[2] Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.
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.
[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.
Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional checkerboard.
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.
In the early 1980s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner.
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).
[11] 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.
Kathleen Carley was a major contributor, especially to models of social networks, obtaining National Science Foundation funding for the annual conference and serving as the first President of NAACSOS.
[citation needed] The Second World Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with George Mason University taking the lead role in local arrangements.
[18] Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at UCLA have also made significant contributions in organizational behavior and decision-making.
In one widely cited paper, agentic language models interacted in a sandbox environment to perform activities like planning birthday parties and holding elections.
The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions.
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.
For example, in a study about CD4+ T cells (a key cell type in the adaptive immune system),[28] 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).
Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics,[29] and the threat of biowarfare, biological applications including population dynamics,[30] stochastic gene expression,[31] plant-animal interactions,[32] vegetation ecology,[33] migratory ecology,[34] landscape diversity,[35] sociobiology,[36] the growth and decline of ancient civilizations, evolution of ethnocentric behavior,[37] forced displacement/migration,[38] language choice dynamics,[39] cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis,[40] the effects of ionizing radiation on mammary stem cell subpopulation dynamics,[41] inflammation,[42] [43] and the human immune system,[44] and the evolution of foraging behaviors.
[55][56] Recently, ABMs such as CovidSim by epidemiologist Neil Ferguson, have been used to inform public health (nonpharmaceutical) interventions against the spread of SARS-CoV-2.
[73] In the domain of peer-to-peer, ad hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown.
[74] The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated.
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.
[83] By modeling a complex system of analysts based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – financial markets were simulated to high accuracy.
Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities.
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.
While such approaches map well to cluster and supercomputer architectures, issues related to communication and synchronization,[103][104] as well as deployment complexity,[105] remain potential obstacles for their widespread adoption.
[100][106][107] 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.