Reward-Based Deception with Cognitive Bias

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.

GPkit: A Human-Centered Approach to Convex Optimization in Engineering Design

“…It did so by embracing a participatory human-centered framework focused on validating workers' knowledge [26] and using connections between that knowledge and the mathematics of optimization to develop GPkit, a toolkit for convex Geometric Programs. GPkit was used by many engineers and researchers during its development [1,2,11,14,16,17,27,28,32,33,36,46,48,49,53,60,64,68], which resulted in features that are:…”

Deceptive Kernel Function on Observations of Discrete POMDP

“…However, the framework has a prerequisite of its deception objective being reward-based. Wu et al [21] have studied the agent's behavior within an MDP environment subject to a reward-based adversarial deception, which in the meantime leverages the cognitive bias of the human. More related researches have emerged recently in reinforcement learning (RL) area regarding the security and stability of RL process.…”

“…While these researches all demonstrate novel and inspirational work from their respective angle, they are meantime subject to several common limitations. Attacks modeled in researches [11], [23] and [14] are subject to certain "objectives" of deception/attack; research in [21] considers human cognitive bias as a reason for agent's potential suboptimal choices; models in [21], [16], [23] and [14] are computed with underlying MDP regardless of agent's partial observability as a potential setting; and some of the tactics introduced above are more attacking rather than deceptive.…”