Common-value all-pay auctions with asymmetric information

We study two-player common-value all-pay auctions in which the players have ex-ante asymmetric information represented by finite connected partitions of the set of states of nature. Our focus is on a family of such auctions in which no player has an information advantage over his opponent. We find sufficient conditions for the existence of equilibrium with monotone strategies, and show that such an equilibrium is unique. We further show that the ex-ante distribution of equilibrium effort is the same for every player (and hence the players’ expected efforts are equal), although their expected payoffs are different and they do not have the same ex-ante probability of winning.     

Markov games with incomplete information

We consider zero-sum Markov games with incomplete information. Here, the second player is never informed about the current state of the underlying Markov chain. The existence of a value and of optimal strategies for both players is shown. In particular, we present finite algorithms for computing optimal strategies for the informed and uninformed player. The algorithms are based on linear programming results.     

Formulation of Bayesian analysis for games with incomplete information

A formal model is given of Harsanyi's infinite hierarchies of beliefs. It is shown that the model closes with some Bayesian game with incomplete information, and that any such game can be approximated by one with a finite number of states of world.     

Lossless outer joins with incomplete information

In the relational model of data, Rissanen's Theorem provides the basis for the usual normalization process based on decomposition of relations. However, many difficulties occur if information is incomplete in databases and nulls are required to represent missing or unknown data. We concentrate here on the notion of outer join and find some reasonable conditions to guarantee that outer join will also preserve the lossless join property for two relations. Next we provide a generalization of this result to several relations.     

Zero-sum sequential games with incomplete information

Repeated zero-sum two-person games of incomplete information on one side are considered. If the one-shot game is played sequentially, the informed player moving first, it is proved that the value of then-shot game is constant inn and is equal to the concavification of the game in which the informed player disregards his extra information. This is a strengthening ofAumann andMaschler's results for simultaneous games. Optimal strategies for both players are constructed explicitly.     

Statistically varyingN-person games with incomplete information

The paper provides a functional relationship between the amount of information available to the players and an equilibrium payoff in a game with statistically varying states of nature. Moreover, it is shown that for a supergame consisting of independent repetitions of a common matrix game equilibrium strategies are available which provide only few sequences of actions with positive probability.

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Zusammenfassung Es werdenN-Personenspiele betrachtet, in denen die Auszahlungen sowohl von den Aktionen der Spieler, als auch von einem im Zeitablauf variierenden, a priori unbekannten, Zustandsparameter beeinflußt werden. Im vorliegenden Ansatz wird die erhaltbare Information in expliziter Weise als strategischer Parameter der Spieler aufgefaßt. Die Spieler sind bei der Beschaffung von Information informationstheoretisch modellierten Beschränkungen and die Menge verfügbarer Information unterworfen. Es gelingt in dem Modell, asymptotisch den funktionalen Zusammenhang zwischen der Menge verfügbarer Information und den erreichbaren Gleichgewichtsauszahlungen inT-fach replizierten Spielen zu bestimmen.

     

Preference Programming with incomplete ordinal information

This paper extends possibilities for analyzing incomplete ordinal information about the parameters of an additive value function. Such information is modeled through preference statements which associate sets of alternatives or attributes with corresponding sets of rankings. These preference statements can be particularly helpful in developing a joint preference representation for a group of decision-makers who may find difficulties in agreeing on numerical parameter values. Because these statements can lead to a non-convex set of feasible parameters, a mixed integer linear formulation is developed to establish a linear model for the computation of decision recommendations. This makes it possible to complete incomplete ordinal information with other forms of incomplete information.     

Repeated games with incomplete information and transportation problems

We consider two person zero-sum repeated games with lack of information on one side and with payoffs of special "separable" form. The solutions for these games are reduced to the solutions for families of special transportation type problems with recursive structure. We illustrate our approach applying it to the game introduced by Mertens/Zamir [1976] and later studied by several authors. The "symmetric" subclass of games under consideration was studied in Domansky, Kreps [1995].     

Minmax and maxmin of repeated games with incomplete information

For a class of 2-Person 0-sum repeated games with incomplete information,Aumann/Masch1er [1967] andStearns [1967] have given a necessary and sufficient condition for the existence of v_∞ (the value of the infinitely repeated game).Mertens/Zamir [1971] andMertens [1971/72] have given the formula (and thus proved the existence) of \(\mathop {\lim }\limits_{n \to \infty } \) v _n , the limit of the values of the games withn repetitions, for a much larger class of games than that treated byAumann/Maschler andSteams. In this paper we extend the Aumann-Maschler-Stearns results to the larger family of games studied byMertens [1971/72].     

Learning algorithms for repeated bimatrix Nash games with incomplete information

The purpose of this paper is to study a particular recursive scheme for updating the actions of two players involved in a Nash game, who do not know the parameters of the game, so that the resulting costs and strategies converge to (or approach a neighborhood of) those that could be calculated in the known parameter case. We study this problem in the context of a matrix Nash game, where the elements of the matrices are unknown to both players. The essence of the contribution of this paper is twofold. On the one hand, it shows that learning algorithms which are known to work for zero-sum games or team problems can also perform well for Nash games. On the other hand, it shows that, if two players act without even knowing that they are involved in a game, but merely thinking that they try to maximize their output using the learning algorithm proposed, they end up being in Nash equilibrium.This research was supported in part by NSF Grant No. ECS-87-14777.     

Incentive compatible strategies for general stackelberg games with incomplete information

We study Stackelberg games with incomplete information in a general setting. In particular, we deduce the follower's reaction function, a set-valued function of the leader's action choice and a parameter specifying the follower's payoff type (a parameter about which the leader has only incomplete information), and using a generalized version of Komlos' Theorem due to Balder (1987), we show the existence of an expected payoff maximizing, incentive compatible strategy for the leader given the follower's reaction function.     

残缺模糊合作对策的加权Shapley值

针对具有模糊联盟且支付值残缺的合作对策问题,给出了E-残缺模糊对策的定义.基于残缺联盟值基数集,提出了一个同时满足对称性和线性性的w-加权Shapley值公式.通过构造模糊联盟间的边际贡献,探讨了w-加权Shapley值公式的等价表示形式,指出w-加权Shapley值与完整合作对策Shapley值的兼容性.在模糊联盟框架里,探讨了w-加权Shapley值所满足的联盟单调性、零正则性等优良性质.最后通过算例验证了该公式的有效性.     

Multivariate logistic regression with incomplete covariate and auxiliary information

In this article, we propose and explore a multivariate logistic regression model for analyzing multiple binary outcomes with incomplete covariate data where auxiliary information is available. The auxiliary data are extraneous to the regression model of interest but predictive of the covariate with missing data. Horton and Laird [N.J. Horton, N.M. Laird, Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information, Biometrics 57 (2001) 34–42] describe how the auxiliary information can be incorporated into a regression model for a single binary outcome with missing covariates, and hence the efficiency of the regression estimators can be improved. We consider extending the method of [9] to the case of a multivariate logistic regression model for multiple correlated outcomes, and with missing covariates and completely observed auxiliary information. We demonstrate that in the case of moderate to strong associations among the multiple outcomes, one can achieve considerable gains in efficiency from estimators in a multivariate model as compared to the marginal estimators of the same parameters.     

A parametric GP model dealing with incomplete information for group decision-making

We consider a group decision-making problem where preferences given by the experts are articulated into the form of pairwise comparison matrices. In many cases, experts are not able to efficiently provide their preferences on some aspects of the problem because of a large number of alternatives, limited expertise related to some problem domain, unavailable data, etc., resulting in incomplete pairwise comparison matrices. Our goal is to develop a computational method to retrieve a group priority vector of the considered alternatives dealing with incomplete information. For that purpose, we have established an optimization problem in which a similarity function and a parametric compromise function are defined. Associated to this problem, a logarithmic goal programming formulation is considered to provide an effective procedure to compute the solution. Moreover, the parameters involved in the method have a clear meaning in the context of group problems.