Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems

2012 ◽  
Vol 18 (3) ◽  
pp. 356-373 ◽  
Author(s):  
Claudio Cioffi-Revilla ◽  
Kenneth De Jong ◽  
Jeffrey K. Bassett
Keyword(s):  
Evolutionary Computation ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Biologically Inspired ◽  
Agent Based ◽  
Complex Social Systems

Development of Software Framework for Large-Scale Agent-Based Modeling of Complex Social Systems

2019 ◽  
Vol 10 (4) ◽  
pp. 167-177
Author(s):  
V. L. Makarov ◽  
◽  
A. R. Bakhtizin ◽  
G. L. Beklaryan ◽  
A. S. Akopov ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Software Framework ◽  
Agent Based ◽  
Complex Social Systems

scholarly journals Dynamic calibration of agent-based models using data assimilation

2016 ◽  
Vol 3 (4) ◽  
pp. 150703 ◽  
Author(s):  
Jonathan A. Ward ◽  
Andrew J. Evans ◽  
Nicolas S. Malleson
Keyword(s):  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Social Systems ◽  
Open Data ◽  
Dynamical Behaviour ◽  
Agent Based Models ◽  
Agent Based ◽  
Simple Question ◽  
Complex Social Systems ◽  
Using Data

A widespread approach to investigating the dynamical behaviour of complex social systems is via agent-based models (ABMs). In this paper, we describe how such models can be dynamically calibrated using the ensemble Kalman filter (EnKF), a standard method of data assimilation. Our goal is twofold. First, we want to present the EnKF in a simple setting for the benefit of ABM practitioners who are unfamiliar with it. Second, we want to illustrate to data assimilation experts the value of using such methods in the context of ABMs of complex social systems and the new challenges these types of model present. We work towards these goals within the context of a simple question of practical value: how many people are there in Leeds (or any other major city) right now? We build a hierarchy of exemplar models that we use to demonstrate how to apply the EnKF and calibrate these using open data of footfall counts in Leeds.

Agent based modeling of the coevolution of hostility and pacifism

2015 ◽  
Vol 26 (09) ◽  
pp. 1550098 ◽  
Author(s):  
Fermin Dalmagro ◽  
Juan Jimenez
Keyword(s):  
Social Systems ◽  
Stable State ◽  
Agent Based Modeling ◽  
Armed Conflicts ◽  
Agent Based ◽  
Public Data ◽  
Prisoners Dilemma ◽  
Quantitative Characteristics ◽  
Global Peace ◽  
Emergent Behaviors

We propose a model based on a population of agents whose states represent either hostile or peaceful behavior. Randomly selected pairs of agents interact according to a variation of the Prisoners Dilemma game, and the probabilities that the agents behave aggressively or not are constantly updated by the model so that the agents that remain in the game are those with the highest fitness. We show that the population of agents oscillate between generalized conflict and global peace, without either reaching a stable state. We then use this model to explain some of the emergent behaviors in collective conflicts, by comparing the simulated results with empirical data obtained from social systems. In particular, using public data reports we show how the model precisely reproduces interesting quantitative characteristics of diverse types of armed conflicts, public protests, riots and strikes.

Multi-Agent Simulation in Organizations

2011 ◽  
pp. 2728-2733
Author(s):  
Nikola Vlahovic ◽  
Vlatko Ceric
Keyword(s):  
Social Systems ◽  
Real Life ◽  
Agent Based Modeling ◽  
Multi Agent Systems ◽  
Social Constructs ◽  
Social Phenomena ◽  
Agent Based ◽  
Agent Systems ◽  
Agent Simulation ◽  
Multi Agent

Most economic and business systems are complex, dynamic, and nondeterministic systems. Different modeling techniques have been used for representing real life economic and business organizations either on a macro level (such as national economics) or micro level (such as business processes within a firm or strategies within an industry). Even though general computer simulation was used for modeling various systems (Zeigler, 1976) since the 1970s the limitation of computer resources did not allow for in-depth simulation of dynamic social phenomena. The dynamics of social systems and impact of the behavior of individual entities in social constructs were modeled using mathematical modeling or system dynamics. With the growing interest in multi agent systems that led to its standardization in the 1990s, multi agent systems were proposed for the use of modeling social systems (Gilbert & Conte, 1995). Multi agent simulation was able to provide a high level disintegration of the models and proper treatment of inhomogeneity and individualism of the agents, thus allowing for simulation of cooperation and competition. A number of simulation models were developed in the research of biological and ecological systems, such as models for testing the behavior and communication between social insects (bees and ants). Artificial systems for testing hypothesis about social order and norms, as well as ancient societies (Kohler, Gumerman, & Reynolds, 2005) were also simulated. Since then, agent-based modeling and simulation (ABMS) established itself as an attractive modeling technique (Klugl, 2001; Moss & Davidsson, 2001). Numerous software toolkits were released, such as Swarm, Repast, MASON and SeSAm. These toolkits make agent-based modeling easy enough to be attractive to practitioners from a variety of subject areas dealing with social interactions. They make agent-based modeling accessible to a large number of analysts with less programming experience.

Afterword: Towards a Complex Science of History

2015 ◽  
Author(s):  
Nathan Coombs
Keyword(s):  
Complexity Theory ◽  
Social Systems ◽  
Political Strategy ◽  
Agent Based ◽  
Empirical Verification ◽  
Agent Based Modelling ◽  
Science Of History ◽  
Complex Science ◽  
Complex Social Systems

This chapter begins by taking stock of the first two parts of the book. It argues that although classical Marxism cannot think events discontinuously, its science of history can at least be subjected to empirical verification. By contrast, while post-Althusserian theory succeeds in thinking events radically it does so on the basis of a self-referential rationalism that grants authority to theorists and is resistant to empirical control. To go beyond these philosophical traditions, the afterword suggests that complexity theory and ‘weak’ notions of emergence provide a way forward. Agent-based modelling of complex social systems offers a mediation of necessity and contingency that could help orient political strategy.

scholarly journals Advances in the agent-based modeling of economic and social behavior

2021 ◽  
Vol 1 (7) ◽  
Author(s):  
Mitja Steinbacher ◽  
Matthias Raddant ◽  
Fariba Karimi ◽  
Eva Camacho Cuena ◽  
Simone Alfarano ◽  
...  
Keyword(s):  
Social Behavior ◽  
Laboratory Experiments ◽  
State Of The Art ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Data Sources ◽  
Future Research ◽  
Economic Networks ◽  
Agent Based ◽  
Agent Behavior

AbstractIn this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.

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