Second-order Inductive Inference: an axiomatic approach

TL;DR

This paper develops an axiomatic framework for higher-order inductive inference, ensuring consistent rankings of outcomes that adapt prudently to new information without requiring extensive data, with applications in finance, information verification, and startup analysis.

Contribution

It introduces a unique numerical representation for higher-order inductive rankings and a robust test of prudence within this framework.

Findings

Derived a unique matrix representation for rankings.

Established a robust test for prudence in inductive inference.

Applied framework to finance, fake news, and startup success.

Abstract

Consider a predictor who ranks eventualities on the basis of past cases: for instance a search engine ranking webpages given past searches. Resampling past cases leads to different rankings and the extraction of deeper information. Yet a rich database, with sufficiently diverse rankings, is often beyond reach. Inexperience demands either "on the fly" learning-by-doing or prudence: the arrival of a novel case does not force (i) a revision of current rankings, (ii) dogmatism towards new rankings, or (iii) intransitivity. For this higher-order framework of inductive inference, we derive a suitably unique numerical representation of these rankings via a matrix on eventualities x cases and describe a robust test of prudence. Applications include: the success/failure of startups; the veracity of fake news; and novel conditions for the existence of a yield curve that is robustly arbitrage-free.