This paper completes the causal reasoning trilogy by extending the resource-rational framework to collective inference and embedding it within the broader Energy-Efficiency Cycle. Part I established backward reasoning as hypothesis-space search; Part II analyzed the temporal dynamics of that search. Part III addresses two higher-order questions: (1) How do groups of bounded agents collectively infer causal structure, and (2) How does causal reasoning fit into the universal grammar of energy-constrained systems—the Energy-Efficiency Cycle (disturbance → response → stabilization → constraint → transition)? We argue that collective causal inference can be understood as distributed search over a shared hypothesis space, where communication costs, consensus mechanisms, and social epistemic practices function as resource-management strategies. Using Energy-Efficiency Theory (EET), we introduce energy parameters to quantify individual search effort and communication overhead. We then show that causal reasoning at both individual and collective levels maps onto the Energy-Efficiency Cycle. Testable predictions for group reasoning, scientific collaboration, and AI systems are derived as exploratory hypotheses.
Hongpu Yang (Thu,) studied this question.