We study a multi-objective finite-horizon game model of renewable, common resource extraction where the players have two separate objectives (one is economic success; other describes players’ environmental concern). derive cooperative strategy and subgame-perfect Pareto equilibrium in linear-state non-stationary feedback strategies by employing dynamic programming approach. Since utility transf...

Different works have shown that the combination of multiple loss functions is beneficial when training deep neural networks for a variety of prediction tasks. Generally, such multi-loss approaches are implemented via a weighted multi-loss objective function in which each term encodes a different desired inference criterion. The importance of each term is often set using empirically tuned hyper-...

Several splitting criteria for binary classification trees are shown to be written as weighted sums of two values of divergence measures. This weighted sum approach is then used to form two families of splitting criteria. One of them contains the chi-squared and entropy criterion, the other contains the mean posterior improvement criterion. Both family members are shown to have the property of ...

Previous results on estimating errors or error bounds on identified transfer functions have relied upon prior assumptions about the noise and the unmodeled dynamics. This prior information took the form of parameterized bounding functions or parameterized probability density functions, in the time or frequency domain with known parameters. Here we show that the parameters that quantify this pri...

"An ethical objection is raised against the Blackorby-Donaldson criterion of optimum population maximizing the sum total of utilities in excess of some critical level, since it may disprefer a social state with more people and with more worthwhile lives. However, the criterion may serve as a practicably reasonable compromise between maximizing total and average utility. Nevertheless, an alter...

Combination methods have been investigated as a possible means to improve performance in multi-variable (multi-criterion or multi-objective) classification, prediction, learning, and optimization problems. In addition, information collected from multi-sensor or multi-source environment also often needs to be combined to produce more accurate information, to derive better estimation, or to make ...

It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and u...

This paper axiomatizes expected multi-utility representations of incomplete preferences under risk and under uncertainty. The von Neumann–Morgenstern expected utility model with incomplete preferences is revisited using a ‘‘constructive’’ approach, as opposed to earlier treatments that use convex analysis. © 2012 Elsevier B.V. All rights reserved. ‘‘Of all the axioms of utility theory, the comp...