Decision Making "Biases" and Support for Assumption-Based Higher-Order Reasoning

TL;DR

This paper challenges the notion that human decision biases are irrational and argues for decision aids that support assumption-based reasoning, exemplified by the Non-Monotonic Probabilist, to improve decision processes.

Contribution

It refutes the idea that biases like confirmation bias are irrational and introduces the Non-Monotonic Probabilist as a tool to support higher-order assumption-based reasoning.

Findings

Human biases are not necessarily irrational when viewed as part of assumption management.

The Non-Monotonic Probabilist aids in extending knowledge and revising assumptions in decision making.

Supports the development of decision aids that enhance assumption-based reasoning.

Abstract

Unaided human decision making appears to systematically violate consistency constraints imposed by normative theories; these biases in turn appear to justify the application of formal decision-analytic models. It is argued that both claims are wrong. In particular, we will argue that the "confirmation bias" is premised on an overly narrow view of how conflicting evidence is and ought to be handled. Effective decision aiding should focus on supporting the contral processes by means of which knowledge is extended into novel situations and in which assumptions are adopted, utilized, and revised. The Non- Monotonic Probabilist represents initial work toward such an aid.