# Reward-Based Deception with Cognitive Bias

## TL;DR

This paper proposes a novel framework for deception in adversarial interactions that exploits human cognitive biases, specifically prospect theory, to optimize resource allocation against bounded rational adversaries.

## Contribution

It introduces a new approach to deception that leverages human cognitive biases and formulates resource allocation as a signomial program within a Markov decision process.

## Key findings

- Effective resource allocation reduces defender's costs.
- Simulation with real-world data demonstrates the framework's practicality.
- Exploiting cognitive biases enhances deception strategies.

## Abstract

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.