Agent-based model
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An agent-based model (ABM) is a
Agent-based models are a kind of microscale model[3] that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence, which some express as "the whole is greater than the sum of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems. Or, macro-scale state changes emerge from micro-scale agent behaviors. Or, simple behaviors (meaning rules followed by agents) generate complex behaviors (meaning state changes at the whole system level).
Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status,[4] using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.[5]
Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) an environment. ABMs are typically implemented as computer simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.
History
The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.
Early developments
The history of the agent-based model can be traced back to the
The Simula programming language, developed in the mid 1960s and widely implemented by the early 1970s, was the first framework for automating step-by-step agent simulations.
1970s and 1980s: the first models
One of the earliest agent-based models in concept was Thomas Schelling's segregation model,[6] which was discussed in his paper "Dynamic Models of Segregation" in 1971. Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome.
In the late 1970s, Paulien Hogeweg and Bruce Hesper began experimenting with individual models of ecology. One of their first results was to show that the social structure of bumble-bee colonies emerged as a result of simple rules that govern the behaviour of individual bees.[7] They introduced the ToDo principle, referring to the way agents "do what there is to do" at any given time.
In the early 1980s,
The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory",[9] based on an earlier conference presentation of theirs. A stronger and earlier candidate is Allan Newell, who in the first Presidential Address of AAAI (published as The Knowledge Level[10]) discussed intelligent agents as a concept.
At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory (CMOT). This field grew as a special interest group of The Institute of Management Sciences (TIMS) and its sister society, the Operations Research Society of America (ORSA).[11]
1990s: expansion
The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by Joshua M. Epstein and Robert Axtell to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.[12] Other notable 1990s developments included Carnegie Mellon University's Kathleen Carley ABM,[13] to explore the co-evolution of social networks and culture. The Santa Fe Institute (SFI) was important in encouraging the development of the ABM modeling platform Swarm under the leadership of Christopher Langton. Research conducted through SFI allowed the expansion of ABM techniques to a number of fields including study of the social and spatial dynamics of small-scale human societies and primates.[11] During this 1990s timeframe Nigel Gilbert published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established a journal from the perspective of social sciences: the Journal of Artificial Societies and Social Simulation (JASSS). Other than JASSS, agent-based models of any discipline are within scope of SpringerOpen journal Complex Adaptive Systems Modeling (CASM).[14]
Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems (CASOS)—incorporated more and more agent-based modeling. Samuelson (2000) is a good brief overview of the early history,[15] and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.[16][17]
In the late 1990s, the merger of TIMS and ORSA to form
2000s and later
More recently,
Theory
Most computational modeling research describes systems in 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 algorithmic 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. 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,
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.[20][21][22] 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[23] and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.[24]
The role of the environment where agents live, both macro and micro,[25] 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.[26]
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),[27] 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.
Applications
In biology
Agent-based modeling has been used extensively in biology, including the analysis of the spread of
In epidemiology
Agent-based models now complement traditional
Program | Year | Citation | Description |
---|---|---|---|
Covasim | 2021 | [61] | SEIR model implemented in Python with an emphasis on features for studying the effects of interventions. |
OpenABM-Covid19 | 2021 | [62] | Epidemic model of the spread of COVID-19, simulating every individual in a population with both R and Python interfaces but using C for heavy computation. |
OpenCOVID | 2021 | [63][64] | An individual-based transmission model of SARS-CoV-2 infection and COVID-19 disease dynamics, developed at the Swiss Tropical and Public Health Institute. |
In business, technology and network theory
Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include
Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science domain (journals versus conferences).
Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.[75]
In team science
In the realm of team science, agent-based modeling has been utilized to assess the effects of team members' characteristics and biases on team performance across various settings.[76] By simulating interactions between agents—each representing individual team members with distinct traits and biases—this modeling approach enables researchers to explore how these factors collectively influence the dynamics and outcomes of team performance. Consequently, agent-based modeling provides a nuanced understanding of team science, facilitating a deeper exploration of the subtleties and variabilities inherent in team-based collaborations.
In economics and social sciences
Prior to, and in the wake of the
ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment[86] and the examination of public policy applications to land-use.[87] There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network.[88] Heterogeneity and dynamics can be easily built in ABM models to address wealth inequality and social mobility.[89]
In water management
ABMs have also been applied in water resources planning and management, particularly for exploring, simulating, and predicting the performance of infrastructure design and policy decisions,[90] and in assessing the value of cooperation and information exchange in large water resources systems.[91]
Organizational ABM: agent-directed simulation
The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems."[92] Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others. Agents for Systems are divided in two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analyses).
Self-driving cars
Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents.[93] Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars.[94][95] It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior. The basic idea of using agent-based modeling to understand self-driving cars was discussed as early as 2003.[96]
Implementation
Many ABM frameworks are designed for serial von-Neumann computer architectures, limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size,[97] scalability restrictions may hinder model validation.[98] Such limitations have mainly been addressed using distributed computing, with frameworks such as Repast HPC[99] specifically dedicated to these type of implementations. While such approaches map well to cluster and supercomputer architectures, issues related to communication and synchronization,[100][101] as well as deployment complexity,[102] remain potential obstacles for their widespread adoption.
A recent development is the use of data-parallel algorithms on Graphics Processing Units
The extreme memory bandwidth combined with the sheer number crunching power of multi-processor GPUs has enabled simulation of millions of agents at tens of frames per second.Integration with other modeling forms
Since Agent-Based Modeling is more of a modeling framework than a particular piece of software or platform, it has often been used in conjunction with other modeling forms. For instance, agent-based models have also been combined with
Verification and validation
Verification and validation (V&V) of simulation models is extremely important.[107][108] Verification involves making sure the implemented model matches the conceptual model, whereas validation ensures that the implemented model has some relationship to the real-world. Face validation, sensitivity analysis, calibration, and statistical validation are different aspects of validation.[109] A discrete-event simulation framework approach for the validation of agent-based systems has been proposed.[110] A comprehensive resource on empirical validation of agent-based models can be found here.[111]
As an example of V&V technique, consider VOMAS (virtual overlay multi-agent system),[112] a software engineering based approach, where a virtual overlay multi-agent system is developed alongside the agent-based model. Muazi et al. also provide an example of using VOMAS for verification and validation of a forest fire simulation model.[113][114] Another software engineering method, i.e. Test-Driven Development has been adapted to for agent-based model validation.[115] This approach has another advantage that allows an automatic validation using unit test tools.
See also
- Agent-based computational economics
- Agent-based model in biology
- Agent-based social simulation (ABSS)
- Artificial society
- Boids
- Comparison of agent-based modeling software
- Complex system
- Complex adaptive system
- Computational sociology
- Conway's Game of Life
- Dynamic network analysis
- Emergence
- Evolutionary algorithm
- Flocking
- Internet bot
- Kinetic exchange models of markets
- Multi-agent system
- Simulated reality
- Social complexity
- Social simulation
- Sociophysics
- Software agent
- Swarming behaviour
- Web-based simulation
- TOTREP
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External links
Articles/general information
- Agent-based models of social networks, java applets.
- On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences
- Introduction to Agent-based Modeling and Simulation. Argonne National Laboratory, November 29, 2006.
- Agent-based models in Ecology – Using computer models as theoretical tools to analyze complex ecological systems[permanent dead link]
- Network for Computational Modeling in the Social and Ecological Sciences' Agent Based Modeling FAQ
- Multiagent Information Systems – Article on the convergence of SOA, BPM and Multi-Agent Technology in the domain of the Enterprise Information Systems. Jose Manuel Gomez Alvarez, Artificial Intelligence, Technical University of Madrid – 2006
- Artificial Life Framework
- Article providing methodology for moving real world human behaviors into a simulation model where agent behaviors are represented
- Agent-based Modeling Resources, an information hub for modelers, methods, and philosophy for agent-based modeling
- An Agent-Based Model of the Flash Crash of May 6, 2010, with Policy Implications, Tommi A. Vuorenmaa (Valo Research and Trading), Liang Wang (University of Helsinki - Department of Computer Science), October, 2013
Simulation models
- Multi-agent Meeting Scheduling System Model by Qasim Siddique
- Multi-firm market simulation by Valentino Piana
- List of COVID-19 simulation models