Quickest Change Detection of Time Inconsistent Anticipatory Agents. Human-Sensor and Cyber-Physical Systems
This work addresses the challenge of integrating behavioral economic models into cyber-physical systems for improved detection, though it appears incremental in applying existing detection concepts to a new agent type.
The paper tackles the problem of quickest change detection in systems involving anticipatory agents, which are time-inconsistent due to their consideration of future decision probabilities, by developing a methodology based on subgame Nash equilibrium. The result shows that this interaction leads to nonconvex structures in detection policies, providing a framework for situation awareness systems.
In behavioral economics, human decision makers are modeled as anticipatory agents that make decisions by taking into account the probability of future decisions (plans). We consider cyber-physical systems involving the interaction between anticipatory agents and statistical detection. A sensing device records the decisions of an anticipatory agent. Given these decisions, how can the sensing device achieve quickest detection of a change in the anticipatory system? From a decision theoretic point of view, anticipatory models are time inconsistent meaning that Bellman's principle of optimality does not hold. The appropriate formalism is the subgame Nash equilibrium. We show that the interaction between anticipatory agents and sequential quickest detection results in unusual (nonconvex) structure of the quickest change detection policy. Our methodology yields a useful framework for situation awareness systems and anticipatory human decision makers interacting with sequential detectors.