Allocating Opportunities in a Dynamic Model of Intergenerational Mobility
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Abstract (arXiv)
Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status -- a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2021.
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Citing papers per year: 2022: 4; 2023: 5; 2024: 1; 2025: 1; 2026: 1.
12 papers.
- 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
- 2022The Long Arc of Fairness: Formalisations and Ethical Discoursecited by 12
- 2022Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrinescited by 10
- 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunitiescited by 5
- 2023Maximizing Submodular Functions for Recommendation in the Presence of Biasescited by 5
- 2023Minimum Levels of Interpretability for Artificial Moral Agentscited by 5
- 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
- 2023Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functionscited by 2
- 2025The Hidden Cost of Waiting for Accurate Predictionscited by 1
- 2022Achievement and Fragility of Long-term Equitabilitycited by 0
- 2024Centralized Selection with Preferences in the Presence of Biasescited by 0
- 2026Price of Fairness in Short-Term and Long-Term Algorithmic Selectionscited by 0
- 2026Price of Fairness in Short-Term and Long-Term Algorithmic Selections7 May 2026
- 2025The Hidden Cost of Waiting for Accurate Predictions1 Mar 2025
- 2024Centralized Selection with Preferences in the Presence of Biases7 Sep 2024
- 2023Minimum Levels of Interpretability for Artificial Moral Agents2 Jul 2023
- 2023Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions16 Jun 2023
- 2023Interpolating Item and User Fairness in Multi-Sided Recommendations12 Jun 2023
- 2023Maximizing Submodular Functions for Recommendation in the Presence of Biases3 May 2023
- 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities13 Jan 2023
- 2022Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines6 Jul 2022
- 2022Achievement and Fragility of Long-term Equitability24 Jun 2022
- 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective8 Jun 2022
- 2022The Long Arc of Fairness: Formalisations and Ethical Discourse8 Mar 2022