[Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review (...) article was originally published in the book chapter and should have carried a note saying that it was used by permission of Oxford University Press.] One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counter-intuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. (shrink)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

Arguably the most influential models of human decision-making today are based on the assumption that two separable systems – intuition and deliberation – underlie the judgments that people make. Our recent work is among the first to present neural evidence contrary to the predictions of these dual-systems accounts. We measured brain activations using functional magnetic resonance imaging (fMRI) while participants were specifically instructed to either intuitively or deliberately judge the authenticity of emotional facial expressions. Results from three different analyses revealed (...) both common brain networks of activation across decision mode and differential activations as a function of strategy adherence. We take our results to contradict popular dual-systems accounts that propose a clear-cut dichotomy of the processing systems, and to support rather a unified model. According to this, intuitive and deliberate judgment processes rely on the same rules, though only the former are thought to be characterized by non-conscious processing. (shrink)

I PSYCHOLOGY 5 The Probabilistic Revolution in Psychology--an Overview Gerd Gigerenzer 7 1 Probabilistic Thinking and the Fight against Subjectivity Gerd Gigerenzer 11 2 Statistical Method and the Historical Development of Research Practice in American Psychology Kurt Danziger 35 3 Survival of the Fittest Probabilist: Brunswik, Thurstone, and the Two Disciplines of Psychology Gerd Gigerenzer 49 4 A Perspective for Viewing the Integration of Probability Theory in Psychology David J. Murray 73 II SOCIOLOGY 101 5 The Two (...) Empirical Roots of Social Theory and the Probability Revolution Anthony Oberschall 103 III ECONOMICS 133 The Probabilistic Revolution in Economics--an Overview Mary S. Morgan 135 6 Why Was There No Probabilistic Revolution in Economic Thought? Claude Mènard 139 7 The Rise of Macroeconomic Calculations in Economic Statistics Robert A. Horváth 147 8 Statistics without Probability and Haavelmo’s Revolution in Econometrics Mary S. Morgan 171 IV PHYSIOLOGY 199 9 Experimental Physiology and Statistical Inference: The Therapeutic Trial in Nineteenth-Century Germany William Coleman 201 V EVOLUTIONARY BIOLOGY 227 The Probabilistic Revolution in Evolutionary Biology--an Overview John H. Beatty 229 10 Natural Selection as a Causal, Empirical, and Probabilistic Theory M. J. S. Hodge 233 11 Dobzhansky and Drift: Facts, Values, and Chance in Evolutionary Biology John H. Beatty 271 12 Random Genetic Drift, R. A. Fisher, and the Oxford School of Ecological Genetics John R. G. Turner 313 13 On the Prior Probability of the Existence of Life Bernd-Olaf Küppers 355 VI PHYSICS 371 The Probabilistic Revolution in Physics--an Overview Lorenz Krüger 373 14 Probabilistic Physics the Classical Way Jan von Plato 379 15 Max Born and the Reality of Quantum Probabilities Nancy Cartwright 409 16 Philosophical Problems of Quantum Theory: The Response of American Physicists Nancy Cartwright. (shrink)

We attack the SSK's rejection of the distinction between discovery and justification (the DJ distinction), famously introduced by Hans Reichenbach and here defended in a "lean" version. Some critics claim that the DJ distinction cannot be drawn precisely, or that it cannot be drawn prior to the actual analysis of scientific knowledge. Others, instead of trying to blur or to reject the distinction, claim that we need an even more fine-grained distinction (e.g. between discovery, invention, prior assessment, test and justification). (...) Adherents of the SSK, however, maintain that the distinction is useless and perhaps nonexistent. We first argue against the assumption that the SSK's objection to the DJ distinction is just the same as Thomas Kuhn's. Second, we point out general weaknesses of the SSK's arguments against the DJ distinction. Finally, we argue that the distinction is useful not only in order to explicate what is meant by an evaluation but even for the empirical explanation of knowledge. We use two case studies from the history of cognitive science to support this point. (shrink)

This is an interdisciplinary collection of new essays by philosophers, psychologists, neuroscientists and historians on the question: What has determined and what should determine the territory or the boundaries of the discipline named "psychology"? Both the contents - in terms of concepts - and the methods - in terms of instruments - are analyzed. Among the contributors are Mitchell Ash, Paul Baltes, Jochen Brandtstädter, Gerd Gigerenzer, Michael Heidelberger, Gerhard Roth, and Thomas Sturm.

En estas páginas hemos presentado varios argumentos para defender que existe una conexión entre el empleo de heurísticas y los procesos de deliberación. En lugar de caracterizar las heurísticas en función de las disciplinas, los campos y/o los ámbitos en que se emplean—tal y como hace, por ejemplo, el grupo ABC, i.e. racionalidad ecológica—las hemos proyectado sobre nuestros enclaves deliberativos, esto es, sobre un espacio eminentemente retórico y político. En dichos enclaves, tal y como solía recordarnos Quintín Racionero, la téchne (...) de la persuasión se nos presenta como parte del órganon de la filosofía práctica porque está aplicada a un espacio ontológico fundado por la comunicación humana. Y es en el seno de dicha comunicación como cabe entender la proliferación de razonamientos heurísticos encaminados al cálculo, la estimación y/o la ponderación, con ayuda de los cuales no sólo se orienta la acción colectiva sino las mismas fases de la deliberación. (shrink)

Even when they are defined with precision, one can often read and hear judgments about the vagueness of heuristics in debates about heuristic reasoning. This opinion is not just frequent but also quite reasonable. In fact, during the 1990s, there was a certain controversy concerning this topic that confronted two of the leading groups in the field of heuristic reasoning research, each of whom held very different perspectives. In the present text, we will focus on two of the papers published (...) in Psychological Review, wherein the arguments of each of these groups were presented:. (shrink)

Las heurísticas son procedimientos de estimación utilizados por todos nosotros al razonar en nuestra vida ordinaria.

The paper will compare two methods used in the design of diagnostic strategies. The first one is a method that precises predictive value of diagnostic tests. The second one is based on the use of Bayes’ theorem. The main aim of this article is to identify the epistemological assumptions underlying both of these methods. For the purpose of this objective, example projects of one and multi-stage diagnostic strategy developed using both methods will be considered.

For more than 20 years, research has proven the beneficial effect of natural frequencies when it comes to solving Bayesian reasoning tasks (Gigerenzer & Hoffrage, 1995). In a recent meta-analysis, McDowell & Jacobs (2017) showed that presenting a task in natural frequency format increases performance rates to 24% compared to only 4% when the same task is presented in probability format. Nevertheless, on average three quarters of participants in their meta-analysis failed to obtain the correct solution for such a (...) task in frequency format. In this paper, we present an empirical study on what participants typically do wrong when confronted with natural frequencies. We found that many of them did not actually use natural frequencies for their calculations, but translated them back into complicated probabilities instead. This switch from the intuitive presentation format to a less intuitive calculation format will be discussed within the framework of psychological theories (e.g., the Einstellung effect). (shrink)

Most models of risky decision making assume that all relevant information is taken into account (e.g., Kahneman & Tversky, 1979; von Neumann & Morgenstern, 1944). However, there are also some models supposing that only part of the information is considered (e.g., Brandstätter, Gigerenzer, & Hertwig, 2006; Gigerenzer & Gaissmaier, 2011). To further investigate the amount of information that is usually used for decision making, and how the use depends on feedback, we conducted a series of three experiments in (...) which participants choose between two lotteries and where no feedback, outcome feedback, and error feedback was provided, respectively. The result show that without feedback participants mostly chose the lottery with the higher winning probability, and largely ignored the potential gains. The same result occurred when the outcome of each decision was fed back. Only after presenting error feedback (i.e., signaling whether a choice was optimal or not), participants considered probabilities as well as gains, resulting in more optimal choices. We propose that outcome feedback was ineffective, because of its probabilistic and ambiguous nature. Participants improve information integration only if provided with a consistent and deterministic signal such as error feedback. (shrink)

In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with (...) the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., “Does this city have a university?”) before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take–the–best or the Weighted Additive heuristics. (shrink)

Chow pays lip service (but not much more!) to Type I errors and thus opts for a hard (all-or-none) .05 level of significance (Superego of Neyman/Pearson theory; Gigerenzer 1993). Most working scientists disregard Type I errors and thus utilize a soft .05 level (Ego of Fisher; Gigerenzer 1993), which lets them report gradations of significance (e.g., p.

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in (...) the environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities. (shrink)

Replacing logical coherence by effectiveness as criteria of rationality, Gigerenzer et al. show that simple heuristics can outperform comprehensive procedures (e.g., regression analysis) that overload human limited information processing capacity. Although their work casts long overdue doubt on the normative status of the Rational Choice Paradigm, their methodology leaves open its relevance as to how decisions are actually made.

Abstract There are two kinds of beliefs. If the ultimate objective is wellbeing (util- ity), the generated beliefs are “practical.” If the ultimate objective is truth, the generated beliefs are “scientific.” This article defends the practical/scientific belief distinction. The proposed distinction has been ignored by standard rational choice theory—as well as by its two major critics, viz., the Tversky/Kahneman program and the Simon/ Gigerenzer program. One ramification of the proposed distinction is clear: agents who make errors with regard to (...) scientific beliefs (e.g., the conjunction fallacy) should not be taken as committing irrationality—because they are most probably engaging the other kind of maximization, the pursuit of wellbeing. (shrink)

Response to commentaries on keynote article.

John Buridan was a fourteenth-century philosopher who enjoyed an enormous reputation for about two hundred years, was then totally neglected, and is now being 'rediscovered' through his relevance to contemporary work in philosophical logic. The final chapter of Buridan's Sophismata deals with problems about self-reference, and in particular with the semantic paradoxes. He offers his own distinctive solution to the well-known 'Liar Paradox' and introduces a number of other paradoxes that will be unfamiliar to most logicians. Buridan also moves on (...) from these problems to more general questions about the nature of propositions, the criteria of their truth and falsity and the concepts of validity and knowledge. This edition of that chapter is intended to make Buridan's ideas and arguments accessible to a wider range of readers. The volume should interest many philosophers, linguists and logicians, who are increasingly finding in medieval work striking anticipations of their own concerns. (shrink)

In this paper we consider an intuitionistic variant of the modal logic S4 (which we call IS4). The novelty of this paper is that we place particular importance on the natural deduction formulation of IS4— our formulation has several important metatheoretic properties. In addition, we study models of IS4— not in the framework of Kirpke semantics, but in the more general framework of category theory. This allows not only a more abstract definition of a whole class of models but also (...) a means of modelling proofs as well as provability. (shrink)

Can the general public learn to deal with risk and uncertainty, or do authorities need to steer people’s choices in the right direction? Libertarian paternalists argue that results from psychological research show that our reasoning is systematically flawed and that we are hardly educable because our cognitive biases resemble stable visual illusions. For that reason, they maintain, authorities who know what is best for us need to step in and steer our behavior with the help of “nudges.” Nudges are nothing (...) new, but justifying them on the basis of a latent irrationality is. In this article, I analyze the scientific evidence presented for such a justification. It suffers from narrow logical norms, that is, a misunderstanding of the nature of rational thinking, and from a confirmation bias, that is, selective reporting of research. These two flaws focus the blame on individuals’ minds rather than on external causes, such as industries that spend billions to nudge people into unhealthy behavior. I conclude that the claim that we are hardly educable lacks evidence and forecloses the true alternative to nudging: teaching people to become risk savvy. (shrink)