Heuristics

When individuals engage in decision-making or judgment it is often necessary to use heuristics to help process the information that they encounter. Heuristics have been called rules of thumb but can be also viewed as cognitive frameworks for processing information during decision-making. Heuristics can be more or less effective based on a number of factors. Examples of types of heuristic include the anchoring and adjustment heuristic and the representative heuristic. Heuristics can be applied in many areas including education and viewed from unique vantage points such as the positive psychology approach.

Keywords Affect Heuristic; Anchoring and Adjustment Heuristic; Attribute Substitution; Decision-Making; Heuristics; Judgment; Moral Heuristics; Problem-Solving; Representative Heuristic

Overview

Individuals use heuristics to make decisions or come to conclusions about any number of events, people, or situations they encounter in their environment. Heuristics can be thought of as rules of thumb individuals use to make decisions across a range of circumstances (Veermans, van Joolingen, & de Jong, 2006). The rules of thumb that are heuristics are really cognitive frameworks that are developed through experience and implemented during problem-solving (Abel, 2003). Heuristics have been conceptualized as one aspect of a broader information-processing system that also entails perception, memory, and processing information in an ordered sequence (Hogarth, 1981). Additionally, the heuristic system has been posited to be one type of reasoning system within the realm of dual-process reasoning theories (De Neys, 2006).

About Heuristics

Characteristics of heuristics include:

• The possibility that their use could result in conclusions that are incorrect,

• The ability to apply them in a variety of circumstances,

• Being domain-specific or general in nature, and

• Being viewed in implicit or explicit terms (Veermans, van Joolingen, & de Jong, 2006).

In regard to the notion that using heuristics may lead to incorrect conclusions, it is also the case that using heuristics can lead to conclusions that may be correct yet inaccurate in some way (Abel, 2003; Smith, 1999). Inaccuracy resulting from the use of heuristics is often due to error that comes into play during the decision-making process.

Hogarth and Karelaia (2007) examined how effective heuristics have been and under what conditions heuristics prove to be more or less accurate. Conditions posited to affect heuristic accuracy and efficacy were the amount of information encountered and the existence of trade-offs concerning cues and attributes in information processing. Hogarth and Karelaia compared linear models of information processing with heuristic use in regard to regions of rationality. Heuristics performed more accurately when there was consonance between the nature of the heuristic and the environment in which they were used. Furthermore, decision-making using heuristics will frequently involve the assessment of the representativeness of the stimuli being evaluated and the outcomes being predicted (Kahneman & Tversky, 1996). Finally, heuristics may prove disadvantageous in decision-making when the settings in which individuals find themselves necessitate analytical and extended reasoning and not the quicker pace of heuristics (De Neys, 2006).

Types of Heuristics

Given the ubiquity of heuristics in everyday life, it stands to reason that there are a variety of heuristics in existence. Researchers continue to investigate how and why certain types of heuristics are utilized or “selected” for particular situations (Marewski & Schooler, 2011). Swinkels (2003) is just one of many researchers who has asserted that individuals use heuristics to help themselves process the social information they receive while attempting to make decisions. He reviewed several types of heuristics:

• Representative,

• Availability,

• Simulation, and

• Anchoring and adjustment heuristics.

Representative Heuristics

According to Swinkels, the representative heuristic involves using information about the more prototypical characteristics of groupings of people or things to make decisions about individual people or members of groups.

Availability Heuristics

In using the availability heuristic, individuals draw upon familiar exemplars of characteristics of groups as they process information.

Simulation Heuristics

The simulation heuristic involves the ability of individuals to create as many possible situations related to the question a hand.

Anchoring & Adjustment Heuristics

The anchoring and adjustment heuristic entails using a point of reference or an "anchor" when processing information during the decision-making process. The initial anchor often undergoes an adjustment before an individual settles on a decision (Swinkles, 2003).

Quite a few researchers have examined the anchoring and adjustment heuristic. For instance, Smith (1999) stated that adults have been documented to use the anchoring and adjustment but less was known about if, and how, children use the anchoring and adjustment heuristic. Smith conducted a study with students in elementary and middle school grades on the use of the anchoring and adjustment heuristic. Results indicated that even students in the youngest grades (i.e., third grade) used the anchoring and adjustment heuristic.

Further investigation of the anchoring and adjustment heuristic has yielded more intriguing findings. Morrow (2002) noted that the anchor in the anchoring and adjustment heuristic may unduly influence subsequent decisions if the information used to make the first estimate in the decision-making process is not sound, or the adjustments that are made fall short in accuracy. Epley and Gilovich (2006) highlighted the lack of appropriate adjustment when individuals use the anchoring and adjustment heuristic such that adjustments still remain in a range close to the anchor. They found that providing individuals with cautionary guidelines about anchoring effects led to more adjustments being made but only when the individuals supplied the anchor themselves.

Affect Heuristic

Finucane, Alhakami, Slovic, & Johnson (2000) posited that an affect heuristic is used in decision-making by individuals. The affect heuristic incorporates the positive and negative valences attributed to various representations when individuals make judgments, particularly about risks and benefits. Finucane and colleagues suggested that the affect heuristic accounts in some part for the negative association between risks and benefits during the decision-making process. Kahneman (2003) proclaimed the affect heuristic to be as seminal in the heuristics arena as the representative and availability heuristic.

Fluency, Generation & Resemblance Heuristics

In addressing additional heuristics, Whittlesea and Leboe (2000) focused on recall and recognition tasks as they related to decision-making and their relevance to the construct of remembering. The fluency, generation, and resemblance heuristics were posited to play roles in remembering. The fluency heuristic pertains to the facility with which individuals can process information about tangible stimuli in the environment (Olds & Westerman, 2012). Generation heuristics are related to the amount of information an individual is able to generate about a stimulus encountered in the environment. The resemblance heuristic refers to how many aspects of a stimulus are concordant with an individual's expectations of the stimulus due to past encounters as opposed to the current setting that the individual engages with the stimulus. The resemblance and generation heuristics are information gathering heuristics while the fluency heuristic is referred to by the authors as a quality-of-performance, or information processing, heuristic.

Priority & Moral Heuristics

Brandstätter, Gigerenzer, and Hertwig (2006) defined the priority heuristic as a framework by which individuals make decisions by prioritizing the gathered information, limiting the amount of information to review, and then making a decision given the information gathered. The priority heuristic stands in contrast to the weighting and summing process that comprises the trade-off theory of information processing. Sunstein (2003) addressed the topic of moral heuristics, or the use of rules of thumb in regard to moral and political topics, and the problems that arise when they are used without taking context into account. An example of a moral heuristic is the outrage heuristic where individuals make judgments about the punishment for a transgression based on the level of outrage the transgression evokes. Kahneman and Frederick (2002) offered the terms indignation heuristic or anger heuristic as possible synonyms for the outrage heuristic.

Applications

Discovery Learning

Heuristics have also been applied within various arenas and in diverse ways. For example, Veermans, van Joolingen, and de Jong (2006) detailed how viewing heuristics in implicit or explicit terms influences the discovery learning process. Discovery learning involves engaging students in the learning process through active and direct exploration of phenomena of interest. The use of implicit heuristics in discovery learning provides students with instructions garnered from a heuristic while explicit heuristic use entails naming the heuristic to be used and detailing the instructions yielded from the heuristic.

Research by Veermans and colleagues found students who received explicit heuristic instruction during discovery learning built upon action related to the instructions related to the heuristics they were exposed to and initiated action on their own. In other words, explicit heuristics promoted discovery learning. Students who received instruction in both implicit and explicit heuristics saw increases in content area learning. In a related study, Nokes, Dole, and Hacker (2007) focused on high school students and how they learn and use history heuristics such as sourcing, corroboration, and contextualization. After implementing an intervention with the students whereby they learned the heuristics, Nokes and colleagues found higher levels of content knowledge for the students in the heuristic intervention as compared to students who did not receive the intervention. Similarly, Chamizo (2012) showed that the use of heuristics (heuristic diagrams) in science teaching was an effective strategy to spur students problem-solving abilities.

Creative Problem Solving

Scott, Lonergan, and Mumford (2005) explored creative problem solving. They stated that creative problem solving is comprised of a number of processes, such as generating ideas and combining concepts, in which heuristics plays a role. Scott and colleagues investigated the process of combining concepts in creative problem solving and the role of case-based and analogical heuristics in this process. The former heuristic was used when a concept was presented to students in a case-format while the latter heuristic was used when principles were used to relay a concept. An association existed between both types of heuristics and the extent to which problem solving was deemed to be of a certain quality, original, and elegant.

Several heuristics have been posited to be integral to problem-solving in the areas of teaching and learning (Abel, 2003). The representation heuristic required individuals to generate a specific representation of the issue to be resolved through problem-solving. The process of specifying a representation was thought to help individuals move the problem being solved from abstract to tangible. Individuals then explore the multiple aspects of the issue of which they are problem-solving through the search heuristic. After using the search heuristic individuals would settle on one of several decisions that best address the issue about which they were problem-solving via the termination heuristic. Finally, the implementation heuristic is the process where individuals evaluate how successful the selected decision was at solving the problem at interest.

Butler and Kline (1998) addressed problem-solving in regard to what they refer to as "ill-structured problems." They discussed the brainstorming, hierarchical, and perspective changing heuristics as methods to problem-solve with ill-structured problems. Brainstorming heuristics incorporate creating possible scenarios for problem-solving and adapting ideas already in place. There are multiple steps to the use of hierarchical heuristics. Potential answers for problems are generated and then grouped with other answers that are similar in nature. This grouping is considered a superordinate and is then used to create even more answers, or subordinates, for the problem at hand. The process continues so that more superordinates and subordinates are created. The changing perspective heuristic involves problem-solving by thinking of alternate stances on the issue at interest. Butler and Kline's research illustrated that once individuals had been trained in the use of the preceding heuristics, the hierarchical heuristic generated the most problem-solving solutions and the brainstorming heuristic yielded the most creative solutions.

Schooler and Hertwig (2007) explored the relationship between forgetting and inferential heuristics such as the recognition heuristic using the fast and frugal heuristics framework as a foundation. They focused on the recognition and fluency heuristics because both types of heuristics involve an experiential evaluation of memory. The recognition heuristic is based on whether an individual can remember encountering stimuli while the fluency heuristic entails how facile an individual is at processing information about the stimuli they encounter. In the case of the recognition heuristic, it is used when an individual encounters similar stimuli and makes a judgment that the stimulus that is recognized by the individual is more valuable, as defined by the criterion of interest, than an unfamiliar stimulus. The more ecologically rational, or within the information-rich environment, the heuristic the more effective the recognition heuristic will be (Goldstein & Gigerenzer, 2002).

Viewpoints

Within the field of decision-making and judgment, there have been differences in perspective on the purpose and efficacy of heuristics. Kahneman and Frederick (2002) stated that at the core of the heuristics and biases approach was the notion that judgments were made by individuals through the dual processes of intuition (System 1) and reasoning (System 2). Kahneman (2003) distinguished between System 1 and System 2 operations in regard to reasoning whereby System 1 was denoted to be quicker, reflexive, and affect laden and System 2 was more purposeful and involved more time.

Revisions to the heuristic framework included delineating attribute substitution as explicating the process of heuristics, expanding heuristics as a construct so that it entails events beyond those that are ambiguous, and highlighting when intuition, or System 1 operations, are changed or controlled by System 2 operations (Kahneman, 2003). Kahneman and Frederick (2002) defined attribute substitution as the substitution of one attribute of a stimulus for another aspect of that same stimulus. Substitution usually occurs because an individual was able to access one attribute more quickly than another while processing information.

In response to the heuristics and biases approach, Gigerenzer & Goldstein (1996) argued that inferential processing should be examined from a standpoint where psychology and ecology are addressed as opposed to solely attending to logic and probability. The latter perspective, according to Girgenzer and Goldstein (1996), leads to focusing more on the error and bias aspect of heuristics instead of the efficacy of heuristics in decision-making.

Relatedly, Goldstein and Gigerenzer (2002) asserted that heuristics have come to be defined as, "poor surrogates for optimal procedures rather than indispensable psychological tools" (p. 75). They argue that heuristics can be ecologically rational and lead to judgment that may be sound as much as it can be limited. Along that vein, Girgenzer and colleagues also introduced the concept of fast and frugal heuristics. Fast and frugal heuristics are "simple but nevertheless fairly accurate strategies that use a minimum of information" (Garcia-Retamero, Takezawa, and Gigerenzer, 2006, p. 1352).

According to Garcia-Retamero, Takezawa, and Gigerenzer (2006), another example of a fast and frugal heuristic included the Take the Best (TTB) heuristic. In regard to the TTB, a pair of stimuli is presented to an individual and the individual must compare the stimuli and decide which is the more valuable. TTB entails searching for cues about the stimuli, knowing when to stop searching, and then making a decision based on the information garnered about the stimuli. The Take the Best algorithm performed comparably with statistical models in computer simulations of its efficacy in decision-making (Gigerenzer & Goldstein, 1996).

Another viewpoint on heuristics comes from work of Griffin and Kahneman (2003) that described heuristics and biases research using a positive psychology framework. They stated that although error may arise from the use of heuristics, their use has also contributed to survival for individuals. Furthermore, Griffin and Kahneman asserted that heuristics are grounded in multifaceted cognitive mechanisms that allow individuals to successfully process large amounts of information. Error may certainly arise but should not diminish the advantages garnered by using heuristics.

Conclusion

Using heuristics is one way that individuals engage in problem-solving in a world where they are often bombarded with increasing amounts of information. Heuristics can, and should, be viewed as more than just rules of thumb. Heuristics are cognitive frameworks for processing information encountered in the environment and ultimately making decisions and judgments about that information. Within this article various characteristics and types of heuristics were reviewed and further elaboration on the concept of heuristics was provided with examples of applications of heuristics in arenas such as the classroom. Finally, perspectives such as a positive psychology approach to heuristics also serve to expand our understanding of heuristics and the decision-making process.

Terms & Concepts

Affect Heuristic: The affect heuristic is a heuristic that attends to affective feelings when making judgments, particularly judgments involving risks and benefits.

Anchoring and Adjustment Heuristic: The anchoring and adjustment heuristic occurs when an individual uses a point of reference or an "anchor" when processing information during the decision-making process. The initial anchor is then adjusted before a decision is made.

Attribute Substitution: Attribute substitution is the substitution of one attribute of a stimulus for another aspect of that same stimulus due to an individual being able to access one attribute more quickly than another while processing information. Attribute substitution is posited to undergird the heuristics process.

Availability Heuristic: The availability heuristic occurs when an individual encounters a stimulus of some sort and then draws upon familiar exemplars of characteristics of groups related to that stimulus as they process information to make a decision.

Fast and Frugal Heuristic: Fast and frugal heuristics are heuristics that use brief and simple methods to effectively solve problems.

Heuristics: Heuristics are also known as rules of thumb. Heuristics are cognitive frameworks used by individuals to process information quickly in order to make decisions or judgments about that information.

Moral Heuristics: Moral heuristics are those heuristics individuals use to make decisions or judgments about moral or political topics. The outrage heuristic is an example of a moral heuristic that refers to the severity of a punishment for a transgression being related to the level of outrage an individual feels at the transgression committed.

Priority Heuristics: The priority heuristic is based on a framework where individuals prioritize gathered information, limit the amount of information to review, and then make a decision.

Representative Heuristic: The representative heuristic, or the representativeness heuristic, occurs when an individual uses information about prototypical characteristics of groupings to make decisions about individual stimuli.

Simulation Heuristic: The simulation heuristic occurs when individuals create as many possible situations related to the question at hand as a means to aid in decision-making.

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Suggested Reading

Eraña, Á., & Martínez, S. (2004). The heuristic structure of scientific knowledge. Journal of Cognition & Culture, 4 (3/4), 701-729. Retrieved November 13, 2007, from EBSCO online database, from Academic Search Premier. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=15109716&site=ehost-live

Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93 , 1449-1475. Retrieved November 27, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=11801370&site=ehost-live

Lubart, T., & Getz, I. (1998). The influence of heuristics on psychological science: A case study of research on creativity. Journal for the Theory of Social Behaviour, 28 , 435- 457. Retrieved November 13, 2007, from EBSCO online database, Academic Search Premier. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=5723184&site=ehost-live