Group Cohesion in Multi-Agent Scenarios as an Emergent Behavior
- URL: http://arxiv.org/abs/2211.02089v1
- Date: Thu, 3 Nov 2022 18:37:05 GMT
- Title: Group Cohesion in Multi-Agent Scenarios as an Emergent Behavior
- Authors: Gianluca Georg Alois Volkmer, Nabil Alsabah
- Abstract summary: We show that imbuing agents with intrinsic needs for group affiliation, certainty and competence will lead to the emergence of social behavior among agents.
This behavior expresses itself in altruism toward in-group agents and adversarial tendencies toward out-group agents.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we elaborate on the design and discuss the results of a
multi-agent simulation that we have developed using the PSI cognitive
architecture. We demonstrate that imbuing agents with intrinsic needs for group
affiliation, certainty and competence will lead to the emergence of social
behavior among agents. This behavior expresses itself in altruism toward
in-group agents and adversarial tendencies toward out-group agents. Our
simulation also shows how parameterization can have dramatic effects on agent
behavior. Introducing an out-group bias, for example, not only made agents
behave aggressively toward members of the other group, but it also increased
in-group cohesion. Similarly, environmental and situational factors facilitated
the emergence of outliers: agents from adversarial groups becoming close
friends.
Overall, this simulation showcases the power of psychological frameworks, in
general, and the PSI paradigm, in particular, to bring about human-like
behavioral patterns in an emergent fashion.
Related papers
- Can Agents Spontaneously Form a Society? Introducing a Novel Architecture for Generative Multi-Agents to Elicit Social Emergence [0.11249583407496219]
We introduce a generative agent architecture called ITCMA-S, which includes a basic framework for individual agents and a framework that supports social interactions among multi-agents.
This architecture enables agents to identify and filter out behaviors that are detrimental to social interactions, guiding them to choose more favorable actions.
arXiv Detail & Related papers (2024-09-10T13:39:29Z) - PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development.
We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
arXiv Detail & Related papers (2024-07-17T08:13:22Z) - Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents [3.7414804164475983]
We study the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting.
We observe several types of non-trivial interactions between pro-social and anti-social agents.
We find that certain types of moral agents are able to steer selfish agents towards more cooperative behavior.
arXiv Detail & Related papers (2024-03-07T04:12:24Z) - AgentCF: Collaborative Learning with Autonomous Language Agents for
Recommender Systems [112.76941157194544]
We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering.
We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together.
Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
arXiv Detail & Related papers (2023-10-13T16:37:14Z) - ProAgent: Building Proactive Cooperative Agents with Large Language
Models [89.53040828210945]
ProAgent is a novel framework that harnesses large language models to create proactive agents.
ProAgent can analyze the present state, and infer the intentions of teammates from observations.
ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
arXiv Detail & Related papers (2023-08-22T10:36:56Z) - AgentVerse: Facilitating Multi-Agent Collaboration and Exploring
Emergent Behaviors [93.38830440346783]
We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system.
Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent.
In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
arXiv Detail & Related papers (2023-08-21T16:47:11Z) - Rethinking Trajectory Prediction via "Team Game" [118.59480535826094]
We present a novel formulation for multi-agent trajectory prediction, which explicitly introduces the concept of interactive group consensus.
On two multi-agent settings, i.e. team sports and pedestrians, the proposed framework consistently achieves superior performance compared to existing methods.
arXiv Detail & Related papers (2022-10-17T07:16:44Z) - A mechanism of Individualistic Indirect Reciprocity with internal and
external dynamics [0.0]
This research proposes a new variant of Nowak and Sigmund model, focused on agents' attitude.
Using Agent-Based Model and a Data Science method, we show on simulation results that the discriminatory stance of the agents prevails in most cases.
The results also show that when the reputation of others is unknown, with a high obstinacy and high cooperation demand, a heterogeneous society is obtained.
arXiv Detail & Related papers (2021-05-28T23:28:50Z) - Deep reinforcement learning models the emergent dynamics of human
cooperation [13.425401489679583]
Experimental research has been unable to shed light on how social cognitive mechanisms contribute to the where and when of collective action.
We leverage multi-agent deep reinforcement learning to model how a social-cognitive mechanism--specifically, the intrinsic motivation to achieve a good reputation--steers group behavior.
arXiv Detail & Related papers (2021-03-08T18:58:40Z) - Learning Latent Representations to Influence Multi-Agent Interaction [65.44092264843538]
We propose a reinforcement learning-based framework for learning latent representations of an agent's policy.
We show that our approach outperforms the alternatives and learns to influence the other agent.
arXiv Detail & Related papers (2020-11-12T19:04:26Z) - Agent-Based Simulation of Collective Cooperation: From Experiment to
Model [0.0]
We present an experiment to observe what happens when humans pass through a dense static crowd.
We derive a model that incorporates agents' perception and cognitive processing of a situation that needs cooperation.
Agents' ability to successfully get through a dense crowd emerges as an effect of the psychological model.
arXiv Detail & Related papers (2020-05-26T13:29:08Z)
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.