Order Effects for Queries in Intelligent Systems
This work addresses the problem of understanding decision-making biases in intelligent agents, which is incremental as it builds on existing theories of order effects.
The paper investigates order effects in intelligent systems, exploring both classical and quantum explanations, and proposes testing the quantum hypothesis on large databases like medical treatment data.
This paper examines common assumptions regarding the decision-making internal environment for intelligent agents and investigates issues related to processing of memory and belief states to help obtain better understanding of the responses. In specific, we consider order effects and discuss both classical and non-classical explanations for them. We also consider implicit cognition and explore if certain inaccessible states may be best modeled as quantum states. We propose that the hypothesis that quantum states are at the basis of order effects be tested on large databases such as those related to medical treatment and drug efficacy. A problem involving a maze network is considered and comparisons made between classical and quantum decision scenarios for it.