Modelling Cournot Games as Multi-agent Multi-armed Bandits
This work addresses the problem of optimizing firm strategies in oligopoly markets for economists and AI researchers, but it is incremental as it builds on existing bandit frameworks with domain-specific adaptations.
The paper tackles modeling repeated Cournot oligopoly games as multi-agent multi-armed bandits, finding that an ε-greedy approach is more viable than traditional methods, and proposes two novel approaches (ε-greedy+HL and ε-greedy+EL) that optimize exploration by focusing on profitable actions, with empirical analysis using simulations to study equilibria and joint cumulative regrets.
We investigate the use of a multi-agent multi-armed bandit (MA-MAB) setting for modeling repeated Cournot oligopoly games, where the firms acting as agents choose from the set of arms representing production quantity (a discrete value). Agents interact with separate and independent bandit problems. In this formulation, each agent makes sequential choices among arms to maximize its own reward. Agents do not have any information about the environment; they can only see their own rewards after taking an action. However, the market demand is a stationary function of total industry output, and random entry or exit from the market is not allowed. Given these assumptions, we found that an $ε$-greedy approach offers a more viable learning mechanism than other traditional MAB approaches, as it does not require any additional knowledge of the system to operate. We also propose two novel approaches that take advantage of the ordered action space: $ε$-greedy+HL and $ε$-greedy+EL. These new approaches help firms to focus on more profitable actions by eliminating less profitable choices and hence are designed to optimize the exploration. We use computer simulations to study the emergence of various equilibria in the outcomes and do the empirical analysis of joint cumulative regrets.