Object-oriented Bayesian networks for a decision support system for antitrust enforcement

TL;DR

This paper introduces an object-oriented Bayesian network model to simulate and analyze the decision-making process of antitrust authorities and firms, incorporating economic variables and strategic interactions to improve enforcement strategies.

Contribution

It presents a novel application of object-oriented Bayesian networks to model complex economic decision processes involving multiple agents and external market factors.

Findings

Monitoring influences firms' cooperation strategies

The integrated model captures decision scenarios effectively

Object-oriented Bayesian networks facilitate complex economic modeling

Abstract

We study an economic decision problem where the actors are two firms and the Antitrust Authority whose main task is to monitor and prevent firms' potential anti-competitive behaviour and its effect on the market. The Antitrust Authority's decision process is modelled using a Bayesian network where both the relational structure and the parameters of the model are estimated from a data set provided by the Authority itself. A number of economic variables that influence this decision process are also included in the model. We analyse how monitoring by the Antitrust Authority affects firms' strategies about cooperation. Firms' strategies are modelled as a repeated prisoner's dilemma using object-oriented Bayesian networks. We show how the integration of firms' decision process and external market information can be modelled in this way. Various decision scenarios and strategies are illustrated.