Much research on judgment and decision making has focussed on the adequacy of classical rationality as a description of human reasoning. But more recently it has been argued that classical rationality should also be rejected even as normative standards for human reasoning. For example, Gigerenzer and Goldstein (1996) and Gigerenzer and Todd (1999a) argue that reasoning involves ‘‘fast and frugal’ ’ algorithms which are not justified by rational norms, but which succeed in the environment. They provide three lines of argument for this view, based on: (A) the importance of the environment; (B) the existence of cognitive limitations; and (C) the fact that an algorithm with no apparent rational basis, Take-the-Best, succeeds in an judgment task (judging which of two cities is the larger, based on lists of features of each city). We reconsider (A)–(C), arguing that standard patterns of explanation in psychology and the social and biological sciences, use rational norms to explain why simple cognitive algorithms can succeed. We also present new computer simulations that compare Take-the-Best with other cognitive models (which use connectionist, exemplarbased, and decision-tree algorithms). Although Take-the-Best still performs well, it does not perform noticeably better than the other models. We conclude that these results provide no strong reason to prefer Take-the-Best over alternative cognitive models.

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The authors develop and test generalized versions of take-the-best (TTB) and rational (RAT) models of multiattribute paired-comparison inference. The generalized models make allowances for subjective attribute weighting, probabilistic orders of attribute inspection, and noisy decision making. A key new test involves a response-time (RT) approach. TTB predicts that RT is determined solely by the expected time required to locate the 1st discriminating attribute, whereas RAT predicts that RT is determined by the difference in summed evidence between the 2 alternatives. Critical test pairs are used that partially decouple these 2 factors. Under conditions in which ideal observer TTB and RAT strategies yield equivalent decisions, both the RT results and the estimated attribute weights suggest that the vast majority of subjects adopted the generalized TTB strategy. The RT approach is also validated in an experimental condition in which use of a RAT strategy is essentially forced upon subjects.

The authors review their own empirical work inspired by the adaptive toolbox metaphor. The review examines factors influencing strategy selection and execution in multi-attribute inference tasks (e.g., information costs, time pressure, memory retrieval, dynamic environments, stimulus formats, intelligence). An emergent theme is the re-evaluation of contingency model claims about the elevated cognitive costs of compensatory in comparison with non-compensatory strategies. Contrary to common assertions about the impact of cognitive complexity, the empirical data suggest that manipulated variables exert their influence at the meta-level of deciding how to decide (i.e., which strategy to select) rather than at the level of strategy execution. An alternative conceptualisation of strategy selection, namely threshold adjustment in an evidence accumulation model, is also discussed and the difficulty in distinguishing empirically between these metaphors is acknowledged.

When serving a customer, service providers typically need to weigh the value of taking time to make an accurate judgement against the cost of delaying the provision of service to others in the system. Our paper presents the first analysis of how to dynamically manage this judgement accuracy/congestion tradeoff. To that end, we study an elementary system where a service provider faces a random stream of customers. The service consists of a basic judgement task. The key feature of our approach is an explicit representation of the agent’s decision process as an elicitation sequence of binary probabilistic cues. When the objective is to maximize average profit to the firm, we show that the maximum number of elicited cues required to make the decision should decrease as the number of customers in the system increases. This structure yields several counter-intuitive results: i) Increasing cue validity may actually decrease overall judgement accuracy; ii) The level of congestion may increase with increasing cue validity; iii) Increasing the cue elicitation rate can increase the level of congestion. Further, as an alternative to optimal policies, we propose several simple cognitive heuristics adapted to congestion, which we construe as a form of endogenous time pressure. Our study suggests that judgements based only on the most relevant piece of information perform reasonably well. When basing judgements on all available information, simple fixed threshold rules appear to be very robust. These findings are consistent with prior results for decision making under exogenous time pressure.

In this paper we examine the role of social and organizational knowledge in managerial decision-making. In a series of experiments, we examined the following questions. (1) How are some implicit organizational variables such as the size of a group and the composition of a group related to risk perception and risky decisions? From a Darwinian perspective, humans have lived in small, nomadic, hunter-gatherers' groups throughout almost the entire evolutionary time. In making decisions at risk, the size of the group thus may serve as a cue signalling the structure and functions of a social group (e.g., kinship, reciprocity, interdependence among group members). To investigate the effects of these organizational variables, Wang (1996a, 1996b, 2001) used a well-known example of irrational decisions, framing effects (Tversky & Kahneman, 1981), as an empirical probe. Framing effects, characterized by an irrational reversal in risk preference due to different ways of presenting / framing the same choice outcomes, appeared only in large group contexts but disappeared in small group and kinship group contexts. Evolutionarily recurrent small group contexts (less than 1000 people) eliminated irrational reversal in risk preference. (2) Would risky choices between a sure option and a gamble of equal expected value vary as a function of the types of information provided in a decision problem? In contrast to verbal framing (e.g., presenting the same choice outcomes as if they are gains or as if they are losses), situational information about the real status of an organization should have independent reflection effects on risky choice. This so called reflection effect has been repeatedly shown in the literature, where people tend to be risk averse in gain situations but risk seeking in l...

The use of the “Take The Best ” Heuristic under different conditions, modeled with ACT-R Empirical evidence is accumulating which suggests that the use of the “Take The Best ” Heuristic (TTB), one of the “Simple Heuristics that make us smart ” (Gigerenzer, Todd & the ABC Research Group) can be induced by factors such as appropriate feedback (Otto & Rieskamp, 2002) or the cost of information search (Bröder, 2000). Using an ACT-R simulation, I demonstrate that these findings can be due to the fact that the utility of heuristics can be learned under some conditions, but not under others: The use of heuristics is conceptualized as being rooted in reaction rather than selection. The theoretical and practical implications of both views are discussed.

Frameworks for search 1

Many models of cognition neglect emotional states that could affect individuals ‘ cognitive processes. The present study explores the effect of emotional stress on people‘s cognitive processes when making probabilistic inferences. It was hypothesized that emotional stress reduces cognitive capacity, leading to the selection of simple inference strategies. Emotional stress was induced with highly arousing negative pictures briefly presented to participants before they made an inference. Emotional stress influenced the selectivity of participants ‘ information search. Emotionally stressed individuals relied on the importance of the cues to a greater extent than the nonstressed participants. They also spent less time on the least important information. Moreover, the proportion of participants ‘ choices consistent with a simple lexicographic heuristic was higher for the emotionally stressed participants than for the nonstressed participants. The results suggest that people respond adaptively to emotional stress by selecting heuristics that require less information and fewer cognitive operations.

ABSTRACT. It is difficult to overestimate Paul Meehl’s influence on judgment and decision-making research. His ‘disturbing little book ’ (Meehl, 1986, p. 370) Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (1954) is known as an attack on human judgment and a call for replacing clinicians with actuarial methods. More than 40 years later, fast and frugal heuristics—proposed as models of human judgment—were formalized, tested, and found to be surprisingly accurate, often more so than the actuarial models that Meehl advocated. We ask three questions: Do the findings of the two programs contradict each other? More generally, how are the programs conceptually connected? Is there anything they can learn from each other? After demonstrating that there need not be a contradiction, we show that both programs converge in their concern to develop (a) domain-specific models of judgment and (b) nonlinear process models that arise from the bounded nature of judgment. We then elaborate the differences between the programs and discuss how these differences can be viewed as mutually instructive: First, we show that the fast and frugal