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Bayesian argumentation and the value of logical validity.

Benjamin Eva¹, Stephan Hartmann²

¹Department of Philosophy.

Psychological Review

|July 20, 2018

Summary

This study shows that Bayesian reasoning, a probabilistic approach, can enrich and justify classical logic in argumentation. It offers new methods for understanding dynamic inferences and the value of logic in uncertain reasoning.

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Area of Science:

Cognitive Science
Psychology of Reasoning
Argumentation Theory

Background:

The Bayesian paradigm suggests probabilistic norms for human cognition, contrasting with traditional logical norms in argumentation.
Existing Bayesian models of argumentation have limitations in handling dynamic and conditional inferences.

Purpose of the Study:

To present a generalized Bayesian approach to argumentation.
To model dynamic and conditional inferences more effectively.
To characterize the value of logical validity within probabilistic reasoning.

Main Methods:

Utilizing a new class of Bayesian learning methods beyond standard conditionalization.
Developing a framework to describe a wider range of argumentative phenomena.
Integrating logical validity into a probabilistic model of reasoning.

Main Results:

The generalized Bayesian approach accommodates dynamic and conditional inferences.
It explains the special value of logically valid argument schemes in uncertain contexts.
The framework extends Bayesian descriptions of argumentation and human cognition.

Conclusions:

Bayesian probabilistic norms and classical logical norms are compatible.
The Bayesian theory of argumentation justifies and enriches classical logic.
This approach challenges dual-process theories of cognition.