LEADER 00000nam  2200325   4500 
001    AAI3431157 
005    20110506125631.5 
008    110506s2010    ||||||||||||||||| ||eng d 
020    9781124318134 
035    (UMI)AAI3431157 
040    UMI|cUMI 
100 1  Penta, Antonio 
245 10 Incomplete information and robustness in strategic 
       environments  
300    214 p 
500    Source: Dissertation Abstracts International, Volume: 71-
       12, Section: A, page:  
500    Adviser: George J. Mailath 
502    Thesis (Ph.D.)--University of Pennsylvania, 2010 
520    Game theoretic modeling involves making assumptions on 
       agents' infinite hierarchies of beliefs. These assumptions
       are understood to be only approximately satisfied in the 
       actual situation. Thus, the significance of game theoretic
       predictions depend on robustness properties of the 
       solution concepts adopted. Chapter 1 discusses recent 
       results in this research area and their relations with the
       results obtained in the subsequent chapters. Chapter 2 
       explores the impact of misspecification of higher order 
       beliefs in static environments, when arbitrary common 
       knowledge assumptions on payoffs are relaxed. (Existing 
       literature focuses on the extreme case in which all such 
       assumptions are relaxed.) Chapter 3 provides a 
       characterization of the strongest predictions, for dynamic
       games, that are "robust" to possible misspecifications of 
       agents' higher order beliefs, and shows that such 
       characterization depends on modeling assumptions that have
       hitherto received little attention in the literature 
       (namely, the distinction between knowledge and certainty ),
       raising novel questions of robustness. Chapter 4 develops 
       a methodology to address classical questions of 
       implementation, when agents' beliefs are unknown to the 
       designer and their private information changes over time. 
       The key idea is the identification of a solution concept 
       that allows a tractable analysis of the full 
       implementation problem: Full "robust" implementation 
       requires that, for all models of agents' beliefs, all the 
       perfect Bayesian equilibria of a mechanism induce outcomes
       consistent with the social choice function (SCF). It is 
       shown that, for a weaker notion of equilibrium and for a 
       general class of games, the set of all such equilibria can
       be computed by means of a "backwards procedure" that 
       combines the logic of rationalizability and backward 
       induction reasoning. It is further shown that a SCF is 
       (partially) implementable for all models of beliefs if and
       only if it is ex-post incentive compatible. In 
       environments with single crossing preferences, strict ex-
       post incentive compatibility and a "contraction property" 
       are sufficient to guarantee full robust implementation in 
       direct mechanisms. This property limits the 
       interdependence in agents' valuations 
590    School code: 0175 
650  4 Economics, General 
650  4 Economics, Theory 
690    0501 
690    0511 
710 2  University of Pennsylvania 
773 0  |tDissertation Abstracts International|g71-12A 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
       advanced?query=3431157