Editing Agent-based model (section)

==Theory==
Most computational modeling research describes systems in [[Steady state|equilibrium]] or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior.  The three ideas central to agent-based models are agents as objects, [[emergence]], and [[complexity]].

Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity.  Typically agents are
[[situated]] in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in [[algorithm]]ic form in computer programs.  In some cases, though not always, the agents may be considered as intelligent and purposeful.  In ecological ABM (often referred to as "individual-based models" in ecology), agents may, for example, be trees in a forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water).
The modeling process is best described as [[inductive reasoning|inductive]]. The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions. Sometimes that result is an equilibrium. Sometimes it is an emergent pattern. Sometimes, however, it is an unintelligible mangle.

In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Agent-based models can explain the emergence of higher-order patterns—network structures of terrorist organizations and the Internet, [[power-law distributions]] in the sizes of traffic jams, wars, and stock-market crashes, and social segregation that persists despite populations of tolerant people. Agent-based models also can be used to identify lever points, defined as moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency.

Rather than focusing on stable states, many models consider a system's robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions.

===Framework===
Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models.<ref>{{cite journal |author=Aditya Kurve |author2=Khashayar Kotobi |author3=George Kesidis |title=An agent-based framework for performance modeling of an optimistic parallel discrete event simulator |journal=Complex Adaptive Systems Modeling |volume=1 |pages=12 |doi=10.1186/2194-3206-1-12 |year=2013 |doi-access=free }}</ref><ref>{{cite journal |first=Muaz A. K. |last=Niazi |title=Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems |hdl=1893/3365 |date=2011-06-30 }} (PhD Thesis)</ref><ref>Niazi, M.A. and Hussain, A (2012), Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods [https://www.springer.com/biomed/neuroscience/book/978-94-007-3851-5 Cognitive Agent-based Computing] {{Webarchive|url=https://web.archive.org/web/20121224084838/http://www.springer.com/biomed/neuroscience/book/978-94-007-3851-5 |date=December 24, 2012 }}</ref> describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:
# Complex Network Modeling Level for developing models using interaction data of various system components.
# Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers.
# Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines.
# Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner.
Other methods of describing agent-based models include code templates<ref>{{cite web |title=Swarm code templates for model comparison |url=http://www.swarm.org/index.php/Software_templates |publisher=[[Swarm Development Group]] |archive-url=https://web.archive.org/web/20080803125909/http://www.swarm.org/index.php/Software_templates |archive-date=August 3, 2008 |url-status=dead}}</ref> and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.<ref>{{cite journal |author1=Volker Grimm |author2=Uta Berger |author3=Finn Bastiansen |author4=Sigrunn Eliassen |author5=Vincent Ginot |author6=Jarl Giske |author7=John Goss-Custard |author8=Tamara Grand |author9=Simone K. Heinz |author10=Geir Huse |author11=Andreas Huth |author12=Jane U. Jepsen |author13=Christian Jørgensen |author14=Wolf M. Mooij |author15=Birgit Müller |author16=Guy Pe'er |author17=Cyril Piou |author18=Steven F. Railsback |author19=Andrew M. Robbins |author20=Martha M. Robbins |author21=Eva Rossmanith |author22=Nadja Rüger |author23=Espen Strand |author24=Sami Souissi |author25=Richard A. Stillman |author26=Rune Vabø |author27=Ute Visser |author28=Donald L. DeAngelis |display-authors=3 |title=A standard protocol for describing individual-based and agent-based models |journal=Ecological Modelling |volume=198 |issue=1–2 |date=September 15, 2006 |pages=115–126 |doi=10.1016/j.ecolmodel.2006.04.023 |bibcode=2006EcMod.198..115G |s2cid=11194736 }} (ODD Paper)</ref>

The role of the environment where agents live, both macro and micro,<ref>Ch'ng, E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, November 20–24, 2012, Kobe, Japan. [http://complexity.io/Publications/chng-MacroMicroEnv.pdf Macro and Micro Environment] {{Webarchive|url=https://web.archive.org/web/20131113173313/http://complexity.io/Publications/chng-MacroMicroEnv.pdf |date=November 13, 2013 }}</ref> is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generate diversity of behavior.<ref>Simon, Herbert A. The sciences of the artificial. MIT press, 1996.</ref>

===Multi-scale modelling===
One strength of agent-based modelling is its ability to mediate information flow between scales. When additional details about an agent are needed, a researcher can integrate it with models describing the extra details. When one is interested in the emergent behaviours demonstrated by the agent population, they can combine the agent-based model with a continuum model describing population dynamics. For example, in a study about CD4+ T cells (a key cell type in the adaptive immune system),<ref>{{Cite journal|last1=Wertheim|first1=Kenneth Y.|last2=Puniy|first2=Bhanwar Lal|last3=Fleur|first3=Alyssa La|last4=Shah|first4=Ab Rauf|last5=Barberis|first5=Matteo|last6=Helikar|first6=Tomáš|date=2021-08-03|title=A multi-approach and multi-scale platform to model CD4+ T cells responding to infections|journal=PLOS Computational Biology|language=en|volume=17|issue=8|pages=e1009209|doi=10.1371/journal.pcbi.1009209|pmid=34343169|pmc=8376204|bibcode=2021PLSCB..17E9209W|issn=1553-7358 |doi-access=free }}</ref> the researchers modelled biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). In the resulting modular model, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models, cell population dynamics are described by an agent-based model, and systemic cytokine concentrations by ordinary differential equations. In this multi-scale model, the agent-based model occupies the central place and orchestrates every stream of information flow between scales.