heuristics problem solving examples

Heuristic Problem Solving: A comprehensive guide with 5 Examples

What are heuristics, advantages of using heuristic problem solving, disadvantages of using heuristic problem solving, heuristic problem solving examples, frequently asked questions.

  • Speed: Heuristics are designed to find solutions quickly, saving time in problem solving tasks. Rather than spending a lot of time analyzing every possible solution, heuristics help to narrow down the options and focus on the most promising ones.
  • Flexibility: Heuristics are not rigid, step-by-step procedures. They allow for flexibility and creativity in problem solving, leading to innovative solutions. They encourage thinking outside the box and can generate unexpected and valuable ideas.
  • Simplicity: Heuristics are often easy to understand and apply, making them accessible to anyone regardless of their expertise or background. They don’t require specialized knowledge or training, which means they can be used in various contexts and by different people.
  • Cost-effective: Because heuristics are simple and efficient, they can save time, money, and effort in finding solutions. They also don’t require expensive software or equipment, making them a cost-effective approach to problem solving.
  • Real-world applicability: Heuristics are often based on practical experience and knowledge, making them relevant to real-world situations. They can help solve complex, messy, or ill-defined problems where other problem solving methods may not be practical.
  • Potential for errors: Heuristic problem solving relies on generalizations and assumptions, which may lead to errors or incorrect conclusions. This is especially true if the heuristic is not based on a solid understanding of the problem or the underlying principles.
  • Limited scope: Heuristic problem solving may only consider a limited number of potential solutions and may not identify the most optimal or effective solution.
  • Lack of creativity: Heuristic problem solving may rely on pre-existing solutions or approaches, limiting creativity and innovation in problem-solving.
  • Over-reliance: Heuristic problem solving may lead to over-reliance on a specific approach or heuristic, which can be problematic if the heuristic is flawed or ineffective.
  • Lack of transparency: Heuristic problem solving may not be transparent or explainable, as the decision-making process may not be explicitly articulated or understood.
  • Trial and error: This heuristic involves trying different solutions to a problem and learning from mistakes until a successful solution is found. A software developer encountering a bug in their code may try other solutions and test each one until they find the one that solves the issue.
  • Working backward: This heuristic involves starting at the goal and then figuring out what steps are needed to reach that goal. For example, a project manager may begin by setting a project deadline and then work backward to determine the necessary steps and deadlines for each team member to ensure the project is completed on time.
  • Breaking a problem into smaller parts: This heuristic involves breaking down a complex problem into smaller, more manageable pieces that can be tackled individually. For example, an HR manager tasked with implementing a new employee benefits program may break the project into smaller parts, such as researching options, getting quotes from vendors, and communicating the unique benefits to employees.
  • Using analogies: This heuristic involves finding similarities between a current problem and a similar problem that has been solved before and using the solution to the previous issue to help solve the current one. For example, a salesperson struggling to close a deal may use an analogy to a successful sales pitch they made to help guide their approach to the current pitch.
  • Simplifying the problem: This heuristic involves simplifying a complex problem by ignoring details that are not necessary for solving it. This allows the problem solver to focus on the most critical aspects of the problem. For example, a customer service representative dealing with a complex issue may simplify it by breaking it down into smaller components and addressing them individually rather than simultaneously trying to solve the entire problem.

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What Are Heuristics?

These mental shortcuts lead to fast decisions—and biased thinking

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

heuristics problem solving examples

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

heuristics problem solving examples

Verywell / Cindy Chung 

  • History and Origins
  • Heuristics vs. Algorithms
  • Heuristics and Bias

How to Make Better Decisions

If you need to make a quick decision, there's a good chance you'll rely on a heuristic to come up with a speedy solution. Heuristics are mental shortcuts that allow people to solve problems and make judgments quickly and efficiently. Common types of heuristics rely on availability, representativeness, familiarity, anchoring effects, mood, scarcity, and trial-and-error.

Think of these as mental "rule-of-thumb" strategies that shorten decision-making time. Such shortcuts allow us to function without constantly stopping to think about our next course of action.

However, heuristics have both benefits and drawbacks. These strategies can be handy in many situations but can also lead to  cognitive biases . Becoming aware of this might help you make better and more accurate decisions.

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History of the Research on Heuristics

Nobel-prize winning economist and cognitive psychologist Herbert Simon originally introduced the concept of heuristics in psychology in the 1950s. He suggested that while people strive to make rational choices, human judgment is subject to cognitive limitations. Purely rational decisions would involve weighing every alternative's potential costs and possible benefits.

However, people are limited by the amount of time they have to make a choice and the amount of information they have at their disposal. Other factors, such as overall intelligence and accuracy of perceptions, also influence the decision-making process.

In the 1970s, psychologists Amos Tversky and Daniel Kahneman presented their research on cognitive biases. They proposed that these biases influence how people think and make judgments.

Because of these limitations, we must rely on mental shortcuts to help us make sense of the world.

Simon's research demonstrated that humans were limited in their ability to make rational decisions, but it was Tversky and Kahneman's work that introduced the study of heuristics and the specific ways of thinking that people rely on to simplify the decision-making process.

How Heuristics Are Used

Heuristics play important roles in both  problem-solving  and  decision-making , as we often turn to these mental shortcuts when we need a quick solution.

Here are a few different theories from psychologists about why we rely on heuristics.

  • Attribute substitution : People substitute simpler but related questions in place of more complex and difficult questions.
  • Effort reduction : People use heuristics as a type of cognitive laziness to reduce the mental effort required to make choices and decisions.
  • Fast and frugal : People use heuristics because they can be fast and correct in certain contexts. Some theories argue that heuristics are actually more accurate than they are biased.

In order to cope with the tremendous amount of information we encounter and to speed up the decision-making process, our brains rely on these mental strategies to simplify things so we don't have to spend endless amounts of time analyzing every detail.

You probably make hundreds or even thousands of decisions every day. What should you have for breakfast? What should you wear today? Should you drive or take the bus? Fortunately, heuristics allow you to make such decisions with relative ease and without a great deal of agonizing.

There are many heuristics examples in everyday life. When trying to decide if you should drive or ride the bus to work, for instance, you might remember that there is road construction along the bus route. You realize that this might slow the bus and cause you to be late for work. So you leave earlier and drive to work on an alternate route.

Heuristics allow you to think through the possible outcomes quickly and arrive at a solution.

Are Heuristics Good or Bad?

Heuristics aren't inherently good or bad, but there are pros and cons to using them to make decisions. While they can help us figure out a solution to a problem faster, they can also lead to inaccurate judgments about others or situations. Understanding these pros and cons may help you better use heuristics to make better decisions.

Types of Heuristics

There are many different kinds of heuristics. While each type plays a role in decision-making, they occur during different contexts. Understanding the types can help you better understand which one you are using and when.


The availability heuristic  involves making decisions based upon how easy it is to bring something to mind. When you are trying to make a decision, you might quickly remember a number of relevant examples.

Since these are more readily available in your memory, you will likely judge these outcomes as being more common or frequently occurring.

For example, imagine you are planning to fly somewhere on vacation. As you are preparing for your trip, you might start to think of a number of recent airline accidents. You might feel like air travel is too dangerous and decide to travel by car instead. Because those examples of air disasters came to mind so easily, the availability heuristic leads you to think that plane crashes are more common than they really are.


The familiarity heuristic refers to how people tend to have more favorable opinions of things, people, or places they've experienced before as opposed to new ones. In fact, given two options, people may choose something they're more familiar with even if the new option provides more benefits.


The representativeness heuristic  involves making a decision by comparing the present situation to the most representative mental prototype. When you are trying to decide if someone is trustworthy, you might compare aspects of the individual to other mental examples you hold.

A soft-spoken older woman might remind you of your grandmother, so you might immediately assume she is kind, gentle, and trustworthy. However, this is an example of a heuristic bias, as you can't know someone trustworthy based on their age alone.

The affect heuristic involves making choices that are influenced by an individual's emotions at that moment. For example, research has shown that people are more likely to see decisions as having benefits and lower risks when in a positive mood.

Negative emotions, on the other hand, lead people to focus on the potential downsides of a decision rather than the possible benefits.

The anchoring bias involves the tendency to be overly influenced by the first bit of information we hear or learn. This can make it more difficult to consider other factors and lead to poor choices. For example, anchoring bias can influence how much you are willing to pay for something, causing you to jump at the first offer without shopping around for a better deal.

Scarcity is a heuristic principle in which we view things that are scarce or less available to us as inherently more valuable. Marketers often use the scarcity heuristic to influence people to buy certain products. This is why you'll often see signs that advertise "limited time only," or that tell you to "get yours while supplies last."

Trial and Error

Trial and error is another type of heuristic in which people use a number of different strategies to solve something until they find what works. Examples of this type of heuristic are evident in everyday life.

People use trial and error when playing video games, finding the fastest driving route to work, or learning to ride a bike (or any new skill).

Difference Between Heuristics and Algorithms

Though the terms are often confused, heuristics and algorithms are two distinct terms in psychology.

Algorithms are step-by-step instructions that lead to predictable, reliable outcomes, whereas heuristics are mental shortcuts that are basically best guesses. Algorithms always lead to accurate outcomes, whereas, heuristics do not.

Examples of algorithms include instructions for how to put together a piece of furniture or a recipe for cooking a certain dish. Health professionals also create algorithms or processes to follow in order to determine what type of treatment to use on a patient.

How Heuristics Can Lead to Bias

Heuristics can certainly help us solve problems and speed up our decision-making process, but that doesn't mean they are always a good thing. They can also introduce errors, bias, and irrational decision-making. As in the examples above, heuristics can lead to inaccurate judgments about how commonly things occur and how representative certain things may be.

Just because something has worked in the past does not mean that it will work again, and relying on a heuristic can make it difficult to see alternative solutions or come up with new ideas.

Heuristics can also contribute to stereotypes and  prejudice . Because people use mental shortcuts to classify and categorize people, they often overlook more relevant information and create stereotyped categorizations that are not in tune with reality.

While heuristics can be a useful tool, there are ways you can improve your decision-making and avoid cognitive bias at the same time.

We are more likely to make an error in judgment if we are trying to make a decision quickly or are under pressure to do so. Taking a little more time to make a decision can help you see things more clearly—and make better choices.

Whenever possible, take a few deep breaths and do something to distract yourself from the decision at hand. When you return to it, you may find a fresh perspective or notice something you didn't before.

Identify the Goal

We tend to focus automatically on what works for us and make decisions that serve our best interest. But take a moment to know what you're trying to achieve. Consider some of the following questions:

  • Are there other people who will be affected by this decision?
  • What's best for them?
  • Is there a common goal that can be achieved that will serve all parties?

Thinking through these questions can help you figure out your goals and the impact that these decisions may have.

Process Your Emotions

Fast decision-making is often influenced by emotions from past experiences that bubble to the surface. Anger, sadness, love, and other powerful feelings can sometimes lead us to decisions we might not otherwise make.

Is your decision based on facts or emotions? While emotions can be helpful, they may affect decisions in a negative way if they prevent us from seeing the full picture.

Recognize All-or-Nothing Thinking

When making a decision, it's a common tendency to believe you have to pick a single, well-defined path, and there's no going back. In reality, this often isn't the case.

Sometimes there are compromises involving two choices, or a third or fourth option that we didn't even think of at first. Try to recognize the nuances and possibilities of all choices involved, instead of using all-or-nothing thinking .

Heuristics are common and often useful. We need this type of decision-making strategy to help reduce cognitive load and speed up many of the small, everyday choices we must make as we live, work, and interact with others.

But it pays to remember that heuristics can also be flawed and lead to irrational choices if we rely too heavily on them. If you are making a big decision, give yourself a little extra time to consider your options and try to consider the situation from someone else's perspective. Thinking things through a bit instead of relying on your mental shortcuts can help ensure you're making the right choice.

Vlaev I. Local choices: Rationality and the contextuality of decision-making .  Brain Sci . 2018;8(1):8. doi:10.3390/brainsci8010008

Hjeij M, Vilks A. A brief history of heuristics: how did research on heuristics evolve? Humanit Soc Sci Commun . 2023;10(1):64. doi:10.1057/s41599-023-01542-z

Brighton H, Gigerenzer G. Homo heuristicus: Less-is-more effects in adaptive cognition .  Malays J Med Sci . 2012;19(4):6-16.

Schwartz PH. Comparative risk: Good or bad heuristic?   Am J Bioeth . 2016;16(5):20-22. doi:10.1080/15265161.2016.1159765

Schwikert SR, Curran T. Familiarity and recollection in heuristic decision making .  J Exp Psychol Gen . 2014;143(6):2341-2365. doi:10.1037/xge0000024

AlKhars M, Evangelopoulos N, Pavur R, Kulkarni S. Cognitive biases resulting from the representativeness heuristic in operations management: an experimental investigation .  Psychol Res Behav Manag . 2019;12:263-276. doi:10.2147/PRBM.S193092

Finucane M, Alhakami A, Slovic P, Johnson S. The affect heuristic in judgments of risks and benefits . J Behav Decis Mak . 2000; 13(1):1-17. doi:10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S

Teovanović P. Individual differences in anchoring effect: Evidence for the role of insufficient adjustment .  Eur J Psychol . 2019;15(1):8-24. doi:10.5964/ejop.v15i1.1691

Cheung TT, Kroese FM, Fennis BM, De Ridder DT. Put a limit on it: The protective effects of scarcity heuristics when self-control is low . Health Psychol Open . 2015;2(2):2055102915615046. doi:10.1177/2055102915615046

Mohr H, Zwosta K, Markovic D, Bitzer S, Wolfensteller U, Ruge H. Deterministic response strategies in a trial-and-error learning task . Inman C, ed. PLoS Comput Biol. 2018;14(11):e1006621. doi:10.1371/journal.pcbi.1006621

Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare .  J Med Ethics . 2020;46(3):205-211. doi:10.1136/medethics-2019-105586

Bigler RS, Clark C. The inherence heuristic: A key theoretical addition to understanding social stereotyping and prejudice. Behav Brain Sci . 2014;37(5):483-4. doi:10.1017/S0140525X1300366X

del Campo C, Pauser S, Steiner E, et al.  Decision making styles and the use of heuristics in decision making .  J Bus Econ.  2016;86:389–412. doi:10.1007/s11573-016-0811-y

Marewski JN, Gigerenzer G. Heuristic decision making in medicine .  Dialogues Clin Neurosci . 2012;14(1):77-89. doi:10.31887/DCNS.2012.14.1/jmarewski

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Heuristics: Definition, Examples, And How They Work

Benjamin Frimodig

Science Expert

B.A., History and Science, Harvard University

Ben Frimodig is a 2021 graduate of Harvard College, where he studied the History of Science.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

Every day our brains must process and respond to thousands of problems, both large and small, at a moment’s notice. It might even be overwhelming to consider the sheer volume of complex problems we regularly face in need of a quick solution.

While one might wish there was time to methodically and thoughtfully evaluate the fine details of our everyday tasks, the cognitive demands of daily life often make such processing logistically impossible.

Therefore, the brain must develop reliable shortcuts to keep up with the stimulus-rich environments we inhabit. Psychologists refer to these efficient problem-solving techniques as heuristics.

Heuristics decisions and mental thinking shortcut approach outline diagram. Everyday vs complex technique comparison list for judgments and fast, short term problem solving method vector

Heuristics can be thought of as general cognitive frameworks humans rely on regularly to reach a solution quickly.

For example, if a student needs to decide what subject she will study at university, her intuition will likely be drawn toward the path that she envisions as most satisfying, practical, and interesting.

She may also think back on her strengths and weaknesses in secondary school or perhaps even write out a pros and cons list to facilitate her choice.

It’s important to note that these heuristics broadly apply to everyday problems, produce sound solutions, and helps simplify otherwise complicated mental tasks. These are the three defining features of a heuristic.

While the concept of heuristics dates back to Ancient Greece (the term is derived from the Greek word for “to discover”), most of the information known today on the subject comes from prominent twentieth-century social scientists.

Herbert Simon’s study of a notion he called “bounded rationality” focused on decision-making under restrictive cognitive conditions, such as limited time and information.

This concept of optimizing an inherently imperfect analysis frames the contemporary study of heuristics and leads many to credit Simon as a foundational figure in the field.

Kahneman’s Theory of Decision Making

The immense contributions of psychologist Daniel Kahneman to our understanding of cognitive problem-solving deserve special attention.

As context for his theory, Kahneman put forward the estimate that an individual makes around 35,000 decisions each day! To reach these resolutions, the mind relies on either “fast” or “slow” thinking.


The fast thinking pathway (system 1) operates mostly unconsciously and aims to reach reliable decisions with as minimal cognitive strain as possible.

While system 1 relies on broad observations and quick evaluative techniques (heuristics!), system 2 (slow thinking) requires conscious, continuous attention to carefully assess the details of a given problem and logically reach a solution.

Given the sheer volume of daily decisions, it’s no surprise that around 98% of problem-solving uses system 1.

Thus, it is crucial that the human mind develops a toolbox of effective, efficient heuristics to support this fast-thinking pathway.

Heuristics vs. Algorithms

Those who’ve studied the psychology of decision-making might notice similarities between heuristics and algorithms. However, remember that these are two distinct modes of cognition.

Heuristics are methods or strategies which often lead to problem solutions but are not guaranteed to succeed.

They can be distinguished from algorithms, which are methods or procedures that will always produce a solution sooner or later.

An algorithm is a step-by-step procedure that can be reliably used to solve a specific problem. While the concept of an algorithm is most commonly used in reference to technology and mathematics, our brains rely on algorithms every day to resolve issues (Kahneman, 2011).

The important thing to remember is that algorithms are a set of mental instructions unique to specific situations, while heuristics are general rules of thumb that can help the mind process and overcome various obstacles.

For example, if you are thoughtfully reading every line of this article, you are using an algorithm.

On the other hand, if you are quickly skimming each section for important information or perhaps focusing only on sections you don’t already understand, you are using a heuristic!

Why Heuristics Are Used

Heuristics usually occurs when one of five conditions is met (Pratkanis, 1989):

  • When one is faced with too much information
  • When the time to make a decision is limited
  • When the decision to be made is unimportant
  • When there is access to very little information to use in making the decision
  • When an appropriate heuristic happens to come to mind at the same moment

When studying heuristics, keep in mind both the benefits and unavoidable drawbacks of their application. The ubiquity of these techniques in human society makes such weaknesses especially worthy of evaluation.

More specifically, in expediting decision-making processes, heuristics also predispose us to a number of cognitive biases .

A cognitive bias is an incorrect but pervasive judgment derived from an illogical pattern of cognition. In simple terms, a cognitive bias occurs when one internalizes a subjective perception as a reliable and objective truth.

Heuristics are reliable but imperfect; In the application of broad decision-making “shortcuts” to guide one’s response to specific situations, occasional errors are both inevitable and have the potential to catalyze persistent mistakes.

For example, consider the risks of faulty applications of the representative heuristic discussed above. While the technique encourages one to assign situations into broad categories based on superficial characteristics and one’s past experiences for the sake of cognitive expediency, such thinking is also the basis of stereotypes and discrimination.

In practice, these errors result in the disproportionate favoring of one group and/or the oppression of other groups within a given society.

Indeed, the most impactful research relating to heuristics often centers on the connection between them and systematic discrimination.

The tradeoff between thoughtful rationality and cognitive efficiency encompasses both the benefits and pitfalls of heuristics and represents a foundational concept in psychological research.

When learning about heuristics, keep in mind their relevance to all areas of human interaction. After all, the study of social psychology is intrinsically interdisciplinary.

Many of the most important studies on heuristics relate to flawed decision-making processes in high-stakes fields like law, medicine, and politics.

Researchers often draw on a distinct set of already established heuristics in their analysis. While dozens of unique heuristics have been observed, brief descriptions of those most central to the field are included below:

Availability Heuristic

The availability heuristic describes the tendency to make choices based on information that comes to mind readily.

For example, children of divorced parents are more likely to have pessimistic views towards marriage as adults.

Of important note, this heuristic can also involve assigning more importance to more recently learned information, largely due to the easier recall of such information.

Representativeness Heuristic

This technique allows one to quickly assign probabilities to and predict the outcome of new scenarios using psychological prototypes derived from past experiences.

For example, juries are less likely to convict individuals who are well-groomed and wearing formal attire (under the assumption that stylish, well-kempt individuals typically do not commit crimes).

This is one of the most studied heuristics by social psychologists for its relevance to the development of stereotypes.

Scarcity Heuristic

This method of decision-making is predicated on the perception of less abundant, rarer items as inherently more valuable than more abundant items.

We rely on the scarcity heuristic when we must make a fast selection with incomplete information. For example, a student deciding between two universities may be drawn toward the option with the lower acceptance rate, assuming that this exclusivity indicates a more desirable experience.

The concept of scarcity is central to behavioral economists’ study of consumer behavior (a field that evaluates economics through the lens of human psychology).

Trial and Error

This is the most basic and perhaps frequently cited heuristic. Trial and error can be used to solve a problem that possesses a discrete number of possible solutions and involves simply attempting each possible option until the correct solution is identified.

For example, if an individual was putting together a jigsaw puzzle, he or she would try multiple pieces until locating a proper fit.

This technique is commonly taught in introductory psychology courses due to its simple representation of the central purpose of heuristics: the use of reliable problem-solving frameworks to reduce cognitive load.

Anchoring and Adjustment Heuristic

Anchoring refers to the tendency to formulate expectations relating to new scenarios relative to an already ingrained piece of information.

 Anchoring Bias Example

Put simply, this anchoring one to form reasonable estimations around uncertainties. For example, if asked to estimate the number of days in a year on Mars, many people would first call to mind the fact the Earth’s year is 365 days (the “anchor”) and adjust accordingly.

This tendency can also help explain the observation that ingrained information often hinders the learning of new information, a concept known as retroactive inhibition.

Familiarity Heuristic

This technique can be used to guide actions in cognitively demanding situations by simply reverting to previous behaviors successfully utilized under similar circumstances.

The familiarity heuristic is most useful in unfamiliar, stressful environments.

For example, a job seeker might recall behavioral standards in other high-stakes situations from her past (perhaps an important presentation at university) to guide her behavior in a job interview.

Many psychologists interpret this technique as a slightly more specific variation of the availability heuristic.

How to Make Better Decisions

Heuristics are ingrained cognitive processes utilized by all humans and can lead to various biases.

Both of these statements are established facts. However, this does not mean that the biases that heuristics produce are unavoidable. As the wide-ranging impacts of such biases on societal institutions have become a popular research topic, psychologists have emphasized techniques for reaching more sound, thoughtful and fair decisions in our daily lives.

Ironically, many of these techniques are themselves heuristics!

To focus on the key details of a given problem, one might create a mental list of explicit goals and values. To clearly identify the impacts of choice, one should imagine its impacts one year in the future and from the perspective of all parties involved.

Most importantly, one must gain a mindful understanding of the problem-solving techniques used by our minds and the common mistakes that result. Mindfulness of these flawed yet persistent pathways allows one to quickly identify and remedy the biases (or otherwise flawed thinking) they tend to create!

Further Information

  • Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: an effort-reduction framework. Psychological bulletin, 134(2), 207.
  • Marewski, J. N., & Gigerenzer, G. (2012). Heuristic decision making in medicine. Dialogues in clinical neuroscience, 14(1), 77.
  • Del Campo, C., Pauser, S., Steiner, E., & Vetschera, R. (2016). Decision making styles and the use of heuristics in decision making. Journal of Business Economics, 86(4), 389-412.

What is a heuristic in psychology?

A heuristic in psychology is a mental shortcut or rule of thumb that simplifies decision-making and problem-solving. Heuristics often speed up the process of finding a satisfactory solution, but they can also lead to cognitive biases.

Bobadilla-Suarez, S., & Love, B. C. (2017, May 29). Fast or Frugal, but Not Both: Decision Heuristics Under Time Pressure. Journal of Experimental Psychology: Learning, Memory, and Cognition .

Bowes, S. M., Ammirati, R. J., Costello, T. H., Basterfield, C., & Lilienfeld, S. O. (2020). Cognitive biases, heuristics, and logical fallacies in clinical practice: A brief field guide for practicing clinicians and supervisors. Professional Psychology: Research and Practice, 51 (5), 435–445.

Dietrich, C. (2010). “Decision Making: Factors that Influence Decision Making, Heuristics Used, and Decision Outcomes.” Inquiries Journal/Student Pulse, 2(02).

Groenewegen, A. (2021, September 1). Kahneman Fast and slow thinking: System 1 and 2 explained by Sue. SUE Behavioral Design. Retrieved March 26, 2022, from https://suebehaviouraldesign.com/kahneman-fast-slow-thinking/

Kahneman, D., Lovallo, D., & Sibony, O. (2011). Before you make that big decision .

Kahneman, D. (2011). Thinking, fast and slow . Macmillan.

Pratkanis, A. (1989). The cognitive representation of attitudes. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp. 71–98). Hillsdale, NJ: Erlbaum.

Simon, H.A., 1956. Rational choice and the structure of the environment. Psychological Review .

Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185 (4157), 1124–1131.

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AP®︎/College Computer Science Principles

Course: ap®︎/college computer science principles   >   unit 4, using heuristics.

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8.2 Problem-Solving: Heuristics and Algorithms

Learning objectives.

  • Describe the differences between heuristics and algorithms in information processing.

When faced with a problem to solve, should you go with intuition or with more measured, logical reasoning? Obviously, we use both of these approaches. Some of the decisions we make are rapid, emotional, and automatic. Daniel Kahneman (2011) calls this “fast” thinking. By definition, fast thinking saves time. For example, you may quickly decide to buy something because it is on sale; your fast brain has perceived a bargain, and you go for it quickly. On the other hand, “slow” thinking requires more effort; applying this in the same scenario might cause us not to buy the item because we have reasoned that we don’t really need it, that it is still too expensive, and so on. Using slow and fast thinking does not guarantee good decision-making if they are employed at the wrong time. Sometimes it is not clear which is called for, because many decisions have a level of uncertainty built into them. In this section, we will explore some of the applications of these tendencies to think fast or slow.

We will look further into our thought processes, more specifically, into some of the problem-solving strategies that we use. Heuristics are information-processing strategies that are useful in many cases but may lead to errors when misapplied. A heuristic is a principle with broad application, essentially an educated guess about something. We use heuristics all the time, for example, when deciding what groceries to buy from the supermarket, when looking for a library book, when choosing the best route to drive through town to avoid traffic congestion, and so on. Heuristics can be thought of as aids to decision making; they allow us to reach a solution without a lot of cognitive effort or time.

The benefit of heuristics in helping us reach decisions fairly easily is also the potential downfall: the solution provided by the use of heuristics is not necessarily the best one. Let’s consider some of the most frequently applied, and misapplied, heuristics in the table below.

Table 8.1. Heuristics that pose threats to accuracy
Heuristic Description Examples of Threats to Accuracy
Representativeness A judgment that something that is more representative of its category is more likely to occur We may overestimate the likelihood that a person belongs to a particular category because they resemble our prototype of that category.
Availability A judgment that what comes easily to mind is common We may overestimate the crime statistics in our own area because these crimes are so easy to recall.
Anchoring and adjustment A tendency to use a given starting point as the basis for a subsequent judgment We may be swayed towards or away from decisions based on the starting point, which may be inaccurate.

In many cases, we base our judgments on information that seems to represent, or match, what we expect will happen, while ignoring other potentially more relevant statistical information. When we do so, we are using the representativeness heuristic . Consider, for instance, the data presented in the table below. Let’s say that you went to a hospital, and you checked the records of the babies that were born on that given day. Which pattern of births do you think you are most likely to find?

Table 8.2. The representativeness heuristic
6:31 a.m. Girl 6:31 a.m. Boy
8:15 a.m. Girl 8:15 a.m. Girl
9:42 a.m. Girl 9:42 a.m. Boy
1:13 p.m. Girl 1:13 p.m. Girl
3:39 p.m. Boy 3:39 p.m. Girl
5:12 p.m. Boy 5:12 p.m. Boy
7:42 p.m. Boy 7:42 p.m. Girl
11:44 p.m. Boy 11:44 p.m. Boy
Using the representativeness heuristic may lead us to incorrectly believe that some patterns of observed events are more likely to have occurred than others. In this case, list B seems more random, and thus is judged as more likely to have occurred, but statistically both lists are equally likely.

Most people think that list B is more likely, probably because list B looks more random, and matches — or is “representative of” — our ideas about randomness, but statisticians know that any pattern of four girls and four boys is mathematically equally likely. Whether a boy or girl is born first has no bearing on what sex will be born second; these are independent events, each with a 50:50 chance of being a boy or a girl. The problem is that we have a schema of what randomness should be like, which does not always match what is mathematically the case. Similarly, people who see a flipped coin come up “heads” five times in a row will frequently predict, and perhaps even wager money, that “tails” will be next. This behaviour is known as the gambler’s fallacy . Mathematically, the gambler’s fallacy is an error: the likelihood of any single coin flip being “tails” is always 50%, regardless of how many times it has come up “heads” in the past.

The representativeness heuristic may explain why we judge people on the basis of appearance. Suppose you meet your new next-door neighbour, who drives a loud motorcycle, has many tattoos, wears leather, and has long hair. Later, you try to guess their occupation. What comes to mind most readily? Are they a teacher? Insurance salesman? IT specialist? Librarian? Drug dealer? The representativeness heuristic will lead you to compare your neighbour to the prototypes you have for these occupations and choose the one that they seem to represent the best. Thus, your judgment is affected by how much your neibour seems to resemble each of these groups. Sometimes these judgments are accurate, but they often fail because they do not account for base rates , which is the actual frequency with which these groups exist. In this case, the group with the lowest base rate is probably drug dealer.

Our judgments can also be influenced by how easy it is to retrieve a memory. The tendency to make judgments of the frequency or likelihood that an event occurs on the basis of the ease with which it can be retrieved from memory is known as the availability heuristic (MacLeod & Campbell, 1992; Tversky & Kahneman, 1973). Imagine, for instance, that I asked you to indicate whether there are more words in the English language that begin with the letter “R” or that have the letter “R” as the third letter. You would probably answer this question by trying to think of words that have each of the characteristics, thinking of all the words you know that begin with “R” and all that have “R” in the third position. Because it is much easier to retrieve words by their first letter than by their third, we may incorrectly guess that there are more words that begin with “R,” even though there are in fact more words that have “R” as the third letter.

The availability heuristic may explain why we tend to overestimate the likelihood of crimes or disasters; those that are reported widely in the news are more readily imaginable, and therefore, we tend to overestimate how often they occur. Things that we find easy to imagine, or to remember from watching the news, are estimated to occur frequently. Anything that gets a lot of news coverage is easy to imagine. Availability bias does not just affect our thinking. It can change behaviour. For example, homicides are usually widely reported in the news, leading people to make inaccurate assumptions about the frequency of murder. In Canada, the murder rate has dropped steadily since the 1970s (Statistics Canada, 2018), but this information tends not to be reported, leading people to overestimate the probability of being affected by violent crime. In another example, doctors who recently treated patients suffering from a particular condition were more likely to diagnose the condition in subsequent patients because they overestimated the prevalence of the condition (Poses & Anthony, 1991).

The anchoring and adjustment heuristic is another example of how fast thinking can lead to a decision that might not be optimal. Anchoring and adjustment is easily seen when we are faced with buying something that does not have a fixed price. For example, if you are interested in a used car, and the asking price is $10,000, what price do you think you might offer? Using $10,000 as an anchor, you are likely to adjust your offer from there, and perhaps offer $9000 or $9500. Never mind that $10,000 may not be a reasonable anchoring price. Anchoring and adjustment does not just happen when we’re buying something. It can also be used in any situation that calls for judgment under uncertainty, such as sentencing decisions in criminal cases (Bennett, 2014), and it applies to groups as well as individuals (Rutledge, 1993).

In contrast to heuristics, which can be thought of as problem-solving strategies based on educated guesses, algorithms are problem-solving strategies that use rules. Algorithms are generally a logical set of steps that, if applied correctly, should be accurate. For example, you could make a cake using heuristics — relying on your previous baking experience and guessing at the number and amount of ingredients, baking time, and so on — or using an algorithm. The latter would require a recipe which would provide step-by-step instructions; the recipe is the algorithm. Unless you are an extremely accomplished baker, the algorithm should provide you with a better cake than using heuristics would. While heuristics offer a solution that might be correct, a correctly applied algorithm is guaranteed to provide a correct solution. Of course, not all problems can be solved by algorithms.

As with heuristics, the use of algorithmic processing interacts with behaviour and emotion. Understanding what strategy might provide the best solution requires knowledge and experience. As we will see in the next section, we are prone to a number of cognitive biases that persist despite knowledge and experience.

Key Takeaways

  • We use a variety of shortcuts in our information processing, such as the representativeness, availability, and anchoring and adjustment heuristics. These help us to make fast judgments but may lead to errors.
  • Algorithms are problem-solving strategies that are based on rules rather than guesses. Algorithms, if applied correctly, are far less likely to result in errors or incorrect solutions than heuristics. Algorithms are based on logic.

Bennett, M. W. (2014). Confronting cognitive ‘anchoring effect’ and ‘blind spot’ biases in federal sentencing: A modest solution for reforming and fundamental flaw. Journal of Criminal Law and Criminology , 104 (3), 489-534.

Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.

MacLeod, C., & Campbell, L. (1992). Memory accessibility and probability judgments: An experimental evaluation of the availability heuristic.  Journal of Personality and Social Psychology, 63 (6), 890–902.

Poses, R. M., & Anthony, M. (1991). Availability, wishful thinking, and physicians’ diagnostic judgments for patients with suspected bacteremia.  Medical Decision Making,  11 , 159-68.

Rutledge, R. W. (1993). The effects of group decisions and group-shifts on use of the anchoring and adjustment heuristic. Social Behavior and Personality, 21 (3), 215-226.

Statistics Canada. (2018). Ho micide in Canada, 2017 . Retrieved from https://www150.statcan.gc.ca/n1/en/daily-quotidien/181121/dq181121a-eng.pdf

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability.  Cognitive Psychology, 5 , 207–232.

Psychology - 1st Canadian Edition Copyright © 2020 by Sally Walters is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home Blog Business Using Heuristic Problem-Solving Methods for Effective Decision-Making

Using Heuristic Problem-Solving Methods for Effective Decision-Making

Using Heuristic Problem Solving Methods for Effective Decision-Making

Problem-solving capability and effective decision making are two of the most prized capabilities of any leader. However, one cannot expect these traits to be simply present by default in an individual, as both require extensive analysis of the root cause of issues and to know what to look for when anticipating a gain. In a previous article, we brought you  5 Problem-Solving Strategies to Become a Better Problem Solver . This time we have something that can help you dig deep to resolve problems, i.e. using heuristic problem-solving methods for effective decision-making.

What are Heuristics?

Heuristics are essentially problem-solving tools that can be used for solving non-routine and challenging problems. A heuristic method is a practical approach for a short-term goal, such as solving a problem. The approach might not be perfect but can help find a quick solution to help move towards a reasonable way to resolve a problem.

Example: A computer that is to be used for an event to allow presenters to play PowerPoint presentations via a projector malfunctions due to an operating system problem. In such a case a system administrator might quickly refresh the system using a backup to make it functional for the event. Once the event concludes the system administrator can run detailed diagnostic tests to see if there are any further underlying problems that need to be resolved.

In this example, restoring the system using a backup was a short-term solution to solve the immediate problem, i.e. to make the system functional for the event that was to start in a few hours. There are a number of heuristic methods that can lead to such a decision to resolve a problem. These are explained in more detail in the sections below.

Examples of Heuristic Methods Used for Challenging and Non-Routine Problems

Heuristic methods can help ease the cognitive load by making it easy to process decisions. These include various basic methods that aren’t rooted in any theory per se but rather rely on past experiences and common sense. Using heuristics one can, therefore, resolve challenging and non-routine problems. Let’s take a look at some examples.

A Rule of Thumb

This includes using a method based on practical experience. A rule of thumb can be applied to find a short-term solution to a problem to quickly resolve an issue during a situation where one might be pressed for time.

Example: In the case of the operating system failure mentioned earlier, we assume that the PC on which PowerPoint presentations are to be run by presenters during an event is getting stuck on the start screen. Considering that the event is about to start in 2 hours, it is not practical for the system administrator to reinstall the operating system and all associated applications, hotfixes and updates, as it might take several hours. Using a rule of thumb, he might try to use various tried and tested methods, such as trying to use a system restore point to restore the PC without deleting essential files or to use a backup to restore the PC to an earlier environment.

An Educated Guess

An educated guess or guess and check can help resolve a problem by using knowledge and experience. Based on your knowledge of a subject, you can make an educated guess to resolve a problem.

Example: In the example of the malfunctioning PC, the system administrator will have to make an educated guess regarding the best possible way to resolve the problem. The educated guess, in this case, can be to restore the system to a backup instead of using system restore, both of which might take a similar amount of time; however, the former is likely to work better as a quick fix based on past experience and knowledge of the system administrator.

Trial and Error

This is another heuristic method to problem-solving where one might try various things that are expected to work until a solution is achieved.

Example: The system administrator might try various techniques to fix the PC using trial and error. He might start with checking if the system is accessible in safe mode. And if so, does removing a newly installed software or update solve the problem? If he can’t access the system at all, he might proceed with restoring it from a backup. If that too fails, he might need to quickly opt for a wipe and load installation and only install PowerPoint to ensure that at least presenters can run presentations on the PC. In this case he can perform other required software installations after the event.

An Intuitive Judgment

Intuitive judgment does not result from a rational analysis of a situation or based on reasoning. It is more of a feeling one has which may or may not lead to the desired outcome. Sometimes, intuitive judgement can help resolve problems. Perhaps the most rational way to describe an intuition is that it is some type of calculation at the subconscious level, where you can’t put your finger on the reason why you think something might be the way it is.

Example: The system administrator might have a feeling that the PC is not working because the hard drive has failed. This might be an intuitive judgment without hard evidence. He might quickly replace the hard drive to resolve the problem. Later, after he runs diagnostics on the old hard drive, he might realize that it was indeed that hard drive that was faulty and trying to fix it would have been a waste of time. In this case, he might be able to solve a problem using intuitive judgment.


A stereotype is an opinion which is judgmental rather than rational. Certain types of possessions for example create a stereotype of social status. A person who wears an expensive watch might be deemed rich, although he might simply have received it as a gift from someone, instead of being rich himself.

Example: A certain company might have developed a bad reputation of developing faulty hard drives. If the systems administrator sees the name of that company on the hard drive when opening the faulty PC, he might think that the hard drive is faulty based on stereotyping and decide to replace it.

Profiling is used to systematically analyze data to understand its dynamics. Profiling as a heuristic method for problem-solving might entail analyzing data to understand and resolve a problem or to look for patterns, just like a root cause analysis .

Example: To solve the issue of the faulty PC, a system administrator might look for similar patterns which might have led to the problem. He might search online for solutions via online forums to understand what might have caused the issue. He might also look at the information associated with recently installed software and updates to see if something conflicted with the operating system. During the profiling process, he might realize that software he installed yesterday before shutting down the PC is the cause of the problem, since similar issues have been reported by other users. He might try to remove the software using Safe Mode or by removing its files by running the computer from a bootable disc drive.

Common Sense

Common sense is the use of practical judgment to understand something. The use of common sense is also a heuristic method used for problem-solving.

Example: When dealing with a faulty PC the system administrator sees smoke coming out of the PC. In this case, it is common sense that a hardware component is faulty. He shuts down the PC, removes the power cord and investigates the issue further based on common sense. This is because keeping the system linked to a power socket amidst smoke emitting from the PC can only make things worse. It is common sense to turn off everything and take the necessary precautions to investigate the issue further.

How are Heuristic Methods Used in Decision-Making?

There are a number of formal and informal models of heuristics used for decision making. Let’s take a look at a few of the formal models of heuristics used for decision making.

Formal Models of Heuristics

Fast-and-frugal tree.

A fast-and-frugal tree is a classification or decision tree. It is a graphical form that helps make decisions. For example, a fast-and-frugal tree might help doctors determine if a patient should be sent to a regular ward or for an emergency procedure. fast-and-frugal trees are methods for making decisions based on hierarchical models, where one has to make a decision based on little information.

Fluency Heuristic

In psychology, fluency heuristic implies an object that can be easily processed and deemed to have a higher value, even if it is not logical to assume this. Understanding the application of fluency heuristic can help make better decisions in a variety of fields. Fluency heuristic is more like sunk cost fallacy .

For example, a designer might design a user interface that is easier for users to process, with fewer buttons and easily labeled options. This can help them think fast, work quicker and improve productivity. Similarly, the concept might be used in marketing to sell products using effective marketing techniques. Even if two products are identical, a consumer might pick one over the other based on fluency heuristic. The consumer might deem the product to be better for his needs, even if it is the same as the other one.

Gaze Heuristic

Assume that you aim to catch a ball. Based on your judgment you would leap to catch the ball. If you were to leave yourself to instinct, you will end up at the same spot to catch the ball at a spot you would predict it to fall. This is essentially gaze heuristic. The concept of gaze heuristic is thought to be applied for simple situations and its applications are somewhat limited.

Recognition Heuristic

If there are two objects, one recognizable and the one isn’t, the person is likely to deem the former to be of greater value. A simple example of recognition heuristic is branding. People get used to brand logos, assuming them to be of high quality. This helps brands to sell multiple products using recognition heuristic. So, if you are looking to buy an air conditioner and come across two products, A and B, where A is a brand you know and B is a new company you don’t recognize, you might opt for A. Even if B is of better quality, you might simply trust A because you have been buying electronics from the brand for many years and they have been of good quality.


Satisficing entails looking for alternatives until an acceptable threshold can be ensured. Satisficing in decision making implies selecting an option which meets most needs or the first option which can meet a need, even if it is not the optimal solution. For example, when choosing between early retirement or continuing service for 2 or 3 more years, one might opt for early retirement assuming that it would meet the individual’s needs.

Similarity Heuristic

Similarity heuristic is judgment based on which is deemed similar, if something reminds someone of good or bad days, something similar might be considered the same. Similarity heuristics is often used by brands to remind people of something that they might have sentimental value for.

Someone might buy a limited-edition bottle of perfume that is being sold in a packaging style that was replaced 20 years ago. Assuming that sales were great in those days, the company might sell such limited-edition perfume bottles in the hope of boosting sales. Consumers might buy them simply because they remind them of the ‘good old days’, even though the product inside might not even be of the same but rather similar to what it used to be. Many consumers claim to buy these types of products claiming that it reminds them of a fond memory, such as their youth, marriage or  first job, when they used the product back in the day.

Final Words

Heuristics play a key role in decision making and affect the way we make decisions. Understanding heuristics can not only help resolve problems but also understand biases that affect effective decision making. A business decision or one that affects one’s health, life, or well-being cannot rely merely on a hunch. Understanding heuristics and applying them effectively can therefore help make the best possible decisions. Heuristic methods are not only used in different professions and personal decision making but are also used in artificial intelligence and programming.

Modern anti-virus software for instance uses heuristic methods to dig out the most elusive malware. The same rule can be essentially applied to decision making, by effectively using heuristics to resolve problems and to make decisions based on better judgment.

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Common Sense, Decision Making, Educated Guess, Heuristics, Judgment, Problem Solving, Profiling, Rule of Thumb, Stereotyping, Trial and Error Filed under Business

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Heuristic Method

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Heuristic Method: this article explains the concept of the Heuristic Method , developed by George Pólya in a practical way. After reading it, you will understand the basics of this powerful Problem Solving tool.

What is the Heuristic Method?

A heuristic method is an approach to finding a solution to a problem that originates from the ancient Greek word ‘eurisko’, meaning to ‘find’, ‘search’ or ‘discover’. It is about using a practical method that doesn’t necessarily need to be perfect. Heuristic methods speed up the process of reaching a satisfactory solution.

Previous experiences with comparable problems are used that can concern problem situations for people, machines or abstract issues. One of the founders of heuristics is the Hungarian mathematician György (George) Pólya , who published a book about the subject in 1945 called ‘How to Solve It’. He used four principles that form the basis for problem solving.

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Heuristic method: Four principles

Pólya describes the following four principles in his book:

  • try to understand the problem
  • make a plan
  • carry out this plan
  • evaluate and adapt

Heuristic Method Principles George Ploya - toolshero

If this sequence doesn’t lead to the right solution, Pólya advises to first look for a simpler problem.

A solution may potentially be found by first looking at a similar problem that was possible to solve. With this experience, it’s possible to look at the current problem in another way.

First principle of the heuristic method: understand the problem

It’s more difficult than it seems, because it seems obvious. In truth, people are hindered when it comes to finding an initially suitable approach to the problem.

It can help to draw the problem and to look at it from another angle. What is the problem, what is happening, can the problem be explained in other words, is there enough information available, etc. Such questions can help with the first evaluation of a problem issue.

Second principle of the heuristic method: make a plan

There are many ways to solve problems. This section is about choosing the right strategy that best fits the problem at hand. The reversed ‘working backwards’ can help with this; people assume to have a solution and use this as a starting point to work towards the problem.

It can also be useful to make an overview of the possibilities, delete some of them immediately, work with comparisons, or to apply symmetry. Creativity comes into play here and will improve the ability to judge.

Third principle of the heuristic method: carry out the plan

Once a strategy has been chosen, the plan can quickly be implemented. However, it is important to pay attention to time and be patient, because the solution will not simply appear.

If the plan doesn’t go anywhere, the advice is to throw it overboard and find a new way.

Fourth principle of the heuristic method: evaluate and adapt

Take the time to carefully consider and reflect upon the work that’s already been done. The things that are going well should be maintained, those leading to a lesser solution, should be adjusted. Some things simply work, while others simply don’t.

There are many different heuristic methods, which Pólya also used. The most well-known heuristics are found below:

1. Dividing technique

The original problem is divided into smaller sub-problems that can be solved more easily. These sub-problems can be linked to each other and combined, which will eventually lead to the solving of the original problem.

2. Inductive method

This involves a problem that has already been solved, but is smaller than the original problem. Generalisation can be derived from the previously solved problem, which can help in solving the bigger, original problem.

3. Reduction method

Because problems are often larger than assumed and deal with different causes and factors, this method sets limits for the problem in advance. This reduces the leeway of the original problem, making it easier to solve.

4. Constructive method

This is about working on the problem step by step. The smallest solution is seen as a victory and from that point consecutive steps are taken. This way, the best choices keep being made, which will eventually lead to a successful end result.

5. Local search method

This is about the search for the most attainable solution to the problem. This solution is improved along the way. This method ends when improvement is no longer possible.

Exact solutions versus the heuristic method

The heuristic approach is a mathmatical method with which proof of a good solution to a problem is delivered. There is a large number of different problems that could use good solutions. When the processing speed is equally as important as the obtained solution, we speak of a heuristic method.

The Heuristic Method only tries to find a good, but not necessarily optimal, solution. This is what differentiates heuristics from exact solution methods, which are about finding the optimal solution to a problem. However, that’s very time consuming, which is why a heuristic method may prove preferable. This is much quicker and more flexible than an exact method, but does have to satisfy a number of criteria.

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It’s Your Turn

What do you think? Is the Heuristic Method applicable in your personal or professional environment? Do you recognize the practical explanation or do you have more suggestions? What are your success factors for solving problems

Share your experience and knowledge in the comments box below.

More information

  • Groner, R., Groner, M., & Bischof, W. F. (2014). Methods of heuristics . Routledge .
  • Newell, A. (1983). The heuristic of George Polya and its relation to artificial intelligence . Methods of heuristics, 195-243.
  • Polya, G. (2014, 1945). How to solve it: A new aspect of mathematical method . Princeton university press .

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Patty Mulder

Patty Mulder

Patty Mulder is an Dutch expert on Management Skills, Personal Effectiveness and Business Communication. She is also a Content writer, Business Coach and Company Trainer and lives in the Netherlands (Europe). Note: all her articles are written in Dutch and we translated her articles to English!


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Heuristics and Problem Solving

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  • Wim Van Dooren 2  

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In a general sense heuristics are guidelines or methods for problem solving. Therefore, we will first define problem solving before presenting a specific definition of heuristics.

Problem Solving

In contrast to a routine task, a problem is a situation in which a person is trying to attain a goal but does not dispose of a ready-made solution or solution method. Problem solving involves then “cognitive processing directed at transforming the given situation into a goal situation when no obvious method of solution is available” (Mayer and Wittrock 2006 , p. 287). An implication is that a task can be a problem for one person, but not for someone else. For instance, the task “divide 120 marbles equally among 8 children” may be a problem for beginning elementary school children, but not for people who master the algorithm for long division, or know how to use a calculator.

The term “heuristic” originates from the Greek word heuriskein which means “to find.” Heuristics ...

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De Corte, E., Verschaffel, L., & Op’t Eynde, P. (2000). Self-regulation: a characteristic and a goal of mathematics education. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 687–726). San Diego, CA: Academic.

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Groner, R., Groner, M., & Bischof, W. F. (Eds.). (1983). Methods of heuristics . Hillsdale, NJ: Erlbaum.

Mayer, R. E., & Wittrock, M. C. (2006). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (pp. 287–303). New York: Macmillan.

Polya, G. (1945). How to solve it . Princeton, NJ: Princeton University Press.

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De Corte, E., Verschaffel, L., Van Dooren, W. (2012). Heuristics and Problem Solving. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_420

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What are heuristics and how do they help us make decisions?

Alicia Raeburn contributor headshot

Heuristics are simple rules of thumb that our brains use to make decisions. When you choose a work outfit that looks professional instead of sweatpants, you’re making a decision based on past information. That's not intuition; it’s heuristics. Instead of weighing all the information available to make a data-backed choice, heuristics enable us to move quickly into action—mostly without us even realizing it. In this article, you’ll learn what heuristics are, their common types, and how we use them in different scenarios.

Green means go. Most of us accept this as common knowledge, but it’s actually an example of a micro-decision—in this case, your brain is deciding to go when you see the color green.

You make countless of these subconscious decisions every day. Many things that you might think just come naturally to you are actually caused by heuristics—mental shortcuts that allow you to quickly process information and take action. Heuristics help you make smaller, almost unnoticeable decisions using past information, without much rational input from your brain.

Heuristics are helpful for getting things done more quickly, but they can also lead to biases and irrational choices if you’re not aware of them. Luckily, you can use heuristics to your advantage once you recognize them, and make better decisions in the workplace.

What is a heuristic?

Heuristics are mental shortcuts that your brain uses to make decisions. When we make rational choices, our brains weigh all the information, pros and cons, and any relevant data. But it’s not possible to do this for every single decision we make on a day-to-day basis. For the smaller ones, your brain uses heuristics to infer information and take almost-immediate action.

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How heuristics work

For example, if you’re making a larger decision about whether to accept a new job or stay with your current one, your brain will process this information slowly. For decisions like this, you collect data by referencing sources—chatting with mentors, reading company reviews, and comparing salaries. Then, you use that information to make your decision. Meanwhile, your brain is also using heuristics to help you speed along that track. In this example, you might use something called the “availability heuristic” to reference things you’ve recently seen about the new job. The availability heuristic makes it more likely that you’ll remember a news story about the company’s higher stock prices. Without realizing it, this can make you think the new job will be more lucrative.

On the flip side, you can recognize that the new job has had some great press recently, but that might be just a great PR team at work. Instead of “buying in” to what the availability heuristic is trying to tell you—that positive news means it’s the right job—you can acknowledge that this is a bias at work. In this case, comparing compensation and work-life balance between the two companies is a much more effective way to choose which job is right for you.

History of heuristics

The term "heuristics," originating from the Greek word meaning “to discover,” has ancient roots, but much of today's understanding comes from twentieth-century social scientists. Herbert Simon's research into "bounded rationality" highlighted the use of heuristics in decision-making, particularly under constraints like limited time and information.

Daniel Kahneman was one of the first researchers to study heuristics in his behavioral economics work in the 1970’s, along with fellow psychologist Amos Tversky. They theorized that many of the decisions and judgments we make aren’t rational—meaning we don’t move through a series of decision-making steps to come to a solution. Instead, the human brain uses mental shortcuts to form seemingly irrational, “fast and frugal” decisions—quick choices that don’t require a lot of mental energy.

Kahneman’s work showed that heuristics lead to systematic errors (or biases), which act as the driving force for our decisions. He was able to apply this research to economic theory, leading to the formation of behavioral economics and a Nobel Prize for Kahneman in 2002.

In the years since, the study of heuristics has grown in popularity with economists and in cognitive psychology. Gerd Gigerenzer’s research , for example, challenges the idea that heuristics lead to errors or flawed thinking. He argues that heuristics are actually indicators that human beings are able to make decisions more effectively without following the traditional rules of logic. His research seems to indicate that heuristics lead us to the right answer most of the time.

Types of heuristics

Heuristics are everywhere, whether we notice them or not. There are hundreds of heuristics at play in the human brain, and they interact with one another constantly. To understand how these heuristics can help you, start by learning some of the more common types of heuristics.

Recognition heuristic

The recognition heuristic uses what we already know (or recognize) as a criterion for decisions. The concept is simple: When faced with two choices, you’re more likely to choose the item you recognize versus the one you don’t.

This is the very base-level concept behind branding your business, and we see it in all well-known companies. Businesses develop a brand messaging strategy in the hopes that when you’re faced with buying their product or buying someone else's, you recognize their product, have a positive association with it, and choose that one. For example, if you’re going to grab a soda and there are two different cans in the fridge, one a Coca-Cola, and the other a soda you’ve never heard of, you are more likely to choose the Coca-Cola simply because you know the name.

Familiarity heuristic

The familiarity heuristic is a mental shortcut where individuals prefer options or information that is familiar to them. This heuristic is based on the notion that familiar items are seen as safer or superior. It differs from the recognition heuristic, which relies solely on whether an item is recognized. The familiarity heuristic involves a deeper sense of comfort and understanding, as opposed to just recognizing something.

An example of this heuristic is seen in investment decisions. Investors might favor well-known companies over lesser-known ones, influenced more by brand familiarity than by an objective assessment of the investment's potential. This tendency showcases how the familiarity heuristic can lead to suboptimal choices, as it prioritizes comfort and recognition over a thorough evaluation of all available options.

Availability heuristic

The availability heuristic is a cognitive bias where people judge the frequency or likelihood of events based on how easily similar instances come to mind. This mental shortcut depends on the most immediate examples that pop into one's mind when considering a topic or decision. The ease of recalling these instances often leads to a distorted perception of their actual frequency, as recent, dramatic, or emotionally charged memories tend to be more memorable.

A notable example of the availability heuristic is the public's reaction to shark attacks. When the media reports on shark attacks, these incidents become highly memorable due to their dramatic nature, leading people to overestimate the risk of such events. This heightened perception is despite statistical evidence showing the rarity of shark attacks. The result is an exaggerated fear and a skewed perception of the actual danger of swimming in the ocean.

Representativeness heuristic

The representativeness heuristic is when we try to assign an object to a specific category or idea based on past experiences. Oftentimes, this comes up when we meet people—our first impression. We expect certain things (such as clothing and credentials) to indicate that a person behaves or lives a certain way.

Without proper awareness, this heuristic can lead to discrimination in the workplace. For example, representativeness heuristics might lead us to believe that a job candidate from an Ivy League school is more qualified than one from a state university, even if their qualifications show us otherwise. This is because we expect Ivy League graduates to act a certain way, such as by being more hard-working or intelligent. Of course, in our rational brains, we know this isn’t the case. That’s why it’s important to be aware of this heuristic, so you can use logical thinking to combat potential biases.

Anchoring and adjustment heuristic

Used in finance for economic forecasting, anchoring and adjustment is when you start with an initial piece of information (the anchor) and continue adjusting until you reach an acceptable decision. The challenge is that sometimes the anchor ends up not being a good enough value to begin with. In other words, you choose the anchor based on unknown biases and then make further decisions based on this faulty assumption.

Anchoring and adjustment are often used in pricing, especially with SaaS companies. For example, a displayed, three-tiered pricing model shows you how much you get for each price point. The layout is designed to make it look like you won’t get much for the lower price, and you don’t necessarily need the highest price, so you choose the mid-level option (the original target). The anchors are the low price (suggesting there’s not much value here) and the high price (which shows that you’re getting a "discount" if you choose another option). Thanks to those two anchors, you feel like you’re getting a lot of value, no matter what you spend.

Affect heuristic

You know the advice; think with your heart. That’s the affect heuristic in action, where you make a decision based on what you’re feeling. Emotions are important ways to understand the world around us, but using them to make decisions is irrational and can impact your work.

For example, let’s say you’re about to ask your boss for a promotion. As a product marketer, you’ve made a huge impact on the company by helping to build a community of enthusiastic, loyal customers. But the day before you have your performance review , you find out that a small project you led for a new product feature failed. You decide to skip the conversation asking for a raise and instead double down on how you can improve.

In this example, you’re using the affect heuristic to base your entire performance on the failure of one small project—even though the rest of your performance (building that profitable community) is much more impactful than a new product feature. If you weighed the options rationally, you would see that asking for a raise is still a logical choice. But instead, the fear of asking for a raise after a failure felt like too big a trade-off.


Satisficing is when you accept an available option that’s satisfactory (i.e., just fine) instead of trying to find the best possible solution. In other words, you’re settling. This creates a “bounded rationality,” where you’re constrained by the choices that are good-enough, instead of pushing past the limits to discover more. This isn’t always negative—for lower-impact scenarios, it might not make sense to invest time and energy into finding the optimal choice. But there are also times when this heuristic kicks in and you end up settling for less than what’s possible.

For example, let’s say you’re a project manager planning the budget for the next fiscal year. Instead of looking at previous spend and revenue, you satisfice and base the budget off projections, assuming that will be good enough. But without factoring in historical data, your budget isn’t going to be as equipped to manage hiccups or unexpected changes. In this case, you can mitigate satisficing with a logically-based data review that, while longer, will produce a more accurate and thoughtful budget plan.

Trial and error heuristic

The trial and error heuristic is a problem-solving method where solutions are found through repeated experimentation. It's used when a clear path to the solution isn't known, relying on iterative learning from failures and adjustments.

For example, a chef might experiment with various ingredient combinations and techniques to perfect a new recipe. Each attempt informs the next, demonstrating how trial and error facilitates discovery in situations without formal guidelines.

Pros and cons of heuristics

Heuristics are effective at helping you get more done quickly, but they also have downsides. Psychologists don’t necessarily agree on whether heuristics and biases are positive or negative. But the argument seems to boil down to these two pros and cons:

Heuristics pros:

Simple heuristics reduce cognitive load, allowing you to accomplish more in less time with fast and frugal decisions. For example, the satisficing heuristic helps you find a "good enough" choice. So if you’re making a complex decision between whether to cut costs or invest in employee well-being , you can use satisficing to find a solution that’s a compromise. The result might not be perfect, but it allows you to take action and get started—you can always adjust later on.

Heuristics cons:

Heuristics create biases. While these cognitive biases enable us to make rapid-fire decisions, they can also lead to rigid, unhelpful beliefs. For example, confirmation bias makes it more likely that you’ll seek out other opinions that agree with your own. This makes it harder to keep an open mind, hear from the other side, and ultimately change your mind—which doesn’t help you build the flexibility and adaptability so important for succeeding in the workplace.

Heuristics and psychology

Heuristics play a pivotal role in psychology, especially in understanding how people make decisions within their cognitive limitations. These mental shortcuts allow for quicker decisions, often necessary in a fast-paced world, but they can sometimes lead to errors in judgment.

The study of heuristics bridges various aspects of psychology, from cognitive processes to behavioral outcomes, and highlights the balance between efficient decision-making and the potential for bias.

Stereotypes and heuristic thinking

Stereotypes are a form of heuristic where individuals make assumptions based on group characteristics, a process analyzed in both English and American psychology.

While these generalizations can lead to rapid conclusions and rational decisions under certain circumstances, they can also oversimplify complex human behaviors and contribute to prejudiced attitudes. Understanding stereotypes as a heuristic offers insight into the cognitive limitations of the human mind and their impact on social perceptions and interactions.

How heuristics lead to bias

Because heuristics rely on shortcuts and stereotypes, they can often lead to bias. This is especially true in scenarios where cognitive limitations restrict the processing of all relevant information. So how do you combat bias? If you acknowledge your biases, you can usually undo them and maybe even use them to your advantage. There are ways you can hack heuristics, so that they work for you (not against you):

Be aware. Heuristics often operate like a knee-jerk reaction—they’re automatic. The more aware you are, the more you can identify and acknowledge the heuristic at play. From there, you can decide if it’s useful for the current situation, or if a logical decision-making process is best.

Flip the script. When you notice a negative bias, turn it around. For example, confirmation bias is when we look for things to be as we expect. So if we expect our boss to assign us more work than our colleagues, we might always experience our work tasks as unfair. Instead, turn this around by repeating that your boss has your team’s best interests at heart, and you know everyone is working hard. This will re-train your confirmation bias to look for all the ways that your boss is treating you just like everyone else.

Practice mindfulness. Mindfulness helps to build self-awareness, so you know when heuristics are impacting your decisions. For example, when we tap into the empathy gap heuristic, we’re unable to empathize with someone else or a specific situation. However, if we’re mindful, we can be aware of how we’re feeling before we engage. This helps us to see that the judgment stems from our own emotions and probably has nothing to do with the other person.

Examples of heuristics in business

This is all well and good in theory, but how do heuristic decision-making and thought processes show up in the real world? One reason researchers have invested so much time and energy into learning about heuristics is so that they can use them, like in these scenarios:

How heuristics are used in marketing

Effective marketing does so much for a business—it attracts new customers, makes a brand a household name, and converts interest into sales, to name a few. One way marketing teams are able to accomplish all this is by applying heuristics.

Let’s use ambiguity aversion as an example. Ambiguity aversion means you're less likely to choose an item you don’t know. Marketing teams combat this by working to become familiar to their customers. This could include the social media team engaging in a more empathetic or conversational way, or employing technology like chat-bots to show that there’s always someone available to help. Making the business feel more approachable helps the customer feel like they know the brand personally—which lessens ambiguity aversion.

How heuristics are used in business strategy

Have you ever noticed how your CEO seems to know things before they happen? Or that the CFO listens more than they speak? These are indications that they understand people in a deeper way, and are able to engage with their employees and predict outcomes because of it. C-suite level executives are often experts in behavioral science, even if they didn’t study it. They tend to get what makes people tick, and know how to communicate based on these biases. In short, they use heuristics for higher-level decision-making processes and execution. 

This includes business strategy . For example, a startup CEO might be aware of their representativeness bias towards investors—they always look for the person in the room with the  fancy suit or car. But after years in the field, they know logically that this isn’t always true—plenty of their investors have shown up in shorts and sandals. Now, because they’re aware of their bias, they can build it into their investment strategy. Instead of only attending expensive, luxury events, they also attend conferences with like-minded individuals and network among peers. This approach can lead them to a greater variety of investors and more potential opportunities.

Heuristics vs algorithms

Heuristics and algorithms are both used by the brain to reduce the mental effort of decision-making, but they operate a bit differently. Algorithms act as guidelines for specific scenarios. They have a structured process designed to solve that specific problem. Heuristics, on the other hand, are general rules of thumb that help the brain process information and may or may not reach a solution.

For example, let's say you’re cooking a well-loved family recipe. You know the steps inside and out, and you no longer need to reference the instructions. If you’re following a recipe step-by-step, you’re using an algorithm. If, however, you decide on a whim to sub in some of your fresh garden vegetables because you think it will taste better, you’re using a heuristic.

How to use heuristics to make better decisions

Heuristics can help us make decisions quickly and with less cognitive strain. While they can be efficient, they sometimes lead to errors in judgment. Understanding how to use heuristics effectively can improve decision-making, especially in complex or uncertain situations.

Take time to think

Rushing often leads to reliance on automatic heuristics, which might not always be suitable. To make better decisions, slow down your thinking process. Take a step back, breathe, and allow yourself a moment of distraction. This pause can provide a fresh perspective and help you notice details or angles you might have missed initially.

Clarify your objectives

When making a decision, it's important to understand the ultimate goal. Our automatic decision-making processes tend to favor immediate benefits, sometimes overlooking long-term impacts or the needs of others involved. Consider the broader implications of your decision. Who else is affected? Is there a common objective that benefits all parties? Such considerations can lead to more holistic and effective decisions.

Manage your emotional influences

Emotions significantly influence our decision-making, often without our awareness. Fast decisions are particularly prone to emotional biases. Acknowledge your feelings, but also separate them from the facts at hand. Are you making a decision based on solid information or emotional reactions? Distinguishing between the two can lead to more rational and balanced choices.

Beware of binary thinking

All-or-nothing thinking is a common heuristic trap, where we see decisions as black or white with no middle ground. However, real-life decisions often have multiple paths and possibilities. It's important to recognize this complexity. There might be compromises or alternative options that weren't initially considered. By acknowledging the spectrum of possibilities, you can make more nuanced and effective decisions.

Heuristic FAQs

What is heuristic thinking.

Heuristic thinking refers to a method of problem-solving, learning, or discovery that employs a practical approach—often termed a "rule of thumb"—to make decisions quickly. Heuristic thinking is a type of cognition that humans use subconsciously to make decisions and judgments with limited time.

What is a heuristic evaluation?

A heuristic evaluation is a usability inspection method used in the fields of user interface (UI) and user experience (UX) design. It involves evaluators examining the interface and judging its compliance with recognized usability principles, known as heuristics. These heuristics serve as guidelines to identify usability problems in a design, making the evaluation process more systematic and comprehensive.

What are computer heuristics?

Computer heuristics are algorithms used to solve complex problems or make decisions where an exhaustive search is impractical. In fields like artificial intelligence and cybersecurity, these heuristic methods allow for efficient problem-solving and decision-making, often based on trial and error or rule-of-thumb strategies.

What are heuristics in psychology?

In psychology, heuristics are quick mental rules for making decisions. They are important in social psychology for understanding how we think and decide. Figures like Kahneman and Tversky, particularly in their work "Judgment Under Uncertainty: Heuristics and Biases," have influenced the study of heuristics in psychology.

Learn heuristics, de-mystify your brain

Your brain doesn’t actually work in mysterious ways. In reality, researchers know why we do a lot of the things we do. Heuristics help us to understand the choices we make that don’t make much sense. Once you understand heuristics, you can also learn to use them to your advantage—both in business, and in life. 

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Reviewed by Psychology Today Staff

A heuristic is a mental shortcut that allows an individual to make a decision, pass judgment, or solve a problem quickly and with minimal mental effort. While heuristics can reduce the burden of decision-making and free up limited cognitive resources, they can also be costly when they lead individuals to miss critical information or act on unjust biases.

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As humans move throughout the world, they must process large amounts of information and make many choices with limited amounts of time. When information is missing, or an immediate decision is necessary, heuristics act as “rules of thumb” that guide behavior down the most efficient pathway.

Heuristics are not unique to humans; animals use heuristics that, though less complex, also serve to simplify decision-making and reduce cognitive load.

Generally, yes. Navigating day-to-day life requires everyone to make countless small decisions within a limited timeframe. Heuristics can help individuals save time and mental energy, freeing up cognitive resources for more complex planning and problem-solving endeavors.

The human brain and all its processes—including heuristics— developed over millions of years of evolution . Since mental shortcuts save both cognitive energy and time, they likely provided an advantage to those who relied on them.

Heuristics that were helpful to early humans may not be universally beneficial today . The familiarity heuristic, for example—in which the familiar is preferred over the unknown—could steer early humans toward foods or people that were safe, but may trigger anxiety or unfair biases in modern times.


The study of heuristics was developed by renowned psychologists Daniel Kahneman and Amos Tversky. Starting in the 1970s, Kahneman and Tversky identified several different kinds of heuristics, most notably the availability heuristic and the anchoring heuristic.

Since then, researchers have continued their work and identified many different kinds of heuristics, including:

Familiarity heuristic

Fundamental attribution error

Representativeness heuristic


The anchoring heuristic, or anchoring bias , occurs when someone relies more heavily on the first piece of information learned when making a choice, even if it's not the most relevant. In such cases, anchoring is likely to steer individuals wrong .

The availability heuristic describes the mental shortcut in which someone estimates whether something is likely to occur based on how readily examples come to mind . People tend to overestimate the probability of plane crashes, homicides, and shark attacks, for instance, because examples of such events are easily remembered.

People who make use of the representativeness heuristic categorize objects (or other people) based on how similar they are to known entities —assuming someone described as "quiet" is more likely to be a librarian than a politician, for instance. 

Satisficing is a decision-making strategy in which the first option that satisfies certain criteria is selected , even if other, better options may exist.


Heuristics, while useful, are imperfect; if relied on too heavily, they can result in incorrect judgments or cognitive biases. Some are more likely to steer people wrong than others.

Assuming, for example, that child abductions are common because they’re frequently reported on the news—an example of the availability heuristic—may trigger unnecessary fear or overprotective parenting practices. Understanding commonly unhelpful heuristics, and identifying situations where they could affect behavior, may help individuals avoid such mental pitfalls.

Sometimes called the attribution effect or correspondence bias, the term describes a tendency to attribute others’ behavior primarily to internal factors—like personality or character— while attributing one’s own behavior more to external or situational factors .

If one person steps on the foot of another in a crowded elevator, the victim may attribute it to carelessness. If, on the other hand, they themselves step on another’s foot, they may be more likely to attribute the mistake to being jostled by someone else .

Listen to your gut, but don’t rely on it . Think through major problems methodically—by making a list of pros and cons, for instance, or consulting with people you trust. Make extra time to think through tasks where snap decisions could cause significant problems, such as catching an important flight.

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Some Helpful Problem-Solving Heuristics

A  heuristic  is a thinking strategy, something that can be used to tease out further information about a problem and thus help you figure out what to do when you don’t know what to do. Here are 25 heuristics that can be useful in solving problems. They help you monitor your thought processes, to step back and watch yourself at work, and thus keep your cool in a challenging situation.

  • Ask somebody else  how to do the problem. This strategy is probably the most used world-wide, though it is not one we encourage our students to use, at least not initially.
  • Guess and try  (guess, check, and revise). Your first guess might be right! But incorrect guesses can often suggest a direction toward a solution. (N.B. A spreadsheet is a powerful aid in guessing and trying. Set up the relationships and plug in a number to see if you get what you want. If you don’t, it is easy to try another number. And another.)
  • Restate the problem  using words that make sense to you. One way to do this is to explain the problem to someone else. Often this is all it takes for the light to dawn.
  • Organize information  into a table or chart. Having it laid out clearly in front of you frees up your mind for thinking. And perhaps you can use the organized data to generate more information.
  • Draw a picture  of the problem. Translate problem information into pictures, diagrams, sketches, glyphs, arrows, or some other kind of representation.
  • Make a model  of the problem. The model might be a physical or mental model, perhaps using a computer. You might vary the problem information to see whether and how the model may be affected.
  • Look for patterns , any kind of patterns: number patterns, verbal patterns, spatial/visual patterns, patterns in time, patterns in sound. (Some people define mathematics as the science of patterns.)
  • Act out the problem , if it is stated in a narrative form. Acting it out can have the same effect as drawing a picture. What’s more, acting out the problem might disclose incorrect assumptions you are making.
  • Invent notation . Name things in the problem (known or unknown) using words or symbols, including relationships between problem components.
  • Write equations . An equation is simply the same thing named two different ways.
  • Check all possibilities  in a systematic way. A table or chart may help you to be systematic.
  • Work backwards  from the end condition to the beginning condition. Working backwards is particularly helpful when letting a variable (letter) represent an unknown.
  • Identify subgoals  in the problem. Break up the problem into a sequence of smaller problems (“If I knew this, then I could get that”).
  • Simplify the problem . Use easier or smaller numbers, or look at extreme cases (e.g., use the minimum or maximum value of one of the varying quantities).
  • Restate the problem again . After working on the problem for a time, back off a bit and put it into your own words in still a different way, since now you know more about it.
  • Change your point of view . Use your imagination to change the way you are looking at the problem. Turn it upside down, or pull it inside out.
  • Check for hidden assumptions  you may be making (you might be making the problem harder than it really is). These assumptions are often found by changing the given numbers or conditions and looking to see what happens.
  • Identify needed and given information clearly . You may not need to find everything you think you need to find, for instance.
  • Make up your own technique . It is your mind, after all; use mental actions that make sense to you. The key is to do something that engages you with the problem.
  • Try combinations of the above heuristics .

These heuristics can be readily pointed out to students as they engage problems in the classroom. However, real-world problems are often confronted many times over or on increasingly complex levels. For those kinds of problems, George Polya, the father of modern problem-solving heuristics, identified a fifth class (E) of looking-back heuristics. We include these here for completeness, but also with the teaching caveat that solutions often improve and insights grow deeper after the initial pressure to produce a solution has been resolved. Subsequent considerations of a problem situation are invariably deeper than the first attempt.

  • Check your solution . Substitute your answer or results back into the problem. Are all of the conditions satisfied?
  • Find another solution . There may be more than one answer. Make sure you have them all.
  • Solve the problem a different way . Your first solution will seldom be the best solution. Now that the pressure is off, you may readily find other ways to solve the problem.
  • Solve a related problem . Steve Brown and Marion Walter in their book,  The Art of Problem Posing , suggest the “What if not?” technique. What if the train goes at a different speed? What if there are 8 children, instead of 9? What if . . .? Fascinating discoveries can be made in this way, leading to:
  • Generalize the solution . Can you glean from your solution how it can be made to fit a whole class of related situations? Can you prove your result?

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22 Heuristics Examples (The Types of Heuristics)

22 Heuristics Examples (The Types of Heuristics)

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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heuristic examples and definition, explained below

A heuristic is a mental shortcut that enables people to make quick but less-than-optimal decisions.

The benefit of heuristics is that they allow us to make fast decisions based upon approximations, fast cognitive strategies, and educated guesses. The downside is that they often lead us to come to inaccurate conclusions and make flawed decisions.

The most common examples of heuristics are the availability, representativeness, and affect heuristics. However, there are many more possible examples, as shown in the 23 listed below.

Heuristics Definition

Psychologists Amos Tversky and Daniel Kahneman created the concept of heuristics in the early 1970s. They can be described in the following way:

“[They are] judgmental shortcuts that generally get us where we need to go – and quickly – but at the cost of occasionally sending us off course.”

Thus, we can see heuristics as being both positive and negative for our lives. But most interestingly, they can be leveraged in marketing situations to manipulate people’s purchasing decisions, as discussed below.

Types of Heuristics with Examples

1. availability heuristic.

Quick Definition: Making decisions based upon information that is easily available.

We often rely upon and place greater emphasis upon information that is easily available when making decisions.

We might make a decision based solely on what we know about a topic rather than conducting deeper research in order to make a more informed decision. This causes mistakes in our thinking and leads us to make decisions that are flawed or not sufficiently thought out.

This bias is one reason why political parties try to be the last person who talks to a voter before they go into a polling booth. The newness of the information may cause someone to vote for that part because the party’s arguments are closest to the top of mind.

> Check out these 15 availability heuristic examples

2. Representativeness Heuristic

Quick Definition: Making judgments based upon the similarity of one thing to its archetype. In social situations, this leads to prejudice.

We often make a snap judgment about something by placing it into a category based on its surface appearance. For example, we might see a tree and immediately assume it’s in the oak family based upon the color of its bark or size of its leaves.

In social sciences, we can also see that people make judgements about other people based upon their race, gender, class, or other aspects of their identity. In these situations, we are using stereotypes to come to snap judgements about others.

In these situations, our stereotypical assumptions about others can lead to bias, prejudice , and even discrimination .

> Check out these 11 representativeness heuristic examples

3. Affect Heuristic

Quick Definition: We often make decisions based on emotions, moods, and “gut feelings” rather than logic.

Emotions, moods, and feelings impact our thoughts. This simple fact can lead people into making emotional decisions that they may regret later on when they reflect using logic.

One affect heuristic example is the fact that we often make emotional outbursts that we regret later on. Yelling at a cashier at the shops, for example, may be followed up with regret when we reflect and realize it really wasn’t the cashier’s fault.

Similarly, shoppers make impulse purchases based on the feelings they have about the handbag or new dress. These purchases may be regretted later on when we use logic and realize we have overspent our budgets.

4. Anchoring Heuristic

Quick Definition: We often make decisions based upon a subjective anchoring point that influences all subsequent thinking on a topic.

An anchoring point is often the original piece of information that we are given. Based upon this original piece of information, all future thinking and decisions look good or bad.

An anchoring heuristic example is when a company sets the cost of their goods high before setting a discount. If a high price is set, then a discount is applied, then people would see the price as a bargain rather than high .

Similarly, if you were looking at two highly-priced products, the product that is a few dollars less than the other is seen as a good deal, even if its price is also inflated.

5. Base Rate Heuristic

Quick Definition: We neglect the base statistics in favor of other more proximate statistics when making a judgment.

Base rate neglect occurs when someone forgets the base rate, or a basic fact about information, and instead makes decisions based upon other information that they place too much importance upon.

For example, we may predict that the next person to walk into a hospital is a man if the last three people who entered were all males.

This assumption neglects the fact that 50% of all people who enter hospitals are women.

Here, we are privileging immediate information: that there appears to be a lot of men entering the hospital right now., instead of the base rate fact: that you’ve generally got a 50% chance of a woman walking into the store.

6. Absurdity Heuristic

Quick Definition: We tend to classify things that are improbably as absurd rather than giving them proper consideration.

Many people who believe themselves to be highly logical fall prey to the absurdity heuristic. This occurs when you hear a claim that is improbable, so you instantly dismiss it out of hand.

The ability to filter out absurdity has been highly useful to humans – allowing us to keep our focus on reality and not get caught up in conspiracy theories day and night.

But this becomes a problem when we dismiss things that are serious problems. For example, rejection of climate change science based on the fact that it seems extreme, or a doctor dismissing symptoms of a rare disease, are cases when absurdity bias leads us to make overly dismissive decisions.

7. Contagion Heuristic

Quick Definition: We can sometimes see people, ideas, and things as being either positively or negatively contagious despite lack of logic.

Sometimes, people will try to avoid contact with something or someone that has been the victim of bad luck. For example, a person may feel uncomfortable touching a cancer patient despite the fact they are not at all contagious.

On the positive end, we may believe lucky people will remain lucky and may even spread good luck if we spend time with them. Sometimes, this could be called the halo effect and horns effect.

8. Effort Heuristic

Quick Definition: Assuming the quality of something correlates with the amount of effort put into it.

We will often think something is more valuable or higher quality if it took a great deal of effort to create it. This assumption may be correct, but it doesn’t always turn out to be true.

For example, a person may spend 20 hours a day, 365 days a year, working on a startup business and it may still fail due to flaws in the business model. Another person may build a business in a week and see instant success.

Here, there is no positive correlation between effort and quality.

Nevertheless, the effort heuristic is utilized by advertisers all the time. Advertisements might talk about the amount of hours spent testing products, the research and development money put into it, and so on, in order to show that a lot of effort was put into it. The insinuation here is that the effort has led to a higher-quality product, when this is not necessarily always true.

9. Familiarity Heuristic

Quick Definition: We can often take mental shortcuts where we decide things that are most familiar to us are better than things that are less familiar.

Humans tend to see safety in the familiar and risk in the unfamiliar. In reality, familiar things may be just as risky, if not more, than unfamiliar things. Nevertheless, we know how to navigate familiar situations and therefore find them less risky.

A good example of this is travel. We may look to a country overseas and see it as potentially dangerous or scary. But, looking at data, our hometown or home city may be far more dangerous!

Similarly, we’re much more likely to die in a car crash than a plane crash. Nevertheless, fear may overcome you getting on a plane despite the fact that you didn’t put a moment’s thought into the drive to the airport.

10. Fluency Heuristic

Quick Definition: If an idea is communicated more fluently or skillfully then it is given more credence than an idea that is clumsily communicated, regardless of the merit of the idea.

The fluency with which an idea is communicated can directly impact how we perceive the idea. This mental shortcut allows us to bypass direct assessment of the merits of a case. Instead, we rely more on the charisma of the communicator.

For example, leaders with charismatic authority can often command a high vote during elections because of their ability to connect with voters moreso than their actual policy positions.

11. Gaze Heuristic

Quick Definition: Animals and humans have developed the ability to fixate on an estimated position rather than conducting complex calculations. Generally, this is in relation to motion.

The most common example of the gaze heuristic is the process humans go through to estimate where a ball will land. We don’t do all the calculations to understand trajectory and angle. Instead, we’ve developed an uncanny ability to identify where the ball will land through mental shortcuts based on previous experience.

Similarly, predatory animals can predict where their prey will flee to in order to intercept it, bats can use it during echolocation to estimate the location of obstacles, and hockey goalkeepers can use it to estimate the eventual position of a puck flying towards the goals.

12. Recognition Heuristic

Quick Definition: We assume that things we recognize have more value than things we do not recognize.

Recognition is an important facet of product marketing. Brand recognition alone can help a brand to thrive among a field of other products on a shelf.

The recognition heuristic states that we take mental shortcuts when looking at a range of options by assuming that the most recognizable option holds greater value. Thus, we assume a well-known household brand is higher-quality than a lesser-known brand.

Similarly, a study in psychology found that people assume cities whose names they recognize have larger populations than those that they don’t recognize. This assumption is based on the mental shortcut that larger cities are more likely to have recognizable names than smaller cities. This mental shortcut is often accurate, showing how heuristics can be beneficial (we call this the “less is more effect”).

13. Scarcity Heuristic

Quick Definition: When something is scarce , we see it as more valuable.

False scarcity is a widely-utilized method in marketing psychology because it encourages consumers to see a product as having greater value than it really does.

When a product is framed as being scarce, it is seen as having value because only a certain number of people can have it. As a result, people want it more. Sometimes, we call this the framing effect .

One way marketers use false scarcity is that they create limited-time discounts. In this case, the low price is a point of scarcity. Another way they can create false scarcity is to have open and closed cart periods so the product is only available for a short period of time.

This is a heuristic because people are encouraged to bypass making cold contemplative decisions about the product and, instead, make rushed decisions based on fear of missing out.

14. Similarity Heuristic

Quick Definition: Similarity between past and present situations impacts decision-making, allowing people to bypass making objective comparisons of two alternatives.

We tend to rely on past experiences to shape future experiences. If we liked something previously, then we may seek out similar situations in the future. If we didn’t like it in the past ,then we may avoid those situations in the future.

This logic allows people to bypass a thorough assessment of something and, instead, make fast decisions based on past experience.

Marketers can take advantage of this tendency. For example, a new fast food restaurant may use colors and a menu similar to McDonald;s in order to lull consumers into seeing the restaurant as similar to their previous positive experiences at McDonald’s, and therefore more likely to give it a go.

Similarly, Netflix may show you shows and movies similar to previous ones you watched to the end, because Netflix knows that you are going to be partisan toward a similar experience to the ones you previously enjoyed.

15. Simulation Heuristic

Quick Definition: We tend to overestimate the likelihood of an event based upon how easy it is to visualize it.

If our minds are able to visualize something happening, then we overstimate its probability.

Generally, the simulation heuristic occurs in relation to regret or near misses. A great example of this is buying a lottery ticket. If you found out that someone bought a winning lottery ticket one hour after you bought your ticket, then you’d easily be able to visualize the potentiality that you had gotten stuck in traffic that day and turned up to buy the ticket an hour later.

In this example, the probability of you ever turning up to buy the lottery ticket at the right time and place remains extremely low. However, because you can so easily visualize that eventuality, it feels as if you were truly very close to winning the lottery.

16. Social Proof Heuristic

Quick Definition: We use social proof as a mental shortcut to verify the quality or veracity of something instead of investigating it ourselves.

The social proof heuristic occurs both in social norms and product marketing.

In social norms, people tend to accept something as normal, correct, or appropriate because the rest of society does.

We could imagine, for example, 200 years ago many people thought the idea of the women’s right to vote as an idea that is strange or worthy of serious critique before being implemented. There weren’t many people supportive of the idea, so it was unquestioned. Today, because women’s right to vote is a social norm, it seems absurd that anyone would take it away.

In both of the above situations, people relied on broader society’s views (i.e. social proof) as an anchoring point for their own thinking on the topic.

Similarly, in marketing, marketers often go to great lengths to get quotes from “average joes” who have used a product in order to provide social proof in their advertisements.

17. Authority Heuristic

Quick Definition: We tend to defer to authorities as a shortcut rather than doing the thinking and research ourselves.

Society is structured in such a way that we defer to authorities and experts constantly. For example, we will defer to doctors on medical issues, engineers when building bridges, and lawyers on legal issues.

It’s just impossible to go about life trying to be an expert and authority on every topic. Instead, we will need to team up with authorities to make intelligent decisions. So, this heuristic is necessary.

However, mistakes can often be made when we see a person as an authority in one topic and, therefore, assume they’re an authority in entirely unrelated topics.

18. Hot-Hand Fallacy

Quick Definition: We overestimate our chances of success after a string of recent successes.

The hot-hand fallacy assumes that successful people will continue to experience success in the future.

The phrase “hot-hand” refers to gambling where a person rolling a dice has a “hot-hand” if they keep rolling the right numbers.

But we can apply this concept to a range of other situations. For example, we can apply it to investment funds, where investors will invest in a fund if it recently saw a lot of success.

However, past success does not guarantee future results. The more important thing would be to look at their investment philosophy rather than take the mental shortcut of “if they have recently been successful, then they will be in the future, too.”

19. Occam’s Razor

Quick Definition: The assumption that the most straightforward explanation is the most accurate.

Occam’s razor refers to the preferencing of more straightforward explanations as opposed to more complex ones. One logical justification for this is that the straightforward explanation has the least possible variables where mistakes in logic can occur.

However, critics of this approach highlight that, by definition, Occam’s razor fails to contemplate all possible variables and therefore causes oversimplification of explanations. Nevertheless, invoking Occam’s razor allows people to step back from a situation and contemplate whether they have over-complicated a simple situation.

>Check out these 15 occam’s razor examples

20. Naive Diversification

Quick Definition: Longer-term planning tends to involve more diversification than shorter-term planning.

Consider a situation where you are asked to purchase 5 weeks’ worth of groceries at once. In this situation, you’re more likely to buy a diverse range of fruit and vegetables for the forthcoming five weeks.

By contrast, if you were to go shopping once a week for five weeks, you’re less likely to diversify. Rather, you would buy a narrow range of products that you want in the short term.

In this example, people tend to diversify when faced with longer-term plans than shorter-term plans.

Naive diversification teaches us a lesson in business and investment. It teaches us that sometimes we are too soon to diversify when making plans because of our inability to make longer-term decisions in the shorter-term. As a result, we try to hedge by diversifying.

21. Peak–End Rule

Quick Definition: People tend to remember and pass judgment on an event based upon its most intense moment of finality rather than the average.

The peak-end rule refers to situations where the peak and end of a situation are the most important in our memories. When describing situations in the past tense, our minds shortcut to the peak and the end and fail to contemplate the other parts of the memory.

For example, a book or movie may be boring for 75% of the film, but the last 25% are excellent. You then go away and tell people how excellent it was, forgetting that there were long boring periods.

This is because our minds are most stimulated at the highly emotive parts of a situation, searing them in our memories.

This rule can be applied in vacation packages, movies, and other experince-based services where the experience is curated so the peak (and end) are highly stimulating to create a ‘wow experience’ that shapes people’s memories.

22. Mere Exposure Effect

Quick Definition: The mere exposure effect occurs when people develop a preference for a stimulus (such as a brand) simply because it is familiar. It is sometimes referred to as the familiarity principle.

The more frequently a person sees, experiences, or is otherwise exposed to something, the more likely it is that they will begin to like and favor it.

This is a cognitive heuristic because it involves a mental shortcut where something that is familiar is assumed to be safer and more trustworthy than unfamiliar things, regardless of the facts of the case.

This is used extensively in advertising, for example, where repeated exposure to advertisements from a particular brand, such as a restaurant, might make people more inclined to go to that restaurant next time they are hungry.

>See our full article on the Mere Exposure Effect

Heuristics are rules of thumb that help us make decisions quickly. They are useful in many situations, and in fact have helped us evolutionarily by filtering out bad information and making decisions quickly.

However, they can can also lead to biases and errors in our thinking. In the worst-case scenarios they can lead to stereotyping and significant social harm. The most common types of heuristics are availability heuristics, representativeness heuristics, and anchoring and adjustment.

Knowing about these biases in our thinking can help marketers to sell products and help reflective people to make better decisions by knowing when and when not to use heuristics.

See Also: Fundamental Attribution Error Examples


  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Number Games for Kids (Free and Easy)
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What Are Heuristics?

Understanding heuristics.

  • Pros and Cons
  • Examples in Behavioral Economics

Heuristics and Psychology

The bottom line.

  • Investing Basics

Heuristics: Definition, Pros & Cons, and Examples

James Chen, CMT is an expert trader, investment adviser, and global market strategist.

heuristics problem solving examples

Heuristics are mental shortcuts that help people make quick decisions. They are rules or methods that help people use reason and past experience to solve problems efficiently. Commonly used to simplify problems and avoid cognitive overload, heuristics are part of how the human brain evolved and is wired, allowing individuals to quickly reach reasonable conclusions or solutions to complex problems. These solutions may not be optimal ones but are often sufficient given limited timeframes and calculative capacity.

These cognitive shortcuts feature prominently in behavioral economics .

Key Takeaways

  • Heuristics are mental shortcuts for solving problems in a quick way that delivers a result that is sufficient enough to be useful given time constraints.
  • Investors and financial professionals use a heuristic approach to speed up analysis and investment decisions.
  • Heuristics can lead to poor decision-making based on a limited data set, but the speed of decisions can sometimes make up for the disadvantages.
  • Behavioral economics has focused on heuristics as one limitation of human beings behaving like rational actors.
  • Availability, anchoring, confirmation bias, and the hot hand fallacy are some examples of heuristics people use in their economic lives.

Investopedia / Danie Drankwalter

People employ heuristics naturally due to the evolution of the human brain. The brain can only process so much information at once and therefore must employ various shortcuts or practical rules of thumb . We would not get very far if we had to stop to think about every little detail or collect every piece of available information and integrate it into an analysis.

Heuristics therefore facilitate timely decisions that may not be the absolute best ones but are appropriate enough. Individuals are constantly using this sort of intelligent guesswork, trial and error, process of elimination, and past experience to solve problems or chart a course of action. In a world that is increasingly complex and overloaded with big data, heuristic methods make decision-making simpler and faster through shortcuts and good-enough calculations.

First identified in economics by the political scientist and organizational scholar Herbert Simon in his work on bounded rationality, heuristics have now become a cornerstone of behavioral economics.

Rather than subscribing to the idea that economic behavior was rational and based upon all available information to secure the best possible outcome for an individual ("optimizing"), Simon believed decision-making was about achieving outcomes that were "good enough" for the individual based on their limited information and balancing the interests of others. Simon called this " satisficing ," a portmanteau of the words "satisfy" and "suffice."

Advantages and Disadvantages of Using Heuristics

The main advantage to using heuristics is that they allow people to make good enough decisions without having all of the information and without having to undertake complex calculations.

Because humans cannot possibly obtain or process all the information needed to make fully rational decisions, they instead seek to use the information they do have to produce a satisfactory result, or one that is good enough. Heuristics allow people to go beyond their cognitive limits.

Heuristics are also advantageous when speed or timeliness matters—for example, deciding to enter a trade or making a snap judgment about some important decision. Heuristics are thus handy when there is no time to carefully weigh all options and their merits.


There are also drawbacks to using heuristics. While they may be quick and dirty, they will likely not produce the optimal decision and can also be wrong entirely. Quick decisions without all the information can lead to errors in judgment, and miscalculations can lead to mistakes.

Moreover, heuristics leave us prone to biases that tend to lead us toward irrational economic behavior and sway our understanding of the world. Such heuristics have been identified and cataloged by the field of behavioral economics.

Quick & easy

Allows decision-making that goes beyond our cognitive capacity

Allows for snap judgments when time is limited

Often inaccurate

Can lead to systemic biases or errors in judgment

Example of Heuristics in Behavioral Economics


A popular shortcut method in problem-solving identified in behavioral economics is called representativeness heuristics. Representativeness uses mental shortcuts to make decisions based on past events or traits that are representative of or similar to the current situation.

Say, for example, Fast Food ABC expanded its operations to India and its stock price soared. An analyst noted that India is a profitable venture for all fast-food chains. Therefore, when Fast Food XYZ announced its plan to explore the Indian market the following year, the analyst wasted no time in giving XYZ a "buy" recommendation.

Although their shortcut approach saved reviewing data for both companies, it may not have been the best decision. Fast Food XYZ may have food that is not appealing to Indian consumers, which research would have revealed.

Anchoring and Adjustment

Anchoring and adjustment is another prevalent heuristic approach. With anchoring and adjustment, a person begins with a specific target number or value—called the anchor—and subsequently adjusts that number until an acceptable value is reached over time. The major problem with this method is that if the value of the initial anchor is not the true value, then all subsequent adjustments will be systematically biased toward the anchor and away from the true value.

An example of anchoring and adjustment is a car salesman beginning negotiations with a very high price (that is arguably well above the  fair value ). Because the high price is an anchor, the final price will tend to be higher than if the car salesman had offered a fair or low price to start.

Availability (Recency) Heuristic

The availability (or recency) heuristic is an issue where people give too much weight to the probability of an event happening again if it recently has occurred. For instance, if a shark attack is reported in the news, those headlines make the event salient and can lead people to stay away from the water, even though shark attacks remain very rare.

Another example is the case of the " hot hand ," or the sense that following a string of successes, an individual is likely to continue being successful. Whether at the casino, in the markets, or playing basketball, the hot hand has been debunked. A string of recent good luck does not alter the overall probability of events occurring.

Confirmation Bias

Confirmation bias is a well-documented heuristic whereby people give more weight to information that fits with their existing worldviews or beliefs. At the same time, information that contradicts these beliefs is discounted or rejected.

Investors should be aware of their own tendency toward confirmation bias so that they can overcome poor decision-making, missing chances, and avoid falling prey to bubbles . Seeking out contrarian views and avoiding affirmative questions are two ways to counteract confirmation bias.

Hindsight Bias

Hindsight is always 20/20. However, the hindsight bias leads us to forget that we made incorrect predictions or estimates prior to them occurring. Rather, we become convinced that we had accurately predicted an event before it occurred, even when we did not. This can lead to overconfidence for making future predictions, or regret for not taking past opportunities.


Stereotypes are a kind of heuristic that allows us to form opinions or judgments about people whom we have never met. In particular, stereotyping takes group-level characteristics about certain social groups—often ones that are racist, sexist, or otherwise discriminatory—and casts those characteristics onto all of the members in that group, regardless of their individual personalities, beliefs, skills, or behaviors.

By imposing oversimplified beliefs onto people, we can quickly judge potential interactions with them or individual outcomes of those people. However, these judgments are often plain wrong, derogatory, and perpetuate social divisions and exclusions.

Heuristics were first identified and taken seriously by scholars in the middle of the 20th century with the work of Herbert Simon, who asked why individuals and firms don't act like rational actors in the real world, even with market pressures punishing irrational decisions. Simon found that corporate managers do not usually optimize but instead rely on a set of heuristics or shortcuts to get the job done in a way that is good enough (to "satisfice").

Later, in the 1970s and '80s, psychologists Amos Tversky and Daniel Kahneman working at the Hebrew University in Jerusalem, built off of Herbert Simon's work and developed what is known as Prospect Theory . A cornerstone of behavioral economics, Prospect Theory catalogs several heuristics used subconsciously by people as they make financial evaluations.

One major finding is that people are loss-averse —that losses loom larger than gains (i.e., the pain of losing $50 is far more than the pleasure of receiving $50). Here, people adopt a heuristic to avoid realizing losses, sometimes spurring them to take excessive risks in order to do so—but often leading to even larger losses.

More recently, behavioral economists have tried to develop policy measures or "nudges" to help correct people's irrational use of heuristics in order to help them achieve more optimal outcomes—for instance, by having people enroll in a retirement savings plan by default instead of having to opt in.

What Are the Types of Heuristics?

To date, several heuristics have been identified by behavioral economics—or else developed to aid people in making otherwise complex decisions. In behavioral economics, representativeness, anchoring and adjustment, and availability (recency) are among the most widely cited. Heuristics may be categorized in many ways, such as cognitive versus emotional biases or errors in judgment versus errors in calculation.

What Is Heuristic Thinking?

Heuristic thinking uses mental shortcuts—often unconsciously—to quickly and efficiently make otherwise complex decisions or judgments. These can be in the form of a "rule of thumb" (e.g., saving 5% of your income in order to have a comfortable retirement) or cognitive processes that we are largely unaware of like the availability bias.

What Is Another Word for Heuristic?

Heuristic may also go by the following terms: rule of thumb; mental shortcut; educated guess; or satisfice.

How Does a Heuristic Differ From an Algorithm?

An algorithm is a step-by-step set of instructions that are followed to achieve some goal or outcome, often optimizing that outcome. They are formalized and can be expressed as a formula or "recipe." As such, they are reproducible in the sense that an algorithm will always provide the same output, given the same input.

A heuristic amounts to an educated guess or gut feeling. Rather than following a set of rules or instructions, a heuristic is a mental shortcut. Moreover, it often produces sub-optimal and even irrational outcomes that may differ even when given the same input.

What Are Computer Heuristics?

In computer science, a heuristic refers to a method of solving a problem that proves to be quicker or more efficient than traditional methods. This may involve using approximations rather than precise calculations or techniques that circumvent otherwise computationally intensive routines.

Heuristics are practical rules of thumb that manifest as mental shortcuts in judgment and decision-making. Without heuristics, our brains would not be able to function given the complexity of the world, the amount of data to process, and the calculative abilities required to form an optimal decision. Instead, heuristics allow us to make quick, good-enough choices.

However, these choices may also be subject to inaccuracies and systemic biases, such as those identified by behavioral economics.

Simon, Herbert. " Herbert Simon, Innovation, and Heuristics ." Mind & Society, vol. 17, 2019, pp. 97-109.

Kahneman, Daniel, and Tversky, Amos. " Prospect Theory: An Analysis of Decision Under Risk ." The Econometric Society, vol. 47, no. 2, 1979, pp. 263-292.

heuristics problem solving examples

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heuristics problem solving examples

Understanding Heuristics in Problem Solving and Decision Making

Heuristics are mental shortcuts or rules of thumb that simplify decision making and problem-solving processes. They are strategies derived from previous experiences with similar problems that help individuals make quick, efficient judgments. The term "heuristic" comes from the Greek word "heuriskein," which means "to discover" or "to find." Heuristics play a crucial role in both everyday life and expert systems, allowing for satisfactory solutions when an exhaustive search is impractical.

Types of Heuristics

There are several types of heuristics commonly identified in cognitive psychology and behavioral economics, including but not limited to:

This involves estimating the likelihood of events based on their availability in memory. If something can be recalled easily, it is thought to be more common or likely.

This heuristic involves judging the probability of an event by finding a ‘representative’ or similar event and assuming the probabilities will be similar.

  • Anchoring and Adjustment Heuristic: This is the process of making decisions based on adjustments to a previously existing value or starting point, known as the anchor.
  • Affect Heuristic: Decisions are made based on the emotions associated with the outcomes or aspects of the decision, rather than a logical assessment.

Heuristics are not perfect and can lead to cognitive biases or systematic errors in thinking. However, they are valuable in that they allow for rapid decision-making, which can be particularly beneficial in fast-paced or emergency situations.

Heuristics in Problem Solving

In problem-solving, heuristics help in creating a simplified model of the world that makes it easier to generate solutions. They reduce the cognitive load by focusing on the most relevant aspects of the problem. For example, a common heuristic in problem-solving is "divide and conquer," where a complex problem is broken down into smaller, more manageable parts.

Heuristics in Decision Making

Heuristics also play a significant role in decision making, especially under conditions of uncertainty. They help individuals make quick decisions without having to analyze extensive information. For instance, a consumer might choose a product based on brand recognition (availability heuristic) rather than comparing all available alternatives.

Advantages and Disadvantages of Heuristics

The primary advantage of heuristics is their efficiency. They allow individuals to make decisions quickly, which is essential in many real-world situations where time is of the essence. However, the use of heuristics can also lead to biases and errors. For example, the availability heuristic can cause people to overestimate the likelihood of dramatic or recently reported events, such as plane crashes or shark attacks.

Heuristics in Artificial Intelligence

In artificial intelligence (AI), heuristics are used to design algorithms that can solve problems more efficiently. In AI, a heuristic function can estimate how close a state in a search space is to a goal state. This is particularly useful in games like chess, where the heuristic might be a function that evaluates who is ahead in a given board position.

Heuristics are an essential aspect of human cognition, aiding in rapid decision-making and problem-solving. While they can sometimes lead to errors or biases, their benefits in terms of speed and efficiency are undeniable. Understanding heuristics is crucial not only for cognitive psychology and AI but also for improving decision-making processes in various fields, including business, medicine, and public policy.

Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under Uncertainty: Heuristics and Biases. Cambridge University Press.

Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.

Newell, A., & Simon, H. A. (1972). Human Problem Solving. Prentice-Hall.

Russell, S. J., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach. Prentice Hall.

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What is Heuristics? Definition, Working, and Examples

Heuristics is an approach that steers an algorithm toward finding workable solutions for complex problems.

Heuristics is defined as a problem-solving or decision-making technique that uses minimum relevant information, past results, and experiences to produce a workable and practical solution for a problem in a reasonable period. This article explains the core principles underlying heuristics, its working, and some key examples in today’s computing world.

Table of Contents

What is heuristics, how does heuristics work, examples of heuristics.

Heuristics is a problem-solving or decision-making technique that uses minimum relevant information, past results, and experiences to produce a workable and practical solution for a problem in a reasonable time. These strategies focus on providing quick results with an acceptable accuracy range rather than offering near-perfect solutions.

Heuristics comprises vital ingredients of the machine learning (ML) and artificial intelligence (AI) disciplines. It is a go-to approach when it is highly impractical to derive a solution for a problem by following a step-by-step algorithm. Moreover, as heuristic strategies look to provide speedy solutions rather than accurate ones, they are generally blended with optimization algorithms to improve the results.

Technically, all iterations are interdependent, implying that each level of a deep neural network is crucial in deciding which solution path to choose and which to discard based on their closeness to the desired result. Thus, the term ‘heuristics’ is synonymous with ‘short-cut’, since it does not employ resources to explore solution paths that do not yield acceptable results.

Heuristic methods in AI are based on cognitive science principles that revolve around ‘how humans think’. Moreover, heuristic algorithms in AI enable systems to produce approximate solutions rather than exact ones. Heuristics do not necessarily provide a cheaper solution. Instead, the ones that do not overestimate the cost of achieving the result are termed ‘admissible heuristics’. This is a crucial characteristic of heuristics that ensures the solution’s optimality. At the fundamental level, an admissible heuristic simplifies the original problem by reducing its constraints.

Although heuristic processes tend to find solutions or results that often work or are correct, they may only sometimes be right, provable, optimal, or accurate. However, decisions based on heuristics are usually good enough to solve small-scale problems and provide solutions in situations of uncertainty where complete information is unavailable. 

Heuristics rely on shortcuts to provide immediate, efficient, and short-term solutions that facilitate timely decisions for businesses. Analysts across industries use specific thumb rules that allow companies to address a problem and make decisions and judgments rapidly and efficiently. These include the process of trial and error, elimination, intelligent guesswork, past results or formulas, and even the analysis of historical data. However, in the computing world, a heuristic model acts as a rule of thumb to speed up and simplify decision-making processes in situations where there’s not enough time for careful consideration of all the aspects of the problem.

Advantages of heuristics

Heuristics facilitate the real-time monitoring of events while using fewer resources and minimizing the system’s load. It allows systems to handle big data and ensure a faster turnaround time for decisions on complex problems. Heuristic rules are vital for computing, cybersecurity , and risk prevention strategies.

Moreover, the approach plays a vital role in detecting newer variations of past problems and issues, combining larger datasets to identify connections between data points eventually. It allows the system to reach a definitive conclusion based on the configuration and helps choose a safe course of action that is void of risks.

Heuristics involve trade-offs when compared to traditional algorithms and decision-making methods. It prioritizes speed over precision, accuracy, or completeness of a solution. Moreover, it involves intelligent guesswork and even cuts corners to return solutions with more errors. Heuristic models rely on minimal calculations that may produce results prone to biases. However, the speed of the outcome overshadows the underlying shortcomings. For example, a heuristic-based system can immediately block financial transactions (online) based on blacklisted data points such as customer ID, contact no., email, browser hash, and so on.

Although heuristics do not offer an ideal mechanism to design solutions, one can consider the general drawbacks of the approach when configuring rules and setting up processes so that it enables you to choose scenarios where heuristics can be applied not only to speed up a task but also free up resources.

See More: What Is HCI (Human-Computer Interaction)? Meaning, Importance, Examples, and Goals

Heuristics generally refers to educated guesses that seem to deliver faster decisions than traditional approaches when dealing with problem-solving factors in the computing industry. Heuristic models typically perform the following tasks that enable faster decision-making:

  • Analyze historical data
  • Monitor real-time data frequently (i.e., 24×7)
  • Compare data patterns in new and old data
  • Make appropriate assumptions that fill unknown gaps in the data
  • Trigger an action upon reaching a pre-set threshold for further processing

Heuristics are suitable for machine learning (i.e., white-box and black-box models), machine reasoning, and other related models that deal with diverse, big, and incomplete data. Heuristic-based techniques are popularly employed in trading & finance, cybersecurity, and fraud detection & prevention sectors. Moreover, they are being increasingly adopted by enterprises to advance their tech to enhance business productivity and efficiency.

Let’s understand the working of heuristics via a simple fraud detection example.

In fraud detection , heuristic models tend to terminate or block transactions by considering flagged data such as customer ID, cookies, contact details, or even specific action sequences in some cases. Let’s break down a simple use case.

  • Assume a scammer or fraudster registers himself for an online gambling game app hoping to manipulate and abuse the ‘bonus packages’ offered by the gaming system.
  • The fraudster has already tried this trick, though with a different device and contact details such as contact no. or user ID.
  • In the second fraudulent attempt, although a few user credentials are different, the fraudster is still on the same IP address since he provided the same email and home address. Also, the individual is following the same steps on the gambling platform he used in his first attempt.
  • Here, heuristics come to the forefront. The system uses a heuristic model to evaluate the newly entered data points picked up from the second attempt and match them to the shared data points associated with the first attempt. The model fills in the gaps and connects the new and old data points to arrive at a conclusion.
  • The risk tolerance threshold is reached as the estimates tend to reveal the similarity between the first and second fraudulent attempts. As a result, the system blocks the fraudster from accessing or using it.

In this example, the IP address used in the second fraud attempt matched that of the earlier fraud event. Also, the fraudster used similar steps to exploit the system. Based on these parameters, the system assumes that the same person is trying to attempt another fraud.

Here, there’s a possibility that the result of risk analysis may just be a false positive. However, risk analysts have already considered several constraints and variables, such as ‘risk vs. reward’, to decide that it is better to proceed with false positives rather than face the consequences of false negatives. This implies that the analysts are okay with blocking legitimate users than missing the opportunity of blocking real fraudsters. Accordingly, the risk analysis team adjusts the risk tolerance threshold for the particular company or scenario.

In some cases, fraudsters can also put in user credentials, such as a house address that matches the address of a legitimate user. Some scammers tend to operate through a shared internet access spot that legitimate users use. These are the techniques used by fraudsters to play safely. However, with machine and browser fingerprinting, these data points are identified. From here on, the heuristic model starts finding connections and making assumptions essential for deriving conclusions.

See More: What Is Quantum Computing? Working, Importance, and Uses

Heuristics is an inevitable and inseparable part of artificial intelligence . Simply put, it is a computer simulation of the human thinking process, used in situations no known algorithm can reach. Hence, heuristics are generally used in conjunction with optimization algorithms to enhance the overall efficiency of the desired results.

Here are some of the key examples where heuristics are routinely used:

1. Traveling salesman problem (TSP)

The traveling salesman problem refers to an optimization problem where a list of cities and the distance between each pair of cities is given. The task is to determine the shortest path to visit each city once and eventually return to the origin city. As multiple cities are traversed, the solution must also check cost (i.e., distance) and time complexity.

The TSP problem is generally considered an NP-hard problem (non-deterministic polynomial-time hardness) as producing an optimal solution even for a small- or moderate-sized dataset is a challenging task. As an alternative, a greedy algorithm can be used in this case to yield an approximate solution in a reasonably shorter period. This implies that the result approximates the optimal answer, which is a good-enough solution for the problem. The algorithm is a type of heuristics in one sense, indicating that the solution is close enough to the desired result. Although one can achieve solutions theoretically, an approximation is the best bet considering time constraints.

TSP is a combinatorial optimization problem having multiple applications in our daily lives. This includes vehicle routing, logistics (planning and scheduling), goods delivery, maritime industry, airport networks, public transportation networks in top-tier cities, and so on. TSP is a good example where heuristics play an important role.

2. Search optimization problems

Heuristics is known to make algorithms faster when handling specific ‘search optimization problems’. In step 1, heuristic rules tend to try out every possibility at each stage, which means executing a full-space search algorithm. However, the system can abort this search at any point if it recognizes that the current solution is worse than the best solution that has already been determined. Thus, heuristics helps optimize such search problems by initially trying out good choices or solutions while eliminating the wrong paths early.

Specific best-first search algorithms such as ‘A* search’ use heuristics to boost the algorithm’s convergence while keeping track of the correctness of the solution as long as the heuristic is admissible; for example, search engine optimization. Search engines help individuals find relevant information from millions of data sources. However, with such vast volumes of information on the internet, finding helpful content can be difficult. To make the process as swift as possible, search engines use heuristics to expedite the search process and ensure that individuals find relevant information in minimal time.

3. Heuristic search hypothesis

Allen Newell and Herbert A. Simon proposed the heuristic search hypothesis. In this hypothesis, solutions to complex problems are revealed as symbol structures. The symbol system then employs intelligence to solve the problem through a search. The process repeatedly generates, modifies, and restructures symbols until the created structure matches the solution structure.

Thus, each step invariably depends on the previous step. As a result, the heuristic model learns which paths to pursue and which ones to eliminate by verifying the closeness of the current step to the desired solution. Consequently, this process saves time and resources as some possible solutions may not be generated based on its measured unlikeliness to complete the solution.

In this context, a heuristic model fulfills its task by exploiting search trees. Rather than generating all possible solution branches in the initial stages, the model selects branches that have a higher probability of producing outcomes than other branches that are less likely to do so. At each decision point, the heuristic picks up the branches that produce acceptable solutions at each decision point.

4. Antivirus software

Antivirus software relies on heuristic rules to identify, detect, and isolate different forms of malware . It scans the software under consideration and looks for the code patterns or even behavioral patterns of the viruses with rules in place for different virus types. During the process, it is identified if the file or executing strategy reveals a particular code pattern or pattern of activities. As a result, it is inferred that the file is infected.

Moreover, with behavior-based heuristic scanning approaches, even self-modifying polymorphic viruses are traceable, unlike other simple scanning methods. Heuristic-based scanning can detect mutating viruses without needing to be detected previously. It can see suspicious new file codes or behavior patterns in real-time without needing to be noticed, analyzed, and labeled as an ‘xyz’ virus. As such, future viruses can be tackled by following the heuristics approach.

5. Knapsack problem (KP)

A knapsack problem consists of a group of items, each having a weight and a value. The task is to determine the total number of things to include in a combination so that the total weight of the item is less than or equal to a specific weight and the entire item value is as high as possible.

A heuristic model in the form of a greedy algorithm can be employed to solve the problem. The algorithm arranges items in descending order of value per weight and then inserts them in the sack. The technique allows the most valuable and heavy things to get into the pack first.

Such KP problems where heuristics play a vital role find applications across various fields such as machine scheduling, space allocation, asset optimization, home energy management, software resource management, optimizing power allocation to electronic equipment, network selection for mobile nodes, and so on.

See More : What Is Super Artificial Intelligence (AI)? Definition, Threats, and Trends

Heuristics in computer science refers to the ‘rules of thumb’ that algorithms use to determine approximate solutions to complex problems. As there’s too much information for systems to scan through before coming to a conclusion in a limited period, heuristic models prioritize speed over the correctness of the solution. However, it is crucial to consider that heuristic rules are specific to the problems you intend to solve, and their specifics may vary for every situation.

For example, let’s say you intend to apply heuristics to your algorithm designed to determine the number of moves a bishop can make on an 8×8 chessboard while traversing every square on the board. In this case, you can create heuristics that enable the bishop to choose a path with the most available diagonal moves. As you make a specific path, generating heuristic rules that allow the bishop to choose a path with the minimum available diagonal moves is better. As the available decision-making space is limited, solutions are also narrow, and as a result, they are found quickly.

Thus, with each definitive problem, you can design your own heuristic rules to finish a task in less time. This is a handy approach as some computationally complex issues may require years of computation to find the exact answer; however, you can produce an approximate result almost instantly with heuristics.

Did this article help you understand the fundamentals of heuristics and its role in computer science? Comment below or let us know on Facebook Opens a new window , Twitter Opens a new window , or LinkedIn Opens a new window . We’d love to hear from you!


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Heuristic Approach to Problem-solving: Examples

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Videos, worksheets, solutions, and activities to help students learn how to use the heuristic approach to solve word problems in Singapore Math.

Use A Picture / Diagram / Model Example: The total cost of 2 similar bags, 3 wallets and 4 belts is $1188. A bag cost thrice as much as a wallet and a wallet costs twice as much as a belt. How much will Ted have to pay for a bag, a wallet and a belt?

Heuristic Approach to problem-solving Example: 7/10 of the boys who participated in a marathon race were Chinese. The rest of the boys were made up of Eurasians and Malays in the ratio 5:7 respectively. There were 756 more Chinese than Malay boys. Find the total number of boys who participated in the marathon race.

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Heuristic Approaches to Problem Solving

“A heuristic technique, often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgement, guesstimate, stereotyping, profiling, or common sense.” (Source: Wikipedia )

“In computer science, a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut.” (Source: Wikipedia )

The objective of a heuristic algorithm is to apply a rule of thumb approach to produce a solution in a reasonable time frame that is good enough for solving the problem at hand. There is no guarantee that the solution found will be the most accurate or optimal solution for the given problem. We often refer the solution as “good enough” in most cases.

Heuristic Algorithms? Heuristic Algorithms can be found in:

Let’s investigate a few basic examples where a heuristic algorithm can be used:


Based on this approach, can you think of how a similar approach could be used for an algorithm to play:

  • Othello (a.k.a. Reversi Game)
  • A Battleship game?
  • Rock/Paper/Scissors?

It is hence essential to use a heuristic approach to quickly discard some moves which would most likely lead to a defeat while focusing on moves that would seem to be a good step towards a win!


Let’s consider the above scenario when investigating all the possible moves for this white pawn. Can the computer make a quick decision as to what would most likely be the best option?

heuristics problem solving examples

Alternatively, a machine learning algorithm could play the game and record and update statistics after playing each card to progressively learn which criteria is more likely to win the round for each card in the deck. You can investigate how machine learning can be used in a game of Top Trumps by reading this blog post. Heuristic methods can be used when developing algorithms which try to understand what the user is saying, or asking for. For instance, by looking for words associations, an algorithm can narrow down the meaning of words especially when a word can have two different meanings:


e.g. When using Google search a user types: “Raspeberry Pi Hardware” We can deduct that in this case Raspberry has nothing to do with the piece of fruit, so there is no need to give results on healthy eating, cooking recipes or grocery stores…

However if the user searches for “Raspeberry Pie ingredients” , we can deduct that the user is searching for a recipe and is less likely to be interested in programming blogs or computer hardware online shops. Short Path Algorithms used by GPS systems and self-driving cars also use a heuristic approach to decide on the best route to go from A to Z. This is for instance the case for the A* Search algorithm which takes into consideration the distance as the crow flies between two nodes to decide which paths to explore first and hence more effectively find the shortest path between two nodes.


You can compare two different algorithms used to find the shortest route from two nodes of a graph:

  • Dijkstra’s Shortest Path Algorithm (Without using a heuristic approach)
  • A* Search Algorithm (Using a heuristic approach)

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As a generalization of two-level hierarchical time minimization transportation problem (2HTMTP), capacitated two-level hierarchical time minimization transportation problem (C2HTMTP) is a vital issue due to the route shipping capacity ...

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In this paper we present a weight coded genetic algorithm (GA) based approach to the capacitated arc routing problem (CARP). In comparison to metaheuristic algorithms, simple constructive heuristic algorithms often produce poor quality solutions to the ...

A Two-Stage Heuristic with Ejection Pools and Generalized Ejection Chains for the Vehicle Routing Problem with Time Windows

The vehicle routing problem with time windows (VRPTW) is an important problem in logistics. The problem is to serve a number of customers at minimum cost without violating the customers' time-window constraints or the vehicle-capacity constraint. In ...


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    Algorithms. In contrast to heuristics, which can be thought of as problem-solving strategies based on educated guesses, algorithms are problem-solving strategies that use rules. Algorithms are generally a logical set of steps that, if applied correctly, should be accurate. For example, you could make a cake using heuristics — relying on your ...

  7. Using Heuristic Problem-Solving Methods for Effective ...

    Heuristics are essentially problem-solving tools that can be used for solving non-routine and challenging problems. A heuristic method is a practical approach for a short-term goal, such as solving a problem. The approach might not be perfect but can help find a quick solution to help move towards a reasonable way to resolve a problem.

  8. Heuristic Methods

    Heuristic methods can also play an important role in your problem-solving processes. The straw man technique, for example, is similar in approach to heuristics, and it is designed to help you to build on or refine a basic idea. Another approach is to adapt the solution to a different problem to fix yours. TRIZ is a powerful methodology for ...

  9. Heuristic Method definition, steps and principles

    A heuristic method is an approach to finding a solution to a problem that originates from the ancient Greek word 'eurisko', meaning to 'find', 'search' or 'discover'. It is about using a practical method that doesn't necessarily need to be perfect. Heuristic methods speed up the process of reaching a satisfactory solution.

  10. Heuristics and Problem Solving

    For example, it is obvious that the heuristic "distinguish the conditions that the solution should satisfy" can be used in a variety of problem situations and subject-matter domains besides mathematical problems, such as writing an essay, designing a plan for a house, diagnosing a disease, solving a physics problem, interpreting historical ...

  11. Heuristics: How Mental Shortcuts Help Us Make Decisions [2024 ...

    Heuristic thinking refers to a method of problem-solving, learning, or discovery that employs a practical approach—often termed a "rule of thumb"—to make decisions quickly. Heuristic thinking is a type of cognition that humans use subconsciously to make decisions and judgments with limited time.

  12. Heuristics

    2. Next. A heuristic is a mental shortcut that allows an individual to make a decision, pass judgment, or solve a problem quickly and with minimal mental effort. While heuristics can reduce the ...

  13. Some Helpful Problem-Solving Heuristics

    A heuristic is a thinking strategy, something that can be used to tease out further information about a problem and thus help you figure out what to do when you don't know what to do. Here are 25 heuristics that can be useful in solving problems. They help you monitor your thought processes, to step back and watch yourself at work, and thus ...

  14. Heuristics

    Firefighters, for example, may have an instinctive sense for when a burning building might collapse: a mental heuristic that they have developed through lots of experience. Heuristics appear to be an evolutionary adaptation that simplifies problem-solving and makes it easier for us to navigate the world.

  15. (PDF) Heuristics and Problem Solving

    Heuristics and Problem Solving: Definitions, Benefits, and Limitations. The term heuristic, from the Greek, means, "serving to find out or discover". (Todd and Gigerenzer, 2000, p. 738). In ...

  16. Heuristic

    A heuristic (/ h j ʊ ˈ r ɪ s t ɪ k /; from Ancient Greek εὑρίσκω (heurískō) 'method of discovery' ) or heuristic technique (problem solving, mental shortcut, rule of thumb) is any approach to problem solving that employs a pragmatic method that is not fully optimized, perfected, or rationalized, but is nevertheless "good enough" as an approximation or attribute substitution.

  17. 22 Heuristics Examples (The Types of Heuristics)

    The benefit of heuristics is that they allow us to make fast decisions based upon approximations, fast cognitive strategies, and educated guesses. The downside is that they often lead us to come to inaccurate conclusions and make flawed decisions. The most common examples of heuristics are the availability, representativeness, and affect ...

  18. Problem-Solving Strategies: Definition and 5 Techniques to Try

    In insight problem-solving, the cognitive processes that help you solve a problem happen outside your conscious awareness. 4. Working backward. Working backward is a problem-solving approach often ...

  19. Heuristics: Definition, Pros & Cons, and Examples

    Heuristics: A problem-solving method that uses short cuts to produce good-enough solutions given a limited time frame or deadline. Heuristics provide for flexibility in making quick decisions ...

  20. Heuristics Definition

    Heuristics in Problem Solving. In problem-solving, heuristics help in creating a simplified model of the world that makes it easier to generate solutions. They reduce the cognitive load by focusing on the most relevant aspects of the problem. For example, a common heuristic in problem-solving is "divide and conquer," where a complex problem is ...

  21. What is Heuristics? Definition, Working, and Examples

    Heuristics is a problem-solving or decision-making technique that uses minimum relevant information, past results, and experiences to produce a workable and practical solution for a problem in a reasonable time. These strategies focus on providing quick results with an acceptable accuracy range rather than offering near-perfect solutions.

  22. Heuristic Approach to Problem-solving: Examples

    Heuristic Approach to problem-solving Example: 7/10 of the boys who participated in a marathon race were Chinese. The rest of the boys were made up of Eurasians and Malays in the ratio 5:7 respectively. There were 756 more Chinese than Malay boys. Find the total number of boys who participated in the marathon race. Show Step-by-step Solutions

  23. Heuristic Approaches to Problem Solving

    Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgement, guesstimate, stereotyping, profiling, or common sense." (Source: Wikipedia) "In computer science, a heuristic is a technique designed for solving a problem ...

  24. A new hybrid genetic algorithm with tabu search for solving the

    An R&D sensor deployment problem was formulated, which attempted to deploy a minimum number of R&D sensors for coverage of a certain set of targets so that each target can be monitored by 0 < d < 1 $0 < d < 1$ ratio of time in each frame. The problem was proved as an NP-hard one and two heuristics were introduced as a solution to the problem.

  25. An efficient solution approach to capacitated three-level hierarchical

    The four algorithms especially two heuristic algorithms with fast descending characteristics have several merits including easy computer implementation, no memory overflow, high computing efficiency, and easy extension to capacitated multilevel hierarchical time minimization transportation problem with level greater than three.