Typical models: minimizing false beliefs

TL;DR

This paper introduces the concepts of typical atoms and models to reduce false beliefs in reasoning with incomplete information, aiming to improve decision-making accuracy.

Contribution

It proposes a novel framework for reasoning with typical models that minimizes false beliefs and analyzes their properties and stability.

Findings

Typical models minimize expected false beliefs.

Properties of typical models include correctness and stability.

Connection established between typical models and oblivious reasoning.

Abstract

A knowledge system S describing a part of real world does in general not contain complete information. Reasoning with incomplete information is prone to errors since any belief derived from S may be false in the present state of the world. A false belief may suggest wrong decisions and lead to harmful actions. So an important goal is to make false beliefs as unlikely as possible. This work introduces the notions of "typical atoms" and "typical models", and shows that reasoning with typical models minimizes the expected number of false beliefs over all ways of using incomplete information. Various properties of typical models are studied, in particular, correctness and stability of beliefs suggested by typical models, and their connection to oblivious reasoning.