Game theory is the de facto standard conceptual framework used to analyze the strategic
interactions among rational agents in multiagent systems. At the heart of this framework is …

We introduce the use of empirical centralization to derive novel practical, probabilistic,
sample-dependent bounds to the Supremum Deviation (SD) of empirical means of functions …

We tackle the problem of learning equilibria in simulation-based games. In such games, the
players' utility functions cannot be described analytically, as they are given through a black …

We introduce a methodology for the design of parametric mechanisms, which are multiagent
systems inhabited by strategic agents, with knobs that can be adjusted to achieve specific …

In non-truthful auctions, agents' utility for a strategy depends on the strategies of the
opponents and also the prior distribution over their private types; the set of Bayes Nash …

In recent years, empirical game-theoretic analysis (EGTA) has emerged as a powerful tool
by which to analyze multiagent systems [2, 22, 23, 28], particularly when only a simulator of …

Empirical game-theoretic analysis (EGTA) is primarily focused on learning the equilibria of
simulation-based games. Recent approaches have tackled this problem by learning a …

We propose a scheme based on active learning to reconstruct private strategies executed by
a population of interacting agents and predict an exact outcome of the underlying multi …

We present a methodology to robustly estimate the competitive equilibria (CE) of
combinatorial markets under the assumption that buyers do not know their precise …

In recent years, empirical game-theoretic analysis (EGTA) has emerged as a powerful tool
for analyzing games in which an exact specification of the utilities is unavailable. Instead …