Plenary speakers

Prof. Dr. Dylan Possamai

ETH Zürich

Title: Contract theory and energy markets

We study how continuous-time contract theory can be used as a quantitative tool for the design of incentives in energy markets, focusing on two applications: demand–response management and pollution regulation. First, we model a demand–response program as a moral-hazard Principal–Agent problem in which a risk-averse consumer can exert costly effort to reduce both the mean and the volatility of her consumption deviations, while the producer observes consumption but not effort. We then extend the model to a large population with common shocks, leading to a mean-field Stackelberg setting where optimal incentives naturally combine (i) a “classical” component indexed on the individual consumption deviation and (ii) a benchmarking component indexed on aggregate behavior (equivalently, on the common noise). This yields explicit contract structures and comparative statics, and illustrates how controlling volatility can materially increase responsiveness. Second, we propose a regulator–producers model for pollution regulation in an electricity network. After an auction phase (cost functions given), the regulator chooses production and power flows subject to network constraints, and designs terminal transfers that induce producers to exert abatement effort affecting the drift of aggregate emissions. The set of implementable remunerations is characterized via a tractable dynamic representation, while the regulator’s problem leads to an HJB characterization and computable optimal contracts. Numerical experiments on a calibrated three-node network (inspired by the Chilean market) show that optimal contracts can substantially curb emissions, reducing pollution increments by more than 30% in the reported simulation study.

 

Prof. Dr. Kenan Zhang

EPFL

Title: Markov population potential games: An alternative framework for Markovian traffic assignment

Traffic assignment models describe how selfish individuals interact on a physical network under prescribed behavioral rules, and how an equilibrium state emerges from these interactions. Although originally developed for long-term travel demand forecasting, their applications extend far beyond road traffic prediction. Among existing models, Markovian traffic assignment (MTA) strikes a balance between model representational power and analytical tractability by assuming memoryless (Markovian) system dynamics and state-dependent decision making. In this talk, I will introduce a new framework for Markovian traffic assignment, namely, Markov population potential game (MPPG), which integrates Markov game, potential game, and population game. By allowing introducing interdependent state transitions, MPPG generalizes classical MTA and substantially broadens the scope of applicability. A key advantage of MPPG, as with potential games in general, is the existence of an equivalent optimization problem whose optimal solution corresponds to the equilibrium. I will present sufficient conditions under which such a potential exist and can be constructed in closed form, along with solution algorithms based on the corresponding optimization formulation, then conclude with several applications of MPPG in transportation.