Conflict and Surprise: Heuristics for Model Revision

TL;DR

This paper introduces heuristics grounded in decision theory to identify when probabilistic models are likely to be inaccurate, aiding users in diagnosing and revising models effectively.

Contribution

It proposes novel heuristics for diagnosing model failures, enhancing the reliability of probabilistic models through decision-theoretic insights.

Findings

Heuristics effectively identify model inadequacies.

Improves dynamic model revision processes.

Supports decision-making under model uncertainty.

Abstract

Any probabilistic model of a problem is based on assumptions which, if violated, invalidate the model. Users of probability based decision aids need to be alerted when cases arise that are not covered by the aid's model. Diagnosis of model failure is also necessary to control dynamic model construction and revision. This paper presents a set of decision theoretically motivated heuristics for diagnosing situations in which a model is likely to provide an inadequate representation of the process being modeled.