The paper shows why and how an empirical study of fast-and-frugal heuristics can provide norms of good reasoning, and thus how (and how far) rationality can be naturalized. We explain the heuristics that humans often rely on in solving problems, for example, choosing investment strategies or apartments, placing bets in sports, or making library searches. We then show that heuristics can lead to judgments that are as accurate as or even more accurate than strategies that use more information and computation, (...) including optimization methods. A standard way to defend the use of heuristics is by reference to accuracy-effort trade-offs. We take a different route, emphasizing ecological rationality (the relationship between cognitive heuristics and environment), and argue that in uncertain environments, more information and computation are not always better (the “less-can-be-more” doctrine). The resulting naturalism about rationality is thus normative because it not only describes what heuristics people use, but also in which specific environments one should rely on a heuristic in order to make better inferences. While we desist from claiming that the scope of ecological rationality is unlimited, we think it is of wide practical use. (shrink)

Knill, Kersten, & Mamassian (Chapter 6) provide an interesting discussion of how the Bayesian formulation can be used to help investigate human vision. In their view, computational theories can be based on an ideal observer that uses Bayesian inference to make optimal use of available information. Four factors are important here: the image information used, the output structures estimated, the priors assumed (i.e., knowledge about the structure of the world), and the likelihood function used (i.e., knowledge about the projection of (...) the world onto the sensors). Knill & Kersten argue that such a framework not only helps analyze a perceptual task, but can also help investigators to define it. Two examples are provided (the interpretation of surface contour and the perception of moving shadows) to show how this approach can be used in practice. As the authors admit, most (if not all) perceptual processes are ill-suited to a "strong" Bayesian approach based on a single consistent model of the world. Instead, they argue for a "weak" variant that assumes Bayesian inference to be carried out in modules of more limited scope. But how weak is "weak"? Are such approaches suitable for only a few relatively low-level tasks, or can they be applied more generally? Could a weak Bayesian approach, for example, explain how we would recognize the return of Elvis Presley? The formal modelling of human perception To help get a fix on things, it is useful to examine the fate of an earlier attempt to formalize human perception: the application of information theory. It was once hoped that this theory—a close cousin of the Bayesian formulation—would provide a way to uncover information-handling laws that were largely independent of physical implementation. In this approach, the human nervous system was assumed to have.. (shrink)

Evidence-based approaches to policy-making are growing in popularity. A generally embraced view is that with the appropriate evidence at hand, decision and policy making will be optimal, legitimate and publicly accountable. In practice, however, evidence-based policy making is constrained by a variety of problems of evidence. Some of these problems will be explored in this article, in the context of the debates on evidence from which they originate. It is argued that the source of much disagreement might be a failure (...) to addressing crucial philosophical assumptions that inform, often silently, these debates. Three controversial questions will be raised which appear central to some of the challenges faced by evidence-based policy making: firstly, how do certain types of facts candidate themselves as evidence; secondly, how do we decide what evidence we have, and how much of it; and thirdly, can we combine evidence. In addressing these questions it will be shown how a philosophically informed debate might prove instrumental in clarifying and settling practical difficulties. (shrink)

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...) explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)

The evidence-based medicine movement advocates basing all medical decisions on certain types of quantitative research data and has stimulated protracted controversy and debate since its inception. Evidence-based medicine presupposes an inaccurate and deficient view of medical knowledge. Michael Polanyi’s theory of tacit knowledge both explains this deficiency and suggests remedies for it. Polanyi shows how all explicit human knowledge depends on a wealth of tacit knowledge which accrues from experience and is essential for problem solving. Edmund Pellegrino’s classic treatment of (...) clinical judgment is examined, and a Polanyian critique of this position demonstrates that tacit knowledge is necessary for understanding how clinical judgment and medical decisions involve persons. An adequate medical epistemology requires much more qualitative research relevant to the clinical encounter and medical decision making than is currently being done. This research is necessary for preventing an uncritical application of evidence-based medicine by health care managers that erodes good clinical practice. Polanyi’s epistemology shows the need for this work and provides the structural core for building an adequate and robust medical epistemology that moves beyond evidence-based medicine. (shrink)

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...) that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

In the present paper we empirically investigate the psychometric properties of some of the most famous statistical and logical cognitive illusions from the “heuristics and biases” research program by Daniel Kahneman and Amos Tversky, who nearly 50 years ago introduced fascinating brain teasers such as the famous Linda problem, the Wason card selection task, and so-called Bayesian reasoning problems (e.g., the mammography task). In the meantime, a great number of articles has been published that empirically examine single cognitive illusions, theoretically (...) explaining people’s faulty thinking, or proposing and experimentally implementing measures to foster insight and to make these problems accessible to the human mind. Yet these problems have thus far usually been empirically analyzed on an individual-item level only (e.g., by experimentally comparing participants’ performance on various versions of one of these problems). In this paper, by contrast, we examine these illusions as a group and look at the ability to solve them as a psychological construct. Based on an sample ofN= 2,643 Luxembourgian school students of age 16–18 we investigate the internal psychometric structure of these illusions (i.e., Are they substantially correlated? Do they form a reflexive or a formative construct?), their connection to related constructs (e.g., Are they distinguishable from intelligence or mathematical competence in a confirmatory factor analysis?), and the question of which of a person’s abilities can predict the correct solution of these brain teasers (by means of a regression analysis). (shrink)

The scientificity of the research should be evaluated according to the methodology used in the study. However, these are usually the research areas or the institutions that are classified as scientific or non-scientific. Because of various reasons, it may turn out that the scientific institutions are not producing science, while the “non-scientists” are doing real science. In the extreme case, the official science system is entirely corrupt, consisting of fraudsters, while the real scientists have been expelled from academic institutions. Since (...) 2016-2017, there has been much talk about the “post-truth era” and the politicians who are “denying science”. However, simultaneously, many complaints about the corruption of science appeared. The outsider cannot tell who is telling the truth as it may be the case that the science fraudsters are defending themselves and these politicians are aware of the corruption. It is also untrue that the censoring or suppression of science started from 2016-2017. Suppression of science because of political and ideological reasons was present already long ago, and during the last few years, it has been increasing. The picture is highly complicated as there are many pretenders, false accusations, etc. For example, because of political reasons, someone may be set up as a pseudoscientist, the real scientist may be expelled using political accusations, justified criticism may be labelled as political pressure, etc. There is something like an inner information war ongoing in and around science. The classical philosophy of science seems unable to handle it because every formal rule can be misapplied. Science, as a whole, may be unable to persist. (shrink)

The Darwinian theory of evolution is itself evolving and this book presents the details of the core of modern Darwinism and its latest developmental directions. The authors present current scientific work addressing theoretical problems and challenges in four sections, beginning with the concepts of evolution theory, its processes of variation, heredity, selection, adaptation and function, and its patterns of character, species, descent and life. The second part of this book scrutinizes Darwinism in the philosophy of science and its usefulness in (...) understanding ecosystems, whilst the third section deals with its application in disciplines beyond the biological sciences, including evolutionary psychology and evolutionary economics, Darwinian morality and phylolinguistics. The final section addresses anti-Darwinism, the creationist view and issues around teaching evolution in secondary schools. The reader learns how current experimental biology is opening important perspectives on the sources of variation, and thus of the very power of natural selection. This work examines numerous examples of the extension of the principle of natural selection and provides the opportunity to critically reflect on a rich theory, on the methodological rigour that presides in its extensions and exportations, and on the necessity to measure its advantages and also its limits. Scholars interested in modern Darwinism and scientific research, its concepts, research programs and controversies will find this book an excellent read, and those considering how Darwinism might evolve, how it can apply to the human sciences and other disciplines beyond its origins will find it particularly valuable. Originally produced in French (Les Mondes Darwiniens), the scope and usefulness of the book have led to the production of this English text, to reach a wider audience. This book is a milestone in the impressive penetration by Francophone scholars into the world of Darwinian science, its historiography and philosophy over the last two decades. Alex Rosenberg, R. Taylor Cole Professor of Philosophy, Duke University Until now this useful and comprehensive handbook has only been available to francophones. Thanks to this invaluable new translation, this collection of insightful and original essays can reach the global audience it deserves. Tim Lewens, University of Cambridge. (shrink)

Quantitative researchers often discuss research ethics as if specific ethical problems can be reduced to abstract normative logics (e.g., virtue ethics, utilitarianism, deontology). Such approaches overlook how values are embedded in every aspect of quantitative methods, including ‘observations,’ ‘facts,’ and notions of ‘objectivity.’ We describe how quantitative research practices, concepts, discourses, and their objects/subjects of study have always been value-laden, from the invention of statistics and probability in the 1600s to their subsequent adoption as a logic made to appear as (...) if it exists prior to, and separate from, ethics and values. This logic, which was embraced in the Academy of Management from the 1960s, casts management researchers as ethical agents who ought to know about a reality conceptualized as naturally existing in the image of statistics and probability (replete with ‘constructs’), while overlooking that S&P logic and practices, which researchers made for themselves, have an appreciable role in making the world appear this way. We introduce a different way to conceptualize reality and ethics, wherein the process of scientific inquiry itself requires an examination of its own practices and commitments. Instead of resorting to decontextualized notions of ‘rigor’ and its ‘best practices,’ quantitative researchers can adopt more purposeful ways to reason about the ethics and relevance of their methods and their science. We end by considering implications for addressing ‘post truth’ and ‘alternative facts’ problems as collective concerns, wherein it is actually the pluralistic nature of description that makes defending a collectively valuable version of reality so important and urgent. (shrink)

Experiments are actions, performed in order to gain information. Like other acts, there are virtues of performing them well. I discuss one virtue of experimentation, that of knowing how to trade its information-gaining potential against other goods.

This article gives a brief history of chance in the Christian tradition, from casting lots in the Hebrew Bible to the discovery of laws of chance in the modern period. I first discuss the deep-seated skepticism towards chance in Christian thought, as shown in the work of Augustine, Aquinas, and Calvin. The article then describes the revolution in our understanding of chance—when contemporary concepts such as probability and risk emerged—that occurred a century after Calvin. The modern ability to quantify chance (...) has transformed ideas about the universe and human nature, separating Christians today from their predecessors, but has received little attention by Christian historians and theologians. (shrink)

This is an important volume of seventeen essays that historicizes observation as a practice, concept and ideal. It belongs to the historiographical tradition of scrutinizing central aspects of the scientific enterprise such as experiments and objectivity that once appeared too self-evident to be probed. The challenge of historicizing such a significant idea is that it has to be a collective enterprise.The volume starts with three essays that provide a chronological survey of the period from 500 to 1800. Katherine Park, covering (...) the period from 500 to 1500, explains how observation for Cicero and Pliny had nothing to do with experiment or systematic investigation, but rather was a collective and largely anonymous process, associated with originary phrases of natural knowledge of a distant past and intelligible even by the illiterate. It was the basis for conjectural knowledge that lacked causal explanation. Observation of celestial events occurred from time to time, but in the context o .. (shrink)

This book proposes to identify three long-term structures in causal reasoning - in particular, in terms of the relationship between cause and identity - that appear to be of value in categorizing and organizing various trends in philosophical thought.<br>Such conceptual schemes involve a host of philosophical dilemmas (such as the problem of relativism), which are examined in the first chapter. A number of naturalistic and transcendental approaches to this problem are also analysed.<br>In particular, the book attempts to construct a theoretical (...) basis for Foucault's tripartite classification of epistemological structures in European thought.<br>The final chapter attempts to buttress the above schema by extending the analysis from cause and identity to growth, change, and stability, critiquing certain ideas of Foucault and Heidegger, as well as examining the contemporary thought of process philosophy and complexity theory.<br><br>Contents<br><br>Contents: Plurality and Causality - What is a Paradigm? - Paradigms and Causal Structure - Universality and Mathesis Universalis - Temporality and Visibility - Classical and Modern Probability Theory - The Environment and Non-Monotonicity - The Marburg School and the Limit Concept; Foucault's concept of 'Man' - Nineteenth Century Philosophy of Science - A Dynamic Model - Foucault and the Birth of the Clinic - Process, Growth and Complexity - Time and History. (shrink)

(2000). Ecological rationality and its contents. Thinking & Reasoning: Vol. 6, No. 4, pp. 375-384.

The Darwinian concept of natural selection was conceived within a set of Newtonian background assumptions about systems dynamics. Mendelian genetics at first did not sit well with the gradualist assumptions of the Darwinian theory. Eventually, however, Mendelism and Darwinism were fused by reformulating natural selection in statistical terms. This reflected a shift to a more probabilistic set of background assumptions based upon Boltzmannian systems dynamics. Recent developments in molecular genetics and paleontology have put pressure on Darwinism once again. Current work (...) on self-organizing systems may provide a stimulus not only for increased problem solving within the Darwinian tradition, especially with respect to origins of life, developmental genetics, phylogenetic pattern, and energy-flow ecology, but for deeper understanding of the very phenomenon of natural selection itself. Since self-organizational phenomena depend deeply on stochastic processes, self-organizational systems dynamics advance the probability revolution. In our view, natural selection is an emergent phenomenon of physical and chemical selection. These developments suggest that natural selection may be grounded in physical law more deeply than is allowed by advocates of the autonomy of biology, while still making it possible to deny, with autonomists, that evolutionary explanations can be modeled in terms of a deductive relationship between laws and cases. We explore the relationship between, chance, self-organization, and selection as sources of order in biological systems in order to make these points. (shrink)

In a certain sense, uncertainty andignorance have been recognized in science andphilosophy from the time of the Greeks.However, the mathematical sciences have beendominated by the pursuit of certainty.Therefore, experiments under simplified andidealized conditions have been regarded as themost reliable source of knowledge. Normally,uncertainty could be ignored or controlled byapplying probability theory and statistics.Today, however, the situation is different.Uncertainty and ignorance have moved intofocus. In particular, the global character ofsome environmental problems has shown that theproblems cannot be disregarded. Therefore,scientists and technologists (...) have in many wayscome into a new situation. The Chernobylaccident is a dramatic example, however,problems such as a possible greenhouse effect,a possible reduction of the ozone layer, and soon are all of the same type. These encompasstotally different problems than scientists andtechnologists are traditionally trained to dealwith. In these cases, the standard use ofstatistics has to change, the burden of proofshould be reversed, one should draw ondifferent kinds of expertise, and, in general,science should be ``democratized.''. (shrink)

The emperor's new mind (hereafter Emperor) is an attempt to put forward a scientific alternative to the viewpoint of according to which mental activity is merely the acting out of some algorithmic procedure. John Searle and other thinkers have likewise argued that mere calculation does not, of itself, evoke conscious mental attributes, such as understanding or intentionality, but they are still prepared to accept the action the brain, like that of any other physical object, could in principle be simulated by (...) a computer. In Emperor I go further than this and suggest that the outward manifestations ofconscious mental activity cannot even be properly simulated by calculation. To support this view, I use various arguments to show that the results of mathematical insight, in particular, do not seem to be obtained algorithmically. The main thrust ofthis work, however, is to present an overview ofthe present state of physical understanding and to show that an important gap exists at the point where, quantum and classical physics meet, as well as to speculate on how the conscious brain might be taking advantage ofwhatever new physics is needed to fill this gap to achieve its nonalgorithmic effects. (shrink)

Historically the concept of risk is rooted in Renaissance lifestyles, in which autonomous agents such as sailors, warriors, and tradesmen ventured upon dangerous enterprises. Thus, the concept of risk inseparably combines objective reality (nature) and social construction (culture): Risk = Danger + Venture. Mathematical probability theory was constructed in this social climate in order to provide a quantitative risk assessment in the face of indeterminate futures. Thus we have the famous formula: Risk = Probability (of events) × the Size (of (...) future harms). Because the concept of harm is always observer relative, however, risk assessment cannot be purely quantitative. This leads to the question, What are the general conditions under which risks can be accepted? There is, after all, a difference between incurring a risk and bearing the costs of risks selected for by other agencies. Against this background, contours of a theology of risk emerge. If God creates a self-organizing world of relatively autonomous agents, and if self-organization is favored by cooperative networks of autopoietic processes, then the theological hypothesis of a risk-taking God is at least initially plausible. Moreover, according to the Christian idea of incarnation, God is not only taking a risk but is also bearing the risks implied by the openness of creation. I thus argue for a twofold divine kenosis---in creation as well as in redemption. I discuss some objections to this view, including the serious counterargument that risk taking on behalf of others remains, even for God, a morally dubious task. What are the conditions under which the notion of a risk-taking God can be affirmed without leaving us with the picture of God as an arbitrary, cosmic tyrant? And what are the practical implications for the ways in which human agents of faith, hope, and love can learn to cope with the risks of everyday life and of political decisions? (shrink)

Recent work on self organization promises an explanation of complex order which is independent of adaptation. Self-organizing systems are complex systems of simple units, projecting order as a consequence of localized and generally nonlinear interactions between these units. Stuart Kauffman offers one variation on the theme of self-organization, offering what he calls a ``statistical mechanics'' for complex systems. This paper explores the explanatory strategies deployed in this ``statistical mechanics,'' initially focusing on the autonomy of statistical explanation as it applies in (...) evolutionary settings and then turning to Kauffman's analysis. Two primary morals emerge as a consequence of this examination: first, the view that adaptation and self-organization should be seen as competing theories or models is misleading and simplistic; and second, while we need a synthesis treating self-organization and adaptation as geared toward different problems, at different levels of organization, and deploying different methods, we do not yet have such a synthesis. (shrink)

The ArgumentQuantification is not merely a strategy for describing the social and natural worlds, but a means of reconfiguring them. It entails the imposition of new meanings and the disappearance of old ones. Often it is allied to systems of experimental or administrative control, and in fact considerable feats of human organization are generally required even to create stable, reasonably standardized measures. This essay urges that the uses of quantification in science, social science, and bureaucratic social and economic policy are (...) analogous in important ways to accountancy. (shrink)

Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...) a residual role for logic in understanding reasoning; and others put forward alternative formalisms for uncertain reasoning, or raise specific technical, methodological, or empirical challenges. In responding to these points, we aim to clarify the scope and limits of probability and logic in cognitive science; explore the meaning of the explanation of cognition; and re-evaluate the empirical case for Bayesian rationality. (shrink)

Nowadays, almost everyone seems to agree that science is facing an epistemological crisis – namely the replicability crisis – and that we need to take action. But as to precisely what to do or how...

Este estudo visa a apresentar e analisar a Carta XXXVIII, de 1º de outubro de 1666, escrita por Espinosa. De conteúdo exclusivamente matemático, seu texto responde a uma questão de probabilidade aplicada a jogos de apostas em que é preciso preservar, para todos os jogadores, a mesma chance de ganhar. Para isso, apresentaremos o contexto histórico e exporemos detalhadamente o uso e o sentido da terminologia empregada por Espinosa ao longo de suas explicações. Subsidiariamente, em apêndice, fornecemos, de maneira espelhada, (...) o original holandês da carta e nossa tradução para o português. (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)