Abstract

This paper naturalizes inductive inference by showing how scientific knowledge of real mechanisms provides large benefits to it. I show how knowledge about mechanisms contributes to generalization, inference to the best explanation, causal inference, and reasoning with probabilities. Generalization from some A are B to all A are B is more plausible when a mechanism connects A to B. Inference to the best explanation is strengthened when the explanations are mechanistic and when explanatory hypotheses are themselves mechanistically explained. Causal inference in medical explanation, counterfactual reasoning, and analogy also benefit from mechanistic connections. Mechanisms also help with problems concerning the interpretation, availability, and computation of probabilities.

Highlights

  • An old philosophy joke says that logic texts are divided into two parts: in the first half, on deductive logic, the fallacies are explained; and in the second half, on inductive logic, they are committed

  • This quip is too hard on inductive inference, which is indispensable in science and everyday life, but it does point to the difference between deduction and induction, which introduces unavoidable uncertainty

  • This paper explores a different, compatible way of naturalizing inductive inference not just by psychologizing it and by showing how scientific knowledge of real mechanisms provides large benefits to it

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Summary

Introduction

An old philosophy joke (dubiously attributed to Morris Cohen) says that logic texts are divided into two parts: in the first half, on deductive logic, the fallacies are explained; and in the second half, on inductive logic, they are committed. Recent philosophy of science has intensely investigated the nature of mechanisms, which can be understood as combinations of connected parts whose interactions produce regular changes [1,8,9,10,11]. These investigations have neglected the contribution that the understanding of mechanisms makes to the interconnected problems of. An important task for inductive logic is to discriminate strong mechanisms from unreliable ones

Mechanisms
Results
Inductive Generalization
Inference to the Best Explanation
Causality and Counterfactuals
Probability
Evaluating Mechanisms
Conclusions
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