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An additive agent has a utility function that is an additive set function: for every additive agent i and item j, there is a value ,, such that () =, for every set Z of items. When all agents are additive, welfare maximization can be done by a simple polynomial-time algorithm: give each item j to an agent for whom v i , j {\displaystyle v_{i,j ...
Thus, : is a "rescaled" utility function which has a minimum value of 0 and a maximum value of 1. The Relative Utilitarian social choice rule selects the element in X {\displaystyle X} which maximizes the utilitarian sum
Figure 3: This shows the utility maximisation problem with a minimum utility function. For a minimum function with goods that are perfect complements, the same steps cannot be taken to find the utility maximising bundle as it is a non differentiable function. Therefore, intuition must be used.
A single-attribute utility function maps the amount of money a person has (or gains), to a number representing the subjective satisfaction he derives from it. The motivation to define a utility function comes from the St. Petersburg paradox: the observation that people are not willing to pay much for a lottery, even if its expected monetary gain is infinite.
The following algorithms can be used to find an envy-free cake-cutting with maximum sum-of-utilities, for a cake which is a 1-dimensional interval, when each person may receive disconnected pieces and the value functions are additive: [6] For partners with piecewise-constant valuations: divide the cake into m totally-constant regions.
In decision theory, the von Neumann–Morgenstern (VNM) utility theorem demonstrates that rational choice under uncertainty involves making decisions that take the form of maximizing the expected value of some cardinal utility function. This function is known as the von Neumann–Morgenstern utility function.
Although there are at least four sources of uncertainty - the attribute outcomes, and a decisionmaker's fuzziness about: a) the specific shapes of the individual attribute utility functions, b) the aggregating constants' values, and c) whether the attribute utility functions are additive, these terms being addressed presently - uncertainty ...
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.