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1. MULTIATTRIBUTE UTILITY THEORY, INTERTEMPORAL UTILITY, AND CORRELATION AVERSION

   https://doi.org/10.1111/iere.12279  

   Andersen, Steffen ; Harrison, Glenn W. ; Lau, Morten I. ; Rutström, E. Elisabet ( May 2018 , International Economic Review)

   Convenient assumptions about qualitative properties of the intertemporal utility function have generated counterintuitive implications for the relationship between atemporal risk aversion and the intertemporal elasticity of substitution. If the intertemporal utility function is additively separable, then the latter two concepts are the inverse of each other. We review a theoretical specification with a long lineage in the literature on multi‐attribute utility and use this theoretical structure to guide the design of a series of experiments that allow us to identify and estimate intertemporal correlation aversion. Our results show that subjects are correlation averse over lotteries with intertemporal income profiles.

    

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2. UTILITY MAXIMIZATION IN A LARGE MARKET: UTILITY MAXIMIZATION IN A LARGE MARKET

   https://doi.org/10.1111/mafi.12123  

   Mostovyi, Oleksii ( May 2016 , Mathematical Finance)
3. Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay

   https://doi.org/10.1145/3466772.3467031  

   Fu, Xinzhe ; Modiano, Eytan ( July 2021 , ACM MobiHoc)

   null (Ed.)
4. Approximate Expected Utility Rationalization

   https://doi.org/10.1093/jeea/jvad028  

   Echenique, Federico ; Imai, Taisuke ; Saito, Kota ( April 2023 , Journal of the European Economic Association)

   We propose a new measure of deviations from expected utility theory. For any positive number e, we give a characterization of the datasets with a rationalization that is within e (in beliefs, utility, or perceived prices) of expected utility (EU) theory, under the assumption of risk aversion. The number e can then be used as a measure of how far the data is to EU theory. We apply our methodology to data from three large-scale experiments. Many subjects in these experiments are consistent with utility maximization, but not with EU maximization. Our measure of distance to expected utility is correlated with the subjects’ demographic characteristics.

    

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5. Network utility maximization with unknown utility functions: A distributed, data-driven bilevel optimization approach

   Ji, K ; Ying, L ( October 2023 , International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing)

   Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave. This paper seeks to answer the following question: how to allocate resources when utility functions are unknown, even to the users? This answer has become increasingly important in the next-generation AI-aware communication networks where the user utilities are complex and their closed-forms are hard to obtain. In this paper, we provide a new solution using a distributed and data-driven bilevel optimization approach, where the lower level is a distributed network utility maximization (NUM) algorithm with concave surrogate utility functions, and the upper level is a data-driven learning algorithm to find the best surrogate utility functions that maximize the sum of true network utility. The proposed algorithm learns from data samples (utility values or gradient values) to autotune the surrogate utility functions to maximize the true network utility, so works for unknown utility functions. For the general network, we establish the nonasymptotic convergence rate of the proposed algorithm with nonconcave utility functions. The simulations validate our theoretical results and demonstrate the great effectiveness of the proposed method in a real-world network. 

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