Equally-distributed equivalent utility,ex post egalitarianism and utilitarianism

We provide an axiomatization of expected equally-distributed equivalent-utility social welfare functions in the context of Harsanyi?s impartial observer theorem. For this family of social welfare functions, we show what additional axiom is necessary and sufficient for the observer to exhibit aversion to ex post inequality. We also relate this axiomatization to our axiomatization in a companion paper of generalized utilitarian social welfare functions. Given certain richness assumptions, the only social welfare functions that belong to both families are the utilitarian.     

A game of timing and visibility

We consider the following abstraction of competing publications. There are n players in the game. Each player i chooses a point x_i in the interval [0,1], and a player's payoff is the distance from its point x_i to the next larger point, or to 1 if x_i is the largest. For this game, we give a complete characterization of the Nash equilibrium for the two-player game, and, more important, we give an efficient approximation algorithm to compute numerically the symmetric Nash equilibrium for the n-player game. The approximation is computed via a discrete version of the game. In both cases, we show that the (symmetric) equilibrium is unique. Our algorithmic approach to the n-player game is non-standard in that it does not involve solving a system of differential equations. We believe that our techniques can be useful in the analysis of other timing games.     

On-Line Portfolio Selection Using Multiplicative Updates

We present an on-line investment algorithm that achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm employs a multiplicative update rule derived using a framework introduced by Kivinen and Warmuth. Our algorithm is very simple to implement and requires only constant storage and computing time per stock in each trading period. We tested the performance of our algorithm on real stock data from the New York Stock Exchange accumulated during a 22-year period. On these data, our algorithm clearly outperforms the best single stock as well as Cover's universal portfolio selection algorithm. We also present results for the situation in which the investor has access to additional "side information."