Daniel Lacker works at the intersection of applied probability, stochastic analysis, and mathematical finance. His primary research areas, mean field game theory and interacting particle systems, form the mathematical foundation for a wide range of models of large-scale systems of interacting agents. This modeling framework originated in statistical physics and, more recently, has been adapted to serve a variety of applications in the social sciences, such as financial markets, income inequality, and pedestrian crowd dynamics.
National Science Foundation Postdoctoral Fellow, Brown University, 2015-2017
Society for Industrial and Applied Mathematics, Activity Group on Financial Mathematics and Engineering (SIAG/FME)
Winner of the SIAG/FME Conference Paper Prize, 2014
- D. Lacker and K. Ramanan, “Rare Nash equilibria and the price of anarchy in large static games,” to appear in Mathematics of Operations Research (2018).
- D. Lacker, “Limit theory for controlled McKean-Vlasov dynamics,” SIAM Journal on Control and Optimization 55 (3), 1641-1672 (2017).
- D. Lacker, “A general characterization of the mean field limit for stochastic differential games,” Probability Theory and Related Fields 165 (3), 581-648 (2016). Winner of the SIAG/FME Conference Paper Prize, 2014.
- R. Carmona, F. Delarue, and D. Lacker, “Mean field games with common noise,” The Annals of Probability 44 (6), 3740-3803 (2016).
- D. Lacker, “Mean field games via controlled martingale problems: Existence of Markovian equilibria,” Stochastic Processes and their Applications 125 (7), 2856-2894 (2015).
- R. Carmona and D. Lacker, “A probabilistic weak formulation of mean field games and applications,” The Annals of Applied Probability 25 (3), 1189-1231 (2015).
- D. Lacker, “Liquidity, risk measures, and concentration of measure,” to appear in Mathematics of Operations Research (2018).