Results 1 
6 of
6
A Guide to the Literature on Learning Probabilistic Networks From Data
, 1996
"... This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the ..."
Abstract

Cited by 172 (0 self)
 Add to MetaCart
This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples. Keywords Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery. I. Introduction Probabilistic networks or probabilistic gra...
Composition of Markets with Conflicting Incentives
"... We study information revelation in scoring rule and prediction market mechanisms in settings in which traders have conflicting incentives due to opportunities to profit from the market operator’s subsequent actions. In our canonical model, an agent Alice is offered an incentivecompatible scoring ru ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
We study information revelation in scoring rule and prediction market mechanisms in settings in which traders have conflicting incentives due to opportunities to profit from the market operator’s subsequent actions. In our canonical model, an agent Alice is offered an incentivecompatible scoring rule to reveal her beliefs about a future event, but can also profit from misleading another trader Bob about her information and then making money off Bob’s error in a subsequent market. We show that, in any weak Perfect Bayesian Equilibrium of this sequence of two markets, Alice and Bob earn payoffs that are consistent with a minimax strategy of a related game. We can then characterize the equilibria in terms of an information channel: the outcome of the first scoring rule is as if Alice had only observed a noisy version of her initial signal, with the degree of noise indicating the adverse effect of the second market on the first. We provide a partial constructive characterization of when this channel will be noiseless. We show that our results on the canonical model yield insights into other settings of information extraction with conflicting incentives.
To Buy Or Not To Buy: Why do People Buy too Much Information?
, 2001
"... Previous studies have shown that individuals exhibit a tendency to acquire an excessive amount of private information if information can only be communicated through a small and discrete action space. In this experiment we investigate demand for information when the action space is continuous. Parti ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Previous studies have shown that individuals exhibit a tendency to acquire an excessive amount of private information if information can only be communicated through a small and discrete action space. In this experiment we investigate demand for information when the action space is continuous. Participants sequentially assess their subjective probability which one out of two apriori equally likely states occurred at the beginning of a game. They observe the probability assessment of their predecessor and can acquire additional private information at a fixed price. Participants interact with either human or computer simulated players. We find that individuals in general acquire too many signals and that behavior does not depend on the rationality of their counterparts. A random utility model is able to explain most of the observed behavior.
List of Figures List of Tables
"... All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. This book was set in Computer Modern by The MIT Press and printed ..."
Abstract
 Add to MetaCart
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. This book was set in Computer Modern by The MIT Press and printed and bound in the United States of America.
5th International Symposium on Imprecise Probability: Theories and Applications, Prague, Czech Republic, 2007 Scoring Rules, Entropy, and Imprecise Probabilities
"... Suppose that a riskaverse expected utility maximizer with a precise probability distribution p bets optimally against a risk neutral opponent (or equivalently invests in an incomplete market for contingent claims) whose beliefs (or prices) are described by a convex set Q of probability distribution ..."
Abstract
 Add to MetaCart
Suppose that a riskaverse expected utility maximizer with a precise probability distribution p bets optimally against a risk neutral opponent (or equivalently invests in an incomplete market for contingent claims) whose beliefs (or prices) are described by a convex set Q of probability distributions. This utilitymaximization problem is the dual of the problem of …nding the distribution q in Q that minimizes a generalized divergence (relative entropy) with respect to p. A special case is that of logarithmic utility, in which the corresponding divergence is the KullbackLeibler divergence, but we present a closedform solution for the entire family of linearrisktolerance (a.k.a. HARA) utility functions and show that this corresponds to a particular parametric family of generalized divergences, which is derived from an entropy measure originally proposed by Arimoto and which is also related to a generalization of pseudospherical scoring rule originally proposed by I.J. Good. A variant of this decision problem, in which the decision maker has quasilinear utility for consumption over two periods, leads to the family of power divergences, which is related to a generalization of the power family of scoring rules.