206 research outputs found
A Formal Separation Between Strategic and Nonstrategic Behavior
It is common in multiagent systems to make a distinction between "strategic"
behavior and other forms of intentional but "nonstrategic" behavior: typically,
that strategic agents model other agents while nonstrategic agents do not.
However, a crisp boundary between these concepts has proven elusive. This
problem is pervasive throughout the game theoretic literature on bounded
rationality and particularly critical in parts of the behavioral game theory
literature that make an explicit distinction between the behavior of
"nonstrategic" level-0 agents and "strategic" higher-level agents (e.g., the
level-k and cognitive hierarchy models). Overall, work discussing bounded
rationality rarely gives clear guidance on how the rationality of nonstrategic
agents must be bounded, instead typically just singling out specific decision
rules and informally asserting them to be nonstrategic (e.g., truthfully
revealing private information; randomizing uniformly). In this work, we propose
a new, formal characterization of nonstrategic behavior. Our main contribution
is to show that it satisfies two properties: (1) it is general enough to
capture all purportedly "nonstrategic" decision rules of which we are aware in
the behavioral game theory literature; (2) behavior that obeys our
characterization is distinct from strategic behavior in a precise sense
Polynomial-time Computation of Exact Correlated Equilibrium in Compact Games
In a landmark paper, Papadimitriou and Roughgarden described a
polynomial-time algorithm ("Ellipsoid Against Hope") for computing sample
correlated equilibria of concisely-represented games. Recently, Stein, Parrilo
and Ozdaglar showed that this algorithm can fail to find an exact correlated
equilibrium, but can be easily modified to efficiently compute approximate
correlated equilibria. Currently, it remains unresolved whether the algorithm
can be modified to compute an exact correlated equilibrium. We show that it
can, presenting a variant of the Ellipsoid Against Hope algorithm that
guarantees the polynomial-time identification of exact correlated equilibrium.
Our new algorithm differs from the original primarily in its use of a
separation oracle that produces cuts corresponding to pure-strategy profiles.
As a result, we no longer face the numerical precision issues encountered by
the original approach, and both the resulting algorithm and its analysis are
considerably simplified. Our new separation oracle can be understood as a
derandomization of Papadimitriou and Roughgarden's original separation oracle
via the method of conditional probabilities. Also, the equilibria returned by
our algorithm are distributions with polynomial-sized supports, which are
simpler (in the sense of being representable in fewer bits) than the mixtures
of product distributions produced previously; no tractable algorithm has
previously been proposed for identifying such equilibria.Comment: 15 page
Local-Effect Games
This talk will survey two graphical models which the authors have proposed
for compactly representing single-shot, finite-action games in which a large
number of agents contend for scarce resources.
The first model considered is Local-Effect Games (LEGs). These games often
(but not always) have pure-strategy Nash equilibria. Finding a potential
function is a good technique for finding such equilibria. We give a complete
characterization of which LEGs have potential functions and provide the
functions in each case; we also show a general case where pure-strategy
equilibria exist in the absence of potential functions.
Action-graph games (AGGs) are a fully expressive game representation which
can compactly express both strict and context-specific independence between
players\u27 utility functions, and which generalize LEGs. We present algorithms
for computing both symmetric and arbitrary equilibria of AGGs, based on a
continuation method proposed by Govindan and Wilson. We analyze the worst-
case cost of computing the Jacobian of the payoff function, the exponential-
time bottleneck step of this algorithm, and in all cases achieve exponential
speedup. When the indegree of G is bounded by a constant and the game is
symmetric, the Jacobian can be computed in polynomial time
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