Reward-Based Deception with Cognitive Bias
This addresses deception in strategic interactions involving human adversaries, offering a domain-specific solution for applications like security and defense.
The paper tackles the problem of deceiving adversaries with bounded rationality, such as humans, by leveraging cognitive biases in reward evaluation under stochastic outcomes, and introduces a framework for optimal resource allocation to minimize defender cost, with simulation results based on real-world data in a police patrol example.
Deception plays a key role in adversarial or strategic interactions for the purpose of self-defence and survival. This paper introduces a general framework and solution to address deception. Most existing approaches for deception consider obfuscating crucial information to rational adversaries with abundant memory and computation resources. In this paper, we consider deceiving adversaries with bounded rationality and in terms of expected rewards. This problem is commonly encountered in many applications especially involving human adversaries. Leveraging the cognitive bias of humans in reward evaluation under stochastic outcomes, we introduce a framework to optimally assign resources of a limited quantity to optimally defend against human adversaries. Modeling such cognitive biases follows the so-called prospect theory from behavioral psychology literature. Then we formulate the resource allocation problem as a signomial program to minimize the defender's cost in an environment modeled as a Markov decision process. We use police patrol hour assignment as an illustrative example and provide detailed simulation results based on real-world data.