The Role of Monotonicity in the Epistemic Analysis of Strategic Games

It is well-known that in finite strategic games true common belief (or common knowledge) of rationality implies that the players will choose only strategies that survive the iterated elimination of strictly dominated strategies. We establish a general theorem that deals with monotonic rationality notions and arbitrary strategic games and allows to strengthen the above result to arbitrary games, other rationality notions, and transfinite iterations of the elimination process. We also clarify what conclusions one can draw for the customary dominance notions that are not monotonic. The main tool is Tarski's Fixpoint Theorem.Comment: 20 page

Proof-theoretic Analysis of Rationality for Strategic Games with Arbitrary Strategy Sets

In the context of strategic games, we provide an axiomatic proof of the statement Common knowledge of rationality implies that the players will choose only strategies that survive the iterated elimination of strictly dominated strategies. Rationality here means playing only strategies one believes to be best responses. This involves looking at two formal languages. One is first-order, and is used to formalise optimality conditions, like avoiding strictly dominated strategies, or playing a best response. The other is a modal fixpoint language with expressions for optimality, rationality and belief. Fixpoints are used to form expressions for common belief and for iterated elimination of non-optimal strategies.Comment: 16 pages, Proc. 11th International Workshop on Computational Logic in Multi-Agent Systems (CLIMA XI). To appea

Epistemic Analysis of Strategic Games with Arbitrary Strategy Sets

We provide here an epistemic analysis of arbitrary strategic games based on the possibility correspondences. Such an analysis calls for the use of transfinite iterations of the corresponding operators. Our approach is based on Tarski's Fixpoint Theorem and applies both to the notions of rationalizability and the iterated elimination of strictly dominated strategies.Comment: 8 pages Proc. of the 11th Conference on Theoretical Aspects of Rationality and Knowledge (TARK XI), 2007. To appea

Auto-Modernity after Postmodernism: Autonomy and Automation in Culture, Technology, and Education

Part of the Volume on Digital Young, Innovation, and the UnexpectedThis chapter argues that in order to understand the implications of how digital youth are now using new media and technologies in unexpected and innovative ways, we have to rethink many of the cultural oppositions that have shaped the Western tradition since the start of the modern era. To be precise, we can no longer base our analysis of culture, identity, and technology on the traditional conflicts between the public and the private, the subject and the object, and the human and the machine. Moreover, the modern divide pitting the isolated individual against the impersonal realm of technological mechanization no longer seems to apply to the multiple ways young people are using new media and technologies. In fact, this chapter argues that we have moved into a new cultural period of automodernity, and a key to this cultural epoch is the combination of technological automation and human autonomy

Adaptive social learning

This paper investigates the learning foundations of economic models of social learning. We pursue the prevalent idea in economics that rational play is the outcome of a dynamic process of adaptation. Our learning approach offers us the possibility to clarify when and why the prevalent rational (equilibrium) view of social learning is likely to capture observed regularities in the field. In particular it enables us to address the issue of individual and interactive knowledge. We argue that knowledge about the private belief distribution is unlikely to be shared in most social learning contexts. Absent this mutual knowledge, we show that the long-run outcome of the adaptive process favors non-Bayesian rational play.social Learning ; informational herding ; adaptation ; analogies ; non-Bayesian updating