1 citations · 1 across the 7 of their papers we have counts for
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Deep Incentive Design with Differentiable Equilibrium Blocks
Vinzenz Thoma, Georgios Piliouras, Luke Marris
Automated design of multi-agent interactions with desirable equilibrium outcomes is inherently difficult due to the computational hardness, non-uniqueness, and instability of the r…
Scalable Neural Incentive Design with Parameterized Mean-Field Approximation
Nathan Corecco, Batuhan Yardim, Vinzenz Thoma +2
Designing incentives for a multi-agent system to induce a desirable Nash equilibrium is both a crucial and challenging problem appearing in many decision-making domains, especially…
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings
Michael Curry, Vinzenz Thoma, Darshan Chakrabarti +5
Dynamic mechanism design is a challenging extension to ordinary mechanism design in which the mechanism designer must make a sequence of decisions over time in the face of possibly…
Learning Best Response Policies in Dynamic Auctions via Deep Reinforcement Learning
Vinzenz Thoma, Michael Curry, Niao He +1
Many real-world auctions are dynamic processes, in which bidders interact and report information over multiple rounds with the auctioneer. The sequential decision making aspect pai…
Computing Perfect Bayesian Equilibria in Sequential Auctions with Verification
Vinzenz Thoma, Vitor Bosshard, Sven Seuken
We present an algorithm for computing pure-strategy epsilon-perfect Bayesian equilibria in sequential auctions with continuous action and value spaces. Importantly, our algorithm i…