Ignoring information in binary choice with continuous variables: When is less 'more'?

When can a single variable be more accurate in binary choice than multiple sources of information? We derive analytically the probability that a single variable (SV) will correctly predict one of two choices when both criterion and predictor are continuous variables. We further provide analogous derivations for multiple regression (MR) and equal weighting (EW) and specify the conditions under which the models differ in expected predictive ability. Key factors include variability in cue validities, intercorrelation between predictors, and the ratio of predictors to observations in MR. Theory and simulations are used to illustrate the differential effects of these factors. Results directly address why and when “one-reason” decision making can be more effective than analyses that use more information. We thus provide analytical backing to intriguing empirical results that, to date, have lacked theoretical justification. There are predictable conditions for which one should expect “less to be more.”Decision making, bounded rationality, lexicographic rules, choice theory, Leex

Regions of rationality: Maps for bounded agents

An important problem in descriptive and prescriptive research in decision making is to identify “regions of rationality,” i.e., the areas for which heuristics are and are not effective. To map the contours of such regions, we derive probabilities that heuristics identify the best of m alternatives (m > 2) characterized by k attributes or cues (k > 1). The heuristics include a single variable (lexicographic), variations of elimination-by-aspects, equal weighting, hybrids of the preceding, and models exploiting dominance. We use twenty simulated and four empirical datasets for illustration. We further provide an overview by regressing heuristic performance on factors characterizing environments. Overall, “sensible” heuristics generally yield similar choices in many environments. However, selection of the appropriate heuristic can be important in some regions (e.g., if there is low inter-correlation among attributes/cues). Since our work assumes a “hit or miss” decision criterion, we conclude by outlining extensions for exploring the effects of different loss functions.Decision making, Bounded rationality, Lexicographic rules, Choice theory, Leex

Entrepreneurial success and failure: Confidence and fallible judgement

Excess entry – or the high failure rate of market-entry decisions – is often attributed to overconfidence exhibited by entreprene urs. We show analytically that whereas excess entry is an inevitable consequence of imperfect assessments of entrepreneurial skill, it does not imply overconfidence. Judgmental fallibility leads to excess entry even when everyone is underconfident. Self-selection implies greater confidence (but not necessarily overconfidence) among those who start new businesses than those who do not and among successful entrants than failures. Our results question claims that “entrepreneurs are overconfident” and emphasize the need to understand the role of judgmental fallibility in producing economic outcomes.Excess entry, fallible judgment, overconfidence, skill uncertainty, entrepreneurship, LeeX

On heuristic and linear models of judgment: Mapping the demand for knowledge

Research on judgment and decision making presents a confusing picture of human abilities. For example, much research has emphasized the dysfunctional aspects of judgmental heuristics, and yet, other findings suggest that these can be highly effective. A further line of research has modeled judgment as resulting from “as if” linear models. This paper illuminates the distinctions in these approaches by providing a common analytical framework based on the central theoretical premise that understanding human performance requires specifying how characteristics of the decision rules people use interact with the demands of the tasks they face. Our work synthesizes the analytical tools of “lens model” research with novel methodology developed to specify the effectiveness of heuristics in different environments and allows direct comparisons between the different approaches. We illustrate with both theoretical analyses and simulations. We further link our results to the empirical literature by a meta-analysis of lens model studies and estimate both human and heuristic performance in the same tasks. Our results highlight the trade-off between linear models and heuristics. Whereas the former are cognitively demanding, the latter are simple to use. However, they require knowledge – and thus “maps” – of when and which heuristic to employ.Decision making; heuristics; linear models; lens model; judgmental biases

Determinants of linear judgment: A meta-analysis of lens model studies

The mathematical representation of Brunswik’s lens model has been used extensively to study human judgment and provides a unique opportunity to conduct a meta-analysis of studies that covers roughly five decades. Specifically, we analyze statistics of the “lens model equation” (Tucker, 1964) associated with 259 different task environments obtained from 78 papers. In short, we find – on average – fairly high levels of judgmental achievement and note that people can achieve similar levels of cognitive performance in both noisy and predictable environments. Although overall performance varies little between laboratory and field studies, both differ in terms of components of performance and types of environments (numbers of cues and redundancy). An analysis of learning studies reveals that the most effective form of feedback is information about the task. We also analyze empirically when bootstrapping is more likely to occur. We conclude by indicating shortcomings of the kinds of studies conducted to date, limitations in the lens model methodology, and possibilities for future research.Judgment, lens model, linear models, learning, bootstrapping

Take-the-best and other simple strategies: Why and when they work 'well' in binary choice

The effectiveness of decision rules depends on characteristics of both rules and environments. A theoretical analysis of environments specifies the relative predictive accuracies of the lexicographic rule 'take-the-best' (TTB) and other simple strategies for binary choice. We identify three factors: how the environment weights variables; characteristics of choice sets; and error. For cases involving from three to five binary cues, TTB is effective across many environments. However, hybrids of equal weights (EW) and TTB models are more effective as environments become more compensatory. In the presence of error, TTB and similar models do not predict much better than a naïve model that exploits dominance. We emphasize psychological implications and the need for more complete theories of the environment that include the role of error.Decision making, bounded rationality, lexicographic rules, Leex

When "hope springs eternal": The role of chance in risk taking

In most naturally occurring situations, success depends on both skill and chance. We contrast experimental market entry decisions where payoffs depend on skill as opposed to combinations of skill and chance. Our data show differential attitudes toward chance by those whose self-assessed skills are low and high. Making chance more important induces greater optimism for the former who start taking more risk, while the latter maintain a belief that high levels of skill are sufficient to overcome the vagaries of chance. Finally, although we observed “excess entry” (i.e., too many participants entered markets), this could not be attributed to overconfidence.Skill, chance, overconfidence, optimism, competition, risk taking, gender differences

A step too far? Leader racism inhibits transgression credit

Prior research established that when in-group leaders commit serious transgressions, such as breaking enforceable rules or engaging in bribery, people treat them leniently compared with similarly transgressive regular group members or out-group leaders (‘transgression credit’). The present studies test a boundary condition of this phenomenon, specifically the hypothesis that transgression credit will be lost if a leader's action implies racist motivation. In study 1, in a corporate scenario, a transgressive in-group leader did or did not express racism. In study 2, in a sports scenario, an in-group or out-group leader or member transgressed rules with or without a racist connotation. Both studies showed that in-group transgressive leaders lost their transgression credit if their transgression included a racial connotation. Wider implications for constraining leaders' transgressions are discussed