A Multidimensional Bayesian Framework for Modeling Collective Dynamics in Human-AI Symbiosis: Integrating Postphenomenological, Biological, and Engineering Perspectives

This conceptual paper proposes a novel multidimensional Bayesian framework for modeling the collective dynamics emerging from human-AI symbiotic interactions. Drawing on postphenomenological insights, we conceptualize human-technology relations as co-constitutive processes that transcend individual agency, incorporating biological analogies (e.g., neural plasticity and ecosystem resilience) and engineering principles (e.g., feedback control systems and fault-tolerant design). The framework employs Bayesian inference and advanced active inference principles to quantify uncertainty and minimize surprise in decision-making processes, supported by rigorous mathematical derivations including stochastic differential equations, detailed free energy minimization, Itô calculus solutions, Fokker-Planck equations, and policy evaluation, Python-based agent-based simulations using real-world datasets, advanced sensitivity analysis with Sobol indices computed via Monte Carlo methods, quantitative statistics including mutual information, Bayesian evidence synthesis, uncertainty quantification via Kullback-Leibler divergence, and falsifiability assessments. We demonstrate the model's applicability through simulated healthcare, social robotics, medical robotics including robotic surgery, AI applications in healthcare, and smart city scenarios, providing a manufacturing roadmap for AI system deployment, laboratory validation protocols, and experimental designs. This work advances the discourse in human-technology relations by offering a falsifiable, replicable model that bridges philosophical, scientific, and practical domains, with implications for ethical AI design in collective contexts.