Computational Simulation of Technology Regulation and Social Outcomes

Abstract

Technology regulation shapes social outcomes through complex causal mechanisms -- direct compliance effects, innovation incentive changes, market structure responses, and second-order social adaptations -- that regulatory impact assessments typically model inadequately. Computational simulation of regulation-outcome relationships provides richer, more dynamic predictions of regulatory effects than conventional static cost-benefit analysis by capturing feedback loops, distributional dynamics, and emergent social behaviours that resist analytical modelling. This paper proposes the Computational Regulation Impact Simulation Framework (CRISF), applying agent-based modelling and system dynamics simulation to four technology regulation scenarios: EU AI Act compliance cost dynamics, GDPR privacy-innovation trade-offs, digital platform competition regulation, and autonomous vehicle safety standard stringency. CRISF simulates social outcomes -- innovation rate, consumer welfare, equity distribution, and safety performance -- across 10,000 Monte Carlo runs per scenario under varied regulatory parameter settings. Key results: EU AI Act compliance costs create a market concentration dynamic that favours large incumbents; GDPR shows a privacy-welfare Pareto frontier with achievable win-wins at intermediate stringency; AV safety standard stringency exhibits a non-monotonic safety outcome -- stricter standards increase safety up to an optimal point, then decrease it through adoption delay. The framework provides computational regulatory impact analysis methodology for policymakers and regulatory economists.