Computational Asymmetry in Strategic Bayesian Networks

TL;DR

This paper introduces Strategic Bayesian Networks (SBN), a game-theoretic framework modeling how computational resource asymmetries among economic actors influence strategic outcomes and payoffs.

Contribution

It proposes SBN as a novel game specification to incorporate computational choices and demonstrates how computational asymmetry affects payoffs in strategic settings.

Findings

Players with greater computational resources achieve higher payoffs.

SBN effectively models strategic interactions involving computational asymmetries.

Two example games illustrate the impact of computational limitations on outcomes.

Abstract

Among the strategic choices made by today's economic actors are choices about algorithms and computational resources. Different access to computational resources may result in a kind of economic asymmetry analogous to information asymmetry. In order to represent strategic computational choices within a game theoretic framework, we propose a new game specification, Strategic Bayesian Networks (SBN). In an SBN, random variables are represented as nodes in a graph, with edges indicating probabilistic dependence. For some nodes, players can choose conditional probability distributions as a strategic choice. Using SBN, we present two games that demonstrate computational asymmetry. These games are symmetric except for the computational limitations of the actors. We show that the better computationally endowed player receives greater payoff.