International Society of Dynamic Games

Dynamic Games and Applications Seminar

Economically Exhaustible Resources in an Oligopoly-Fringe Model with Renewables

September 21, 2023
11:00 AM — 12:00 PM (Montreal time)

Webinar link

We consider a game between oligopolistic and fringe suppliers of fossil fuel from an exhaustible resource, and of producers of a renewable perfect substitute. Extraction costs are stock-dependent and strictly convex in the rate of extraction. We characterize the open-loop equilibrium analytically and perform numerical simulations with calibrated parameter values. The effects of our cost assumptions are (i) to have asymptotic, economical instead of physical exhaustion of the non-renewable resource and (ii) the existence of a non-traditional limit-pricing phase in which both fossil and renewables suppliers are active. We decompose the welfare loss of imperfect competition in a conservation and a sequence effect and show that both can be substantial: 3.8 and 4.2 trillion US$ in the calibrated model, respectively. We also find that initial carbon emissions depend non-monotonically on the renewables subsidy rate.

(with Gerard van der Meijden et Cees Withagen)

Dynamic Games and Applications Seminar

Network games with large populations: non-uniqueness and learning dynamics

September 14, 2023
11:00 AM — 12:00 PM (Montreal time)

Webinar link.

Understanding the role of network interactions is fundamental for improving efficiency, resilience, and welfare in many socio-economic settings. The large size of these systems (e.g., involving billions of users in the case of social platforms) however introduces some challenges from the perspective of a planner that aims at regulating interactions. In fact in many cases, collecting exact network data is either too costly or impossible due to privacy concerns. In these cases however it might be feasible for the planner to collect statistical information about agents’ interactions that can be used to infer a random graph model. A key question is then whether knowledge of such a random graph model is sufficient to infer relevant features of the realized network or to control a dynamical process evolving over it. This question has been addressed in a number of recent works by focusing on network games with unique Nash equilibrium. Yet, many relevant networks may exhibit multiple equilibria. In this talk, we will present novel results in this direction. Specifically, we will present a convergence theory for graphon games with multiple equilibria and algorithms for learning in time-varying settings.