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

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  1. Iris Rooij, Cory D. Wright & Todd Wareham (2012). Intractability and the Use of Heuristics in Psychological Explanations. Synthese 187 (2):471-487.score: 24.0
    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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  2. William C. Wimsatt (1997). Aggregativity: Reductive Heuristics for Finding Emergence. Philosophy of Science 64 (4):372-84.score: 24.0
    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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  3. William C. Wimsatt (2006). Reductionism and its Heuristics: Making Methodological Reductionism Honest. [REVIEW] Synthese 151 (3):445 - 475.score: 24.0
    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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  4. 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.score: 24.0
    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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  5. Peter M. Todd & Gerd Gigerenzer (2000). Précis of Simple Heuristics That Make Us Smart. Behavioral and Brain Sciences 23 (5):727-741.score: 24.0
    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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  6. Eugen Fischer (2014). Philosophical Intuitions , Heuristics , and Metaphors. Synthese 191 (3):569-606.score: 24.0
    : 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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  7. Mark Alfano (2012). Wilde Heuristics and Rum Tum Tuggers: Preference Indeterminacy and Instability. Synthese 189 (S1):5-15.score: 24.0
    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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  8. 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.score: 24.0
    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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  9. András Szigeti (2012). No Need to Get Emotional? Emotions and Heuristics. Ethical Theory and Moral Practice 16 (4):845-862.score: 24.0
    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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  10. David Magnus (1996). Heuristics and Biases in Evolutionary Biology. Biology and Philosophy 12 (1):21-38.score: 24.0
    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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  11. Andrea Polonioli (2012). Gigerenzer's 'External Validity Argument' Against the Heuristics and Biases Program: An Assessment. [REVIEW] Mind and Society 11 (2):133-148.score: 24.0
    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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  12. Horacio Arló-Costa & Arthur Paul Pedersen (2013). Fast and Frugal Heuristics: Rationality and the Limits of Naturalism. Synthese 190 (5):831-850.score: 24.0
    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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  13. Timothy R. Colburn (1995). Heuristics, Justification, and Defeasible Reasoning. Minds and Machines 5 (4):467-487.score: 24.0
    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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  14. Peter L. Samuelson & Ian M. Church (forthcoming). When Cognition Turns Vicious: Heuristics and Biases in Light of Virtue Epistemology. Philosophical Psychology:1-19.score: 24.0
    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 (or neo-Aristotelianism)—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 (...)
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  15. Gabor Tahin (2011). Rhetorical Heuristics: Probabilistic Strategies in Complex Oratorical Arguments. [REVIEW] Argumentation 25 (1):1-21.score: 24.0
    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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  16. Laura Martignon & Michael Schmitt (1999). Simplicity and Robustness of Fast and Frugal Heuristics. Minds and Machines 9 (4):565-593.score: 24.0
    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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  17. Mark Blokpoel, Marlieke van Kesteren, Arjen Stolk, Pim Haselager, Ivan Toni & Iris Van Rooij (2012). Recipient Design in Human Communication: Simple Heuristics or Perspective Taking? Frontiers in Human Neuroscience 6.score: 24.0
    Humans have a remarkable capacity for tuning their communicative behaviors to different addressees, a phenomenon also known as recipient design. It remains unclear how this tuning of communicative behavior is implemented during live human interactions. Classical theories of communication postulate that recipient design involves perspective taking, i.e., the communicator selects her behavior based on her hypotheses about beliefs and knowledge of the recipient. More recently, researchers have argued that perspective taking is computationally too costly to be a plausible mechanism in (...)
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  18. Adam Feltz & Stephanie Samayoa (2012). Heuristics and Life-Sustaining Treatments. Journal of Bioethical Inquiry 9 (4):443-455.score: 24.0
    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. Jay Weinroth (1989). Heuristics and Pedagogy. AI and Society 3 (4):315-322.score: 24.0
    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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  20. 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.score: 24.0
    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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  21. Spencer Phillips Hey (forthcoming). Heuristics and Meta-Heuristics in Scientific Judgment. British Journal for the Philosophy of Science 2015.score: 24.0
    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 comes at the cost of a systematic bias. As Wimsatt (1980, 2007) showed, biased modeling heuristics can conceal errors, leading to poor decisions or inaccurate models. This liability to produce errors presents a fundamental (...)
     
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  22. 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.score: 21.0
    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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  23. 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.score: 21.0
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  24. 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.score: 21.0
    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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  25. Peter M. Todd (1999). Simple Inference Heuristics Versus Complex Decision Machines. Minds and Machines 9 (4):461-477.score: 21.0
  26. 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.score: 21.0
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  27. 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.score: 21.0
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  28. Hang Zhang, Soumya V. Maddula & Laurence T. Maloney (2010). Planning Routes Across Economic Terrains: Maximizing Utility, Following Heuristics. Frontiers in Psychology 1.score: 21.0
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  29. Machiel Keestra & Stephen J. Cowley (2011). Concepts – Not Just Yardsticks, but Also Heuristics: Rebutting Hacker and Bennett. Language Sciences 33 (3):464-472.score: 20.0
    In their response to our article (Keestra and Cowley, 2009), Hacker and Bennett charge us with failing to understand the project of their book Philosophical Foundations of Neuroscience (PFN; Bennett and Hacker, 2003) and do this by discussing foundationalism, linguistic conservatism and the passivity of perception. In this rebuttal we explore disagreements that explain the alleged errors. First, we reiterate our substantial disagreement with Bennett and Hacker (B&H) regarding their assumption that, even regarding much debated concepts like ‘consciousness’, we can (...)
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  30. Louise Cummings (2014). Informal Fallacies as Cognitive Heuristics in Public Health Reasoning. Informal Logic 34 (1):1-37.score: 20.0
    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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  31. How Good Are Fast & Frugal Heuristics (2002). Gerd Gigerenzer, Jean Czerlinski, & Laura Martignon. In Renée Elio (ed.), Common Sense, Reasoning, & Rationality. Oxford University Press.score: 20.0
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  32. Gerd Gigerenzer (1999). Simple Heuristics That Make Us Smart. Oxford University Press.score: 20.0
    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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  33. Cass R. Sunstein (2005). Moral Heuristics. Behavioral and Brain Sciences 28 (4):531-542.score: 18.0
    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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  34. Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.score: 18.0
    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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  35. Peter Singer (2005). Intuitions, Heuristics, and Utilitarianism. Behavioral and Brain Sciences 28 (4):560-561.score: 18.0
    A common objection to utilitarianism is that it clashes with our common moral intuitions. Understanding the role that heuristics play in moral judgments undermines this objection. It also indicates why we should not use John Rawls' model of reflective equilibrium as the basis for testing normative moral theories.
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  36. William D. Casebeer (2005). Neurobiology Supports Virtue Theory on the Role of Heuristics in Moral Cognition. Behavioral and Brain Sciences 28 (4):547-548.score: 18.0
    Sunstein is right that poorly informed heuristics can influence moral judgment. His case could be strengthened by tightening neurobiologically plausible working definitions regarding what a heuristic is, considering a background moral theory that has more strength in wide reflective equilibrium than “weak consequentialism,” and systematically examining what naturalized virtue theory has to say about the role of heuristics in moral reasoning.
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  37. Elizabeth Anderson (2005). Moral Heuristics: Rigid Rules or Flexible Inputs in Moral Deliberation? Behavioral and Brain Sciences 28 (4):544-545.score: 18.0
    Sunstein represents moral heuristics as rigid rules that lead us to jump to moral conclusions, and contrasts them with reflective moral deliberation, which he represents as independent of heuristics and capable of supplanting them. Following John Dewey's psychology of moral judgment, I argue that successful moral deliberation does not supplant moral heuristics but uses them flexibly as inputs to deliberation. Many of the flaws in moral judgment that Sunstein attributes to heuristics reflect instead the limitations of (...)
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  38. Cass R. Sunstein (2005). On Moral Intuitions and Moral Heuristics: A Response. Behavioral and Brain Sciences 28 (4):565-570.score: 18.0
    Moral heuristics are pervasive, and they produce moral errors. We can identify those errors as such even if we do not endorse any contentious moral view. To accept this point, it is also unnecessary to make controversial claims about moral truth. But the notion of moral heuristics can be understood in diverse ways, and a great deal of work remains to be done in understanding the nature of moral intuitions, especially those that operate automatically and nonreflectively, and in (...)
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  39. 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.score: 18.0
    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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  40. Edward Stein (2005). Wide Reflective Equilibrium as an Answer to an Objection to Moral Heuristics. Behavioral and Brain Sciences 28 (4):561-562.score: 18.0
    If, as is not implausible, the correct moral theory is indexed to human capacity for moral reasoning, then the thesis that moral heuristics exist faces a serious objection. This objection can be answered by embracing a wide reflective equilibrium account of the origins of our normative principles of morality.
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  41. Matthew D. Adler (2005). Cognitivism, Controversy, and Moral Heuristics. Behavioral and Brain Sciences 28 (4):542-543.score: 18.0
    Sunstein aims to provide a nonsectarian account of moral heuristics, yet the account rests on a controversial meta-ethical view. Further, moral theorists who reject act consequentialism may deny that Sunstein's examples involve moral mistakes. But so what? Within a theory that counts consequences as a morally weighty feature of actions, the moral judgments that Sunstein points to are indeed mistaken, and the fact that governmental action at odds with these judgments will be controversial doesn't bar such action.
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  42. José Luis Bermúdez (2000). Rationality, Logic, and Fast and Frugal Heuristics. Behavioral and Brain Sciences 23 (5):744-745.score: 18.0
    Gigerenzer and his co-workers make some bold and striking claims about the relation between the fast and frugal heuristics discussed in their book and the traditional norms of rationality provided by deductive logic and probability theory. We are told, for example, that fast and frugal heuristics such as “Take the Best” replace “the multiple coherence criteria stemming from the laws of logic and probability with multiple correspondence criteria relating to real-world decision performance.” This commentary explores just how we (...)
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  43. Michael E. Gorman (2005). Heuristics, Moral Imagination, and the Future of Technology. Behavioral and Brain Sciences 28 (4):551-551.score: 18.0
    Successful application of heuristics depends on how a problem is represented, mentally. Moral imagination is a good technique for reflecting on, and sharing, mental representations of ethical dilemmas, including those involving emerging technologies. Future research on moral heuristics should use more ecologically valid problems and combine quantitative and qualitative methods.
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  44. Raymundo Morado & Leah Savion, Rationality, Logic, and Heuristics.score: 18.0
    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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  45. Carole J. Lee (2007). The Representation of Judgment Heuristics and the Generality Problem. Proceedings of the 29th Annual Cognitive Science Society:1211-6.score: 18.0
    In his debates with Daniel Kahneman and Amos Tversky, Gerd Gigerenzer puts forward a stricter standard for the proper representation of judgment heuristics. I argue that Gigerenzer’s stricter standard contributes to naturalized epistemology in two ways. First, Gigerenzer’s standard can be used to winnow away cognitive processes that are inappropriately characterized and should not be used in the epistemic evaluation of belief. Second, Gigerenzer’s critique helps to recast the generality problem in naturalized epistemology and cognitive psychology as the methodological (...)
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  46. William C. Wimsatt (2000). Heuristics Refound. Behavioral and Brain Sciences 23 (5):766-767.score: 18.0
    Gigerenzer et al.'s is an extremely important book. The ecological validity of the key heuristics is strengthened by their relation to ubiquitous Poisson processes. The recognition heuristic is also used in conspecific cueing processes in ecology. Three additional classes of problem-solving heuristics are proposed for further study: families based on near-decomposability analysis, exaptive construction of functional structures, and robustness.
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  47. Richard Cooper (2000). Simple Heuristics Could Make Us Smart; but Which Heuristics Do We Apply When? Behavioral and Brain Sciences 23 (5):746-746.score: 18.0
    Simple heuristics are clearly powerful tools for making near optimal decisions, but evidence for their use in specific situations is weak. Gigerenzer et al. (1999) suggest a range of heuristics, but fail to address the question of which environmental or task cues might prompt the use of any specific heuristic. This failure compromises the falsifiability of the fast and frugal approach.
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  48. Barbara H. Fried (2005). Moral Heuristics and the Means/End Distinction. Behavioral and Brain Sciences 28 (4):549-550.score: 18.0
    A mental heuristic is a shortcut (means) to a desired end. In the moral (as opposed to factual) realm, the means/end distinction is not self-evident: How do we decide whether a given moral intuition is a mere heuristic to achieve some freestanding moral principle, or instead a freestanding moral principle in its own right? I discuss Sunstein's solution to that threshold difficulty in translating “heuristics” to the moral realm.
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  49. Susan Hurley (2005). Social Heuristics That Make Us Smarter. Philosophical Psychology 18 (5):585 – 612.score: 18.0
    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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  50. Nigel Harvey (2007). Use of Heuristics: Insights From Forecasting Research. Thinking and Reasoning 13 (1):5 – 24.score: 18.0
    Tversky and Kahneman (1974) originally discussed three main heuristics: availability, representativeness, and anchoring-and-adjustment. Research on judgemental forecasting suggests that the type of information on which forecasts are based is the primary factor determining the type of heuristic that people use to make their predictions. Specifically, availability is used when forecasts are based on information held in memory; representativeness is important when the value of one variable is forecast from explicit information about the value of another variable; and anchoring-and-adjustment is (...)
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