1 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
Efficient Inverse Multiagent Learning
Denizalp Goktas, Amy Greenwald, Sadie Zhao +2
In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. s…
Infinite Horizon Markov Economies
Denizalp Goktas, Sadie Zhao, Yiling Chen +1
In this paper, we study a generalization of Markov games and pseudo-games that we call Markov pseudo-games, which, like the former, captures time and uncertainty, and like the latt…
Tractable General Equilibrium
Denizalp Goktas, Klára Chura, Alp Emin Guneri +1
We study Walrasian economies (or general equilibrium models) and their solution concept, the Walrasian equilibrium. A key challenge in this domain is identifying price-adjustment p…
Banzhaf Power in Hierarchical Voting Games
John Randolph, Denizalp Goktas, Amy Greenwald
The Banzhaf Power Index (BPI) is a method of measuring the power of voters in determining the outcome of a voting game. Some voting games exhibit a hierarchical structure, includin…
Convex-Concave Zero-sum Markov Stackelberg Games
Denizalp Goktas, Arjun Prakash, Amy Greenwald
Zero-sum Markov Stackelberg games can be used to model myriad problems, in domains ranging from economics to human robot interaction. In this paper, we develop policy gradient meth…
Stackelberg POMDP: Learning to Lead via Reinforcement Learning
Gianluca Brero, Alon Eden, Darshan Chakrabarti +6
Many real-world domains--including e-commerce platform design, security planning, and multi-agent coordination--feature leader-follower problems where one decision-maker commits to…