40,578 research outputs found
Patience of Matrix Games
For matrix games we study how small nonzero probability must be used in
optimal strategies. We show that for nxn win-lose-draw games (i.e. (-1,0,1)
matrix games) nonzero probabilities smaller than n^{-O(n)} are never needed. We
also construct an explicit nxn win-lose game such that the unique optimal
strategy uses a nonzero probability as small as n^{-Omega(n)}. This is done by
constructing an explicit (-1,1) nonsingular nxn matrix, for which the inverse
has only nonnegative entries and where some of the entries are of value
n^{Omega(n)}
Social dilemmas, time preferences and technology adoption in a commons problem
Agents interacting on a body of water choose between technologies to catch fish. One is harmless to the resource, as it allows full recovery; the other yields high immediate catches, but low(er) future catches. Strategic interaction in one 'objective'resource game may induce several 'subjective' games in the class of social dilemmas. Which unique 'subjective'game is actually played depends crucially on how the agents discount their future payo¤s. We examine equilibrium behavior and its consequences on sustainability of the common-pool resource system under exponential and hyperbolic discounting. A sufficient degree of patience on behalf of the agents may lead to equilibrium behavior averting exhaustion of the resource, though full restraint (both agents choosing the ecologically or environmentally sound technology) is not necessarily achieved. Furthermore, if the degree of patience between agents is sufficiently dissimilar, the more patient is exploited by the less patient one in equilibrium. We demonstrate the generalizability of our approach developed throughout the paper. We provide recommendations to reduce the enormous complexity surrounding the general cases
The Value 1 Problem Under Finite-memory Strategies for Concurrent Mean-payoff Games
We consider concurrent mean-payoff games, a very well-studied class of
two-player (player 1 vs player 2) zero-sum games on finite-state graphs where
every transition is assigned a reward between 0 and 1, and the payoff function
is the long-run average of the rewards. The value is the maximal expected
payoff that player 1 can guarantee against all strategies of player 2. We
consider the computation of the set of states with value 1 under finite-memory
strategies for player 1, and our main results for the problem are as follows:
(1) we present a polynomial-time algorithm; (2) we show that whenever there is
a finite-memory strategy, there is a stationary strategy that does not need
memory at all; and (3) we present an optimal bound (which is double
exponential) on the patience of stationary strategies (where patience of a
distribution is the inverse of the smallest positive probability and represents
a complexity measure of a stationary strategy)
The Big Match in Small Space
In this paper we study how to play (stochastic) games optimally using little
space. We focus on repeated games with absorbing states, a type of two-player,
zero-sum concurrent mean-payoff games. The prototypical example of these games
is the well known Big Match of Gillete (1957). These games may not allow
optimal strategies but they always have {\epsilon}-optimal strategies. In this
paper we design {\epsilon}-optimal strategies for Player 1 in these games that
use only O(log log T ) space. Furthermore, we construct strategies for Player 1
that use space s(T), for an arbitrary small unbounded non-decreasing function
s, and which guarantee an {\epsilon}-optimal value for Player 1 in the limit
superior sense. The previously known strategies use space {\Omega}(logT) and it
was known that no strategy can use constant space if it is {\epsilon}-optimal
even in the limit superior sense. We also give a complementary lower bound.
Furthermore, we also show that no Markov strategy, even extended with finite
memory, can ensure value greater than 0 in the Big Match, answering a question
posed by Abraham Neyman
Exact Algorithms for Solving Stochastic Games
Shapley's discounted stochastic games, Everett's recursive games and
Gillette's undiscounted stochastic games are classical models of game theory
describing two-player zero-sum games of potentially infinite duration. We
describe algorithms for exactly solving these games
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