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Bruno Ziliotto

Publications and source records attributed to Bruno Ziliotto.

27 records · Page 2Linked to original sources
arXiv2019

Prophet Secretary Through Blind Strategies

In the classic prophet inequality, samples from independent random variables arrive online. A gambler that knows the distributions must decide at each point in time whether to stop and pick the current sample or to continue and lose that sample forever. The goal of the gambler is to maximize the expected value of what she picks and the performance measure is the worst case ratio between the expected value the gambler gets and what a prophet, that sees all the realizations in advance, gets. In the late seventies, Krengel and Sucheston, and Gairing (1977) established that this worst case ratio is a universal constant equal to 1/2. In the last decade prophet inequalities has resurged as an important problem due to its connections to posted price mechanisms, frequently used in online sales. A very interesting variant is the Prophet Secretary problem, in which the only difference is that the samples arrive in a uniformly random order. For this variant several algorithms achieve a constant of 1-1/e and very recently this barrier was slightly improved. This paper analyzes strategies that set a nonincreasing sequence of thresholds to be applied at different times. The gambler stops the first time a sample surpasses the corresponding threshold. Specifically we consider a class of strategies called blind quantile strategies. They consist in fixing a function which is used to define a sequence of thresholds once the instance is revealed. Our main result shows that they can achieve a constant of 0.665, improving upon the best known result of Azar et al. (2018), and on Beyhaghi et al. (2018) (order selection). Our proof analyzes precisely the underlying stopping time distribution, relying on Schur-convexity theory. We further prove that blind strategies cannot achieve better than 0.675. Finally we prove that no algorithm for the gambler can achieve better than 0.732.

cs.DS↗
arXiv2016

Tauberian theorems for general iterations of operators: applications to zero-sum stochastic games

This paper proves several Tauberian theorems for general iterations of operators, and provides two applications to zero-sum stochastic games where the total payoff is a weighted sum of the stage payoffs. The first application is to provide conditions under which the existence of the asymptotic value implies the convergence of the values of the weighted game, as players get more and more patient. The second application concerns stochastic games with finite state space and action sets. This paper builds a simple class of asymptotically optimal strategies in the weighted game, that at each stage play optimally in a discounted game with a discount factor corresponding to the relative weight of the current stage.

math.OC↗
arXiv2016

Zero-sum repeated games: Counterexamples to the existence of the asymptotic value and the conjecture $\operatorname{maxmin}=\operatorname{lim}v_n$

Mertens [In Proceedings of the International Congress of Mathematicians (Berkeley, Calif., 1986) (1987) 1528-1577 Amer. Math. Soc.] proposed two general conjectures about repeated games: the first one is that, in any two-person zero-sum repeated game, the asymptotic value exists, and the second one is that, when Player 1 is more informed than Player 2, in the long run Player 1 is able to guarantee the asymptotic value. We disprove these two long-standing conjectures by providing an example of a zero-sum repeated game with public signals and perfect observation of the actions, where the value of the $λ$-discounted game does not converge when $λ$ goes to 0. The aforementioned example involves seven states, two actions and two signals for each player. Remarkably, players observe the payoffs, and play in turn.

math.OC↗
arXiv2015

General limit value in zero-sum stochastic games

Bewley and Kohlberg (1976) and Mertens and Neyman (1981) have proved, respectively, the existence of the asymptotic value and the uniform value in zero-sum stochastic games with finite state space and finite action sets. In their work, the total payoff in a stochastic game is defined either as a Cesaro mean or an Abel mean of the stage payoffs. This paper presents two findings: first, we generalize the result of Bewley and Kohlberg to a more general class of payoff evaluations and we prove with a counterexample that this result is tight. We also investigate the particular case of absorbing games. Second, for the uniform approach of Mertens and Neyman, we provide another counterexample to demonstrate that there is no natural way to generalize the result of Mertens and Neyman to a wider class of payoff evaluations.

math.OC↗
arXiv2015

Pathwise uniform value in gambling houses and Partially Observable Markov Decision Processes

In several standard models of dynamic programming (gambling houses, MDPs, POMDPs), we prove the existence of a very robust notion of value for the infinitely repeated problem, namely the pathwise uniform value. This solves two open problems. First, this shows that for any epsilon>0, the decision-maker has a pure strategy sigma which is epsilon-optimal in any n-stage game, provided that n is big enough (this result was only known for behavior strategies, that is, strategies which use randomization). Second, the strategy sigma can be chosen such that under the long-run average payoff criterion (expectation of the liminf of the average payoffs), the decision-maker has more than lim v(n)-epsilon.

math.OC↗
arXiv2015

A Tauberian theorem for nonexpansive operators and applications to zero-sum stochastic games

We prove a Tauberian theorem for nonexpansive operators, and apply it to the model of zero-sum stochastic game. Under mild assumptions, we prove that the value of the lambda-discounted game v_{lambda} converges uniformly when lambda goes to 0 if and only if the value of the n-stage game v_n converges uniformly when n goes to infinity. This generalizes the Tauberian theorem of Lehrer and Sorin (1992) to the two-player zero-sum case. We also provide the first example of a stochastic game with public signals on the state and perfect observation of actions, with finite state space, signal sets and action sets, in which for some initial state k_1 known by both players, (v_{lambda}(k_1)) and (v_n(k_1)) converge to distinct limits.

math.OC↗
arXiv2014

Hidden Stochastic Games and Limit Equilibrium Payoffs

We consider 2-player stochastic games with perfectly observed actions, and study the limit, as the discount factor goes to one, of the equilibrium payoffs set. In the usual setup where current states are observed by the players, we show that the set of stationary equilibrium payoffs always converges, and provide a simple example where the set of equilibrium payoffs has no limit. We then introduce the more general model of hidden stochastic game, where the players publicly receive imperfect signals over current states. In this setup we present an example where not only the limit set of equilibrium payoffs does not exist, but there is no converging selection of equilibrium payoffs. This second example is robust in many aspects, in particular to perturbations of the payoffs and to the introduction of correlation or communication devices.

math.OC↗