Research on bounded rationality has two cultures, which I call ‘idealistic’ and ‘pragmatic’. Technically, the cultures differ on whether they build models based on normative axioms or empirical facts, assume that people's goal is to optimize or to satisfice, do not or do model psychological processes, let parameters vary freely or fix them, aim at explanation or prediction and test models from one or both cultures. Each culture tells a story about people's rationality. The story of the idealistic culture is (...) frustrating, with people in principle being able to know what they should do, but in practice systematically failing to do it. This story makes one hide in books for intellectual solace or surrender to the designs of someone smarter. The story of the pragmatic culture is empowering: If people are educated to use the right tool in the right situation, they do well. (shrink)

This paper presents an axiomatic framework for the priority heuristic, a model of bounded rationality in Selten’s (in: Gigerenzer and Selten (eds.) Bounded rationality: the adaptive toolbox, 2001) spirit of using empirical evidence on heuristics. The priority heuristic predicts actual human choices between risky gambles well. It implies violations of expected utility theory such as common consequence effects, common ratio effects, the fourfold pattern of risk taking and the reflection effect. We present an axiomatization of a parameterized version of the (...) heuristic which generalizes the heuristic in order to account for individual differences and inconsistencies. The axiomatization uses semiorders (Luce, Econometrica 24:178–191, 1956), which have an intransitive indifference part and a transitive strict preference component. The axiomatization suggests new testable predictions of the priority heuristic and makes it easier for theorists to study the relation between heuristics and other axiomatic theories such as cumulative prospect theory. (shrink)

Goldstein and Gigerenzer (2002) [Models of ecological rationality: The recognition heuristic. Psychological Review, 109 (1), 75–90] found evidence for the use of the recognition heuristic. For example, if an individual recognizes only one of two cities, they tend to infer that the recognized city has a larger population. A prediction that follows is that of the less‐is‐more effect: Recognizing fewer cities leads, under certain conditions, to more accurate inferences than recognizing more cities. We extend the recognition heuristic to group decision‐making (...) by developing majority and lexicographic models of how recognition information is used by groups. We formally show when the less‐is‐more effect is predicted in groups and we present a study where three‐member groups performed the population comparison task. Several aspects of our data indicate that members who can use the recognition heuristic are, not in all but in most cases, more influential in the group decision process than members who cannot use the heuristic. We also observed the less‐is‐more effect and found that models assuming that members who can use the recognition heuristic are more influential better predict when the effect occurs. (shrink)

It is difficult to overestimate Paul Meehl's influence on judgment and decision-making research. His 'disturbing little book' Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence 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 domain-specific models of judgment and 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 heuristic models can help bridge the clinical - actuarial divide, that is, they can be developed into actuarial methods that are both accurate and easy to implement by the unaided clinical judge. We then argue that Meehl's insistence on improving judgment makes clear the importance of examining the degree to which heuristics are used in the clinical domain and how acceptable they would be as actuarial tools. (shrink)

Nudge and boost are two competing approaches to applying the psychology of reasoning and decision making to improve policy. Whereas nudges rely on manipulation of choice architecture to steer people towards better choices, the objective of boosts is to develop good decision-making competences. Proponents of both approaches claim capacity to enhance social welfare through better individual decisions. We suggest that such efforts should involve a more careful analysis of how individual and social welfare are related in the policy context. First, (...) individual rationality is not always sufficient or necessary for improving collective outcomes. Second, collective outcomes of complex social interactions among individuals are largely ignored by the focus of both nudge and boost on individual decisions. We suggest that the design of mechanisms and social norms can sometimes lead to better collective outcomes than nudge and boost, and present conditions under which the three approaches (nudge, boost, and design) can be expected to enhance social welfare. (shrink)

The five of us research and teach risk analysis with an eye towards decision support. Our work has been dedicated to taming risks and helping to make challenging decisions. But nothing had prepared us for the Covid-19 pandemic. We first had to grapple with the news coming from abroad, including, for some of us, our home countries. Then, some information and research, but mostly opinions, started coming in from our academic community, and we felt the tensions. Finally, the UK went (...) into an unofficial and then official lockdown, and all University staff were asked to redirect their research capacity so as to support the national effort for risk analysis and decision support. As we write this on the 20th of April, many countries, including the UK, are starting to consider how to get out of lockdown. Like the previous stages of the pandemic, there is little data, perhaps a bit more research, surely many more opinions, and definitely an overwhelming amount of personal experiences and thoughts. Here we reflect on all of the above, just in case it can help the readers of this Minds in Society flash editorial to think and act, or at least, to not have to do so entirely on their own. As it can be expected, our collage introduces more questions than it can answer. (shrink)

ABSTRACTThe financial crisis of 2008 was unforeseen partly because the academic theories that underpin policy making do not sufficiently account for uncertainty and complexity or learned and evolved human capabilities for managing them. Mainstream theories of decision making tend to be strongly normative and based on wishfully unrealistic “idealized” modeling. In order to develop theories of actual decision making under uncertainty, we need new methodologies that account for how human actors often manage uncertain situations “well enough.” Some possibly helpful methodologies, (...) drawing on digital science, focus on the role of emotions in determining people's choices; others examine how people construct narratives that enable them to act; still others combine qualitative with quantitative data. (shrink)