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Money is more than memory
(2014)
Impersonal exchange is the hallmark of an advanced society. One key institution for impersonal exchange is money, which economic theory considers just a primitive arrangement for monitoring past conduct in society. If so, then a public record of past actions — or memory — supersedes the function performed by money. This intriguing theoretical postulate remains untested. In an experiment, we show that the suggested functional equality between money and memory does not translate into an empirical equivalence. Monetary systems perform a richer set of functions than just revealing past behaviors, which proves to be crucial in promoting large-scale cooperation.
Asymmetric social norms
(2017)
Studies of cooperation in infinitely repeated matching games focus on homogeneous economies, where full cooperation is efficient and any defection is collectively sanctioned. Here we study heterogeneous economies where occasional defections are part of efficient play, and show how to support those outcomes through contagious punishments.
In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT’s cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT’s behavior isn’t random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our esearch highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents.