8,586 research outputs found
Coordination in Networks Formation: Experimental Evidence on Learning and Salience
We present experiments on repeated non-cooperative network formation games, based on Bala and Goyal (2000). We treat the one-way and the two-ways flow models, each for high and low link costs. The models show both multiple equilibria and coordination problems. We conduct experiments under various conditions which control for salient labeling and learning dynamics. Contrary to previous experiments, we find that coordination on non-empty Strict Nash equilibria is not an easy task for subjects to achieve, even in the mono-directional model where the Strict Nash equilibria is a wheel. We find that salience significantly helps coordination, but only when subjects are pre-instructed to think of the wheel network as a reasonable way to play the networking game. Evidence on learning behavior provides support for subjects choosing strategies consistent with various learning rules, which include as the main ones Reinforcement and Fictitious Play.Experiments, Networks, Behavioral game theory, Salience, Learning dynamics
Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data
We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic settings. Applications include the identification of the most
influential individuals in large (social) networks. Such tasks can also support
policy-making analysis. Motivated by the computational work on LIGs, we cast
the learning problem as maximum-likelihood estimation (MLE) of a generative
model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation
uncovers the fundamental interplay between goodness-of-fit and model
complexity: good models capture equilibrium behavior within the data while
controlling the true number of equilibria, including those unobserved. We
provide a generalization bound establishing the sample complexity for MLE in
our framework. We propose several algorithms including convex loss minimization
(CLM) and sigmoidal approximations. We prove that the number of exact PSNE in
LIGs is small, with high probability; thus, CLM is sound. We illustrate our
approach on synthetic data and real-world U.S. congressional voting records. We
briefly discuss our learning framework's generality and potential applicability
to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending
publication.) Last conference version: submitted March 30, 2012 to UAI 2012.
First conference version: entitled, Learning Influence Games, initially
submitted on June 1, 2010 to NIPS 201
- …