Proper rationalizability and backward induction

This paper introduces a new normal form rationalizability concept, which in reduced normal form games corresponding to generic finite extensive games of perfect information yields the unique backward induction outcome. The basic assumption is that every player trembles “more or less rationally” as in the definition of a ε-proper equilibrium by Myerson (1978). In the same way that proper equilibrium refines Nash and perfect equilibrium, our model strengthens the normal form rationalizability concepts by Bernheim (1984), B?rgers (1994) and Pearce (1984). Common knowledge of trembling implies the iterated elimination of strategies that are strictly dominated at an information set. The elimination process starts at the end of the game tree and goes backwards to the beginning. Received: October 1996/Final version: May 1999     

Proper belief revision and rationalizability in dynamic games

In this paper we develop an epistemic model for dynamic games in which players may revise their beliefs about the opponents’ utility functions as the game proceeds. Within this framework, we propose a rationalizability concept that is based upon the following three principles: (1) at every instance of the game, a player should believe that his opponents are carrying out optimal strategies, (2) a player, at information set h, should not change his belief about an opponent’s relative ranking of two strategies s and s′ if both s and s′ could have led to h, and (3) the players’ initial beliefs about the opponents’ utility functions should agree on a given profile u of utility functions. Common belief in these events leads to the concept of persistent rationalizability for the profile u of utility functions. It is shown that for a given game tree with observable deviators and a given profile u of utility functions, every properly point-rationalizable strategy is a persistently rationalizable strategy for u. This result implies that persistently rationalizable strategies always exist for all game trees with observable deviators and all profiles of utility functions. We provide an algorithm that can be used to compute the set of persistently rationalizable strategies for a given profile u of utility functions. For generic games with perfect information, persistent rationalizability uniquely selects the backward induction strategy for every player.     

Action-timing problem with sequential Bayesian belief revision process

We consider the problem of deciding the best action time when observations are made sequentially. Specifically we address a special type of optimal stopping problem where observations are made from state-contingent distributions and there exists uncertainty on the state. In this paper, the decision-maker's belief on state is revised sequentially based on the previous observations. By using the independence property of the observations from a given distribution, the sequential Bayesian belief revision process is represented as a simple recursive form. The methodology developed in this paper provides a new theoretical framework for addressing the uncertainty on state in the action-timing problem context. By conducting a simulation analysis, we demonstrate the value of applying Bayesian strategy which uses sequential belief revision process. In addition, we evaluate the value of perfect information to gain more insight on the effects of using Bayesian strategy in the problem.     

Behavior and deliberation in perfect-information games: Nash equilibrium and backward induction

Doxastic characterizations of the set of Nash equilibrium outcomes and of the set of backward-induction outcomes are provided for general perfect-information games (where there may be multiple backward-induction solutions). We use models that are behavioral, rather than strategy-based, where a state only specifies the actual play of the game and not the hypothetical choices of the players at nodes that are not reached by the actual play. The analysis is completely free of counterfactuals and no belief revision theory is required, since only the beliefs at reached histories are specified.     

Normal form backward induction for decision trees with coherent lower previsions

We examine normal form solutions of decision trees under typical choice functions induced by lower previsions. For large trees, finding such solutions is hard as very many strategies must be considered. In an earlier paper, we extended backward induction to arbitrary choice functions, yielding far more efficient solutions, and we identified simple necessary and sufficient conditions for this to work. In this paper, we show that backward induction works for maximality and E-admissibility, but not for interval dominance and Γ-maximin. We also show that, in some situations, a computationally cheap approximation of a choice function can be used, even if the approximation violates the conditions for backward induction; for instance, interval dominance with backward induction will yield at least all maximal normal form solutions.     

Forward,backward and symmetric stochastic integration

Summary We define three types of non causal stochastic integrals: forward, backward and symmetric. Our approach consists in approximating the integrator. Two optics are considered: the first one is based on traditional usual stochastic calculus and the second one on Wiener distributions.     

Behaviorism and belief

The examination of now-abandoned behaviorist analysis of the concept of belief can bring to light defects in perspectives such as functionalism and physicalism that are still considered viable. Most theories have in common that they identify the holding of the belief that p by a subject S with some matter of fact in or about S that is distinct from and independent of p. In the case of behaviorism it is easy to show that this feature of the theory generates incoherence in the first-person point of view since it gives footing to the possibility that S could correctly assert “I believe that p,” (that is, “I have the complex disposition the behaviorist theory identifies with holding the belief that p”) and at the same time deny that p is the case. Parallel incoherence can be developed in the context of other philosophically popular accounts of the nature of belief.     

Probabilist,possibilist and belief objects for knowledge analysis

The main aim of the symbolic approach in data analysis is to extend problems, methods and algorithms used on classical data to more complex data called symbolic objects which are well adapted to representing knowledge and which are generic unlike usual observations which characterize individual things. We introduce several kinds of symbolic objects: Boolean, possibilist, probabilist and belief. We briefly present some of their qualities and properties; three theorems show how Probability, Possibility and Evidence theories may be extended on these objects. Finally, four kinds of data analysis problems including the symbolic extension are illustrated by several algorithms which induce knowledge from classical data or from a set of symbolic objects.     

The logic of justified belief,explicit knowledge,and conclusive evidence

We present a complete, decidable logic for reasoning about a notion of completely trustworthy (“conclusive”) evidence and its relations to justifiable (implicit) belief and knowledge, as well as to their explicit justifications. This logic makes use of a number of evidence-related notions such as availability, admissibility, and “goodness” of a piece of evidence, and is based on an innovative modification of the Fitting semantics for Artemov?s Justification Logic designed to preempt Gettier-type counterexamples. We combine this with ideas from belief revision and awareness logics to provide an account for explicitly justified (defeasible) knowledge based on conclusive evidence that addresses the problem of (logical) omniscience.     

Log-log convexity and backward uniqueness

We study backward uniqueness properties for equations of the form

Under mild regularity assumptions on and , it is shown that implies for . The argument is based on -log and log-log convexity. The results apply to mildly nonlinear parabolic equations and systems with rough coefficients and the 2D Navier-Stokes system.

     

The backward group preserving scheme for multi-dimensional nonhomogeneous and nonlinear backward wave problems

In this study, we utilize a backward group preserving scheme (BGPS) to cope with the nonhomogeneous as well as nonlinear backward wave problems (BWPs). Because the solution does not continuously count on the given information, the BWP is well-known to be seriously ill-posed. When six numerical instances are examined, we reveal that the BGPS is capable of tackling the nonhomogeneous and nonlinear BWPs. Besides, the BGPS is also robust enough against the perturbation even with the boisterous final data, of which the numerical results are rather accurate, effective and stable.     

Doxastic decisions and controlling belief

I critique Matthias Steup’s account of exercising direct voluntary control over coming to have doxastic attitudes via doxastic decisions. I show that the sort of agency Steup argues is exercised in doxastic decision-making is not sufficient for agents to exercise direct voluntary control over their doxastic attitudes. This counts against such putative decisions being the locus of direct control in doxastic agency. Finally, I briefly consider what, if any, consequences the failure of Steup’s theory of doxastic agency may have for epistemic deontologism.     

Large derivatives,backward contraction and invariant densities for interval maps

In this paper, we study the dynamics of a smooth multimodal interval map f with non-flat critical points and all periodic points hyperbolic repelling. Assuming that |Dfⁿ(f(c))|→∞ as n→∞ holds for all critical points c, we show that f satisfies the so-called backward contracting property with an arbitrarily large constant, and that f has an invariant probability μ which is absolutely continuous with respect to Lebesgue measure and the density of μ belongs to L^p for all p<ℓ_max/(ℓ_max-1), where ℓ_max denotes the maximal critical order of f. In the appendix, we prove that various growth conditions on the derivatives along the critical orbits imply stronger backward contraction.     

On linear, degenerate backward stochastic partial differential equations

In this paper we study the well-posedness and regularity of the adapted solutions to a class of linear, degenerate backward stochastic partial differential equations (BSPDE, for short). We establish new a priori estimates for the adapted solutions to BSPDEs in a general setting, based on which the existence, uniqueness, and regularity of adapted solutions are obtained. Also, we prove some comparison theorems and discuss their possible applications in mathematical finance. Received: 24 September 1997 / Revised version: 3 June 1998     

Leibenzon's backward shift and composition operators

We apply Leibenzon's backward shift to show that the composition operator on the unit ball of always maps the weighted Hardy space into the Hardy class .

     

The Kitai criterion and backward shifts

It is proved that for any separable infinite dimensional Banach space , there is a bounded linear operator on such that satisfies the Kitai criterion. The proof is based on a quasisimilarity argument and on showing that satisfies the Kitai criterion for certain backward weighted shifts .

     

Nucleation and backward motion of discrete interfaces

We use a discrete approximation of the motion by crystalline curvature to define an evolution of sets from a single point (nucleation) following a criterion of “maximization” of the perimeter, formally giving a backward version of the motion by crystalline curvature. This evolution depends on the approximation chosen.