Object-oriented Bayesian networks for a decision support system for antitrust enforcement
This work addresses decision support for antitrust enforcement, providing a domain-specific model that is incremental in applying existing methods to a new economic context.
The paper tackles the problem of modeling the Antitrust Authority's decision process to monitor and prevent anti-competitive behavior by firms, using Bayesian networks estimated from real data, and shows how this integration affects firms' cooperation strategies in a repeated prisoner's dilemma framework.
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.