Rethinking Optimization: A Systems-Based Approach to Social Externalities

2025-06-15

AI Summary

Conventional optimization practices frequently overlook externalities and their feedback loops within socio-economic systems, leading to decisions characterized by ignorance, misjudgment, and short-termism. Method: This paper introduces an integrative framework that unifies systems thinking with externality economics—achieving, for the first time, deep coupling of normativity (value trade-offs and responsibility calibration), dynamics (feedback-loop modeling), and quantifiability (shadow pricing and social cost accounting). It employs system dynamics modeling, stakeholder mapping, and structured externality assessment to systematically identify affected parties, clarify externality transmission mechanisms, and specify *when* and *how* externalities should be incorporated into optimization processes. Contribution/Results: The framework delivers actionable pathways for embedding optimization in algorithmic governance, public policy, and AI ethics, alongside methods for responsibility calibration. It overcomes key limitations of traditional optimization—its neglect of interconnectivity, dynamic feedback, and pluralistic value structures.

Technology Category

• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Address unintended consequences of optimization in socioeconomic contexts
• Integrate externalities and feedback loops into optimization processes
• Combine systems thinking and economics to improve decision-making frameworks

Innovation

• Combines systems thinking with economic externalities
• Characterizes stakeholders and subpar practices
• Balances descriptive accuracy with normative objectives