Finance as Extended Biology: Reciprocity as the Cognitive Substrate of Financial Behavior

A central challenge in economics and artificial intelligence is explaining how financial behaviors—such as credit, insurance, and trade—emerge without formal institutions. We argue that these functions are not products of institutional design, but structured extensions of a single behavioral substrate: reciprocity. Far from being a derived strategy, reciprocity served as the foundational logic of early human societies—governing the circulation of goods, regulation of obligation, and maintenance of long-term cooperation well before markets, money, or formal rules.

Trade, commonly regarded as the origin of financial systems, is reframed here as the canonical form of reciprocity: simultaneous, symmetric, and partner-contingent. Building on this logic, we reconstruct four core financial functions—credit, insurance, token exchange, and investment—as expressions of the same underlying principle under varying conditions.

By grounding financial behavior in minimal, simulateable dynamics of reciprocal interaction, this framework shifts the focus from institutional engineering to behavioral computation—offering a new foundation for modeling decentralized financial behavior in both human and artificial agents.

Despite centuries of financial theory, the true origins of core financial behaviors—like credit, insurance, and investment—remain underspecified. Most models assume formal contracts, utility-maximizing agents, or symbolic trust, but rarely explain how these systems arise from real social behavior.

This missing foundation isn’t just theoretical. It limits our ability to design artificial agents or systems that can engage in decentralized finance in a stable, scalable way. Without grounding in memory, expectation, and behavioral payoff, “trust” becomes a placeholder, not a mechanism.

By modeling finance as a structured extension of basic cognitive capacities—rather than as a product of institutional design—we offer a new way to simulate how financial functions emerge from social interaction and reciprocal behavior.

This framework is designed to be simulateable from the ground up. Each of the three behavioral primitives—partner recognition, reciprocal credence, and cost–return sensitivity—can be implemented as modular memory structures within LLM-based agents.

Instead of relying on reinforcement learning or fixed policy rules, agents maintain identity-indexed memory logs, scalar expectations shaped by prior outcomes, and adaptive heuristics for evaluating when and how to cooperate. These mechanisms support the emergence of distinct financial behaviors—such as credit, insurance, token exchange, and investment—as structured expressions of reciprocity under different conditions, including time delay, risk asymmetry, indirect mediation, and expected future return.

These components can be layered into existing multi-agent environments through structured memory and prompt-level reasoning. This allows scalable simulation of how complex financial behaviors emerge from simple social interaction dynamics—without institutional scaffolding.