Viral Agency: Integrating Intentionality, Purposeful Behavior, and Information Integration in Viral Dynamics

This manuscript introduces a robust conceptual framework redefining biological agency through functional attributes—intentionality and goal-directed behavior—independent of structural hallmarks like self-replication or autonomous metabolism. Through detailed examination of viral tropism, host-cell targeting, and molecular decision processes, we show viruses display emergent traits akin to those in Integrated Information Theory (IIT). Utilizing sophisticated models—an intentionality-infused Susceptible-Infected-Recovered (SIR) extension, Bayesian parameter inference, and comprehensive sensitivity analyses—we affirm the framework's falsifiability and testability. Python simulations yield evidence of non-random viral entry, contesting chemical determinism. Cross-domain comparisons of informational integration in viruses, bacteria, and fungi uncover hierarchical causal efficacy and evolutionary paths. Ramifications span virology (agency-focused therapies), AI (enhanced vaccine design, forecasting, surveillance), and neuroscience (AI-driven neural modeling). Grounded in over 100 peer-reviewed articles from leading journals (2014–2024), this framework beckons interdisciplinary validation and application.本研究提出一套稳健的概念框架，通过意向性与目标导向行为两类功能属性，重新界定生物能动性（biological agency），且该框架不受限于自我复制、自主代谢等结构特征。通过对病毒嗜性（viral tropism）、宿主细胞靶向及分子决策过程的细致剖析，本研究证实病毒展现出类似整合信息理论（Integrated Information Theory, IIT）所描述的突现性状。本研究运用注入意向性的易感-感染-恢复（Susceptible-Infected-Recovered, SIR）扩展模型、贝叶斯参数推断方法与全面的敏感性分析等精密建模手段，验证了该框架具备可证伪性与可检验性。通过Python模拟得到病毒入侵并非随机的实证证据，这一发现对化学决定论提出了挑战。通过对病毒、细菌与真菌的信息整合过程开展跨域比较，本研究揭示了层级化因果效力与演化路径。该框架的研究影响波及病毒学（聚焦生物能动性的治疗手段）、人工智能（Artificial Intelligence, AI）领域的疫苗设计优化、疫情预测与监测，以及神经科学领域的人工智能驱动神经建模研究。本框架基于2014至2024年间国际顶级学术期刊发表的百余篇同行评议论文，呼吁学界开展跨学科的验证与应用实践。