Probability Reversal and the Disjunction Effect in Reasoning Systems

TL;DR

This paper explores how beliefs in reasoning systems can exhibit paradoxical reversals like the disjunction effect, suggesting they should be modeled as quantum superpositions rather than classical variables.

Contribution

It introduces a quantum-inspired model of beliefs to explain the disjunction effect and data reversal phenomena in reasoning systems.

Findings

Beliefs behave as superposition states rather than classical variables.

Quantum vector representation explains the disjunction effect.

Classical statistical tests may not capture belief dynamics.

Abstract

Data based judgments go into artificial intelligence applications but they undergo paradoxical reversal when seemingly unnecessary additional data is provided. Examples of this are Simpson's reversal and the disjunction effect where the beliefs about the data change once it is presented or aggregated differently. Sometimes the significance of the difference can be evaluated using statistical tests such as Pearson's chi-squared or Fisher's exact test, but this may not be helpful in threshold-based decision systems that operate with incomplete information. To mitigate risks in the use of algorithms in decision-making, we consider the question of modeling of beliefs. We argue that evidence supports that beliefs are not classical statistical variables and they should, in the general case, be considered as superposition states of disjoint or polar outcomes. We analyze the disjunction effect from the perspective of the belief as a quantum vector.