Strategic Play By Resource-Bounded Agents in Security Games

TL;DR

This paper models resource-bounded agents in security games as probabilistic automata, demonstrating how limited memory induces human-like behaviors and can enhance strategic performance in ranger-poacher scenarios.

Contribution

It introduces a novel automata-based framework to explain human-like deviations in security game strategies as rational responses to computational limitations.

Findings

Large memory PFAs learn Nash equilibrium strategies.

Limited memory PFAs exhibit probability matching and event overweighting.

Human-like behaviors can improve agent performance.

Abstract

Many studies have shown that humans are "predictably irrational": they do not act in a fully rational way, but their deviations from rational behavior are quite systematic. Our goal is to see the extent to which we can explain and justify these deviations as the outcome of rational but resource-bounded agents doing as well as they can, given their limitations. We focus on the well-studied ranger-poacher game, where rangers are trying to protect a number of sites from poaching. We capture the computational limitations by modeling the poacher and the ranger as probabilistic finite automata (PFAs). We show that, with sufficiently large memory, PFAs learn to play the Nash equilibrium (NE) strategies of the game and achieve the NE utility. However, if we restrict the memory, we get more "human-like" behaviors, such as probability matching (i.e., visiting sites in proportion to the probability of a rhino being there), and avoiding sites where there was a bad outcome (e.g., the poacher was caught by the ranger), that we also observed in experiments conducted on Amazon Mechanical Turk. Interestingly, we find that adding human-like behaviors such as probability matching and overweighting significant events (like getting caught) actually improves performance, showing that this seemingly irrational behavior can be quite rational.