6,700 research outputs found
The Complexity of Nash Equilibria in Limit-Average Games
We study the computational complexity of Nash equilibria in concurrent games
with limit-average objectives. In particular, we prove that the existence of a
Nash equilibrium in randomised strategies is undecidable, while the existence
of a Nash equilibrium in pure strategies is decidable, even if we put a
constraint on the payoff of the equilibrium. Our undecidability result holds
even for a restricted class of concurrent games, where nonzero rewards occur
only on terminal states. Moreover, we show that the constrained existence
problem is undecidable not only for concurrent games but for turn-based games
with the same restriction on rewards. Finally, we prove that the constrained
existence problem for Nash equilibria in (pure or randomised) stationary
strategies is decidable and analyse its complexity.Comment: 34 page
Incentive Stackelberg Mean-payoff Games
We introduce and study incentive equilibria for multi-player meanpayoff
games. Incentive equilibria generalise well-studied solution concepts such as
Nash equilibria and leader equilibria (also known as Stackelberg equilibria).
Recall that a strategy profile is a Nash equilibrium if no player can improve
his payoff by changing his strategy unilaterally. In the setting of incentive
and leader equilibria, there is a distinguished player called the leader who
can assign strategies to all other players, referred to as her followers. A
strategy profile is a leader strategy profile if no player, except for the
leader, can improve his payoff by changing his strategy unilaterally, and a
leader equilibrium is a leader strategy profile with a maximal return for the
leader. In the proposed case of incentive equilibria, the leader can
additionally influence the behaviour of her followers by transferring parts of
her payoff to her followers. The ability to incentivise her followers provides
the leader with more freedom in selecting strategy profiles, and we show that
this can indeed improve the payoff for the leader in such games. The key
fundamental result of the paper is the existence of incentive equilibria in
mean-payoff games. We further show that the decision problem related to
constructing incentive equilibria is NP-complete. On a positive note, we show
that, when the number of players is fixed, the complexity of the problem falls
in the same class as two-player mean-payoff games. We also present an
implementation of the proposed algorithms, and discuss experimental results
that demonstrate the feasibility of the analysis of medium sized games.Comment: 15 pages, references, appendix, 5 figure
Bounding Rationality by Discounting Time
Consider a game where Alice generates an integer and Bob wins if he can
factor that integer. Traditional game theory tells us that Bob will always win
this game even though in practice Alice will win given our usual assumptions
about the hardness of factoring.
We define a new notion of bounded rationality, where the payoffs of players
are discounted by the computation time they take to produce their actions. We
use this notion to give a direct correspondence between the existence of
equilibria where Alice has a winning strategy and the hardness of factoring.
Namely, under a natural assumption on the discount rates, there is an
equilibriumwhere Alice has a winning strategy iff there is a linear-time
samplable distribution with respect to which Factoring is hard on average.
We also give general results for discounted games over countable action
spaces, including showing that any game with bounded and computable payoffs has
an equilibrium in our model, even if each player is allowed a countable number
of actions. It follows, for example, that the Largest Integer game has an
equilibrium in our model though it has no Nash equilibria or epsilon-Nash
equilibria.Comment: To appear in Proceedings of The First Symposium on Innovations in
Computer Scienc
Exact solution of a modified El Farol's bar problem: Efficiency and the role of market impact
We discuss a model of heterogeneous, inductive rational agents inspired by
the El Farol Bar problem and the Minority Game. As in markets, agents interact
through a collective aggregate variable -- which plays a role similar to price
-- whose value is fixed by all of them. Agents follow a simple
reinforcement-learning dynamics where the reinforcement, for each of their
available strategies, is related to the payoff delivered by that strategy. We
derive the exact solution of the model in the ``thermodynamic'' limit of
infinitely many agents using tools of statistical physics of disordered
systems. Our results show that the impact of agents on the market price plays a
key role: even though price has a weak dependence on the behavior of each
individual agent, the collective behavior crucially depends on whether agents
account for such dependence or not. Remarkably, if the adaptive behavior of
agents accounts even ``infinitesimally'' for this dependence they can, in a
whole range of parameters, reduce global fluctuations by a finite amount. Both
global efficiency and individual utility improve with respect to a ``price
taker'' behavior if agents account for their market impact.Comment: 38 pages + 5 figures (needs elsart.sty). New results adde
Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria
We study the distribution of strategies in a large game that models how
agents choose among different double auction markets. We classify the possible
mean field Nash equilibria, which include potentially segregated states where
an agent population can split into subpopulations adopting different
strategies. As the game is aggregative, the actual equilibrium strategy
distributions remain undetermined, however. We therefore compare with the
results of Experience-Weighted Attraction (EWA) learning, which at long times
leads to Nash equilibria in the appropriate limits of large intensity of
choice, low noise (long agent memory) and perfect imputation of missing scores
(fictitious play). The learning dynamics breaks the indeterminacy of the Nash
equilibria. Non-trivially, depending on how the relevant limits are taken, more
than one type of equilibrium can be selected. These include the standard
homogeneous mixed and heterogeneous pure states, but also \emph{heterogeneous
mixed} states where different agents play different strategies that are not all
pure. The analysis of the EWA learning involves Fokker-Planck modeling combined
with large deviation methods. The theoretical results are confirmed by
multi-agent simulations.Comment: 35 pages, 16 figure
Multiplayer Cost Games with Simple Nash Equilibria
Multiplayer games with selfish agents naturally occur in the design of
distributed and embedded systems. As the goals of selfish agents are usually
neither equivalent nor antagonistic to each other, such games are non zero-sum
games. We study such games and show that a large class of these games,
including games where the individual objectives are mean- or discounted-payoff,
or quantitative reachability, and show that they do not only have a solution,
but a simple solution. We establish the existence of Nash equilibria that are
composed of k memoryless strategies for each agent in a setting with k agents,
one main and k-1 minor strategies. The main strategy describes what happens
when all agents comply, whereas the minor strategies ensure that all other
agents immediately start to co-operate against the agent who first deviates
from the plan. This simplicity is important, as rational agents are an
idealisation. Realistically, agents have to decide on their moves with very
limited resources, and complicated strategies that require exponential--or even
non-elementary--implementations cannot realistically be implemented. The
existence of simple strategies that we prove in this paper therefore holds a
promise of implementability.Comment: 23 page
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