Search results for 'Heuristics' (try it on Scholar)

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  1.  5
    How Good Are Fast & Frugal Heuristics (2002). Gerd Gigerenzer, Jean Czerlinski, & Laura Martignon. In Renée Elio (ed.), Common Sense, Reasoning, & Rationality. Oxford University Press
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  2. Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
    The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...
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  3.  53
    Roberto Franzini Tibaldeo (2015). The Heuristics of Fear: Can the Ambivalence of Fear Teach Us Anything in the Technological Age? Ethics in Progress 6 (1):225-238.
    The paper assumes that fear presents a certain degree of ambivalence. To say it with Hans Jonas (1903-1993), fear is not only a negative emotion, but may teach us something very important: we recognize what is relevant when we perceive that it is at stake. Under this respect, fear may be assumed as a guide to responsibility, a virtue that is becoming increasingly important, because of the role played by human technology in the current ecological crisis. Secondly, fear and responsibility (...)
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  4.  45
    Roberto Franzini Tibaldeo (2015). The Heuristics of Fear: Can the Ambivalence of Fear Teach Us Anything in the Technological Age? Ethics in Progress 6 (1):225-238.
    The paper assumes that fear presents a certain degree of ambivalence. To say it with Hans Jonas (1903-1993), fear is not only a negative emotion, but may teach us something very important: we recognize what is relevant when we perceive that it is at stake. Under this respect, fear may be assumed as a guide to responsibility, a virtue that is becoming increasingly important, because of the role played by human technology in the current ecological crisis. Secondly, fear and responsibility (...)
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  5.  16
    Spencer Phillips Hey (2016). Heuristics and Meta-Heuristics in Scientific Judgement. British Journal for the Philosophy of Science 67 (2):471-495.
    Despite the increasing recognition that heuristics may be involved in myriad scientific activities, much about how to use them prudently remains obscure. As typically defined, heuristics are efficient rules or procedures for converting complex problems into simpler ones. But this increased efficiency and problem-solving power comes at the cost of a systematic bias. As Wimsatt showed, biased modelling heuristics can conceal errors, leading to poor decisions or inaccurate models. This liability to produce errors presents a fundamental challenge (...)
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  6. William C. Wimsatt (1997). Aggregativity: Reductive Heuristics for Finding Emergence. Philosophy of Science 64 (4):372-84.
    Most philosophical accounts of emergence are incompatible with reduction. Most scientists regard a system property as emergent relative to properties of the system's parts if it depends upon their mode of organization--a view consistent with reduction. Emergence can be analyzed as a failure of aggregativity--a state in which "the whole is nothing more than the sum of its parts." Aggregativity requires four conditions, giving tools for analyzing modes of organization. Differently met for different decompositions of the system, and in different (...)
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  7. Iris Rooij, Cory D. Wright & Todd Wareham (2012). Intractability and the Use of Heuristics in Psychological Explanations. Synthese 187 (2):471-487.
    Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.
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  8.  57
    Peter M. Todd & Gerd Gigerenzer (2000). Précis of Simple Heuristics That Make Us Smart. Behavioral and Brain Sciences 23 (5):727-741.
    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), (...)
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  9.  76
    William C. Wimsatt (2006). Reductionism and its Heuristics: Making Methodological Reductionism Honest. [REVIEW] Synthese 151 (3):445 - 475.
    Methodological reductionists practice ‘wannabe reductionism’. They claim that one should pursue reductionism, but never propose how. I integrate two strains in prior work to do so. Three kinds of activities are pursued as “reductionist”. “Successional reduction” and inter-level mechanistic explanation are legitimate and powerful strategies. Eliminativism is generally ill-conceived. Specific problem-solving heuristics for constructing inter-level mechanistic explanations show why and when they can provide powerful and fruitful tools and insights, but sometimes lead to erroneous results. I show how traditional (...)
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  10.  49
    Peter L. Samuelson & Ian M. Church (2014). When Cognition Turns Vicious: Heuristics and Biases in Light of Virtue Epistemology. Philosophical Psychology 28 (8):1095-1113.
    In this paper, we explore the literature on cognitive heuristics and biases in light of virtue epistemology, specifically highlighting the two major positions—agent-reliabilism and agent-responsibilism —as they apply to dual systems theories of cognition and the role of motivation in biases. We investigate under which conditions heuristics and biases might be characterized as vicious and conclude that a certain kind of intellectual arrogance can be attributed to an inappropriate reliance on Type 1, or the improper function of Type (...)
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  11.  76
    Eugen Fischer (2014). Philosophical Intuitions , Heuristics , and Metaphors. Synthese 191 (3):569-606.
    : Psychological explanations of philosophical intuitions can help us assess their evidentiary value, and our warrant for accepting them. To explain and assess conceptual or classificatory intuitions about specific situations, some philosophers have suggested explanations which invoke heuristic rules proposed by cognitive psychologists. The present paper extends this approach of intuition assessment by heuristics-based explanation, in two ways: It motivates the proposal of a new heuristic, and shows that this metaphor heuristic helps explain important but neglected intuitions: general factual (...)
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  12.  20
    María G. Navarro (2015). Vague Heuristics. In Rudolf Seising and Luis Argüelles (ed.), Conjectures, Hypotheses, and Fuzzy Logic. Springer 281-294.
    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 (...)
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  13.  12
    Roberto Franzini Tibaldeo (2015). The Heuristics of Fear: Can the Ambivalence of Fear Teach Us Anything in the Technological Age? Ethics in Progress 6 (1):225-238.
    The paper assumes that fear presents a certain degree of ambivalence. To say it with Hans Jonas (1903-1993), fear is not only a negative emotion, but may teach us something very important: we recognize what is relevant when we perceive that it is at stake. Under this respect, fear may be assumed as a guide to responsibility, a virtue that is becoming increasingly important, because of the role played by human technology in the current ecological crisis. Secondly, fear and responsibility (...)
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  14.  2
    Mark Battersby (2016). Enhancing Rationality: Heuristics, Biases, and The Critical Thinking Project. Informal Logic 36 (2):99-120.
    : This paper develops four related claims: 1. Critical thinking should focus more on decision making, 2. the heuristics and bias literature developed by cognitive psychologists and behavioral economists provides many insights into human irrationality which can be useful in critical thinking instruction, 3. unfortunately the “rational choice” norms used by behavioral economists to identify “biased” decision making narrowly equate rational decision making with the efficient pursuit of individual satisfaction; deviations from these norms should not be treated as an (...)
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  15.  79
    Philippe Gagnon (2012). A Look at the Inference Engine Underlying ‘Evolutionary Epistemology’ Accounts of the Production of Heuristics. In Dirk Evers, Antje Jackelén, Michael Fuller & Taede A. Smedes (eds.), Is Religion Natural? Studies in Science and Theology, No. 13. ESSSAT Biennial Yearbook 2011-2012. Martin-Luther-Universität
    This paper evaluates the claim that it is possible to use nature’s variation in conjunction with retention and selection on the one hand, and the absence of ultimate groundedness of hypotheses generated by the human mind as it knows on the other hand, to discard the ascription of ultimate certainty to the rationality of human conjectures in the cognitive realm. This leads to an evaluation of the further assumption that successful hypotheses with specific applications, in other words heuristics, seem (...)
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  16.  8
    Roberto Franzini Tibaldeo (2015). The Heuristics of Fear: Can the Ambivalence of Fear Teach Us Anything in the Technological Age? Ethics in Progress 6 (1).
    The paper assumes that fear presents a certain degree of ambivalence. To say it with Hans Jonas (1903-1993), fear is not only a negative emotion, but may teach us something very important: we recognize what is relevant when we perceive that it is at stake. Under this respect, fear may be assumed as a guide to responsibility, a virtue that is becoming increasingly important, because of the role played by human technology in the current ecological crisis. Secondly, fear and responsibility (...)
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  17.  9
    Jean-François Bonnefon, Didier Dubois, Hélène Fargier & Sylvie Leblois (2008). Qualitative Heuristics For Balancing the Pros and Cons. Theory and Decision 65 (1):71-95.
    Balancing the pros and cons of two options is undoubtedly a very appealing decision procedure, but one that has received scarce scientific attention so far, either formally or empirically. We describe a formal framework for pros and cons decisions, where the arguments under consideration can be of varying importance, but whose importance cannot be precisely quantified. We then define eight heuristics for balancing these pros and cons, and compare the predictions of these to the choices made by 62 human (...)
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  18.  5
    Adam Feltz & Stephanie Samayoa (2012). Heuristics and Life-Sustaining Treatments. Journal of Bioethical Inquiry 9 (4):443-455.
    Surrogates’ decisions to withhold or withdraw life-sustaining treatments (LSTs) are pervasive. However, the factors influencing surrogates’ decisions to initiate LSTs are relatively unknown. We present evidence from two experiments indicating that some surrogates’ decisions about when to initiate LSTs can be predictably manipulated. Factors that influence surrogate decisions about LSTs include the patient’s cognitive state, the patient’s age, the percentage of doctors not recommending the initiation of LSTs, the percentage of patients in similar situations not wanting LSTs, and default treatment (...)
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  19.  35
    Mark Alfano (2012). Wilde Heuristics and Rum Tum Tuggers: Preference Indeterminacy and Instability. Synthese 189 (S1):5-15.
    Models in decision theory and game theory assume that preferences are determinate: for any pair of possible outcomes, a and b, an agent either prefers a to b, prefers b to a, or is indifferent as between a and b. Preferences are also assumed to be stable: provided the agent is fully informed, trivial situational influences will not shift the order of her preferences. Research by behavioral economists suggests, however, that economic and hedonic preferences are to some degree indeterminate and (...)
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  20.  8
    John R. Shook (2016). Abduction, Complex Inferences, and Emergent Heuristics of Scientific Inquiry. Axiomathes 26 (2):157-186.
    The roles of abductive inference in dynamic heuristics allows scientific methodologies to test novel explanations for the world’s ways. Deliberate reasoning often follows abductive patterns, as well as patterns dominated by deduction and induction, but complex mixtures of these three modes of inference are crucial for scientific explanation. All possible mixed inferences are formulated and categorized using a novel typology and nomenclature. Twenty five possible combinations among abduction, induction, and deduction are assembled and analyzed in order of complexity. There (...)
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  21.  20
    Horacio Arló-Costa & Arthur Paul Pedersen (2013). Fast and Frugal Heuristics: Rationality and the Limits of Naturalism. Synthese 190 (5):831-850.
    Gerd Gigerenzer and Thomas Sturm have recently proposed a modest form of what they describe as a normative, ecological and limited naturalism. The basic move in their argument is to infer that certain heuristics we tend to use should be used in the right ecological setting. To address this argument, we first consider the case of a concrete heuristic called Take the Best (TTB). There are at least two variants of the heuristic which we study by making explicit the (...)
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  22.  8
    Jason Dana & Clintin P. Davis-Stober (2016). Rational Foundations of Fast and Frugal Heuristics: The Ecological Rationality of Strategy Selection Via Improper Linear Models. Minds and Machines 26 (1-2):61-86.
    Research on “improper” linear models has shown that predetermined weighting schemes for the linear model, such as equally weighting all predictors, can be surprisingly accurate on cross-validation. We review recent advances that can characterize the optimal choice of an improper linear model. We extend this research to the understanding of fast and frugal heuristics, particularly to the ecologically rational goal of understanding in which task environments given heuristics are optimal. We demonstrate how to test this model using the (...)
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  23.  21
    Andrea Polonioli (2012). Gigerenzer's 'External Validity Argument' Against the Heuristics and Biases Program: An Assessment. [REVIEW] Mind and Society 11 (2):133-148.
    Gigerenzer’s ‘external validity argument’ plays a pivotal role in his critique of the heuristics and biases research program (HB). The basic idea is that (a) the experimental contexts deployed by HB are not representative of the real environment and that (b) the differences between the setting and the real environment are causally relevant, because they result in different performances by the subjects. However, by considering Gigerenzer’s work on frequencies in probability judgments, this essay attempts to show that there are (...)
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  24.  47
    Henry Brighton & Gerd Gigerenzer (2011). Towards Competitive Instead of Biased Testing of Heuristics: A Reply to Hilbig and Richter (2011). Topics in Cognitive Science 3 (1):197-205.
    Our programmatic article on Homo heuristicus (Gigerenzer & Brighton, 2009) included a methodological section specifying three minimum criteria for testing heuristics: competitive tests, individual-level tests, and tests of adaptive selection of heuristics. Using Richter and Späth’s (2006) study on the recognition heuristic, we illustrated how violations of these criteria can lead to unsupported conclusions. In their comment, Hilbig and Richter conduct a reanalysis, but again without competitive testing. They neither test nor specify the compensatory model of inference they (...)
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  25.  23
    András Szigeti (2012). No Need to Get Emotional? Emotions and Heuristics. Ethical Theory and Moral Practice 16 (4):845-862.
    Many believe that values are crucially dependent on emotions. This paper focuses on epistemic aspects of the putative link between emotions and value by asking two related questions. First, how exactly are emotions supposed to latch onto or track values? And second, how well suited are emotions to detecting or learning about values? To answer the first question, the paper develops the heuristics-model of emotions. This approach models emotions as sui generis heuristics of value. The empirical plausibility of (...)
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  26.  23
    Gabor Tahin (2011). Rhetorical Heuristics: Probabilistic Strategies in Complex Oratorical Arguments. [REVIEW] Argumentation 25 (1):1-21.
    The study describes a method created for the analysis of persuasive strategies, called rhetorical heuristics, which can be applied in speeches where the argument focuses primarily on questions of fact. First, the author explains how the concept emerged from the study of classical oratory. Then the theoretical background of rhetorical heuristics is outlined through briefly discussing relevant aspects of the psychology of decision-making. Finally, an exposition of how one could find these persuasive strategies introduces rhetorical heuristics in (...)
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  27.  15
    Laura Martignon & Michael Schmitt (1999). Simplicity and Robustness of Fast and Frugal Heuristics. Minds and Machines 9 (4):565-593.
    Intractability and optimality are two sides of one coin: Optimal models are often intractable, that is, they tend to be excessively complex, or NP-hard. We explain the meaning of NP-hardness in detail and discuss how modem computer science circumvents intractability by introducing heuristics and shortcuts to optimality, often replacing optimality by means of sufficient sub-optimality. Since the principles of decision theory dictate balancing the cost of computation against gain in accuracy, statistical inference is currently being reshaped by a vigorous (...)
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  28.  23
    David Magnus (1996). Heuristics and Biases in Evolutionary Biology. Biology and Philosophy 12 (1):21-38.
    Approaching science by considering the epistemological virtues which scientists see as constitutive of good science, and the way these virtues trade-off against one another, makes it possible to capture action that may be lost by approaches which focus on either the theoretical or institutional level. Following Wimsatt (1984) I use the notion of heuristics and biases to help explore a case study from the history of biology. Early in the 20th century, mutation theorists and natural historians fought over the (...)
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  29.  21
    Timothy R. Colburn (1995). Heuristics, Justification, and Defeasible Reasoning. Minds and Machines 5 (4):467-487.
    Heuristics can be regarded as justifying the actions and beliefs of problem-solving agents. I use an analysis of heuristics to argue that a symbiotic relationship exists between traditional epistemology and contemporary artificial intelligence. On one hand, the study of models of problem-solving agents usingquantitative heuristics, for example computer programs, can reveal insight into the understanding of human patterns of epistemic justification by evaluating these models'' performance against human problem-solving. On the other hand,qualitative heuristics embody the justifying (...)
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  30.  5
    Jay Weinroth (1989). Heuristics and Pedagogy. AI and Society 3 (4):315-322.
    In its focus on heuristics as opposed to hierarchically structured general principles, expert systems technology suggests a pedagogic strategy with affinities to the approaches of some of the creative philosophers of East and West, and a challenge to the reliance on presentation of general principles found in academic tradition. A tutoring approach to classroom presentation may be seen to relate to the point that non-trivial general principles cannot be verbally expressed without substantial loss of meaning.
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  31. María G. Navarro (2014). Poor People of the World Unite! Poverty and the Future of Research in Heuristics. Social Epistemology Review and Reply Collective 3 (2):19-21.
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  32.  68
    William C. Wimsatt (2006). Aggregate, Composed, and Evolved Systems: Reductionistic Heuristics as Means to More Holistic Theories. [REVIEW] Biology and Philosophy 21 (5):667-702.
    Richard Levins’ distinction between aggregate, composed and evolved systems acquires new significance as we recognize the importance of mechanistic explanation. Criteria for aggregativity provide limiting cases for absence of organization, so through their failure, can provide rich detectors for organizational properties. I explore the use of failures of aggregativity for the analysis of mechanistic systems in diverse contexts. Aggregativity appears theoretically desireable, but we are easily fooled. It may be exaggerated through approximation, conditions of derivation, and extrapolating from some conditions (...)
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  33.  18
    Torsten Reimer & Ulrich Hoffrage (2006). The Ecological Rationality of Simple Group Heuristics: Effects of Group Member Strategies on Decision Accuracy. Theory and Decision 60 (4):403-438.
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  34.  20
    Rüdiger W. Waldkirch, Matthias Meyer & Karl Homann (2009). Accounting for the Benefits of Social Security and the Role of Business: Four Ideal Types and Their Different Heuristics. [REVIEW] Journal of Business Ethics 89 (3):247 - 267.
    Germany is considered to be a pioneer of social security systems; nonetheless, globalization and demographic changes have put enormous pressure on them. A solution is not yet in sight as the debate on the future of the German social security systems still lacks consensus. We argue that ideas matter and that the debate can benefit from a deeper reflection on the concept of social security. This objective is pursued along two lines. First, we take a historical perspective and reconstruct the (...)
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  35.  2
    Chris Buse (2013). Intersectoral Action for Health Equity as It Relates to Climate Change in Canada: Contributions From Critical Systems Heuristics. Journal of Evaluation in Clinical Practice 19 (6):1095-1100.
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  36.  16
    Peter M. Todd (1999). Simple Inference Heuristics Versus Complex Decision Machines. Minds and Machines 9 (4):461-477.
  37. Nathan Dinneen (2014). Hans Jonas’s Noble ‘Heuristics of Fear’: Neither the Good Lie Nor the Terrible Truth. Cosmos and History: The Journal of Natural and Social Philosophy 10 (2):1-21.
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  38. Gerd Gigerenzer (1999). Simple Heuristics That Make Us Smart. Oxford University Press.
    Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is (...)
     
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  39. Cass R. Sunstein (2005). Moral Heuristics. Behavioral and Brain Sciences 28 (4):531-542.
    With respect to questions of fact, people use heuristics – mental short-cuts, or rules of thumb, that generally work well, but that also lead to systematic errors. People use moral heuristics too – moral short-cuts, or rules of thumb, that lead to mistaken and even absurd moral judgments. These judgments are highly relevant not only to morality, but to law and politics as well. Examples are given from a number of domains, including risk regulation, punishment, reproduction and sexuality, (...)
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  40. Asbjørn Steglich-Petersen & Sara Praëm (2015). Philosophical Thought Experiments as Heuristics for Theory Discovery. Synthese 192 (9):2827-2842.
    The growing literature on philosophical thought experiments has so far focused almost exclusively on the role of thought experiments in confirming or refuting philosophical hypotheses or theories. In this paper we draw attention to an additional and largely ignored role that thought experiments frequently play in our philosophical practice: some thought experiments do not merely serve as means for testing various philosophical hypotheses or theories, but also serve as facilitators for conceiving and articulating new ones. As we will put it, (...)
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  41. Robert Prentice (2004). Teaching Ethics, Heuristics, and Biases. Journal of Business Ethics Education 1 (1):55-72.
    Although economists often model decision makers as rational actors, the heuristics and biases literature that springs from the work of Nobel Prize winner Daniel Kahneman and his late colleague Amos Tversky demonstrates that people make decisions that depart from the optimal model in systematic ways. These cognitive and behavioral limitations not only cause inefficient decision making, but also lead people to make decisions that are unethical. This article seeks to introduce a selected portion of the heuristics and biases (...)
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  42.  4
    Hidehito Honda, Toshihiko Matsuka & Kazuhiro Ueda (2016). Memory‐Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models. Cognitive Science 40 (5):n/a-n/a.
    Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use. In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. (...)
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  43. Raymundo Morado & Leah Savion, Rationality, Logic, and Heuristics.
    The notion of rationality is crucial to Computer Science and Artificial Intelligence, Economics, Law, Philosophy, Psychology, Anthropology, etc. Most if not all of these disciplines presuppose the agent's capacity to infer in a logical manner. Theories about rationality tend toward two extremes: either they presuppose an unattainable logical capacity, or they tend to minimize the role of logic, in light of vast data on fallacious inferential performance. We analyze some presuppositions in the classical view of logic, and suggest empirical and (...)
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  44.  7
    Yixing Shan & Lili Yang (forthcoming). Fast and Frugal Heuristics and Naturalistic Decision Making: A Review of Their Commonalities and Differences. [REVIEW] Thinking and Reasoning:1-23.
    ABSTRACTBoth the fast and frugal heuristics and the naturalistic decision making research programmes have identified important areas of inquiry previously neglected in the traditional study of human judgment and decision making, and have greatly contributed to the understanding of people's real-world decision making under environmental constraints. The two programmes share similar theoretical arguments regarding the rationality, optimality, and role of experience in decision making. Their commonalities have made them appealing to each other, and efforts have been made, by their (...)
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  45.  4
    J. S. Blumenthal-Barby (2016). Biases and Heuristics in Decision Making and Their Impact on Autonomy. American Journal of Bioethics 16 (5):5-15.
    Cognitive scientists have identified a wide range of biases and heuristics in human decision making over the past few decades. Only recently have bioethicists begun to think seriously about the implications of these findings for topics such as agency, autonomy, and consent. This article aims to provide an overview of biases and heuristics that have been identified and a framework in which to think comprehensively about the impact of them on the exercise of autonomous decision making. I analyze (...)
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  46.  35
    Laura Martignon & Ulrich Hoffrage (2002). Fast, Frugal, and Fit: Simple Heuristics for Paired Comparison. Theory and Decision 52 (1):29-71.
    This article provides an overview of recent results on lexicographic, linear, and Bayesian models for paired comparison from a cognitive psychology perspective. Within each class, we distinguish subclasses according to the computational complexity required for parameter setting. We identify the optimal model in each class, where optimality is defined with respect to performance when fitting known data. Although not optimal when fitting data, simple models can be astonishingly accurate when generalizing to new data. A simple heuristic belonging to the class (...)
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  47.  10
    Louise Cummings (2014). Informal Fallacies as Cognitive Heuristics in Public Health Reasoning. Informal Logic 34 (1):1-37.
    The public must make assessments of a range of health-related issues. However, these assessments require scientific know-ledge which is often lacking or ineffectively utilized by the public. Lay people must use whatever cognitive resources are at their disposal to come to judgement on these issues. It will be contended that a group of arguments—so-called informal fallacies—are a valuable cognitive resource in this regard. These arguments serve as cognitive heuristics which facilitate reasoning when knowledge is limited or beyond the grasp (...)
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  48.  99
    Peter Carruthers (2006). Simple Heuristics Meet Massive Modularity. In Peter Carruthers, Stephen Laurence & Stephen P. Stich (eds.), The Innate Mind: Culture and Cognition. Oxford University Press
    This chapter investigates the extent to which claims of massive modular organization of the mind (espoused by some members of the evolutionary psychology research program) are consistent with the main elements of the simple heuristics research program. A number of potential sources of conflict between the two programs are investigated and defused. However, the simple heuristics program turns out to undermine one of the main arguments offered in support of massive modularity, at least as the latter is generally (...)
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  49. Amos Tversky & Daniel Kahneman (1974). Judgment Under Uncertainty: Heuristics and Biases. Science 185 (4157):1124-1131.
    This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant (...)
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    Susan Hurley (2005). Social Heuristics That Make Us Smarter. Philosophical Psychology 18 (5):585 – 612.
    I argue that an ecologically distributed conception of instrumental rationality can and should be extended to a socially distributed conception of instrumental rationality in social environments. The argument proceeds by showing that the assumption of exogenously fixed units of activity cannot be justified; different units of activity are possible and some are better means to independently given ends than others, in various circumstances. An important social heuristic, the mirror heuristic, enables the flexible formation of units of activity in game theoretic (...)
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