Synthetic Minds in Normative Sandboxes: Exaptation, Spandrels, and Multilevel Selection in Agent-Based Legal Simulation

This paper introduces the Synthetic Minds Normative Sandbox (SNMS), a second-generation agent-based simulation framework for modeling the emergence and transformation of legal norms within multi-agent institutional environments. SNMS advances beyond first-generation normative automata by equipping agents with cognitively differentiated MindProfiles — composite structures encoding cognitive, affective, and doctrinal dimensions — calibrated empirically to eight Argentine institutional archetypes. Central to the architecture is the integration of TribeV2 (King, Benchetrit, Rapin, Brooks, Begany, Raugel (2) Gould and (3) Wilson & Sober's (1994) multilevel selection. Four novel metrics operationalize the framework: the Functional Drift Index (FDI), the Spandrel Emergence Metric (SEM), the Multilevel Selection Ratio (MSR), and the Mean Compliance Index (MCI). A proof of concept applied to a simulated Argentine labor reform scenario across 100 agents, 20 rounds, and 5 random seeds yields a Compliance Legitimacy Score (CLS) of 0.89, an FDI of 0.42, two normative spandrels by round 14, and an MSR of 0.73.