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      Conviction Narrative Theory: A theory of choice under radical uncertainty

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      Behavioral and Brain Sciences
      Cambridge University Press (CUP)

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          Abstract

          Conviction Narrative Theory (CNT) is a theory of choice under radical uncertainty – situations where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas most theories of choice assume that people rely on (potentially biased) probabilistic judgments, such theories cannot account for adaptive decision-making when probabilities cannot be assigned. CNT proposes that people use narratives – structured representations of causal, temporal, analogical, and valence relationships – rather than probabilities, as the currency of thought that unifies our sense-making and decision-making faculties. According to CNT, narratives arise from the interplay between individual cognition and the social environment, with reasoners adopting a narrative that feels “right” to explain the available data; using that narrative to imagine plausible futures; and affectively evaluating those imagined futures to make a choice. Evidence from many areas of the cognitive, behavioral, and social sciences supports this basic model, including lab experiments, interview studies, and econometric analyses. We identify 12 propositions to explain how the mental representations (narratives) interact with four inter-related processes (explanation, simulation, affective evaluation, and communication), examining the theoretical and empirical basis for each. We conclude by discussing how CNT can provide a common vocabulary for researchers studying everyday choices across areas of the decision sciences.

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          Prospect Theory: An Analysis of Decision under Risk

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            A Behavioral Model of Rational Choice

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              Judgment under Uncertainty: Heuristics and Biases.

              This article described three heuristics that are employed in making judgements under uncertainty: (i) 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; (ii) 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 (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Behavioral and Brain Sciences
                Behav Brain Sci
                Cambridge University Press (CUP)
                0140-525X
                1469-1825
                2023
                May 30 2022
                2023
                : 46
                Article
                10.1017/S0140525X22001157
                65920317-2c86-41ca-adbc-420daae66a6d
                © 2023

                https://www.cambridge.org/core/terms

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