We aim to understand fundamental preferences over fairness and cooperation embedded in artificial intelligence (AI). We do this by having a large language model (LLM), GPT-3.5, play two classic games: the dictator game and the prisoner's dilemma. We compare the decisions of the LLM to those of...

As technology has advanced, many have wondered whether (or simply when) artificial intelligent devices will replace the humans who perform complex, interactive, interpersonal tasks such as dispute resolution. Has science now progressed to the point that artificial intelligence devices can...

The Holy Grail of a decentralised stablecoin is achieved on rigorous mathematical frameworks, obtaining multiple advantageous proofs: stability, convergence, truthfulness, faithfulness, and malicious-security. These properties could only be attained by the novel and interdisciplinary combination...

Do large language models (LLMs) - such as ChatGPT 3.5, ChatGPT 4.0, and Google's Gemini 1.0 Pro - simulate human behavior in the context of the Prisoner's Dilemma (PD) game with varying stake sizes? This paper investigates this question, examining how LLMs navigate scenarios where...

We use machine learning to uncover regularities in the initial play of matrix games. We first train a prediction algorithm on data from past experiments. Examining the games where our algorithm predicts correctly, but existing economic models don't, leads us to add a parameter to the best...

Artificially intelligent (AI) applications make data-driven predictions that enable personalization on a large scale. As such, recent advances in AI's predictive power might have the potential to create more productive work environments. Using a principal-agent model and understanding AI as...

The paper studies how common codes of artificial language in communication are developed in the laboratory. We find that codes emerging from an environment with more variable spatial positions tend to use a limited set of symbols to represent positions, whereas codes emerging from an environment...

Classic artificial intelligence (Q-learning) algorithms have been capable of consistently learning supra-competitive pricing strategies in infinitely repeated Nash-Bertrand pricing games without human communication. Such algorithms have been able to converge due to the temporal correlation of...

Can algorithms help people predict behavior in high-stakes prisoner’s dilemmas? Participants watching the pre-play communication of contestants in the TV show Golden Balls display a limited ability to predict contestants’ behavior, while algorithms do significantly better. We provide...