60,208 research outputs found
An information theory for preferences
Recent literature in the last Maximum Entropy workshop introduced an analogy
between cumulative probability distributions and normalized utility functions.
Based on this analogy, a utility density function can de defined as the
derivative of a normalized utility function. A utility density function is
non-negative and integrates to unity. These two properties form the basis of a
correspondence between utility and probability. A natural application of this
analogy is a maximum entropy principle to assign maximum entropy utility
values. Maximum entropy utility interprets many of the common utility functions
based on the preference information needed for their assignment, and helps
assign utility values based on partial preference information. This paper
reviews maximum entropy utility and introduces further results that stem from
the duality between probability and utility
GME versus OLS - Which is the best to estimate utility functions?
This paper estimates von Neumann andMorgenstern utility functions comparing the generalized maximum entropy (GME) with OLS, using data obtained by utility elicitation methods. Thus, it provides a comparison of the performance of the two estimators in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover the GME estimator is more precise than the OLS one. Overall the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data is generated by utility elicitation methods.Generalized maximum entropy; Maximum entropy principle; von Neumann and Morgenstern utility; Utility elicitation.
Utility function estimation: The entropy approach
The maximum entropy principle can be used to assign utility values when only partial information is available about the decision
maker’s preferences. In order to obtain such utility values it is necessary to establish an analogy between probability and utility
through the notion of a utility density function. In this paper we explore the maximum entropy principle to estimate the utility
function of a risk averse decision maker
An application to general maximum entropy to utility
Methodologies related to information theory have been increasingly
used in studies in economics and management. In this paper, we use
generalised maximum entropy as an alternative to ordinary least squares in the
estimation of utility functions. Generalised maximum entropy has some
advantages: it does not need such restrictive assumptions and could be used
with both well and ill-posed problems, for example, when we have small
samples, which is the case when estimating utility functions. Using linear,
logarithmic and power utility functions, we estimate those functions and
confidence intervals and perform hypothesis tests. Results point to the greater
accuracy of generalised maximum entropy, showing its efficiency in
estimation
Estimating utility functions using generalized maximum entropy
This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum
entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages
of this approach, we provide a comparison of the performance of the GME estimator with ordinary least
square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples
through Monte Carlo simulations. The difference between the two estimators is small and it decreases as
the width of the parameter support vector increases. Moreover, the GME estimator is more precise than
the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation
of utility functions when data are generated by utility elicitation methods
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