Agent-Based And Individual-Based Modeling: A Pr...
In response to concerns about possible bioterrorism, the authors developed an individual-based (or "agent-based") computational model of smallpox epidemic transmission and control. The model explicitly represents an "artificial society" of individual human beings, each implemented as a distinct object, or data structure in a computer program. These agents interact locally with one another in code-represented social units such as homes, workplaces, schools, and hospitals. Over many iterations, these microinteractions generate large-scale macroscopic phenomena of fundamental interest such as the course of an epidemic in space and time. Model variables (incubation periods, clinical disease expression, contagiousness, and physical mobility) were assigned following realistic values agreed on by an advisory group of experts on smallpox. Eight response scenarios were evaluated at two epidemic scales, one being an introduction of ten smallpox cases into a 6,000-person town and the other an introduction of 500 smallpox cases into a 50,000-person town. The modeling exercise showed that contact tracing and vaccination of household, workplace, and school contacts, along with prompt reactive vaccination of hospital workers and isolation of diagnosed cases, could contain smallpox at both epidemic scales examined.
Agent-Based and Individual-Based Modeling: A Pr...
This book aims to partly address this gap by showing readers how to create individual-based models (IBMs, also known as agent-based models, or ABMs) of cultural evolution. We provide example code written in the programming language R, which has been widely adopted in the scientific community. We will go from very simple models of the basic processes of cultural evolution, such as biased transmission and cultural mutation, to more advanced topics such as the evolution of social learning, demographic effects, and social network analysis. Where possible we recreate existing models in the literature, so that readers can better understand those existing models, and perhaps even extend them to address questions of their own interest.
The desire to better understand the transmission of infectious disease in the real world has motivated the representation of epidemic diffusion in the context of quantitative simulation. In recent decades, both individual-based (such as Agent-Based) models and aggregate models (such as System Dynamics) are widely used in epidemiological modeling. This paper compares the difference between system dynamics models and agent-based models in the context of Tuberculosis (TB) transmission, considering smoking as a risk factor. The merits and impact of capturing individual heterogeneity is examined via representing Bacillus Calmette-Gurin vaccination and reactivation in both models. The simulation results of the two models exhibit distinct discrepancies in TB incidence rate and prevalence. Results also suggest that, at the level of practical application, agent-based models offer signifcantly greater accuracy and easier extension, especially when representing a decreasing reactivation rate, waning of immunity and heterogeneous individual attributes. Another experiment sought to evaluate the impact of network structure on TB diffusion. Simulations are conducted under three widely used network topologies, namely random, scale-free and small world. The results reveal large differences between results of agent-based models and system dynamics models, which further give insights into the difference between these two model types in the context of practical decision-making in healthcare.
Complex systems modeling can provide useful insights when designing and anticipating the impact of public health interventions. We developed an agent-based, or individual-based, computation model (ABM) to aid in evaluating and refining implementation of behavior change interventions designed to increase physical activity and healthy eating and reduce unnecessary weight gain among school-aged children. The potential benefits of applying an ABM approach include estimating outcomes despite data gaps, anticipating impact among different populations or scenarios, and exploring how to expand or modify an intervention. The practical challenges inherent in implementing such an approach include data resources, data availability, and the skills and knowledge of ABM among the public health obesity intervention community. The aim of this article was to provide a step-by-step guide on how to develop an ABM to evaluate multifaceted interventions on childhood obesity prevention in multiple settings. We used data from 2 obesity prevention initiatives and public-use resources. The details and goals of the interventions, overview of the model design process, and generalizability of this approach for future interventions is discussed.
When considering mathematical models, two commonly used types of implementation can be distinguished: compartmental and individual-based models (IBMs). While compartmental models simulate population counts, IBMs (also called agent-based models or micro-simulation models) keep track of the history of each individual in the population separately.
Agent-based models consist of agents that interact within an environment. Agent-based modeling has been called by various names in the broad base of its applications, which could refer to completely different methodologies. In a computing scientific domain (e.g., AI or distributed autonomous systems), agent-based modeling typically refers to a computational method and simulation for studying the actions and interactions of a set of autonomous entities. It is also called a multi-agent system (MAS) or agent-based system. In non-computing-related scientific domains (e.g., ecological science or life science), Agent-Based models usually refer to the individual-based models.
In social sciences, agent-based modeling could refer to an actor in the social world. In recent years, in agent-based social simulation (ABSS) that mimics social phenomena, the concept of autonomous agents has become well-known. Davidsson(5) classifies research areas in ABSS into social aspects of agent systems (SAAS), multi-agent-based simulation (MABS), and social simulation (SocSim). This classification depends upon different combinations of focus areas, which include agent-based computing, computer simulation, and social science. First, SAAS focuses more on social science and agent-based computing and includes the study of norms, institutions, organizations, cooperation, and competition, among others. Second, research in the intersection between computer simulation and agent-based computing is referred to as MABS and uses agent technology for simulating any phenomena other than social phenomena. Third, SocSim is in the intersection between social science and computer simulation and corresponds to the simulation of social phenomena on a computer that uses typically simple models of the simulated social entities, such as cellular automata. In transportation research and applications, which are the focus of this primer, the keyword of agent-based modeling is mostly seen referring to an individual-based model and simulation or an autonomous computing method.
Like in individual-based modeling, agent behavior is typically modeled as a collection of heuristic, context-dependent rules that are iterated over one or more discrete time steps. Foraging behavior, for example, might be modeled as a random walk over the landscape, or as a more complex search routine in which the animal assesses its environment and moves in a deliberate fashion to seek out preferred food sources. One of the challenges of agent-based modeling is the elaboration of such rules. Empirical observations of animal
There is considerable confusion in the ecosystem modeling literature concerning the difference between individual-based models (IBMs) and agent-based models. Individual-based modeling has a long tradition in ecological modeling and many authors use the terms individual-based and agent-based interchangeably. Adaptive agents are similar to individuals in IBM, with the exception that agents are provided with mechanisms by which they can adapt, learn, or evolve. In the agent literature, one generally differentiates between proactive and reactive agents. A proactive agent is motivated and goal oriented, whereas a reactive agent simply reacts to stimuli. Individuals in IBMs are usually more similar to reactive agents.
An agent-based (or individual-based) model is a computational simulation of autonomous agents that react to their environment (including other agents) given a predefined set of rules [1]. ABMs have been adopted and studied in a variety of research disciplines. One reason for their popularity is that they enable a relaxation of many simplifying assumptions usually made by mathematical models. Relaxing such assumptions of a "perfect world" can change a model's behavior [2]. 041b061a72