SMS scnews item created by Anna Aksamit at Mon 20 Sep 2021 1331
Type: Seminar
Distribution: World
Expiry: 27 Sep 2021
Calendar1: 22 Sep 2021 1400-1510
CalLoc1: zoom talk
Auth: (aaks9559) in SMS-SAML

Stochastics and Finance: Zhou Zhou -- Optimal relative performance criteria in mean field contribution games

Dear All, 

You are kindly invited to attend the next Stochastics and Finance seminar.  On Wednesday
September 22 at 2pm Zhou Zhou will give a talk via Zoom.  

Zoom link: 

Speaker: Dr Zhou Zhou (University of Sydney) 

Title: Optimal relative performance criteria in mean field contribution games 

Abstract: We consider mean-field contribution games, where players in a team choose some
effort levels at each time period, and the aggregate reward for the team depends on the
aggregate cumulative performance of all the players.  Each player aims to maximize the
expected reward of her own share subject to her cost of effort.  To reduce free-rider
issue, we propose some relative performance criteria (RPC), based on which the reward is
redistributed to each player.  We are interested in those RPCs which implement the
optimal solution for the corresponding centralized problem, and we call such RPC an
optimal one.  That is, the expected payoff of each player under the equilibrium
associated with an optimal RPC is as large as the value induced by the corresponding
problem where players completely cooperate.  We first analyze a one-period model with
homogeneous players, and obtain natural RPCs of different forms.  Then we generalize
these results to a multi-period model in discrete time.  Next, we investigate a
two-layer mean-field game: The top-layer is an inter-team game (team-wise competition)
in which the reward of a team is impacted by the relative achievement of the team with
respect to other teams; the bottom layer is an intra-team contribution game where an RPC
is implemented for reward redistribution among team members.  We establish the existence
of equilibria for the two-layer game and characterize the intra-team optimal RPC.
Finally, we extend the (one-layer) results of optimal RPCs to the continuous-time setup
as well as to the case with heterogenous players. 

Please feel free to forward this message to anyone who might be interested in this

Kind regards, 


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