Individual Recognition
Identify and remember specific agents.
Reciprocal Credence
Estimate likelihood of return cooperation.
Cost–Return Sensitivity
Adapt investment based on benefit feedback.
The origins of economic behavior remain unresolved—not only in the social sciences but also in AI, where dominant theories often rely on predefined incentives or institutional assumptions. Contrary to the longstanding myth of barter as the foundation of exchange, converging evidence from early human societies suggests that reciprocity—not barter—was the foundational economic logic, enabling communities to sustain exchange and social cohesion long before formal markets emerged.
Yet despite its centrality, reciprocity lacks a simulateable and cognitively grounded account. Here, we introduce a minimal behavioral framework based on three empirically supported cognitive primitives—individual recognition, reciprocal credence, and cost--return sensitivity—that enable agents to participate in and sustain reciprocal exchange, laying the foundation for scalable economic behavior. These mechanisms scaffold the emergence of cooperation, proto-economic exchange, and institutional structure from the bottom up.
By bridging insights from primatology, developmental psychology, and economic anthropology, this framework offers a unified substrate for modeling trust, coordination, and economic behavior in both human and artificial systems.
Despite centuries of theory, the true origin of economic exchange remains underspecified. Most models assume institutions, utility, or symbolic trust—but say little about how these systems emerge from real social behavior.
This missing foundation isn’t just a theoretical gap. It prevents us from building artificial agents that behave socially in a stable, scalable way. Without grounding in memory, expectation, and behavioral payoff, trust becomes just a label, not a mechanism.
By modeling exchange as something that emerges from basic cognitive capacities—not something imposed by rules—we open a new path for simulating how institutions form, rather than assuming they already exist.
This framework is designed to be simulateable from the ground up. Each of the three cognitive primitives—recognition, credence, and cost–return sensitivity—can be implemented as modular memory functions in LLM-based agents.
Rather than relying on reinforcement learning or hardcoded rules, agents can be equipped with identity tracking, scalar expectations, and cumulative cost-return logs to support adaptive reciprocity over time.
These components can be layered into existing multi-agent systems via structured memory and prompt-level reasoning—enabling scalable simulations of how social exchange and institutions emerge from simple cognitive building blocks.
