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Agent-based social simulation

Agent-based social simulation (social simulations) uses agent-based modeling and artificial agents to simulate social phenomena within computer-based multiagent systems. This interdisciplinary field combines social science, computer simulation, and agent-based computing to model social systems by representing individuals or groups as agents in a simulated society. ABSS helps analyze agent behaviors and predict outcomes of social events that are difficult to observe directly, offering valuable insights into social processes. It provides tools and models to explore complex social interactions, enhancing our understanding of societal dynamics through artificial environments.

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Multi-agent system

A multi-agent system is a system created from multiple autonomous elements interacting with each other. These are called agents. In a multi-agent system, each agent is represented by an individual algorithm. See Agent-based model.

Agents can be used to simulate many different active elements, including organisms, machines, persons, corporations and other organizations, nations, and so on. Agent-based models can be used to simulate a wide variety of social phenomena, including transportation, market failures, cooperation and escalation and spreading of conflicts. In agent-based models illustrate how models based on simple rules can results in complex dynamics and emergent behavior (Kontopoulos, 1993; Archer, 1995; Sawyer, 2001).

History

Sugarscape

Main article: Sugarscape

The first widely known multi-agent generative social model was developed in 1996 by Joshua M. Epstein and Robert Axtell.3 The purpose of this model was simulation and research of social phenomena like seasonal migration, environmental pollution, procreation, combat, disease spreading and cultural features. Their model is based on the work of economist Thomas Schelling, presented in his paper "Models of Segregation". This model defined the first generation of computer-based social simulations. Epstein and Axtell's model was implemented using concepts from the "Game of Life" developed by John Horton Conway.

Usage for social sciences

There are three main objects of scientific implementation of ABSS (Gilbert, Trotzsch; 2005)

Understanding basic aspects of social phenomena

Like aspects involving its diffusion, dynamics or results. Such basic models should be based on simple rules, so that a system's resulting emergent behavior can be easily observable.

Prediction

These models are implemented to predict real life events and phenomena. Examples of use could be transportation (prediction of traffic in future to find places where traffic jams could occur), prediction of future unemployment rates etc. Problem of models made to accurately predict such an events is increasing complexity of model with number of dynamically changing parameters.

Research, testing and formulation of hypothesis

Unlike other two main objects, which have use outside Social sciences, latter one is used mainly on the field of social science. Agent-based social simulations are often used during research of new hypothesis. Simulation could be useful when there is no other way to observe agents during their actions. For example, during creation of new language, which is long-term process. Another benefit of simulation lies in fact, that to be able to prove theory in simulation, it has to be represented in formal and logical form. This leads to more coherent formulation of theory.

Multi-agent simulation suites (MASS) usage for problem solving

Society and culture

Models of information diffusion in social environment

An academic article investigates an agent-based simulation of information diffusion in Facebook online social network.4

Organizing networks

Emergence of social phenomena

Altruism and cooperation Ethnocentrism

Crowd behaviour

Models for natural disasters (evacuation – fire)

Economical science

Business

Market behavior models

Religion

Software used for implementing ABSS

Different agent based software have been used for implementing ABSS (Tobias & Hofmann 2004) such as

See also

Further studies

  • EPSTEIN, Joshua M.; AXTELL, Robert. Growing Artificial Societies: social science from the bottom up. MIT Press. 1996, ISBN 0-262-55025-3.
  • EPSTEIN, Joshua M. Generative Social Science: studies in agent-based computational modeling. Princeton University Press. 2006
  • GILBERT, N. and Troitzsch, K. G. (1999). Simulation for the Social Scientist, Open University Press.

References

  1. Li, Xiaochen; Mao, Wenji; Zeng, Daniel; Wang, Fei-Yue (2008). "Agent-Based Social Simulation and Modeling in Social Computing". Intelligence and Security Informatics. Lecture Notes in Computer Science. Vol. 5075/2008. pp. 401–412. doi:10.1007/978-3-540-69304-8_41. ISBN 978-3-540-69136-5. 978-3-540-69136-5 ↩

  2. Davidsson, Paul (2002). "Agent Based Social Simulation: A Computer Science View". Journal of Artificial Societies and Social Simulation. 5 (1). http://jasss.soc.surrey.ac.uk/5/1/7.html ↩

  3. EPSTEIN J M & Axtell R L (1996) ↩

  4. Nasrinpour, Hamid Reza; Friesen, Marcia R.; McLeod, Bob (2016-11-22). "An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network". arXiv:1611.07454 [cs.SI]. /wiki/ArXiv_(identifier) ↩

  5. Ascape http://ascape.sourceforge.net/ ↩

  6. INGENIAS Development Kit Archived July 5, 2009, at the Wayback Machine (IDK) http://grasia.fdi.ucm.es/main/?q=en/node/127 ↩