18,044 research outputs found
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
Free Riding in the Lab and in the Field
We run a public good experiment in the field and in the lab with (partly) the same subjects. The field experiment is a true natural field experiment as subjects do not know that they are exposed to an experimental variation. We can show that subjects' behavior in the classic lab public good experiment correlates with their behavior in the structurally comparable public good treatment in the field but not with behavior in any of two control treatments we ran in the field. This effect is also economically significant. We conclude that a) the classic lab public good experiment captures important aspects of structurally equivalent real life situations and b) that behavior in lab and field at least in our setting is driven by the same underlying forces
What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality?
In an earlier experiment, participants played a perfect information game
against a computer, which was programmed to deviate often from its backward
induction strategy right at the beginning of the game. Participants knew that
in each game, the computer was nevertheless optimizing against some belief
about the participant's future strategy. In the aggregate, it appeared that
participants applied forward induction. However, cardinal effects seemed to
play a role as well: a number of participants might have been trying to
maximize expected utility.
In order to find out how people really reason in such a game, we designed
centipede-like turn-taking games with new payoff structures in order to make
such cardinal effects less likely. We ran a new experiment with 50
participants, based on marble drop visualizations of these revised payoff
structures. After participants played 48 test games, we asked a number of
questions to gauge the participants' reasoning about their own and the
opponent's strategy at all decision nodes of a sample game. We also checked how
the verbalized strategies fit to the actual choices they made at all their
decision points in the 48 test games.
Even though in the aggregate, participants in the new experiment still tend
to slightly favor the forward induction choice at their first decision node,
their verbalized strategies most often depend on their own attitudes towards
risk and those they assign to the computer opponent, sometimes in addition to
considerations about cooperativeness and competitiveness.Comment: In Proceedings TARK 2017, arXiv:1707.0825
Neural correlates of mentalizing-related computations during strategic interactions in humans
Competing successfully against an intelligent adversary requires the ability to mentalize an opponent's state of mind to anticipate his/her future behavior. Although much is known about what brain regions are activated during mentalizing, the question of how this function is implemented has received little attention to date. Here we formulated a computational model describing the capacity to mentalize in games. We scanned human subjects with functional MRI while they participated in a simple two-player strategy game and correlated our model against the functional MRI data. Different model components captured activity in distinct parts of the mentalizing network. While medial prefrontal cortex tracked an individual's expectations given the degree of model-predicted influence, posterior superior temporal sulcus was found to correspond to an influence update signal, capturing the difference between expected and actual influence exerted. These results suggest dissociable contributions of different parts of the mentalizing network to the computations underlying higher-order strategizing in humans
Manager Power, Member Behavior and Capital Structure: Portuguese Douro Wine Cooperatives
Leverage is one of the most important financial factors to the survival and viability of agricultural cooperatives (e.g., wine cooperatives) during a period of intense competition. Leverage is influenced both by the behavior of managers and cooperative members. An empirical study for the Douro Demarcated Region Wine Cooperatives (DDRWC) supports the hypothesis that managers have a positive influence in the determination of the equity/total assets ratio and that individualistic behavior of cooperative members has a negative influence in the value of this ratio. This paper suggests that there may be value in reconsidering cooperatives in the context of a so-called Mediterranean model.Agribusiness, Agricultural cooperatives, governance, behavior and leverage,
Personality Preferences and Pre-Commitment: Behavioral Explanations in Ultimatum Games
This paper uses responder pre-commitment and the Jungian theory of mental activity and psychological type, as measured by the widely-used Myers-Briggs Type Indicator (MBTI), to gain insight into subject behavior in a laboratory ultimatum bargaining experiment. Three experiment design details are noteworthy: (1) one design requires responders to make a nonbinding pre-commitment rejection level prior to seeing the offer, (2) one design requires responders to make a binding pre-commitment rejection level, and (3) one design includes a third person (or “hostage”) who makes no decision, but whose payment depends on the proposal being accepted. In general, we find behavior in our experiment to be consistent with hypotheses based on theoretical underpinnings of the MBTI and its descriptions of psychological type.
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