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Information and Explanatory Goodness.

David H Glass¹

¹School of Computing, Ulster University, York St, Belfast, BT15 1ED UK.

|February 10, 2025

Summary

This summary is machine-generated.

This study introduces a Bayesian framework for evaluating explanatory goodness. It proposes that good explanations minimize complexity cost relative to explanatory gain, using ad hoc hypotheses as examples.

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Area of Science:

Philosophy of Science
Bayesian Epistemology
Scientific Explanation

Background:

Current Bayesian accounts primarily focus on confirmation.
A quantitative measure of explanatory goodness is lacking.
Ad hoc hypotheses present a challenge for evaluating explanations.

Purpose of the Study:

To propose a qualitative Bayesian account of explanatory goodness.
To introduce a complexity criterion for evaluating explanations.
To illustrate the account using ad hoc hypotheses.

Main Methods:

Developing a Bayesian framework analogous to incremental confirmation.
Defining explanatory goodness based on a complexity criterion (explanatory gain vs. cost).
Applying the framework to analyze ad hoc hypotheses.

Main Results:

Explanations are deemed good if their complexity reduction (gain) outweighs their introduced complexity (cost).
The proposed account offers a qualitative assessment of explanatory power.
The framework provides a novel way to analyze ad hoc hypotheses.

Conclusions:

The qualitative Bayesian account offers a new perspective on explanatory goodness.
The complexity criterion provides a clear metric for evaluating scientific explanations.
This framework can be extended to various contexts in scientific reasoning.