Epimorphisms of C*-algebras are surjective

We answer a question raised by V.G.Pestov in the affirmative.Comment: 2 page

Distribution-Free Learning

We select among rules for learning which of two actions in a stationary decision problem achieves a higher expected payo¤when payoffs realized by both actions are known in previous instances. Only a bounded set containing all possible payoffs is known. Rules are evaluated using maximum risk with maximin utility, minimax regret, competitive ratio and selection procedures being special cases. A randomized variant of fictitious play attains minimax risk for all risk functions with ex-ante expected payoffs increasing in the number of observations. Fictitious play itself has neither of these two properties. Tight bounds on maximal regret and probability of selecting the best action are included.fictitious play, nonparametric, finite sample, matched pairs, foregone payoffs, minimax risk, ex-ante improving, selection procedure

How to Attain Minimax Risk with Applications to Distribution-Free Nonparametric Estimation and Testing

We show how to a derive exact distribution-free nonparametric results for minimax risk when underlying random variables have known finite bounds and means are the only parameters of interest. Transform the data with a randomized mean preserving transformation into binary data and then apply the solution to minimax risk for the case where random variables are binary valued. This shows that minimax risk is attained by a linear strategy and the the set of binary valued distributions contains a least favorable prior. We apply these results to statistics. All unbiased symmetric non-randomized estimates for a function of the mean of a single sample are presented. We find a most powerful unbiased test for the mean of a single sample. We present tight lower bounds on size, type II error and minimal accuracy in terms of expected length of confidence intervals for a single mean and for the difference between two means. We show how to transform the randomized tests that attain the lower bounds into non-randomized tests that have at most twice the type I and II errors. Relative parameter efficiency can be measured in finite samples, in an example on anti-selfdealing indices relative (parameter) efficiency is 60% as compared to the tight lower bound. Our method can be used to generate distribution-free nonparametric estimates and tests when variance is the only parameter of interest. In particular we present a uniformly consistent estimator of standard deviation together with an upper bound on expected quadratic loss. We use our estimate to measure income inequality.exact, distribution-free, nonparametric inference, finite sample theory

Why Imitate, and if so, How? Exploring a Model of Social Evolution

In consectutive rounds, each agent in a finite population chooses an action, is randomly matched, obtains a payoff and then observes the performance of another agent. An agent determines future behavior based on the information she receives from the present round. She chooses among the behavioral rules that increase expected payoffs in any specifications of the matching scenario. The rule that outperforms all other such rules specifies to imitate the action of an agent that performed better with probability proportional to how much better she performed. The evolution of a large population in which each agent uses this rule can be approximated in the short run by the replicator dynamics.Random matching, learning, imitation, replicator dynamics

A Study of Truck Platooning Incentives Using a Congestion Game

We introduce an atomic congestion game with two types of agents, cars and trucks, to model the traffic flow on a road over various time intervals of the day. Cars maximize their utility by finding a trade-off between the time they choose to use the road, the average velocity of the flow at that time, and the dynamic congestion tax that they pay for using the road. In addition to these terms, the trucks have an incentive for using the road at the same time as their peers because they have platooning capabilities, which allow them to save fuel. The dynamics and equilibria of this game-theoretic model for the interaction between car traffic and truck platooning incentives are investigated. We use traffic data from Stockholm to validate parts of the modeling assumptions and extract reasonable parameters for the simulations. We use joint strategy fictitious play and average strategy fictitious play to learn a pure strategy Nash equilibrium of this game. We perform a comprehensive simulation study to understand the influence of various factors, such as the drivers' value of time and the percentage of the trucks that are equipped with platooning devices, on the properties of the Nash equilibrium.Comment: Updated Introduction; Improved Literature Revie

Commitments, Intentions, Truth and Nash Equilibria

Games with multiple Nash equilibria are believed to be easier to play if players can communicate. We present a simple model of communication in games and investigate the importance of when communication takes place. Sending a message before play captures talk about intentions, after play captures talk about past commitments. We focus on equilibria where messages are believed whenever possible. Applying our results to Aumann’s Stag Hunt game we find that communication is useless if talk is about commitments, while the efficient outcome is selected if talk is about intentions. This confirms intuition and empirical findings in the literature