Strategic Inference with a Single Private Sample

TL;DR

This paper models a game where an attacker with a single private sample updates its belief about a target's quality, influencing strategic decisions in a cyber security context with asymmetric information.

Contribution

It introduces a simple game model capturing how a private sample affects inference and strategic choices under asymmetric information.

Findings

Characterization of pure strategy equilibria

Influence of prior knowledge on decisions

Impact of payoffs and costs on strategies

Abstract

Motivated by applications in cyber security, we develop a simple game model for describing how a learning agent's private information influences an observing agent's inference process. The model describes a situation in which one of the agents (attacker) is deciding which of two targets to attack, one with a known reward and another with uncertain reward. The attacker receives a single private sample from the uncertain target's distribution and updates its belief of the target quality. The other agent (defender) knows the true rewards, but does not see the sample that the attacker has received. This leads to agents possessing asymmetric information: the attacker is uncertain over the parameter of the distribution, whereas the defender is uncertain about the observed sample. After the attacker updates its belief, both the attacker and the defender play a simultaneous move game based on their respective beliefs. We offer a characterization of the pure strategy equilibria of the game and explain how the players' decisions are influenced by their prior knowledge and the payoffs/costs.