Are People Programmed to Commit Fallacies? Further Thoughts about the Interpretation of Experimental Data on Probability Judgment

Cohen, L. Jonathan

doi:10.1111/j.1468-5914.1982.tb00450.x

Cited by 168 publications

(35 citation statements)

References 37 publications

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“…As noted by Hogarth (1981), Cohen (1982) and Brehmer (1988), real world decision environments are rarely one-shot. In decision settings that provide unambiguous, plentiful and meaningful feedback, humans typically learn to perform well (Stanovich and West, 2000).…”

Section: Ambiguous and Non-transparent Feedbackmentioning

confidence: 99%

“…In decision settings that provide unambiguous, plentiful and meaningful feedback, humans typically learn to perform well (Stanovich and West, 2000). So when people make poor decisions (Kahnemann, 2002), this is commonly explained by the fact that people have not had the opportunity to learn from feedback (Hogarth, 1981;Cohen, 1982). It has consequently been argued that findings relating to decision errors (Kahnemann, 2002) are not so much evidence of inadequate human behaviour than of unnatural and untypical experimental decision ecologies (Stanovich and West, 2000;Klein, 2004).…”

Section: Ambiguous and Non-transparent Feedbackmentioning

confidence: 99%

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On improving dynamic decision‐making: implications from multiple‐process cognitive theory

Bakken

2008

Syst. Res.

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Decision environments that afford unambiguous and transparent feedback allow humans to build up corresponding mental models that give them good decision guidance. But in many complex decision situations, such feedback is not available. In such environments people not only show initial misperceptions, but also fail to learn. Education and training have therefore been proposed, but with limited success. This paper applies multipleprocess cognitive theory to explain findings of poor learning and sub-optimal decisionmaking. The applied theory suggests that intuitive processes are default in decisionmaking and natural learning. Analytic processes are more seldom applied. Implications for education and training in dynamic decision-making are suggested, and in light of the primacy of intuitive processes, include massive exposure to complex dynamic decisionmaking in order to improve upon intuition in such environments.

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Section: Ambiguous and Non-transparent Feedbackmentioning

confidence: 99%

Section: Ambiguous and Non-transparent Feedbackmentioning

confidence: 99%

On improving dynamic decision‐making: implications from multiple‐process cognitive theory

Bakken

2008

Syst. Res.

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“…However, the interpretation of these empirical results and the criteria used to assess people's performance are not straightforward matters. As a result, these issues have been source of important debates not only among psychologists but also among philosophers (see, for example, Kahneman and Tversky 1996;Gigerenzer 1996;Samuels and Stich 2004;Cohen 1981Cohen , 1982Adler 1984Adler , 1991.…”

Section: Introductionmentioning

confidence: 99%

On the nature of the conjunction fallacy

Moro

2008

Synthese

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In a seminal work, Tversky and Kahneman showed that in some contexts people tend to believe that a conjunction of events (e.g., Linda is a bank teller and is active in the feminist movement) is more likely to occur than one of the conjuncts (e.g., Linda is a bank teller). This belief violates the conjunction rule in probability theory. Tversky and Kahneman called this phenomenon the "conjunction fallacy". Since the discovery of the phenomenon in 1983, researchers in psychology and philosophy have engaged in important controversies around the conjunction fallacy. The goal of this paper is to explore the most important of these controversies, namely, the controversy about the nature of the conjunction fallacy. Is the conjunction fallacy mainly due to a misunderstanding of the problem by participants (misunderstanding hypothesis) or is it mainly due to a genuine reasoning bias (reasoning bias hypothesis)? A substantial portion of research on the topic has been directed to test the misunderstanding hypothesis. I review this literature and argue that a stronger case can be made against the misunderstanding hypothesis. Thus, I indirectly provide support for the reasoning bias hypothesis.

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“…Second, the fit of the data to an alternative normative model does not guarantee in itself that this is the correct model of human inference, but it generates new research questions. The criterion also fits what Cohen (1982) calls ''the norm extraction method'' as opposed to ''the preconceived norm method.'' In the former, the investigator is trying to discover what conceptions of probability people endorse in solving a particular problem, whereas in the latter she imputes a predetermined theory to them.…”

mentioning

confidence: 66%

“…The debate between these two interpretations of probability is the most heated, but they are not the only ones. There are at least three other interpretations (Cohen, 1982). In the system of deductive logic, though much more developed than inductive logic, one can also find different logical subsystems, e.g.…”

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confidence: 99%

Another Look at Reasoning Experiments: Rationality, Normative Models and Conversational Factors

Todorov

1997

J Theory Soc Behav

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In many studies, human reasoning has been depicted as “biased” or deviating from normative models in both areas of deductive and inductive reasoning. Two criteria for evaluation of reasoning studies are proposed in this paper. The first criterion concerns the selection and application of normative models against which human performance is assessed. The second criterion concerns the role of conversational factors in the differential selection of information used in the subsequent judgment. These two criteria were applied to studies on two tasks from the fields of deductive and inductive reasoning, the Wason selection task and the base rate fallacy. In view of alternative normative systems, control of the application of the normative models used and the perceived conversational relevance of the information presented in the experimental tasks, the alleged biases have been demonstrated to be largely unsupported. The analysis is consistent with the notion that human reasoning is optimal and rational. The implications of the present account for reasoning experiments are discussed.

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Are People Programmed to Commit Fallacies? Further Thoughts about the Interpretation of Experimental Data on Probability Judgment

Cited by 168 publications

References 37 publications

On improving dynamic decision‐making: implications from multiple‐process cognitive theory

On improving dynamic decision‐making: implications from multiple‐process cognitive theory

On the nature of the conjunction fallacy

Another Look at Reasoning Experiments: Rationality, Normative Models and Conversational Factors

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