NEW COLLECTIVE AGGREGATION FUNCTION OF ADDITIVE VALUE FUNCTIONS BY THE QUADRATIC MEAN

2021 ◽  
Vol 26 (2) ◽  
pp. 179-196
Author(s):  
Zoïnabo Savadogo ◽  
Saïdou Ouedraogo ◽  
Frédéric Nikiema ◽  
Somdouda Sawadogo ◽  
Blaise Some
Keyword(s):  
Aggregation Function ◽  
Value Functions ◽  
Quadratic Mean

The Received Wisdom

2018 ◽  
Author(s):  
Meghan Sullivan
Keyword(s):  
Temporal Discounting ◽  
Value Functions ◽  
Distant Future ◽  
Rational Planning ◽  
Future Discounting ◽  
The Future

This chapter introduces the reader to future discounting and some received wisdom. The received wisdom about rational planning tends to assume that it is irrational to have near‐biased preferences (i.e., preferences for lesser goods now compared to greater goods further in the future).Thechapter describes these preferences by introducing the reader to value functions. Value functions are then used to model different kinds of distant future temporal discounting (e.g., hyperbolic, exponential, absolute). Finally, the chapter makes a distinction between temporal discounting and risk discounting. It offers a reverse lottery test to tease apart these two kinds of discounting.

Value functions and two classes of binary codes

2021 ◽  
Vol 344 (3) ◽  
pp. 112261
Author(s):  
Zihui Liu
Keyword(s):  
Binary Codes ◽  
Value Functions

scholarly journals Mixed aggregation functions for outliers detection

2021 ◽  
pp. 1-14
Author(s):  
Hengshan Zhang ◽  
Chunru Chen ◽  
Tianhua Chen ◽  
Zhongmin Wang ◽  
Yanping Chen
Keyword(s):  
Aggregation Function ◽  
Outliers Detection ◽  
Aggregation Functions ◽  
Novel Approach ◽  
Consensus Measure

A scenario that often encounters in the event of aggregating options of different experts for the acquisition of a robust overall consensus is the possible existence of extremely large or small values termed as outliers in this paper, which easily lead to counter-intuitive results in decision aggregation. This paper attempts to devise a novel approach to tackle the consensus outliers especially for non-uniform data, filling the gap in the existing literature. In particular, the concentrate region for a set of non-uniform data is first computed with the proposed searching algorithm such that the domain of aggregation function is partitioned into sub-regions. The aggregation will then operate adaptively with respect to the corresponding sub-regions previously partitioned. Finally, the overall aggregation is operated with a proposed novel consensus measure. To demonstrate the working and efficacy of the proposed approach, several illustrative examples are given in comparison to a number of alternative aggregation functions, with the results achieved being more intuitive and of higher consensus.

scholarly journals Assessing the Performance of Interactive Multiobjective Optimization Methods

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Bekir Afsar ◽  
Kaisa Miettinen ◽  
Francisco Ruiz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Interactive Methods ◽  
Value Functions ◽  
Technical Aspects ◽  
Preference Information ◽  
Multiobjective Optimization Problems ◽  
Human Decision

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

scholarly journals Lax connection and conserved quantities of quadratic mean field games

2021 ◽  
Vol 62 (8) ◽  
pp. 083302
Author(s):  
Thibault Bonnemain ◽  
Thierry Gobron ◽  
Denis Ullmo
Keyword(s):  
Mean Field ◽  
Mean Field Games ◽  
Conserved Quantities ◽  
Quadratic Mean

scholarly journals Sensitivity Analysis for Two-Level Value Functions with Applications to Bilevel Programming

2012 ◽  
Vol 22 (4) ◽  
pp. 1309-1343 ◽  
Author(s):  
S. Dempe ◽  
B. S. Mordukhovich ◽  
A. B. Zemkoho
Keyword(s):  
Sensitivity Analysis ◽  
Bilevel Programming ◽  
Value Functions
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