# Rethinking Optimization: A Systems-Based Approach to Social Externalities

arXiv: https://arxiv.org/abs/2506.12825 · HTML: https://mlsanity.com/paper/2506.12825/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 78 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [Equalizing Recourse across Groups](https://mlsanity.com/paper/1909.03166/) (2019)
- [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022)
- [Towards a Systems Theory of Algorithms](https://mlsanity.com/paper/2401.14029/) (2024)
- [Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability](https://mlsanity.com/paper/2104.12037/) (2021)
- [Optimization's Neglected Normative Commitments](https://mlsanity.com/paper/2305.17465/) (2023)
- [Towards a Theory of Control Architecture: A quantitative framework for layered multi-rate control](https://mlsanity.com/paper/2401.15185/) (2024)
- [Questioning the assumptions behind fairness solutions](https://mlsanity.com/paper/1811.11293/) (2018)
- [Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security](https://mlsanity.com/paper/2504.07719/) (2025)

## Built on by (most cited first, 2 of 2)

- [Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks](https://mlsanity.com/paper/2508.06411/) (2025)
- [Consumer Autonomy or Illusion? Rethinking Consumer Agency in the Age of Algorithms](https://mlsanity.com/paper/2508.13440/) (2025)

## Abstract (arXiv)

Optimization is widely used for decision making across various domains, valued for its ability to improve efficiency. However, poor implementation practices can lead to unintended consequences, particularly in socioeconomic contexts where externalities (costs or benefits to third parties outside the optimization process) are significant. To propose solutions, it is crucial to first characterize involved stakeholders, their goals, and the types of subpar practices causing unforeseen outcomes. This task is complex because affected stakeholders often fall outside the direct focus of optimization processes. Also, incorporating these externalities into optimization requires going beyond traditional economic frameworks, which often focus on describing externalities but fail to address their normative implications or interconnected nature, and feedback loops. This paper suggests a framework that combines systems thinking with the economic concept of externalities to tackle these challenges. This approach aims to characterize what went wrong, who was affected, and how (or where) to include them in the optimization process. Economic externalities, along with their established quantification methods, assist in identifying "who was affected and how" through stakeholder characterization. Meanwhile, systems thinking (an analytical approach to comprehending relationships in complex systems) provides a holistic, normative perspective. Systems thinking contributes to an understanding of interconnections among externalities, feedback loops, and determining "when" to incorporate them in the optimization. Together, these approaches create a comprehensive framework for addressing optimization's unintended consequences, balancing descriptive accuracy with normative objectives. Using this, we examine three common types of subpar practices: ignorance, error, and prioritization of short-term goals.