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A guide to formulating fairness in an optimization model.

Violet Xinying Chen1, J N Hooker2

  • 1Stevens Institute of Technology, Hoboken, USA.

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|June 26, 2023
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Summary
This summary is machine-generated.

This study surveys mathematical methods for incorporating fairness into optimization models, moving beyond simple cost-benefit analysis. It presents practical approaches for integrating ethical criteria into decision-making processes.

Keywords:
Distributive justiceFairness

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Area of Science:

  • Operations Research
  • Decision Science
  • Computer Science

Background:

  • Optimization models traditionally focus on maximizing benefits or minimizing costs.
  • Integrating fairness and ethical considerations into mathematical models presents significant challenges.
  • Existing literature offers various approaches, but a unified framework is lacking.

Purpose of the Study:

  • To critically survey and analyze diverse mathematical formulations for fairness in optimization.
  • To explore methods integrating efficiency and fairness concerns.
  • To identify practical approaches for implementing these criteria in optimization models.

Main Methods:

  • Comprehensive literature review of fairness criteria in optimization and social choice theory.
  • Analysis of inequality measures, Rawlsian criteria, bargaining solutions (e.g., Nash, Kalai-Smorodinsky), and group parity metrics.
  • Examination of utility-threshold and fairness-threshold schemes.
  • Evaluation of formulation in linear, nonlinear, and mixed-integer programming.

Main Results:

  • Detailed overview of multiple fairness formulations, including alpha fairness, proportional fairness, and group parity.
  • Identification of practical implementation strategies for various optimization modeling techniques.
  • Survey of axiomatic and bargaining derivations considering interpersonal utility comparability.

Conclusions:

  • Provides a structured overview of mathematical fairness criteria for optimization.
  • Offers guidance on selecting and implementing appropriate fairness formulations.
  • Highlights the importance of ethical considerations in decision-making models.