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Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
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Related Experiment Video

Updated: Dec 22, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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Generative Adversarial Construction of Parallel Portfolios.

Shengcai Liu, Ke Tang, Xin Yao

    IEEE Transactions on Cybernetics
    |May 2, 2020
    PubMed
    Summary

    This study introduces an adversarial approach for constructing parallel algorithm portfolios, enhancing generalization even with scarce training data. The method generates challenging instances to improve solver performance, outperforming existing techniques.

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    Last Updated: Dec 22, 2025

    An R-Based Landscape Validation of a Competing Risk Model
    05:37

    An R-Based Landscape Validation of a Competing Risk Model

    Published on: September 16, 2022

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Optimization

    Background:

    • Automatic algorithm configuration is effective for solver construction.
    • Current methods assume training data sufficiently represents target use cases for generalization.
    • Scarce and biased training data can hinder solver generalization.

    Purpose of the Study:

    • To develop effective construction approaches for parallel algorithm portfolios resilient to scarce and biased training data.
    • To improve the generalization capabilities of automatically constructed solvers.

    Main Methods:

    • An adversarial process simultaneously considers instance generation and portfolio construction.
    • Instance generation aims to create challenging problems for the current portfolio.
    • Portfolio construction seeks new component solvers to address newly generated instances.

    Main Results:

    • The proposed approach yielded parallel portfolios with significantly better generalization than existing methods under scarce and biased training data.
    • Applied to Boolean satisfiability problems (SAT) and traveling salesman problems (TSPs).

    Conclusions:

    • The adversarial approach effectively constructs parallel algorithm portfolios with superior generalization.
    • Generated portfolios demonstrated performance comparable to state-of-the-art manually designed parallel solvers.