Agent-based models (ABMs) are increasingly recognized as valuable tools in modelling human environmental systems, but challenges and critics remain. One pressing challenge in the era of "Big Data" and given the flexibility of representation afforded by ABMs, is identifying the appropriate level of...
Agent-based models (ABMs) simulate individual agents and their interactions, enabling the study of complex phenomena in biological systems. These models are widely used to understand processes such as cell migration, molecular dynamics, ecological interactions, and infectious disease spread. However, AB...
The human dimension is one major source of uncertainty in the management of social-ecological systems such as fisheries. Agent-based models (ABMs) can help to reduce these uncertainties by making it possible to model and simulate human behavior. To understand how ABMs can be applied in fisherie...
Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, a significant drawback of ABMs is their inability to estimate agent-specific (or “micro”) variables, which hinders their ability to make accurate predictions ...
This chapter introduces agent-based models (ABMs). These are computational semi-realistic models where every important part of the system is explicitly represented. ABMs can be very valuable in biological modeling because they can represent very complicated systems that cannot be represented using, fo...
Agent-based models (ABMs) offer promise to fill this role, and in this study a new approach to agent-based modeling is introduced to simulate the behavior and interactions of the parties participating in a conflict scenario, which is modeled as a game. To develop this framework, we ...
In the study of emergent behaviour in complex adaptive systems, Agent-based Modelling & Simulation (ABMS) is being used in many different domains such as healthcare, energy, evacuation, commerce, manufacturing and defense. This collection of articles presents a convenient introduction to ABMS with...
Agent-based models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but...
The agent-based models manage objects called agents, which are equipped with certain “intelligence.” They can take decisions, optimize their actions, and interact with each other and with the environment. Agent-based models (ABMs) are a type of microscale models that simulate the simultaneous ...
Agent-based models (ABMs)/multi-agent systems (MASs) are one of the most widely used modeling-simulation-analysis approaches for understanding the dynamical behavior of complex systems. These models can be often characterized by several parameters with nonlinear interactions which together determine the...