arXiv subjects

Egil Diau

Publications and source records attributed to Egil Diau.

4 recordsLinked to original sources

arXiv2025

The Cognitive Foundations of Economic Exchange: A Modular Framework Grounded in Behavioral Evidence

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. For an interactive visualization of the framework, see: https://egil158.github.io/cogfoundations-econ/

cs.CY↗

arXiv2025

Reciprocity as the Foundational Substrate of Society: How Reciprocal Dynamics Scale into Social Systems

Prevailing accounts in both multi-agent AI and the social sciences explain social structure through top-down abstractions-such as institutions, norms, or trust-yet lack simulateable models of how such structures emerge from individual behavior. Ethnographic and archaeological evidence suggests that reciprocity served as the foundational mechanism of early human societies, enabling economic circulation, social cohesion, and interpersonal obligation long before the rise of formal institutions. Modern financial systems such as credit and currency can likewise be viewed as scalable extensions of reciprocity, formalizing exchange across time and anonymity. Building on this insight, we argue that reciprocity is not merely a local or primitive exchange heuristic, but the scalable substrate from which large-scale social structures can emerge. We propose a three-stage framework to model this emergence: reciprocal dynamics at the individual level, norm stabilization through shared expectations, and the construction of durable institutional patterns. This approach offers a cognitively minimal, behaviorally grounded foundation for simulating how large-scale social systems can emerge from decentralized reciprocal interaction.

cs.CY↗

arXiv2025

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.

cs.CY↗

arXiv2024

A Survey of Useful LLM Evaluation

LLMs have gotten attention across various research domains due to their exceptional performance on a wide range of complex tasks. Therefore, refined methods to evaluate the capabilities of LLMs are needed to determine the tasks and responsibility they should undertake. Our study mainly discussed how LLMs, as useful tools, should be effectively assessed. We proposed the two-stage framework: from ``core ability'' to ``agent'', clearly explaining how LLMs can be applied based on their specific capabilities, along with the evaluation methods in each stage. Core ability refers to the capabilities that LLMs need in order to generate high-quality natural language texts. After confirming LLMs possess core ability, they can solve real-world and complex tasks as agent. In the "core ability" stage, we discussed the reasoning ability, societal impact, and domain knowledge of LLMs. In the ``agent'' stage, we demonstrated embodied action, planning, and tool learning of LLMs agent applications. Finally, we examined the challenges currently confronting the evaluation methods for LLMs, as well as the directions for future development.

cs.CL↗