2,148 research outputs found
Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
We describe and develop a close relationship between two problems that have
customarily been regarded as distinct: that of maximizing entropy, and that of
minimizing worst-case expected loss. Using a formulation grounded in the
equilibrium theory of zero-sum games between Decision Maker and
Nature, these two problems are shown to be dual to each other, the solution
to each providing that to the other. Although Tops\oe described this connection
for the Shannon entropy over 20 years ago, it does not appear to be widely
known even in that important special case. We here generalize this theory to
apply to arbitrary decision problems and loss functions. We indicate how an
appropriate generalized definition of entropy can be associated with such a
problem, and we show that, subject to certain regularity conditions, the
above-mentioned duality continues to apply in this extended context.
This simultaneously provides a possible rationale for maximizing entropy and
a tool for finding robust Bayes acts. We also describe the essential identity
between the problem of maximizing entropy and that of minimizing a related
discrepancy or divergence between distributions. This leads to an extension, to
arbitrary discrepancies, of a well-known minimax theorem for the case of
Kullback-Leibler divergence (the ``redundancy-capacity theorem'' of information
theory). For the important case of families of distributions having certain
mean values specified, we develop simple sufficient conditions and methods for
identifying the desired solutions.Comment: Published by the Institute of Mathematical Statistics
(http://www.imstat.org) in the Annals of Statistics
(http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/00905360400000055
Prediction with expert advice for the Brier game
We show that the Brier game of prediction is mixable and find the optimal
learning rate and substitution function for it. The resulting prediction
algorithm is applied to predict results of football and tennis matches. The
theoretical performance guarantee turns out to be rather tight on these data
sets, especially in the case of the more extensive tennis data.Comment: 34 pages, 22 figures, 2 tables. The conference version (8 pages) is
published in the ICML 2008 Proceeding
A Geometric Proof of Calibration
We provide yet another proof of the existence of calibrated forecasters; it
has two merits. First, it is valid for an arbitrary finite number of outcomes.
Second, it is short and simple and it follows from a direct application of
Blackwell's approachability theorem to carefully chosen vector-valued payoff
function and convex target set. Our proof captures the essence of existing
proofs based on approachability (e.g., the proof by Foster, 1999 in case of
binary outcomes) and highlights the intrinsic connection between
approachability and calibration
Assessing probabilistic forecasts about particular situations
How useful are probabilistic forecasts of the outcomes of particular situations? Potentially, they contain more information than unequivocal forecasts and, as they allow a more realistic representation of the relative likelihood of different outcomes, they might be more accurate and therefore more useful to decision makers. To test this proposition, I first compared a Squared-Error Skill Score (SESS) based on the Brier score with an Absolute-Error Skill Score (AESS), and found the latter more closely coincided with decision-makers’ interests. I then analysed data obtained in researching the problem of forecasting the decisions people make in conflict situations. In that research, participants were given lists of decisions that might be made and were asked to make a prediction either by choosing one of the decisions or by allocating percentages or relative frequencies to more than one of them. For this study I transformed the percentage and relative frequencies data into probabilistic forecasts. In most cases the participants chose a single decision. To obtain more data, I used a rule to derive probabilistic forecasts from structured analogies data, and transformed multiple singular forecasts for each combination of forecasting method and conflict into probabilistic forecasts. When compared using the AESS, probabilistic forecasts were not more skilful than unequivocal forecasts.accuracy, error measures, evaluation, forecasting methods, prediction
Belief Elicitation: A Horse Race among Truth Serums
In survey studies, probabilistic expectations about uncertain events are typically elicited by asking respondents for their introspective beliefs. If more complex procedures are feasible, beliefs can be elicited by incentive compatible revealed preference mechanisms (“truth serums”). Various mechanisms have been proposed in the literature, which differ in the degree to which they account for respondents’ deviations from expected value maximization. In this paper, we pit non-incentivized introspection against five truth serums, to elicit beliefs in a simple two-player game. We test the internal validity (additivity and predictive power for own behavior), and the external validity (predictive power for other players’ behavior, or accuracy) of each method. We find no differences among the truth serums. Beliefs from incentivized methods are better predictors of subjects’ own behavior compared to introspection. However, introspection performs equally well as the truth serums in terms of accuracy and additivity.belief measurement;subjective probability;scoring rules;outcome matching;probability matching;internal validity;external validity
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