The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?
- URL: http://arxiv.org/abs/2305.07970v2
- Date: Sat, 29 Jun 2024 12:29:28 GMT
- Title: The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?
- Authors: Steve Phelps, Yvan I. Russell,
- Abstract summary: We investigated the capability of the GPT-3.5 large language model (LLM) to operationalize natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior.
We used a prompt to describe the task environment using a similar protocol to that used in experimental psychology studies with human subjects.
Our results provide evidence that LLMs can, to some extent, translate natural language descriptions of different cooperative stances into corresponding descriptions of appropriate task behaviour.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We investigated the capability of the GPT-3.5 large language model (LLM) to operationalize natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior in two social dilemmas: the repeated Prisoners Dilemma and the one-shot Dictator Game. Using a within-subject experimental design, we used a prompt to describe the task environment using a similar protocol to that used in experimental psychology studies with human subjects. We tested our research question by manipulating the part of our prompt which was used to create a simulated persona with different cooperative and competitive stances. We then assessed the resulting simulacras' level of cooperation in each social dilemma, taking into account the effect of different partner conditions for the repeated game. Our results provide evidence that LLMs can, to some extent, translate natural language descriptions of different cooperative stances into corresponding descriptions of appropriate task behaviour, particularly in the one-shot game. There is some evidence of behaviour resembling conditional reciprocity for the cooperative simulacra in the repeated game, and for the later version of the model there is evidence of altruistic behaviour. Our study has potential implications for using LLM chatbots in task environments that involve cooperation, e.g. using chatbots as mediators and facilitators in public-goods negotiations.
Related papers
- Explaining Decisions of Agents in Mixed-Motive Games [11.792961910129684]
In recent years, agents have become capable of communicating seamlessly via natural language.
Such environments and scenarios have rarely been explored in the context of explainable AI.
In this work, we design explanation methods to address inter-agent competition, cheap-talk, or implicit communication by actions.
arXiv Detail & Related papers (2024-07-21T19:56:04Z) - Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma? [0.1474723404975345]
We study the cooperative behavior of Llama2 when playing the Iterated Prisoner's Dilemma against random adversaries displaying various levels of hostility.
We find that Llama2 tends not to initiate defection but it adopts a cautious approach towards cooperation.
In comparison to prior research on human participants, Llama2 exhibits a greater inclination towards cooperative behavior.
arXiv Detail & Related papers (2024-06-19T14:51:14Z) - A Dialogue Game for Eliciting Balanced Collaboration [64.61707514432533]
We present a two-player 2D object placement game in which the players must negotiate the goal state themselves.
We show empirically that human players exhibit a variety of role distributions, and that balanced collaboration improves task performance.
arXiv Detail & Related papers (2024-06-12T13:35:10Z) - Sharing the Cost of Success: A Game for Evaluating and Learning Collaborative Multi-Agent Instruction Giving and Following Policies [19.82683688911297]
We propose a challenging interactive reference game that requires two players to coordinate on vision and language observations.
We show that a standard Proximal Policy Optimization (PPO) setup achieves a high success rate when bootstrapped with partner behaviors.
We find that a pairing of neural partners indeed reduces the measured joint effort when playing together repeatedly.
arXiv Detail & Related papers (2024-03-26T08:58:28Z) - GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment [72.96949760114575]
We propose a novel cooperative communication framework, Goal-Oriented Mental Alignment (GOMA)
GOMA formulates verbal communication as a planning problem that minimizes the misalignment between parts of agents' mental states that are relevant to the goals.
We evaluate our approach against strong baselines in two challenging environments, Overcooked (a multiplayer game) and VirtualHome (a household simulator)
arXiv Detail & Related papers (2024-03-17T03:52:52Z) - Building Cooperative Embodied Agents Modularly with Large Language
Models [104.57849816689559]
We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments.
We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework.
Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
arXiv Detail & Related papers (2023-07-05T17:59:27Z) - Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination [36.33334853998621]
We introduce the Cooperative Open-ended LEarning (COLE) framework to solve cooperative incompatibility in learning.
COLE formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy.
We show that COLE could effectively overcome the cooperative incompatibility from theoretical and empirical analysis.
arXiv Detail & Related papers (2023-06-05T16:51:38Z) - Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
This work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior.
Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents.
arXiv Detail & Related papers (2022-08-22T14:06:06Z) - Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria [57.74495091445414]
Social deduction games offer an avenue to study how individuals might learn to synthesize potentially unreliable information about others.
In this work, we present Hidden Agenda, a two-team social deduction game that provides a 2D environment for studying learning agents in scenarios of unknown team alignment.
Reinforcement learning agents trained in Hidden Agenda show that agents can learn a variety of behaviors, including partnering and voting without need for communication in natural language.
arXiv Detail & Related papers (2022-01-05T20:54:10Z) - Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
We study the task of few-shot $textitlanguage coordination$.
We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities.
This requires the ability to model the partner's beliefs, a vital component of human communication.
arXiv Detail & Related papers (2021-07-12T19:26:11Z) - Loss aversion fosters coordination among independent reinforcement
learners [0.0]
We study what are the factors that can accelerate the emergence of collaborative behaviours among independent selfish learning agents.
We model two versions of the game with independent reinforcement learning agents.
We prove experimentally the introduction of loss aversion fosters cooperation by accelerating its appearance.
arXiv Detail & Related papers (2019-12-29T11:22:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.