EVOLUTION AND INFORMATION

Abstract: What is a signal worth, how much information does it carry? The notions of entropy and mutual information have not made it into mainstream biology. Instead, the value of signals is measured in more tangible currency, such as number of offspring or fitness. In this talk I will show that the notion of biological fitness, or growth rate, and measures of information are tightly linked. I will then explore the information content of the genome. How does information enter the genome, and what benefit can it give once it is there?

SIMPLE FORAGING FOR SIMPLE FORAGERS (joint work with Bezalel Peleg, Mor Amita and Avi Shmida)

Abstract: We try to explain how solitary bees (as examples of simple foragers), without any communication, will distribute themselves in a field of flowers according to something known as the Ideal Free Distribution, while any individual exhibits the so-called Matching Law when alone. We discuss two types of simple foraging strategies. Each of these strategies explains the appearance of the Ideal Free Distribution in a multi-bee community as well as as the Matching Law in an experimental set-up.

MODELS OF ORDER IN KABBALAH

Abstract:The lecture will deal with the ascent of the importance of objective order in medievalJudaism, in comparison to earlier forms of Judaism, and I will address forms of order like astronomical, theosophical, intellectual, and linguistic order.

MAKING RETIREMENT DECISIONS WITHOUT ELICITING UTILITIES (JOINT WITH S. KAKADE AND O. RONEN)

Abstract:We study the problem of how one should invest for retirement.Typically before this problem can be solved, a detailed utilityfunction needs to be elicited: the utility for consumption, thedis-utility for working, the utility for passing on inheritance, thediscount factor to trade-off utilities at different times, etc.  Oncethese are all known, backwards induction solves the problem.  But,people are notoriously bad at making these judgments.  So can we offeradvice which doesn't depend on knowing their utility?This talk will present some simple stylized facts that are useful inmaking

APPROXIMATION MECHANISMS AND CHARACTERIZATION OF IMPLEMENTABLE SOCIAL CHOICE RULES

Abstract: The emerging field of Algorithmic Mechanism Design studies strategic implementations of social-choice functions that arise in computational settings -- most importantly, various resource allocation rules. The clash between computational constraints and incentive constraints is at the heart of this field. This happens whenever one wishes to implement a computationally-hard social choice function ( e.g. an allocation rule). In such cases, approximations or heuristics are computationally required, but it is not at all clear whether these can be strategically implemented.

MIXED BUNDLING AUCTIONS

Abstract: We study multi-object auctions where agents have multi-dimensional, private and additive valuations for heterogeneous objects. The revenue-maximizing auction for such environments is not known. I will first explain the relevant economic ideas / relations to monopoly pricing, and then the technical difficulties appearing in the auction context.  I will then focus on the revenue properties of a class of dominant strategy mechanisms where a weight is assigned to each partition of objects. The weights influence the probability with which partitions are chosen in the mechanism.

COUNTERFACTUALS

In this talk I will survey my account of counterfactuals. It consists of probabilistic conditions, backed up by causal conditions, themselves probabilistic (although such causal conditions will not be unpacked probabilistically in this talk). It makes uses of a probabilistic notion of processes. It does not make any use of the standard notion of closer or closest possible world or world state, as in some main-stream conceptions of counterfactuals. The talk will be to a large extent informal, but it will exhibit the basis on which a formal theory can be established.

An Operational Measure of Riskiness (joint work with Dean P. Foster)

AbstractWe define the riskiness of a gamble g as that unique number R(g) such that no-bankruptcy is guaranteed if and only if one never accepts gambles whose riskiness exceeds the current wealth.Paper: http://www.ma.huji.ac.il/hart/abs/risk.html   

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