Current psychology of human reasoning is divided into several different approaches. For instance, there is a major dispute over the question whether human beings are able to apply norms of the formal models of rationality such as rules of logic, or probability and decision theory, correctly. While researchers following the “heuristics and biases” approach argue that we deviate systematically from these norms, and so are perhaps deeply irrational, defenders of the “bounded rationality” approach think not only that the evidence for (...) this conclusion is problematic but also that we should not, at least not very often, use formal norms in reasoning. I argue that while the evidence for heuristics and biases is indeed questionable, the bounded rationality approach has its limits too. Most especially, we should not infer that formal norms play no role in a comprehensive theory of rationality. Instead, formal and bounded rules of reasoning might even be connected in a more comprehensive theory of rationality. (shrink)

Although radical forms of relativism are perhaps beyond the epistemological pale, I argue here that a more moderate form may be plausible, and articulate the conditions under which moderate epistemic relativism could well serve our epistemic goals. In particular, as a result of our limitations as human cognizers, we find ourselves needing to investigate the dappled and difficult world by means of competing communities of highly specialized researchers. We would do well, I argue, to admit of the existence of unresolvable (...) disputes between such communities, but only so long as there is a sufficient amount of fruitful exchange between them as well. I close with some speculation about when it is or is not legitimate to make an “appeal to discipline”: responding to another’s argument by saying something like, “we should do it this way, because we are philosophers (/linguists/psychologists/…), and that’s just what we do”. (shrink)

Mark Kelman’s recent book, The Heuristics Debate (HD), has two main goals. First, it seeks to reconstruct the controversy in decision science between Kahneman et al.’s heuristics-and-biases approach and Gigerenzer et al.’s fast-and-frugal heuristics approach. Second, it tries to discuss its implications for jurisprudence and policy-making. This study focuses on the first task only. The study attempts to show that, although HD has several important merits, its interpretation of the controversy misses some crucial aspects. Specifically, HD fails to appreciate that (...) the debate is fundamentally about what a “rational” judgment is in the first place. Moreover, because of this, HD also fails to acknowledge the interplay between normative and methodological considerations. With regard to this aspect, HD’s treatment of the controversy fits into a long tradition. This study takes the opportunity to rectify the error. (shrink)

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction betweendirect quantitative uncertaintyandindirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of knowledge (...) concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty. (shrink)

This paper focuses on the opposition between two contemporary research programs in economics: behavioral economics (BE) and experimental market economics (EME). Our claim is that the arguments of this opposition can be clarified through the lens of another opposition in the philosophy of probability and in probability theory, between Bayesianism and frequentism. We show how this probabilistic opposition has indirectly shaped a controversy in psychology that opposes two research programs – Heuristics and Biases and Ecological Rationality – which play respective (...) roles in the foundations of individual rationality in BE and EME. To understand these theoretical interrelationships, we investigate the 1996 controversy between Kahneman, Tversky, and Gigerenzer. Those psychologists held different views on how probabilistic representations influence the context-dependency of rationality. This provides a rationale to suggest that a probabilistic ghost may be haunting the experimental machine in economics, and explai.. (shrink)

Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages), both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on “transparent” Bayesian problems varies widely, and generally remains rather unimpressive. (...) We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures) at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point. (shrink)

This essay is my review of Philip Booth’s Wellbeing and the Role of Government. The book is an anthology of original articles by eminent researchers in modern happiness economics, such as: Booth himself; Paul Omerod; David Sacks, Betsey Stephenson, and Justin Wolfers; Christopher Snowden; J. R. Shackleton; Christian Bjornskov; Peter Boettke and Christopher Coyne; and Pedro Schwartz. I conclude by offering several criticisms of the work.

The modelling of bounded rationality is currently pursued by approaches that exhibit a wide diversity of methodologies. This special issue collects five contributions that discuss different methodological aspects of these approaches. In our introduction, we map the variety of methodological positions with respect to three questions. First, what kinds of evidence do the respective approaches consider relevant for modelling bounded rationality? Second, what kind of modelling desiderata do the respective approaches focus on? And third, how do the respective approaches justify (...) the normative validity of bounded rationality? To broaden the picture, we not only discusss the five contributions of this issue, but also include relevant positions from the extant literature. (shrink)

I elaborate on Quilty-Dunn et al.'s integration of the language-of-thought hypothesis in social reasoning by outlining two discrepancies between the experimental paradigms referred to by the authors and the social world: Self-referential projection and deliberate thinking in experiments. Robust tests of the hypothesis in social reasoning should include observational, natural, and cross-cultural approaches.

A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world—the actual “statistics of the environment.” But in fact the idea of a “true” prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional Bayesian (...) epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that “ground truth” probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be “tuned” to its environment. (shrink)

The notion of risk plays a central role in economics, finance, health, psychology, law and elsewhere, and is prevalent in managing challenges and resources in day-to-day life. In recent work, Duncan Pritchard (2015, 2016) has argued against the orthodox probabilistic conception of risk on which the risk of a hypothetical scenario is determined by how probable it is, and in favour of a modal conception on which the risk of a hypothetical scenario is determined by how modally close it is. (...) In this article, we use Pritchard’s discussion as a springboard for a more wide-ranging discussion of the notion of risk. We introduce three different conceptions of risk: the standard probabilistic conception, Pritchard’s modal conception, and a normalcy conception that is new (though it has some precursors in the psychological literature on risk perception). Ultimately, we argue that the modal conception is ill-suited to the roles that a notion of risk is required to play and explore the prospects for a form of pluralism about risk, embracing both the probabilistic and the normalcy conceptions. (shrink)

Bayesian reasoning, defined here as the updating of a posterior probability following new information, has historically been problematic for humans. Classic psychology experiments have tested human Bayesian reasoning through the use of word problems and have evaluated each participant’s performance against the normatively correct answer provided by Bayes’ theorem. The standard finding is of generally poor performance. Over the past two decades, though, progress has been made on how to improve Bayesian reasoning. Most notably, research has demonstrated that the use (...) of frequencies in a natural sampling framework —as opposed to single-event probabilities—can improve participants’ Bayesian estimates. Furthermore, pictorial aids and certain individual difference factors also can play significant roles in Bayesian reasoning success. The mechanics of how to build tasks which show these improvements is not under much debate. The explanations for why naturally-sampled frequencies and pictures help Bayesian reasoning remain hotly contested, however, with many researchers falling into ingrained “camps” organized around two dominant theoretical perspectives. The present paper evaluates the merits of these theoretical perspectives, including the weight of empirical evidence, theoretical coherence, and predictive power. By these criteria, the ecological rationality approach is clearly better than the heuristics and biases view. Progress in the study of Bayesian reasoning will depend on continued research that honestly, vigorously, and consistently engages across these different theoretical accounts rather than staying “siloed” within one particular perspective. The process of science requires an understanding of competing points of view, with the ultimate goal being integration. (shrink)

A heuristic is a rule of thumb. In psychology, heuristics are relatively simple rules for making judgments. A fast heuristic is easy to use and allows one to make judgments quickly. A frugal heuristic relies on a small fraction of the available evidence in making judgments. Typically, fast and frugal heuristics (FFHs) have, or are claimed to have, a further property: They are very reliable, yielding judgments that are about as accurate in the long run as ideal non-fast, non-frugal rules. (...) This paper introduces some well-known examples of FFHs, raises some objections to the FFH program, and looks at the implications of those parts of the FFH program about which we can have some reasonable degree of confidence. (shrink)

The author reviewed the research on the impact of cognitive biases on professionals’ decision-making in four occupational areas. Two main findings emerged. First, the literature reviewed shows that a dozen of cognitive biases has an impact on professionals’ decisions in these four areas, overconfidence being the most recurrent bias. Second, the level of evidence supporting the claim that cognitive biases impact professional decision-making differs across the areas covered. Research in finance relied primarily upon secondary data while research in medicine and (...) law relied mainly upon primary data from vignette studies. Two research gaps are highlighted. The first one is a potential lack of ecological validity of the findings from vignette studies, which are numerous. The second is the neglect of individual differences in cognitive biases, which might lead to the false idea that all professionals are susceptible to biases, to the same extent. To address that issue, we suggest that reliable, specific measures of cognitive biases need to be improved or developed. (shrink)

In the rationality debate, Gerd Gigerenzer and his colleagues have argued that human’s apparent inability to follow probabilistic principles does not mean our irrationality, because we can do probabilistic reasoning successfully if probability information is given in frequencies, not percentages (the natural frequency hypothesis). They also offered an evolutionary argument to this hypothesis, according to which using frequencies was evolutionarily more advantageous to our hominin ancestors than using percentages, and this is why we can reason correctly about probabilities in the (...) frequency format. This paper offers a critical review of this evolutionary argument. I show that there are reasons to believe using the frequency format was not more adaptive than using the standard (percentage) format. I also argue that there is a plausible alternative explanation (the nested-sets hypothesis) for the improved test performances of experimental subjects—one of Gigerenzer’s key explananda—which undermines the need to postulate mental mechanisms for probabilistic reasoning tuned to the frequency format. The explanatory thrust of the natural frequency hypothesis is much less significant than its advocates assume. (shrink)

Gerd Gigerenzer's views on probabilistic reasoning in humans have come under close scrutiny. Very little attention, however, has been paid to his evolutionary component of his argument. According to Gigerenzer, reasoning about probabilities as frequencies is so common today because it was favored by natural selection in the past. This paper presents a critical examination of this argument. It will show first, that, _pace_ Gigerenzer, there are some reasons to believe that using the frequency format was not more adaptive than (...) using the standard (percentage) format and, second, that Gigerenzer's evolutionary argument and his other arguments such as his historical description of the notion of probability are in tension with each other. (shrink)

A theory of rationality is a theory that evaluates instances of reasoning as rational, irrational, or (ir)rational to some degree. Theories can be categorized as rule-based or consequentialist. Rule-based theories say that rational reasoning accords with certain rules (e.g., of logic or probability). Consequentialist theories say that rational reasoning tends to produce good consequences. For instance, the reliabilist takes rationality to be reasoning that tends to produce mostly true beliefs. The pragmatist takes it to be reasoning that tends to produce (...) mostly useful beliefs. This article reviews some of the features and the challenges of rule-based, reliabilist, and pragmatist theories of rationality. (shrink)