Herbert Simon's bounded rationality offers three principles for building a theory of behavior: (i) to study the process of actual decision making (as opposed to as-if models of expected utility maximization), (ii) to study how decisions are made in situations of uncertainty and intractability (as opposed to risk and ambiguity alone), and (iii) to study how minds adapt to environments (as opposed to modeling solely the mind or the environment). Economists hijacked bounded rationality by reinterpreting it to mean optimization under constraints, and psychologists by reinterpreting it as the study of cognitive biases, that is, deviations from optimization. This contradictory double-takeover silenced the revolutionary essence of Simon's program. My colleagues and I have revived and extended Simon's lost program, choosing the term ecological rationality to avoid any confusion. As opposed to the cognitive biases program, the study of ecological rationality is both descriptive and prescriptive. It investigates the repertoire of heuristics individuals or institutions have at their disposal (their adaptive toolbox) as well as the conditions under which each heuristic is successful and thus should be used, as measured by real-world criteria (the ecological rationality of heuristics). The study of the adaptive toolbox relies on observation and experimentation; the study of the conditions under which various heuristics should be used relies on mathematical analyses and computer simulations. This combination of descriptive and prescriptive analysis offers a novel perspective for behavioral economics and the study of decision-making in general.

Defensive decision making occurs when employees do not decide in the best interest of the organization but rather opt for a personally safer alternative. Analyzing defensive decision making through the lens of the behavioral theory of the firm, we hypothesized that psychological safety and authentic leadership mitigate defensive decisions as they reduce the perceived uncertainty should anything go wrong. An experimental scenario study with 315 managers in a large organization provides causal evidence that the combination of low psychological safety and low authentic leadership increases defensive decisions. Whereas a leader's authenticity offset a lack of psychological safety, it did not further reduce defensive decisions if psychological safety was present. We developed a tool that provides a first estimate of the consequences of defensive decision making in terms of forgone opportunities which equate to 10.8% of the annual revenue for the organization studied. Effectively coping with uncertainty is thus highly relevant.

I distinguish two meanings of the term bias in the social sciences. In the first, biases are functional: they are necessary, and simultaneously enable and constrain perception and cognition. In the second, biases are viewed as errors and ideally should be reduced to zero. In the functional view, bias is value-neutral, neither good nor bad. This pragmatic perspective accepts that cognition must operate under conditions of uncertainty (rather than the certainty of a “small world”) and intractability (where the optimal solution cannot be calculated). Biases enable cognition to deal with these situations where probability theory offers no guidance, for instance, through intelligent heuristics. In contrast, the error view assigns a negative value to bias. It assumes that cognition deals with problems where the true state of the world is known with certainty – at least to some authority. This distinction has profound implications for research design: the two views lead not only to different answers, but also to different questions. Researchers who adopt the error view take the deviation between judgment and true state as the explanandum, not the judgment itself. As a consequence, the functional question – What does a bias achieve? – is virtually never asked, nor is the possibility considered that certain biases might lead to better judgments. This is one reason why less-is-more effects –conditions under which ignoring information yield more accurate inferences – were discovered only recently. Ultimately, views about the nature of bias can themselves become a bias in research on biases.

Accepted by: Aris SyntetosTrust your gut. Do not rely on urges-analyze! Decisions are either logical or psychological & mldr; Such popular maxims reveal a complicated, even confused approach to how one should make decisions. The sciences of decision theory and practice suggest that managers should leverage the power of mathematics, whilst also reserving a role for personal insights. But how exactly can that be achieved? Combining analysis and intuition sounds like trying to have one's cake and eat it too; enticing but impossible. We believe that it is indeed possible to combine analysis and intuition to make decisions. And that doing so in a systematic way is now within reach. A slow yet powerful series of discoveries has culminated in a vision of decision making that brings ever closer together mathematics, psychology and management in the form of analytical models of intuition. The concept that enables these advances is fast-and-frugal heuristics. The present piece (i) provides a background for fast-and-frugal heuristics; (ii) introduces their basics, outlining conditions under which fast-and-frugal heuristics perform well or not; (iii) challenges established beliefs and clears common misconceptions related to fast-and-frugal heuristics; (iv) outlines principles of smart (heuristics-based) management; and (v) surveys open questions to explore how the potential of fast-and-frugal heuristics for management mathematics can be fully realized.

The preference for simple explanations, known as the parsimony principle, has long guided the development of scientific theories, hypotheses, and models. Yet recent years have seen a number of successes in employing highly complex models for scientific inquiry (e.g., for 3D protein folding or climate forecasting). In this paper, we reexamine the parsimony principle in light of these scientific and technological advancements. We review recent developments, including the surprising benefits of modeling with more parameters than data, the increasing appreciation of the context-sensitivity of data and misspecification of scientific models, and the development of new modeling tools. By integrating these insights, we reassess the utility of parsimony as a proxy for desirable model traits, such as predictive accuracy, interpretability, effectiveness in guiding new research, and resource efficiency. We conclude that more complex models are sometimes essential for scientific progress, and discuss the ways in which parsimony and complexity can play complementary roles in scientific modeling practice.

ABSTRACT Background Integrity of academic publishing is increasingly undermined by fake science publications massively produced by commercial “editing services” (so-called “paper mills”). They use AI-supported, automated production techniques at scale and sell fake publications to students, scientists, and physicians under pressure to advance their careers. Because the scale of fake publications in biomedicine is unknown, we developed a simple method to red-flag them and estimate their number. Methods To identify indicators able to red-flag fake publications (RFPs), we sent questionnaires to authors. Based on author responses, a classification rule was applied initially using the two-indicators “non-institutional email AND no international authors” (“email+NIA”) to sub-samples of 15,120 PubMed®-listed publications regarding publication date, journal, impact factor, country and RFP citations. Using the indicator “hospital affiliation” (“email+hospital”), this classification (tallying) rule was validated by comparing 400 known fakes with 400 matched presumed non-fakes. Results Two initial indicators (“email+NIA”) revealed a rapid rise of RFP from 2010 to 2020. Countries with the highest RFP proportion were Russia, Turkey, China, Egypt, India and China (39%-55%). When using the “email+hospital” tallying-rule, sensitivity of RFP identification was 86%, the false alarm rate 44%, and the estimated RFP rate in 2020 was 11.0%. Adding a RFP-citation indicator (“email+hospital+RFP-citations”) increased the sensitivity to 90% and reduced the false alarm rate to 37%. Given 1.3 million biomedical Scimago-listed publications, the estimated annual RFP number in 2020 is about 150,000. Conclusions Potential fake publications can be red-flagged using simple-to-use, validated classification rules to earmark them for subsequent scrutiny. RFP rates are increasing, suggesting higher actual fake rates than previously reported. The large scale and proliferation of fake publications in biomedicine can damage trust in science, endanger public health, and impact economic spending and security. Easy-to-apply fake detection methods, as proposed here, or more complex automated methods can enable the retraction of fake publications at scale and help prevent further damage to the permanent scientific record.

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

Deliberate ignorance is the willful choice not to know the answer to a question of personal relevance. The question of whether a man is the biological father of his child is a sensitive issue in many cultures and can lead to litigation, divorce, and disinheritance. Thanks to DNA tests, men are easily able to resolve the uncertainty. Psychological theories that picture humans as informavores who are averse to ambiguity suggest men would do a DNA test, as does evolutionary theory, which considers investing in raising a rival’s offspring a mistake. We conducted two representative studies using computer-based face-to-face interviews in Germany (n = 969) and Spain (n = 1,002) to investigate whether men actually want to know and how women would react to this desire. As a base line, Germans (Spanish) estimated that 10% (20%) of fathers mistakenly believe that they are the biological father of their child. Nevertheless, in both countries, only 4% of fathers reported that they had performed a DNA paternity test, while 96% said they had not. In contrast, among men without children, 38% (33%) of Germans (Spanish) stated they would do a DNA test if they had children, mostly without telling their partners. Spanish women with children would more often disapprove of a paternity test or threaten their husbands with divorce (25%) than would German women (13%). We find that a simple test of risk aversion, measured also by the purchase of non-mandatory insurances, is correlated with not wanting to know.

How do firms set prices when faced with an uncertain market? We study the pricing strategies of car dealers for used cars using online data and interviews. We find that 97% of 628 dealers employ an aspiration-level heuristic similar to a Dutch auction. Dealers adapt the parameters of the heuristic-initial price, duration, and change in price-to their local market conditions, such as number of competitors, population density, and GDP per capita. At the same time, the aggregate market is described by a model of equilibrium price dispersion. Unlike the equilibrium model, the heuristic correctly predicts systematic pricing characteristics such as high initial price, price stickiness, and the "cheap twin paradox." We also find first evidence that heuristic pricing can generate higher profits given uncertainty than the equilibrium strategy.

I argue that psychology can learn from the natural sciences and focus on the weight that physics attributes to precise theories. Much of psychology can be increasingly characterized by theory aversion—yet not the kind motivated by positivism. Theory aversion in psychology arises from a conflict between two desires: to come up with a theory, and to avoid the necessary mental effort and time as well as the risk of refutation. The results are ersatz theories, or surrogates. I outline three common, but independent, research practices that avoid building precise theories of psychological processes: the null ritual, which allows researchers to get away with not specifying their research hypothesis; as-if theories, which refrain from modeling psychological processes; and lists of binary oppositions, as in dual-system theories, which consist of vague dichotomies. Psychologists could learn from physics to walk forward on two feet—theory and experiment—rather than hobble on one.

During the Cold War, logical rationality - consistency axioms, subjective expected utility maximization, Bayesian probability updating - became the bedrock of economics and other social sciences. In the 1970s, logical rationality underwent attack by the heuristics- and-biases program, which interpreted the theory as a universal norm of how individuals should make decisions, although such an interpretation is absent in von Neumann and Morgenstern's foundational work and dismissed by Savage. Deviations in people's judgments from the theory were thought to reveal stable cognitive biases, which were in turn thought to underlie social problems, justifying governmental paternalism. In the 1990s, the ecological rationality program entered the field, based on the work of Simon. It moves beyond the narrow bounds of logical rationality and analyzes how individuals and institutions make decisions under uncertainty and intractability. This broader view has shown that many supposed cognitive biases are marks of intelligence rather than irrationality, and that heuristics are indispensable guides in a world of uncertainty. The passionate debate between the three research programs became known as the rationality wars. I provide a brief account from the 'front line' and show how the parties understood in strikingly different ways what the war entailed.

Why successful leaders must embrace simple strategies in an increasingly uncertain and complex world. Making decisions is one of the key tasks of managers, leaders, and professionals. In Smart Management, Jochen Reb, Shenghua Luan, and Gerd Gigerenzer demonstrate how business leaders can utilize heuristics—simple decision-making strategies adapted to the task at hand. In a world that has become increasingly volatile, uncertain, complex, and ambiguous (VUCA), the authors make the case against complex analytical methods that quickly reach their limits. This against-the-grain approach leads to decisions that are not only faster but also more accurate, transparent, and easier to learn about, communicate, and teach. Smart Management offers an evidence-based yet practical discussion of how business leaders can use smart heuristics to make good decisions in a VUCA world. Building on the fast-and-frugal heuristics program, Smart Management demonstrates the efficacy of heuristic decision making in a twofold approach. First, it introduces the concept of ecological rationality, which prescribes the environmental conditions under which specific heuristics work well. Second, the book describes a repertoire of heuristics, referred to as the adaptive toolbox, that leaders, managers, and professionals can develop and rely on to make a variety of decisions, such as on business strategy, negotiation, and personnel selection. The toolbox not only showcases the practical usefulness of these heuristics but also inspires readers to discover and develop their own smart heuristics.