We study the relative performance of disclosure and auditing in decentralized institutions. We consider the information transmission problem between two decision makers who take actions in sequence at two decision dates. The first decision maker has private information about a state of nature that is relevant for both decisions, and sends a message to the second. The second decision maker can c...

1. OVERVIEW Robust control considers the design of decision or control rules that fare well across a range of alternative models. Thus robust control is inherently about model uncertainty, particularly focusing on the implications of model uncertainty for decisions. Robust control originated in the 1980s in the control theory branch of the engineering and applied mathematics literature, and it ...

We consider the fair Hamiltonian cycle Maker-Breaker game, played on the edge set of the complete graph Kn on n vertices. It is known that Maker wins this game if n is sufficiently large. We are interested in the minimum number of moves needed for Maker in order to win the Hamiltonian cycle game, and in the smallest n for which Maker has a winning strategy for this game. We prove the following ...

Abstract: The author addresses the emergence of cognitive level D in the Four-Level-Cognitive-Development Theory (FLCDT). He argues that the concept of transcendent God, ‘Maker of Heaven and Earth and of all things visible and invisible’ was an important factor for that. The problem of God’s qualities and the view of numbers in medieval Christian and Arabic philosophy are examined as some argum...

We show the effects of uncertainty, ecological and economic irreversibility, and endogenous learning on land preservation decisions for species conservation. We compare the optimal decisions of three types of decision makers: an experimenting decision maker who recognizes the endogeneity of information, a real options decision maker who ignores the endogeneity but recognizes the future availabi...

This paper proposes a novel framework for Relevance Feedback based on the Fisher Kernel (FK). Specifically, we train a Gaussian Mixture Model (GMM) on the top retrieval results (without supervision) and use this to create a FK representation, which is therefore specialized in modelling the most relevant examples. We use the FK representation to explicitly capture temporal variation in video via...