Games with Unawareness

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yossi Feinberg

AbstractWe provide a tool to model and solve strategic situations where players’ perceptions are limited, as well as situations where players realize that other players’ perceptions may be limited and so on. We define normal, repeated, incomplete information, and extensive form games with unawareness using a unified methodology. A game with unawareness is defined as a collection of standard games (of the corresponding form). The collection specifies how each player views the game, how she views the other players’ perceptions of the game and so on. The modeler’s description of perceptions, the players’ description of other players’ perceptions, etc. are shown to have consistent representations. We extend solution concepts such as rationalizability and Nash equilibrium to these games and study their properties. It is shown that while unawareness in normal form games can be mapped to incomplete information games, the extended Nash equilibrium solution is not mapped to a known solution concept in the equivalent incomplete information games, implying that games with unawareness generate novel types of behavior.

Author(s):  
Felix Munoz-Garcia

Abstract This paper provides a non-technical introduction to a procedure to find Perfect Bayesian equilibria (PBEs) in incomplete information games. Despite the rapidly expanding literature on industrial organization that uses PBE as its main solution concept, most undergraduate and graduate textbooks still present a relatively theoretical introduction to PBEs. This paper offers a systematic five-step procedure that helps students find all pure-strategy PBEs in incomplete information games. Furthermore, it illustrates a step-by-step application of this procedure to a signaling game, using a worked-out example.


Games ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 19
Author(s):  
Asha Sadanand

Refinements of the Nash equilibrium have followed the strategy of extending the idea of subgame perfection to incomplete information games. This has been achieved by appropriately restricting beliefs at unreached information sets. Each new refinement gives stricter and more mathematically-complicated limitations on permitted beliefs. A simpler approach is taken here, where the whole idea of beliefs is dispensed with, and a new equilibrium concept, called the ideal reactive equilibrium, that builds on some pioneering work by Amershi, Sadanand and Sadanand on thought process dynamics, is developed.


2008 ◽  
Vol 16 (3) ◽  
pp. 250-273 ◽  
Author(s):  
Justin Esarey ◽  
Bumba Mukherjee ◽  
Will H. Moore

Private information characteristics like resolve and audience costs are powerful influences over strategic international behavior, especially crisis bargaining. As a consequence, states face asymmetric information when interacting with one another and will presumably try to learn about each others' private characteristics by observing each others' behavior. A satisfying statistical treatment would account for the existence of asymmetric information and model the learning process. This study develops a formal and statistical framework for incomplete information games that we term the Bayesian Quantal Response Equilibrium Model (BQRE model). Our BQRE model offers three advantages over existing work: it directly incorporates asymmetric information into the statistical model's structure, estimates the influence of private information characteristics on behavior, and mimics the temporal learning process that we believe takes place in international politics.


Author(s):  
Christian Kroer ◽  
Gabriele Farina ◽  
Tuomas Sandholm

Nash equilibrium is a popular solution concept for solving imperfect-information games in practice. However, it has a major drawback: it does not preclude suboptimal play in branches of the game tree that are not reached in equilibrium. Equilibrium refinements can mend this issue, but have experienced little practical adoption. This is largely due to a lack of scalable algorithms.Sparse iterative methods, in particular first-order methods, are known to be among the most effective algorithms for computing Nash equilibria in large-scale two-player zero-sum extensive-form games. In this paper, we provide, to our knowledge, the first extension of these methods to equilibrium refinements. We develop a smoothing approach for behavioral perturbations of the convex polytope that encompasses the strategy spaces of players in an extensive-form game. This enables one to compute an approximate variant of extensive-form perfect equilibria. Experiments show that our smoothing approach leads to solutions with dramatically stronger strategies at information sets that are reached with low probability in approximate Nash equilibria, while retaining the overall convergence rate associated with fast algorithms for Nash equilibrium. This has benefits both in approximate equilibrium finding (such approximation is necessary in practice in large games) where some probabilities are low while possibly heading toward zero in the limit, and exact equilibrium computation where the low probabilities are actually zero.


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