This paper examines a general model of sales contests in which agents have heterogeneous attitudes toward risk. It shows that agents that are less risk averse have a higher probability of success. A corollary to this result shows that when absolute risk aversion is decreasing in wealth,...

We consider the problem of designing (perhaps massively distributed) collectives of computational processes to maximize a provided "world utility" function. We consider this problem when the behavior of each process in the collective can be cast as striving to maximize its own payoff utility...

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve distributed optimization and control problems. This is done by translating the distributed problem into an...

Part I: Bayes Estimators and the Shannon Entropy. <p> This paper is the first of two on the problem of estimation a function of a probability distribution from a finite set of samples of that distribution. In this paper a Bayerian analysis of this problem is presented, the optimal properties of the...</p>

For systems usually characterized as complex/living/intelligent, the spatio-temporal patterns exhibited on different scales differ markedly from one another. (E.g., the biomass distribution of a human body looks very different depending on the spatial scale at which one examines that biomass.)...

The ``evidence'' procedure for setting hyperparameters is essentially the same as the techniques of ML-II and generalized maximum likelihood. Unlike those older techniques however, the evidence procedure has been justified (and used) as an approximation to the hierarchical Bayesian calculation....

This paper presents two Bayesian alternatives to the chi-squared test for determining whether a pair of categorical data sets were generated from the same underlying distribution. It then discusses such alternatives for the Kolmogorov-Smirnov test, which is often used when the data sets consist...

This paper presents a Bayesian "correction" to the familiar quadratic loss bias-plus-variance formula. It then discusses some other loss-function-specific aspects of supervised learning. It ends by presenting a version of the bias-plus-variance formula appropriate for log loss.

In bagging [Bre94a] one uses bootstrap replicates of the training set [Efr79, ET93] to try to improve a learning algorithm's performance. The computational requirements for estimating the resultant generalization error on a test set by means of cross-validation are often prohibitive; for...