A review is presented of the book “Heuristics and Biases: The Psychology of Intuitive Judgment,” edited by Thomas Gilovich, Dale Griffin, and Daniel Kahneman.

Heuristics and Biases: The Psychology of Intuitive Judgment

Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P (A&B) cannot exceed the probabilities of its constituents, P (A) and P (B), because the extension (or the possibility set) of the conjunction is included in the extension of its constituents. Judgments under uncertainty, however, are often mediated by intuitive heuristics that are not bound by the conjunction rule. A conjunction can be more representative than one of its constituents, and instances of a specific category can be easier to imagine or to retrieve than instances of a more inclusive category. The representativeness and availability heuristics therefore can make a conjunction appear more probable than one of its constituents. This phenomenon is demonstrated in a variety of contexts including estimation of word frequency, personality judgment, medical prognosis, decision under risk, suspicion of criminal acts, and political forecasting. Systematic violations of the conjunction rule are observed in judgments of lay people and of experts in both between-subjects and within-subjects comparisons. Alternative interpretations of the conjunction fallacy are discussed and attempts to combat it are explored.

/pdf/extensional-versus-intuitive-reasoning-the-conjunction-2cmoy4v6op.pdf

Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment.

Much research in the last two decades has demon- strated that human responses deviate from the performance deemed normative according to various models of decision mak- ing and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be inter- preted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experi- menter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the modal response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implica- tions of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance er- rors are a minor factor in the gap; computational limitations un- derlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Un- expected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X00003435

Individual differences in reasoning: Implications for the rationality debate?

A theory of norms and normality is presented and applied to some phenomena of emotional responses, social judgment, and conversations about causes. Norms are assumed to be constructed ad hoc by recruiting specific representations. Category norms are derived by recruiting exemplars. Specific objects or events generate their own norms by retrieval of similar experiences stored in memory or by construction of counterfactual alternatives. The normality of a stimulus is evaluated by comparing it to the norms that it evokes after the fact, rather than to precomputed expectations. Norm theory is applied in analyses of the enhanced emotional response to events that have abnormal causes, of the generation of predictions and inferences from observations of behavior, and of the role of norms in causal questions and answers. This article is concerned with category norms that represent knowledge of concepts and with stimulus norms that govern comparative judgments and designate experiences as surprising. In the tradition of adaptation level theory (Appley, 1971; Helson, 1964), the concept of norm is applied to events that range in complexity from single visual displays to social interactions. We first propose a model of an activation process that produces norms, then explore the role of norms in social cognition. The central idea of the present treatment is that norms are computed after the event rather than in advance. We sketch a supplement to the generally accepted idea that events in the stream of experience are interpreted and evaluated by consulting precomputed schemas and frames of reference. The view developed here is that each stimulus selectively recruits its own alternatives (Garner, 1962, 1970) and is interpreted in a rich context of remembered and constructed representations of what it could have been, might have been, or should have been. Thus, each event brings its own frame of reference into being. We also explore the idea that knowledge of categories (e.g., "encounters with Jim") can be derived on-line by selectively evoking stored representations of discrete episodes and exemplars. The present model assumes that a number of representations can be recruited in parallel, by either a stimulus event or an

https://www3.canyons.edu/faculty/rafterm/0%200LLI%20LOVEandLOSS/Day%206%20Files/1986%20-%20Norm%20Theory%20-%20Comparing%20Reality%20to%20Its%20Alternatives.pdf

Norm theory: Comparing reality to its alternatives

The program of research now known as the heuristics and biases approach began with a survey of 84 participants at the 1969 meetings of the Mathematical Psychology Society and the American Psychological Association (Tversky & Kahneman, 1971). The respondents, including several authors of statistics texts, were asked realistic questions about the robustness of statistical estimates and the replicability of research results. The article commented tongue-in-heek on the prevalence of a belief that the law of large numbers applies to small numbers as well: Respondents placed too much confidence in the results of small samples, and their statistical judgments showed little sensitivity to sample size. The mathematical psychologists who participated in the survey not only should have known better – they did know better. Although their intuitive guesses were off the mark, most of them could have computed the correct answers on the back of an envelope. These sophisticated individuals apparently had access to two distinct approaches for answering statistical questions: one that is spontaneous, intuitive, effortless, and fast; and another that is deliberate, rule-governed, effortful, and slow. The persistence of large biases in the guesses of experts raised doubts about the educability of statistical intuitions. Moreover, it was known that the same biases affect choices in the real world, where researchers commonly select sample sizes that are too small to provide a fair test of their hypotheses (Cohen, 1969, 1992).

Representativeness revisited: Attribute substitution in intuitive judgment.

: In the last few years two fundamentally different measurement approaches have been developed to model multiattribute preferences. The first is based on expected utility theory, which uses risky preferences among gambles to construct a utility function over multiattribute outcomes. The second is founded in difference measurement, which asks for riskless judgments about strength of preferences to derive a value function. This paper presents conditions under which both representations are related by closed form functional relationships. We reduce functional equations which relate the two representations to basic Cauchy type equations, and show that these functional relationships are very simple, if the value function and the utility function is either additive or multiplicative. The functional relationships are then used to predict empirical relationships between value functions and utility functions in an experimental evaluation of hypothetical job offers. The results indicate that predictions based on the theoretical relationship are equal or better than a simple linear prediction for nine subjects out of ten. Implications for the interpretation and application of value functions and utility functions are discussed.

Theoretical and Empirical Relationships Between Risky and Riskless Utility Functions

: Twenty-two credit officers from a major California lending institution served as subjects in a criterion validation of multiattribute utility elicitation techniques. The techniques tested were the Holistic Orthogonal Parameter Estimation (HOPE) technique (Barron and Person, 1978), Simple Multiattribute Rating Technique (SMART: Edwards, 1977), point distribution, and three rank weighting techniques as discussed in Stillwell and Edwards, 1979. Equal weighting of importance dimensions was also investigated. The criterion against which the judgments were compared was the lending institutions own credit scoring model. This model is based on statistical analysis of over 8,000 cases from the bank records and is a best fit prediction model. Results demonstrate that subjective judgments of importance weighting show a high degree of agreement in application selection and in total utility realized from that selection. Decomposition techniques did somewhat better than holistic techniques.

/pdf/evaluating-credit-applications-a-validation-of-4c43gfm8he.pdf

Evaluating Credit Applications: A Validation of Multiattribute Utility Techniques Against a Real World Criterion,

: Most predictions are intended as a basis for decision making. The point of this paper is that prediction and decision require different methods. Equal weights, while often useful for prediction, are less useful for decision making. The action options available in any decision problem fall into three classes: sure winners, sure losers, and contenders. Sure winners and sure losers are defined by dominance, accepting sure winners and rejecting sure losers is trivial. Good decision rules should discriminate well among contenders. In the familiar pick-1 decision problem, options on the Pareto frontier (i.e. undominated options) almost always show negative correlations among attributes. Such negative correlations make equal weights inappropriate. This paper extends that result to the case in which a decision maker must pick k options out of n. In this case, the set of sure winners is usually not empty. It develops general procedures for identifying the set of contenders, given the options, k, and n. This set is a generalized Pareto frontier, of which the traditional kind is a special case. Simulations show that attribute intercorrelations among contenders are substantially depressed and typically negative, even if the intercorrelations in the whole set are positive. Such negative correlations among contenders strongly question the usefulness of equal weights for decision making.

Equal Weights, Flat Maxima, and Trivial Decisions.

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Theoretical and Empirical Relationships Between Risky and Riskless Utility Functions

Evaluating Credit Applications: A Validation of Multiattribute Utility Techniques Against a Real World Criterion,

Equal Weights, Flat Maxima, and Trivial Decisions.