Decision Making "Biases" and Support for Assumption-Based Higher-Order Reasoning
This work critiques foundational assumptions in decision theory and proposes a new direction for decision aids, which could impact fields like psychology and AI, though it appears incremental as it builds on existing critiques.
The paper challenges the view that human decision-making biases justify formal decision-analytic models, arguing that the 'confirmation bias' is misunderstood and that effective decision aiding should support assumption-based reasoning processes.
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.