What are the cognitive shortcuts used to simplify the decision making process called?

III.A. Heuristics for the Judgment of Probability and Frequencies: Availability, Representativeness, and Anchoring and Adjustment

The heuristics most widely studied within psychology are those that people use to make judgments or estimates of probabilities and frequencies in situations of uncertainty (i.e., in situations in which people lack exact knowledge). Most prominent among these are the availability, representativeness, and anchoring and adjustment heuristics.

The availability heuristic leads one to assess the frequency of a class or the probability of an event by the number of instances or occurrences that can be brought to mind or by how easy it seems to call up those instances. For instance, which class of words is more common: seven-letter English words of the form “_ _ _ _ _ n _” or the form “_ _ _ _ i n g”? According to the availability heuristic, to estimate the frequency of occurrences people draw a sample of the events in question from memory. Specifically, for this case they retrieve words ending in –ing (e.g., “jumping”) and retrieve words with “n” in the sixth position (e.g., “raisins”) and then count the number of words retrieved in some period or assess the ease with which such words could be retrieved. They then answer that the more numerous or easier class of words is more common. Because people find it easier to think of words ending with –ing than to think of words with the letter “n” in the next-to-last position, they usually estimate the class “_ _ _ _ i n g” to be more common. This judgment, however, is wrong because all words ending with –ing also have “n” in the sixth position; in addition, there are seven-letter words with “n” the sixth position that do not end in –ing.

The availability heuristic has been suggested to underlie diverse judgment errors, ranging from the tendency to overestimate how many people die from some specific causes of death (e.g., tornado) and underestimate the death toll of others causes (e.g., diabetes) to why people's answers to life satisfaction questions (“How happy are you?”) may be overly influenced by events that are especially memorable.

The representativeness heuristic has been proposed as a means to assess the probability that an object A belongs to a class B (e.g., that a person described as meek is a pilot) or that an event A is generated by a process B (e.g., that the sequence HTHTHT was generated by randomly throwing a fair coin). This heuristic produces probability judgments according to the extent that object A is representative of or similar to the class or process B (e.g., meekness is not representative of pilots, so a meek person is judged as having a low probability of being a pilot). This heuristic can lead to errors because similarity or representativeness judgments are not always influenced by factors that should affect judgments of probability, such as base rates. The representativeness heuristic has also been evoked to explain numerous judgment phenomena, including “hot hand” observations in basketball (the belief that a player is more likely to score again after he or she already scored successfully than after missing a shot) and the gambler's fallacy (the belief that a successful outcome is due after a run of bad luck).

Another heuristic, anchoring and adjustment, produces estimates of quantities by starting with a particular value (the anchor) and adjusting upward or downward from it. For instance, people asked to quickly estimate the product of either 8×7×6×5×4×3×2×1 or 1×2×3×4×5×6×7×8 give a higher value in the former case. According to the anchoring and adjustment heuristic, this happens because the first few numbers presented are multiplied together to create a higher or lower anchor, which is then adjusted upwards in both cases, yielding a higher final estimate for the first product.

Although it has been pointed out that availability, representativeness, and anchoring and adjustment are quite useful heuristics (because they often lead to good judgments without much time or mental effort), most of the large body of evidence amassed that is consistent with the use of these heuristics comes from studies showing where they break down and lead to cognitive illusions or biases (i.e., deviations from some normative standards). This heuristics-and-biases research program has caught the attention of numerous social scientists, including economists and legal scholars. There are good reasons for this attention, since systematic biases question the empirical validity of classic rational choice models (i.e., models of unbounded rationality) and may have important economic, legal, and other implications.

However, the exclusive focus on cognitive illusions has evoked the criticism that research in the heuristics-and-biases tradition equates the notion of bounded rationality with human irrationality and portrays the human mind in an overly negative light, with some researchers even arguing that cognitive illusions are the rule rather than the exception. It has also been criticized that, to date, the cognitive heuristics posited have not been precisely formalized such that one could either simulate or mathematically analyze their behavior, leaving them free to account for all kinds of experimental performance in a post hoc fashion. For instance, it is still an open question of how people assess similarity to make probability judgments with the representativeness heuristic or how many items (e.g., words ending with –ing) the availability heuristic retrieves before it affords a frequency estimate of a class of object (albeit theoretical progress has been made, for instance, by testing whether availability works in terms of ease of recall or number of items recalled). Moreover, the heuristics-and-biases program focuses on human computational capabilities (the first blade of Simon's scissors), largely ignoring the role of the environment by not specifying how such heuristics capitalize on information structure to make inferences. Finally, this program appears to consider heuristics as dispensable mechanisms (that would not be needed if people had the right tools of probability and logic to call on), in contrast to Simon's view of indispensable heuristics as the only available tools for solving many real-world problems.

Kahneman and Tversky have countered some of this critique by drawing a parallel between their heuristic principles and the qualitative principles of Gestalt psychology—the latter being still valuable despite not being precisely specified. Irrespective of the various criticisms, the heuristic and biases program has undoubtedly led to a tremendous amount of research into the idea that people rely on cognitive heuristics made up of simple psychological processes rather than on complex procedures to make inferences about an uncertain world. As a result, this insight has been firmly established as a central topic of psychology.

Introduction

Heuristics are approximate strategies or ‘rules of thumb’ for decision making and problem solving that do not guarantee a correct solution but that typically yield a reasonable solution or bring one closer to hand. As such, they stand in contrast to algorithms that will produce a correct solution given complete and correct inputs. More specifically, heuristics are usually thought of as shortcuts that allow decisions or solutions to be reached more rapidly and in conditions of incomplete or uncertain information—often because they do not process all the available information. Decision heuristics have been studied in different research traditions, primarily one that has focused on when and where verbally described heuristics can break down and yield biases, that is, deviations from classical norms of rationality, and another that has investigated how specific computationally modeled heuristicscan exploit structured information to yield fast and accurate decisions. Heuristics proposed for probability judgments include representativeness, availability, and anchoring-and-adjustment; for choices between alternatives, heuristics include recognition, one-reason decision making, and cue tallying; and for sequential search across alternatives, satisficing (searching with an aspiration level) is a common heuristic approach.

Heuristics

Heuristics are usually contrasted with ‘algorithms’ in problem-solving. Solving a problem by an algorithm or fail-safe rule is supposed to yield an exact, reliable solution that works for every case. ‘Solving’ it by heuristics – by an unintegrated and incomplete set of suggestive ‘rules of thumb’ that work in some cases, but not in all, and not for fully understood or unified reasons – is just as uncreative as solving it by algorithm. However, many people have noticed that heuristic procedures (such as sampling many special cases by trial and error) occasionally lead to insights, sometimes through inductive generalization and analogy with cases in which heuristics succeed, and sometimes because of the stimulus provided by cases in which heuristics (or even algorithms) fail (see the discussion of anomalies, below).

Evaluating the Performance of Heuristics

Heuristics, as well as any model of decision processes, can be evaluated according to how well they describe people's choices and how well they correspond to normative criteria of accuracy. What normative criteria can be used depends on the nature of the task. In preference tasks, such as choosing between consumer products or monetary gambles, the accuracy of a heuristic can only be determined by assessing how closely its predictions match that of some gold standard for rational preferences, such as maximizing expected utility. In inference tasks, such as which soccer team will win or which of two cities is larger, the accuracy of heuristics is judged by their ability to predict real-world outcomes. The same, or similar, heuristics can be used in both preference tasks and inference tasks, but some were traditionally studied in the preference and some in the inference context. There is also a tradition of studying heuristics that are used primarily in social contexts. Success of these heuristics is also judged by criteria such as fairness, coordination, and equality.

Heuristics

Heuristics are the actual mental rules humans employ when making decisions such as choosing an action from a set of alternatives or deciding when to stop searching for a better option. A simple illustration is ‘one-reason decision making’ according to which individuals make choices based on the first relevant criterion that discriminates between the alternatives (Gigerenzer and Todd, 1999). Heuristics generally deviate from optimizing behavior in the sense that individuals are not assumed to gather all relevant information about all available alternatives before making a decision, even when such an exhaustive search was possible. Heuristics are adaptive decision-making rules (Gigerenzer and Selten, 2001) because they are in accord not only with human cognitive abilities but also with the information structure of the environment (Simon, 1956). Since they deviate from optimizing behavior, heuristics are necessarily linked to satisficing behavior (March and Simon, 1958). Originally conceptualized as ‘deviations from perfect rationality’ in the biases and heuristics program of Kahneman et al. (1982), work on heuristic decision making has shown that simple heuristics can actually outperform more advanced decision models. Recent developments in the decision literature focus on a ‘dual mode model of decision making’ distinguishing two mental systems, one fast, requiring little effort, and driven by heuristics, and one slow, effortful, and driven by conscious deliberation (Kahneman, 2011). Examining the interactions between these two systems and the conditions that determine which of the two systems is activated is a main frontier of this field of research.

2.4.5.2 Heuristics for PDPTW

Recall that exact methods usually are used for small problems (due to long computation time). Exact algorithms include dynamic programming techniques, column generation methods, branch-and-cut, branch-and-price solvers, and more [32]. Among approximate methods, the local search heuristics are commonly used to solve PDP or PDPTW.

The heuristics operators can be classified as follows [229]:

Intraroute operators, for example, 2-opt or rearrange operator.

Interroute operators, for example, an exchange or shift operator.

Ropke and Pisinger [210] used the heuristic called Adaptive Large Neighbor Search (ALNS) to solve PDPTW. It is an extended version of Large Neighbor Search (LNS), which removes and reinserts requests for a given solution to find a better solution. ALNS uses various removal and insertion heuristics. The removal heuristics use relatedness measure (the measure of similarity between two requests), worst removal measure (the cost of solution without the given request), or random selection. The insertion heuristic may use the regret heuristic, which improves the basic greedy algorithm by inserting the request at the position with the lowest cost.

Heuristics

Heuristics are cognitive shortcuts that can be applied consciously or unconsciously to reduce the complexity of decisions. Initially research emphasized the shortcomings of heuristics. The ‘heuristics-and-biases’ approach showed that people often rely on simple decision rules but that they would be better off – in terms of accuracy – if they would not. This approach goes back to the seminal work of Kahneman and Tversky. The three heuristics that received most attention were availability, representativeness, and anchoring and adjustment. The availability heuristic refers to the tendency to assess the probability of an event based on the ease with which instances of that event come to mind. (The tendency to assess the probability of an event based on the ease with which instances of that event come to mind.) Generally people overestimate the probability of an event if concrete instances of that event are easily accessible in memory. Ease of recall and frequency of occurrence are often correlated. A number of factors that affect memory are, however, unrelated to probability. For example, vivid images are easier to recall than pallid ones. Thus having been involved in a serious car accident is likely to be better remembered than annual statistics about the frequency of (types of) traffic accidents. The former is likely to have more impact on probability estimates than the latter.

The representativeness heuristic refers to the tendency to assess the probability that a stimulus belongs to a particular class by judging the degree to which that event corresponds to an appropriate mental model. A well-known example of how ignoring prior probabilities can affect judgment was reported by Kahneman and Tversky in 1973 (see Kahneman, 2011). In their study, respondents were provided with brief personality sketches, supposedly of engineers and lawyers. They were asked to assess the probability that each sketch described a member of one profession or the other. Half the respondents were told the population from which the sketches were drawn consisted of 30 engineers and 70 lawyers, the remaining respondents were told that there were 70 engineers and 30 lawyers. Findings showed that the prior probabilities were essentially ignored, and that respondents estimated the probability of class membership by judging how similar each personality sketch was to their mental model of an engineer or a lawyer.

Anchoring and adjustment refers to a general judgment process in which an initially given or generated response serves as an anchor, and other information is insufficiently used to adjust that response. The anchoring and adjustment heuristic is based on the assumption that people often start their judgmental process by focusing on some initial value that serves as an anchor. The biases related to this heuristic stem from the use of irrelevant anchors and the tendency to insufficiently adjust up or down from an original starting value or anchor.

Initially these three heuristics were described along with a dozen systematic biases such as nonregressive prediction, neglect of base-rate information, and overconfidence. Both heuristics and biases were seen as causing systematic errors in estimates of known quantities and statistical facts. Kahneman and Frederick (see Kahneman, 2011) revised the early conception of heuristics and biases and proposed a new formulation – highlighting a common process of attribute substitution – to explain how heuristics work. Moreover, their new model also includes an explicit treatment of the conditions under which intuitive judgments are modified or overrated by more elaborate cognitive processes. In this new approach, the word heuristic is used in two senses: The noun refers to the cognitive process, and the adjective in heuristic attribute specifies the attribute that is substituted in a particular judgment. Kahneman and Frederick added a family of prototype heuristics in which an attribute of a prototype is substituted for an extensional attribute of its category; the original instance of a so-called prototype heuristic is the use of representativeness in category prediction we discussed earlier. (A heuristic in which an attribute of a prototype is substituted for an extensional attribute of its category.)

Gigerenzer and colleagues stressed the adaptive role of heuristics. In their view, heuristics are efficient cognitive processes that ignore part of the information: Moreover, how well cognitive heuristics function in decisions under uncertainty is in their view an empirical question. Gigerenzer and Gaissmaier (2011) refer to simple, fast, and frugal heuristics that ‘make people smart.’ (Various classes of adaptive heuristics that people use to make decisions faster, more frugally, and/or more accurately.) They present an overview of the various heuristics people use in different types of situations, and also attempt to answer the question when people should rely on a given heuristic rather than a more complex decision strategy. Gigerenzer and colleagues stress the conscious and deliberate use of heuristics as strategies that ignore information to make decisions faster, more frugally, and/or more accurately than more complex methods. They also refer to less-is-more effects; i.e., when less information or computation leads to more accurate judgments than more information or computation. They thus disagree with the view of Payne et al. (1992) that people rely on heuristics because information search and computation costs time and effort, and that heuristics save time and effort at the expense of accuracy.

A first class of heuristics introduced by Gigerenzer and colleagues can be related to an important capacity of our memory; i.e., the fact that a sense of recognition appears in consciousness earlier than recollection. The recognition heuristic simply states that if one of two alternatives is recognized and the other is not, then one should infer that the recognized alternative has the higher value with respect to the criterion. An example is name recognition of cities; this turns out to be a reasonably valid predictor of their population. The higher the recognition validity (α) for a given criterion, the more ecologically rational it is to rely on the recognition heuristic. For each individual, α can be computed by α = C/(C + W) where C is the number of correct inferences the recognition heuristic would make, computed across all pairs in which one alternative is recognized and the other is not, and W is the number of wrong inferences.

The fluency heuristic states that if both alternatives are recognized but one is recognized faster, then infer that this alternative has the higher value with respect to the criterion. The fluency heuristic is ecologically rational if the speed of recognition is correlated with the criterion. Fluency also plays a role when alternatives are not given but need to be generated from memory. Johnson and Raab (2003) introduced the take-the-first heuristic: Choose the first alternative that comes to mind. They tested the ecological validity of this approach with experienced handball players when choosing how to proceed in a given situation (e.g., pass the ball to another player, or take a shot). A third class of heuristics are so-called one-reason decisions: a class of heuristics that bases judgments on one good reason only, ignoring other cues. Obviously this heuristic is related to the lexicographic decision rule: The latter is defined in terms of the ‘most important’ attribute. One-clever-clue heuristics (an example of one-reason decision making) focuss on the usefulness and predictive value of the selected cue of the overall outcome of the choice. Similarly, the take-the-best heuristic is a model of how people infer which of two alternatives has a higher value on a criterion, based on binary cue values retrieved from memory. Take-the-best consists of three steps: (1) search through cues in order of their validity, (2) stop on finding the first cue that discriminates between the alternatives, and (3) the alternative with the more positive cue value has the higher criterion value.

Karelaia (2006) showed that a confirmatory stopping rule – stop after two cues are found that point to the same alternative – leads to quite robust results. This heuristic tends to be ecologically valid in situations where the decision maker knows little about the validity of cues. This class of heuristics weighs cues or alternatives equally and thus makes simple trade-offs. Tallying is an example. Tallying entails simply counting the number of cues favoring one alternative in comparison to others. In three steps: (1) search through cues, (2) stopping rule: if the number of cues is the same for both alternatives, search for another cue. If no more cues are found, guess. Otherwise (3) decide for the alternative that is favored by more cues.

Basically Gigerenzer's work showed that heuristics are not good or bad and – in some circumstances – can be more accurate than more complex strategies. More research is needed on how people learn to use heuristics in an adaptive way; i.e., when to use the proper strategy from their adaptive heuristic tool box.

In their review article on heuristic decision making, Gigerenzer and Gaissmaier (2011) also ask whether gut feelings are based on heuristics, and if so, on which? This brings us to the distinction between intuitive versus deliberative decision making.

Evolved Capacities

Heuristics can exploit evolved human capacities. For instance, the recognition heuristic and the fluency heuristic (see above for definitions) take advantage of the evolved capacities for recognition memory and systematic forgetting (i.e., pushing aside information that is unlikely to be needed again; Schooler and Hertwig, 2005). The simple tit-for-tat heuristic for making decisions in social exchange situations – cooperate first and then imitate your counterpart's behavior (Axelrod, 1984) – takes advantage of a suite of evolved capacities, including memory (players must remember the exchanged value and the accrued debt), numerical discrimination (to evaluate whether an exchange was equitable), and temporal discounting (as reciprocity involves paying an immediate cost for future benefits, those future benefits must be time-discounted appropriately; Stevens and Hauser, 2004).

How Much Improvement Over Shortest Path-Based Heuristic?

A heuristic approach for solving the problem considered here would be to use a distance-based shortest path heuristic to route the demand on a single path between origin and destination first, then separately for each hour, and finally taking the maximum capacity required on each link by using the maximum over all the hours subject to modularity requirement. We have made a comparison between this heuristic and the Lagrangian-based dual approach, SGOPT. We found that for problem R7, R10, and R15, the solution obtained from the shortest-path based heuristic is 19.51%, 6.27%, and 9.47% more costly than that of SGOPT.