Modeling Cooperation as Information Flow: Ripple Structures and Behavioral Digital Twins

Recent advances in articial intelligence (AI), particularly large language models, havesignicantly improved performance in language understanding, vision, and reasoning. How-ever, a fundamental limitation remains: current AI systems still lack an explicit under-standing of the logical structure by which humans make decisions within specic situationsand relationships. This limitation is not merely a shortage of intelligence, but a missinglayer of context-dependent decision logic. This study proposes a theoretical framework andmethodological protocol that redenes human cooperation as an observable information owrather than a psychological or moral phenomenon. We model cooperative propagation asRipple structures, which are directional causal chains of actions in which A helps B and B inturn helps C, and we introduce the concept of a Behavioral Digital Twin, dened as a map-ping of decision logic derived from situation, relationships, and action history. We furtherpresent a non-interventional observational protocol for capturing naturally occurring coop-erative behaviors through naturalistic observation environments, and outline a validationframework for future empirical work. All behavioral events remain voluntary; the proto-col introduces observation infrastructure without prescribing or incentivizing actions. Allgures and examples in this manuscript are conceptual illustrations intended to clarify the-oretical constructs. This work aims to provide a foundational layer connecting AI research,social systems research, and Web3 governance.