---
title: "Social simulation"
chunk: 3/4
source: "https://en.wikipedia.org/wiki/Social_simulation"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:58:43.417449+00:00"
instance: "kb-cron"
---

"Generative e-Social Science for Socio-Spatial Simulation" or (GENESIS) is a research node of the UK National Centre for e-Social Science funded by the UK research council ESRC.
"National e-Infrastructure for Social Simulation" or (NeISS) is a UK-based project funded by JISC.
"Network Models Governance and R&D collaboration networks" or (N.E.M.O) is a research centre whose main focus is to identify ways to create and to assess desirable network structures for typical functions; (e.g. knowledge, creation, transfer, and distribution.) This research will ultimately aid policy-makers at all political levels in improving the effectiveness and efficiency of network-based policy instruments at promoting the knowledge economy in Europe.
"Agent-based Simulations of Market and Consumer Behavior" is another research group that is funded by the Unilever Corporate Research. The current research that is being conducted is investigating the usefulness of agent-based simulations for modeling consumer behavior and to show the potential value and insights it can add to long-established marketing methods.
"New and Emergent World Models Through Individual, Evolutionary and Social Learning" or (New Ties) is a three-year project that will ultimately create a virtual society developed by agent-based simulation. The project will develop a simulated society capable of exploring the environment and developing its own image of this environment and the society through interaction. The goal of the research project is for the simulated society to exhibit individual learning, evolutionary learning and social learning.
Bruch and Mare's project on neighborhood segregation: The purpose of the study is to figure out the reasoning for neighborhood segregation based on race, and to figure out the tipping point or when people become uncomfortable with the integration levels into their neighborhood, and decide to flee from the neighborhood. They set up a model using flash cards, and put the agent's house in the middle and put houses of different races surrounding the agent's house. They asked people how comfortable they would feel with different situations; if they were okay with one situation, they asked another until the neighborhood was fully integrated. Bruch and Mare's results showed that the tipping point was at 50%. When a neighborhood became 50% minority and 50% white, people of both races began to become uncomfortable and white flight began to rise. The use of agent-based modeling showed how useful it can be in the world of sociology, people did not have to answer why they would become uncomfortable, just which situation they were uncomfortable with.
The MAELIA Program (Multi-Agent Emergent Norms Assessment) is a project dealing with the relationships between the users and managers of a natural resource, in that case water, and the related norms and laws that are to be built within them (conventions) or are imposed to them by other actors (institutions). The purpose of the project is to build a generic multiscale platform which is planned to deal with water conflict-related issues.
The Mosi-Agil project is a four-year program funded by the Autonomous Region of Madrid through the program MOSI-AGIL-CM (grant S2013/ICE-3019, co-funded by EU Structural Funds FSE and FEDER). It aims at creating a body of knowledge and practical tools which are necessary to handle more effectively the behavior of occupants of large facilities. Therefore, the project studies the development of ambient intelligence and intelligent environments supported by the use of Agent-Based Social Simulation.
Agent-based modeling is most useful in providing a bridge between micro and macro levels, which is a large part of what sociology studies. Agent-based models are most appropriate for studying processes that lack central coordination, including the emergence of institutions that, once established, impose order from the top down. The models focus on how simple and predictable local interactions generate familiar but highly detailed global patterns, such as emergence of norms and participation of collective action. Michael W. Macy and Robert Willer researched a recent survey of applications and found that there were two main problems with agent-based modeling the self-organization of social structure and the emergence of social order (Macy & Willer 2002). Below is a brief description of each problem Macy and Willer believe there to be;

"Emergent structure. In these models, agents change location or behavior in response to social influences or selection pressures. Agents may start out undifferentiated and then change location or behavior so as to avoid becoming different or isolated (or in some cases, overcrowded). Rather than producing homogeneity, however, these conformist decisions aggregate to produce global patterns of cultural differentiation, stratification, and homophilic clustering in local networks. Other studies reverse the process, starting with a heterogeneous population and ending in convergence: the coordination, diffusion, and sudden collapse of norms, conventions, innovations, and technological standards."
"Emergent social order. These studies show how egoistic adaptation can lead to successful collective action without either altruism or global (top down) imposition of control. A key finding across numerous studies is that the viability of trust, cooperation, and collective action depends decisively on the embeddedness of interaction."
These examples simply show the complexity of our environment and that agent-based models are designed to explore the minimal conditions, the simplest set of assumptions about human behavior, required for a given social phenomenon to emerge at a higher level of organization.

== Criticisms ==
Since its creation, computerized social simulation has been the target of some criticism in regard to its practicality and accuracy. Social simulation's simplification of the complex to form models from which we can better understand the latter is sometimes seen as a draw back, as using fairly simple models to simulate real life with computers is not always the best way to predict behavior.
Most of the criticism seems to be aimed at agent-based models and simulation and how they work: