From Optimization to Satisficing: A Generative AI Framework for Robust Policy Design in Evolving Socio-Technical Ecosystems

Abstract

Traditional optimization approaches to policy design in socio-technical ecosystems increasingly fail under conditions of deep uncertainty, rapid environmental change, and incomplete information. This paper proposes a paradigm shift from optimization to satisficing—the pursuit of "good enough" solutions that satisfy core constraints rather than optimal but fragile outcomes. We introduce a novel Generative AI Framework for Satisficing Policy Design (GAI-SPD) that leverages large language models and diffusion-based generative architectures to produce diverse, context-aware policy alternatives as system conditions evolve. The framework integrates (1) a multi-agent simulation environment for scenario generation, (2) a generative policy synthesizer that produces satisficing solutions, and (3) an adaptive evaluation module that assesses policy robustness across multiple futures. We validate our approach through three case studies: smart grid demand-response management, pandemic response policy design, and sustainable manufacturing supply chain optimization. Results demonstrate that GAI-SPD generates policies that achieve 87-94% of optimal performance while exhibiting 3.2-5.7× greater robustness to unforeseen perturbations compared to conventional optimization methods. The framework reduces computational time by 68% and provides interpretable justifications for selected policies, enabling human decision-makers to understand and trust AI-generated recommendations. Our findings suggest that embracing satisficing through generative AI offers a viable pathway toward resilient, adaptive, and human-centric policy design for complex socio-technical systems.