Social Immunology and Information Oncology: Transdisciplinary Models of Network Dynamics for Biomedical Investigation

Abstract

The growing complexity of biological and social systems demands modeling paradigms that transcend disciplinary boundaries. This report explores a profound conceptual and methodological analogy between the dynamics of artificial intelligence (AI) in human societies and the pathogenesis of complex diseases such as cancer and autoimmune disorders. We argue that computational models developed to understand the spread of (dis)information, the formation of echo chambers, and adversarial information warfare offer robust frameworks for generating new hypotheses in biomedical investigation. By framing cell-cell interactions and immune responses through the lenses of network theory, game theory, and control theory, we propose a transdisciplinary synthesis. This report examines contagion models as analogues for metastatic proliferation, frames immune evasion as a form of computational propaganda, and models the breakdown of self-tolerance as a "social autoimmunity" manifested in echo chambers. Furthermore, we explore how intervention strategies, from immunotherapy to information inoculation theory, can be understood through a unified framework of network control. By cross-pollinating insights from information science and biomedicine, this work aims to catalyze novel approaches to network medicine, systems biology, and the development of targeted therapies.