Applications
in biology
Agent-based modeling has been widely used in biology, including analysis of the spread of epidemics,[35] and the threat of biological warfare, biological applications") including population dynamics,[36] stochastic gene expression,[37] plant-animal interactions,[38] vegetation ecology,[39] migratory ecology,[40] landscape diversity,[41] sociobiology,[42] growth and decline of ancient civilizations, evolution of ethnocentric behavior,[43] forced displacement/migration,[44] dynamics of language choice,[45] cognitive modeling, and biomedical applications including 3D modeling of breast tissue formation/morphogenesis,[46] the effects of ionizing radiation on mammary stem cell subpopulation dynamics,[47] inflammation,[48]
[49]
and the human immune system"),[50] and the evolution of foraging behaviors.[51] Agent-based models have also been used to develop decision support systems such as for breast cancer.[52] Agent-based models are increasingly used to model drug systems in early phase and preclinical research to aid in drug development and gain insights into biological systems that would not be possible a priori.[53] Military applications have also been evaluated.[54] In addition, agent-based models have recently been used to study biological systems at the molecular level.[55][56][57] Agent-based models have also been written to describe ecological processes acting in ancient systems, such as those in dinosaur environments and other more recent ancient systems.[58][59][60].
in epidemiology
Agent-based models now complement traditional compartmental models, which are the most common type of epidemiological models. MBAs have been shown to be superior to compartmental models in terms of prediction accuracy.[61][62] Recently, MBAs such as CovidSim") by epidemiologist Neil Ferguson&action=edit&redlink=1 "Neil Ferguson (epidemiologist) (not yet written)"), have been used to inform public health (non-pharmacological) interventions against the spread of SARS-CoV-2.[63] Epidemiological MBAs have been criticized for simplification. and use unrealistic assumptions.[64][65] Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases where MBAs are accurately calibrated.[66] MBAs for such simulations are primarily based on synthetic populations"), as actual population data are not always available.[67].
In business, technology and network theory
Agent-based models have been used since the mid-1990s to solve various business and technology problems. Some examples of applications are marketing,[72] organizational behavior and cognition,[73] teamwork,[74][75] supply chain optimization") and logistics, modeling consumer behavior, including word of mouth, the effects of social networks, distributed computing, workforce management") and portfolio management").[76].
Recently, agent-based modeling and simulation have been applied to various areas, such as studying the impact of researchers' publication forums in the field of computing (journals versus conferences).[77] In addition, MBAs have been used to simulate information delivery in environmental care environments.[78] A November 2016 article on arXiv analyzed an agent-based simulation of the diffusion of posts on Facebook.[79] In the networking domain peer-to-peer, ad hoc, and other complex, self-organizing networks, the utility of agent-based modeling and simulation has been demonstrated.[80] Recently, the use of a formal computer-based specification framework has been demonstrated, along with wireless sensor networks and agent-based simulation.[81]
Agent-based search or evolutionary algorithm is a new research topic to solve complex optimization problems.[82].
In team science
In the field of team science, agent-based modeling has been used to evaluate the effects of team member characteristics and biases on team performance in various settings.[83] By simulating interactions between agents—each representing individual team members with distinct traits and biases—this modeling approach allows researchers to explore how these factors collectively influence team performance dynamics and outcomes. Consequently, agent-based modeling provides a nuanced understanding of team science, facilitating a deeper exploration of the subtleties and variabilities inherent to team-based collaborations.
In economics and social sciences
Before and after the 2008 financial crisis, interest in MBAs as potential tools for economic analysis has grown.[84][85] MBAs do not assume that the economy can reach equilibrium, and "representative agents" are replaced by agents with diverse, dynamic, and interdependent behavior, including herding behavior. MBAs take a “bottom-up approach” and can generate extremely complex and volatile simulated economies. MBAs can represent unstable systems with busts and booms that develop from non-linear (disproportionate) responses to proportionally small changes.[86] A July 2010 article in The Economist examined MBAs as alternatives to EGDE models). and other economic complexities than standard models[87] along with an essay by J. Doyne Farmer") and Duncan Foley that argued that MBAs could satisfy both Keynes's desire to represent a complex economy and Robert Lucas's desire to build models based on microfoundations. Farmer and Foley noted the progress that has been made using MBAs to model parts of an economy, but argued for the creation of a very large model that incorporates low-level models. level.[89] Simulating a complex system of financial analysts") based on three different behavioral profiles - imitator, anti-imitator and indifferent - financial markets were simulated with great precision. The results showed a correlation between the morphology of the network and the stock index.[90] However, the MBA approach has been criticized for its lack of robustness between models, where similar models can give very different results.[91][92].
MBAs have been deployed in architecture and urban planning to evaluate the design and simulate the flow of pedestrians in the urban environment[93] and the examination of public policy applications to land use.[94] There is also a growing field of socioeconomic analysis of the impact of infrastructure investment using the ability of MBAs to discern systemic impacts across a socioeconomic network.[95] Heterogeneity and dynamics can be easily integrated into MBA models to address wealth inequality and social mobility.[96].
MBAs have also been proposed as applied educational tools for diplomats in the field of international relations[97] and for national and international policymakers to improve their evaluation of public policies.[98].
In water management
MBAs have also been applied in water resources planning and management, particularly to explore, simulate and predict the performance of infrastructure design and policy decisions,[99] and to evaluate the value of cooperation and information sharing in large water resources systems.[100].
Organizational MBA: Agent-led simulation
The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely, "Systems for agents" and "Agents for systems".[101] Agent systems (sometimes called agent systems) are systems that implement agents for use in engineering, human dynamics and social dynamics, military and other applications. Agent systems for systems are divided into two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or improving cognitive abilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analysis).
autonomous cars
Hallerbach et al. analyzed the application of agent-based approaches to the development and validation of automated driving systems through a digital twin of the tested vehicle and microscopic traffic simulation based on independent agents.[102] Waymo has created a Carcraft multi-agent simulation environment to test algorithms for autonomous cars.[103][104] It simulates traffic interactions between human drivers, pedestrians, and automated vehicles. People's behavior is imitated by artificial agents based on data from real human behavior. The basic idea of using agent-based modeling to understand self-driving cars was discussed as early as 2003.[105].