A competitive search game with a moving target

TL;DR

This paper introduces a discrete-time competitive search game with a moving target modeled by a Markov chain, establishing the existence of approximate strategies and analyzing their robustness and structural properties.

Contribution

It develops the theory of $ ext{eps}$-optimal strategies in a competitive search game with a moving target, including their robustness and structural analysis.

Findings

Existence of a game value and $ ext{eps}$-optimal strategies.

Robustness of strategies over finite horizons and discounted games.

Analysis of finite truncation strategies and special results for time-homogeneous cases.

Abstract

We introduce a discrete-time search game, in which two players compete to find an object first. The object moves according to a time-varying Markov chain on finitely many states. The players know the Markov chain and the initial probability distribution of the object, but do not observe the current state of the object. The players are active in turns. The active player chooses a state, and this choice is observed by the other player. If the object is in the chosen state, this player wins and the game ends. Otherwise, the object moves according to the Markov chain and the game continues at the next period. We show that this game admits a value, and for any error-term $\veps>0$, each player has a pure (subgame-perfect) $\veps$-optimal strategy. Interestingly, a 0-optimal strategy does not always exist. The $\veps$-optimal strategies are robust in the sense that they are $2\veps$-optimal on all finite but sufficiently long horizons, and also $2\veps$-optimal in the discounted version of the game provided that the discount factor is close to 1. We derive results on the analytic and structural properties of the value and the $\veps$-optimal strategies. Moreover, we examine the performance of the finite truncation strategies, which are easy to calculate and to implement. We devote special attention to the important time-homogeneous case, where additional results hold.