Qualitative probabilistic inference under varied entropy levels

Thorn, Paul D.; Schurz, Gerhard

doi:10.1016/j.jal.2016.05.004

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…In light of these results, Schurz and Thorn (2012) concluded that System Z achieves the optimal balance in the trade-off between deriving true and informative conclusions and avoiding false or uninformative ones. 13 Schurz and Thorn (2012) and Thorn and Schurz (2014) investigated three additional scoring rules: PIR (price is right), sPIR (subtle price is right), and EU (expected utility). The qualitative orderings of the ecological validity, applicability, and cognitive success of the four systems were the same across all four success measures, demonstrating the robustness of the results.…”

Section: Cognitive Success and Reasoning With Uncertain Conditionalsmentioning

confidence: 99%

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Cognitive Success: A Consequentialist Account of Rationality in Cognition

Schurz

Hertwig

2019

Topics in Cognitive Science

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One of the most discussed issues in psychology—presently and in the past—is how to define and measure the extent to which human cognition is rational. The rationality of human cognition is often evaluated in terms of normative standards based on a priori intuitions. Yet this approach has been challenged by two recent developments in psychology that we review in this article: ecological rationality and descriptivism. Going beyond these contributions, we consider it a good moment for psychologists and philosophers to join forces and work toward a new foundation for the definition of rational cognition. We take a first step in this direction by proposing that the rationality of both cognitive and normative systems can be measured in terms of their cognitive success. Cognitive success can be defined and gauged in terms of two factors: ecological validity (the system's validity in conditions in which it is applicable) and the system's applicability (the scope of conditions under which it can be applied). As we show, prominent systems of reasoning—deductive reasoning, Bayesian reasoning, uncertain conditionals, and prediction and choice—perform rather differently on these two factors. Furthermore, we demonstrate that conceptualizing rationality according to its cognitive success offers a new perspective on the time‐honored relationship between the descriptive (“is”) and the normative (“ought”) in psychology and philosophy.

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Section: Cognitive Success and Reasoning With Uncertain Conditionalsmentioning

confidence: 99%

“… The investigation of these four reasoning systems in application to natural environments is work for the future. Thorn and Schurz () investigated the dependence of the systems’ success rate on the entropy of the environment, with the result that System Z is optimal in those environments in which entropy is not low. …”

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Cognitive Success: A Consequentialist Account of Rationality in Cognition

Schurz

Hertwig

2019

Topics in Cognitive Science

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“…Questions concerning inheritance inference have also been central to the study of nonmonotonic logics. A central concern within the field of nonmonotonic logic is to decide which weakening of the monotonicity property of classical logic (e.g., cautious monotonicity, rational monotonicity, or some stronger principle) is appropriate for respective nonmonotonic logics, given a logic's interpretation and intended application (Adams, 1975(Adams, , 1986Brewka, 2009;Gabbay, 1984;Goldszmidt & Pearl, 1991, 1996Halpern, 2009;Hawthorne & Makinson, 2007;Kraus, Lehmann, & Magidor, 1990;McDermott & Doyle, 1980;Pearl, 1988Pearl, , 1990Poole, 1988Poole, , 1994Reiter, 1980Reiter, , 1987Schurz, 1997Schurz, , 1998Schurz & Thorn, 2012;Thorn, Eichhorn, Kern-Isberner, Schurz, 2015;Thorn & Schurz, 2014, 2016. Inheritance inference is often at center stage in discussions of nonmonotonic logic, because inheritance inference is one of the central forms of nondeductive inference that nonmonotonic logics are meant to codify, and because claims about which weakening of classical monotonicity is appropriate translate directly into claims about which sorts of inheritance inference should be permitted.…”

Section: Introductionmentioning

confidence: 99%

How Category Selection Impacts Inference Reliability: Inheritance Inference From an Ecological Perspective

Thorn

Schurz

2021

Cognitive Science

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This article presents results from a simulation‐based study of inheritance inference, that is, inference from the typicality of a property among a “base” class to its typicality among a subclass of the class. The study aims to ascertain which kinds of inheritance inferences are reliable, with attention to the dependence of their reliability upon the type of environment in which inferences are made. For example, the study addresses whether inheritance inference is reliable in the case of “exceptional subclasses” (i.e., subclasses that are known to be atypical in some respect) and attends to variations in reliability that result from variations in the entropy level of the environment. A further goal of the study is to show that the reliability of inheritance inference depends crucially on which sorts of base classes are used in making inferences. One approach to inheritance inference treats the extension of any atomic predicate as a suitable base class. A second approach identifies suitable base classes with the cells of a partition (of a preselected size k) of the domain of objects that satisfies the condition of maximizing the similarity of objects that are assigned to the same class. In addition to permitting more inferences, our study shows that the second approach results in inheritance inferences that are far more reliable, particularly in the case of exceptional subclasses.

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Compositionality Meets Belief Revision: a Bayesian Model of Modification

Strößner

2020

Rev.Phil.Psych.

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The principle of compositionality claims that the content of a complex concept is determined by its constituent concepts and the way in which they are composed. However, for prototype concepts this principle is often too rigid. Blurring the division between conceptual composition and belief update has therefore been suggested (Hampton and Jönsson 2012). Inspired by this idea, we develop a normative account of how belief revision and meaning composition should interact in modifications such as “red apple” or “pet hamster”. We do this by combining the well-known selective modification model (Smith et al. Cognitive science 12(4):485–527 1988) with the rules of Bayesian belief update. Moreover, we relate this model to systems of defeasible reasoning as discussed in the field of artificial intelligence.

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Qualitative probabilistic inference under varied entropy levels

Cited by 4 publications

References 14 publications

Cognitive Success: A Consequentialist Account of Rationality in Cognition

Cognitive Success: A Consequentialist Account of Rationality in Cognition

How Category Selection Impacts Inference Reliability: Inheritance Inference From an Ecological Perspective

Compositionality Meets Belief Revision: a Bayesian Model of Modification

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