Algorithm Instance Games
Samuel D. Johnson, Tsai-Ching Lu

TL;DR
This paper introduces algorithm instance games (AIGs) to analyze how the specific algorithms used to determine game outcomes influence strategic behavior, focusing on set-cover based games with deterministic and non-deterministic algorithms.
Contribution
It characterizes Nash equilibria in set-cover based AIGs, revealing how algorithm determinism affects agents' strategic choices and equilibrium structures.
Findings
Deterministic algorithm leads to equilibria with single-element strategies.
Non-deterministic algorithm allows multiple strategies, including zero or all elements.
Equilibrium strategies differ significantly between the two algorithm types.
Abstract
This paper introduces algorithm instance games (AIGs) as a conceptual classification applying to games in which outcomes are resolved from joint strategies algorithmically. For such games, a fundamental question asks: How do the details of the algorithm's description influence agents' strategic behavior? We analyze two versions of an AIG based on the set-cover optimization problem. In these games, joint strategies correspond to instances of the set-cover problem, with each subset (of a given universe of elements) representing the strategy of a single agent. Outcomes are covers computed from the joint strategies by a set-cover algorithm. In one variant of this game, outcomes are computed by a deterministic greedy algorithm, and the other variant utilizes a non-deterministic form of the greedy algorithm. We characterize Nash equilibrium strategies for both versions of the game, finding…
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Taxonomy
TopicsAuction Theory and Applications · Artificial Intelligence in Games · Sports Analytics and Performance
