Designing Intelligent Agents to Judge Intrinsic Quality of Human Decisions

Abstract

Research on judging decisions made by fallible (human) agents is not as much advanced as research on finding optimal decisions. Human decisions are often influenced by various factors, such as risk, uncertainty, time pressure, and depth of cognitive capability, whereas decisions by an intelligent agent (IA) can be effectively optimal without these limitations. The concept of 'depth', a well-defined term in game theory (including chess), does not have a clear formulation in decision theory. To quantify 'depth' in decision theory, we can configure an IA of supreme competence to 'think' at depths beyond the capability of any human, and in the process collect evaluations of decisions at various depths. One research goal is to create an intrinsic measure of the depth of thinking required to answer certain test questions, toward a reliable means of assessing their difficulty apart from item-response statistics. We relate the depth of cognition by humans to depths of search, and use this information to infer the quality of decisions made, so as to judge the decision-maker from his decisions. We use large data from real chess tournaments and evaluations from chess programs (AI agents) of strength beyond all human players. We then seek to transfer the results to other decision-making fields in which effectively optimal judgments can be obtained from either hindsight, answer banks, powerful AI agents or from answers provided by judges of various competency.

Measuring Level-K Reasoning, Satisficing, and Human Error in Game-Play Data

“…A longer objective is a learning model for humans that generates problems for humans, provides feedback based on the responses, and tailors the difficulty and complexity of subsequent problems accordingly in order to aid in the learning process of the human. We hope that if artificial agents can be produced for other decision domains, this same approach can be applied to judge human decisions more accurately [40], replacing reactive criticism of mistakes by considerations of depth as a measure of expertise.…”