On convexity and quasiconvexity of extremal value functions in set optimization

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.

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Qualitative properties of solutions to set optimization problems

Computational and Applied Mathematics ◽

10.1007/s40314-021-01458-x ◽

2021 ◽

Vol 40 (2) ◽

Author(s):

Lam Quoc Anh ◽

Nguyen Huu Danh ◽

Pham Thanh Duoc ◽

Tran Ngoc Tam

Keyword(s):

Optimization Problems ◽

Set Optimization ◽

Qualitative Properties ◽

Qualitative Properties Of Solutions

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Value functions and two classes of binary codes

Discrete Mathematics ◽

10.1016/j.disc.2020.112261 ◽

2021 ◽

Vol 344 (3) ◽

pp. 112261

Author(s):

Zihui Liu

Keyword(s):

Binary Codes ◽

Value Functions

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Two-stage Approach to Feature Set Optimization for Unsupervised Dataset with Heterogeneous Attributes

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114563 ◽

2021 ◽

pp. 114563

Author(s):

Arpita Chaudhuri ◽

Debasis Samanta ◽

Monalisa Sarma

Keyword(s):

Set Optimization ◽

Two Stage

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Assessing the Performance of Interactive Multiobjective Optimization Methods

ACM Computing Surveys ◽

10.1145/3448301 ◽

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.

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