Agent-based Modeling

2021 ◽  
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Urban Systems ◽  
Dynamic Processes ◽  
Modeling Tools ◽  
Individual Agent ◽  
Bottom Up ◽  
Agent Based

Agent-based modeling (ABM) has become widely accepted as a methodological tool to model and simulate dynamic processes of geographical phenomena. A growing number of ABM studies across a variety of domains and disciplines is partially explained by the development of agent-modeling tools and platforms, the availability of micro-data, and the advancement in computer technology and cyberinfrastructure. In addition to these technical reasons, another key motivation underlying ABM research is to address challenges embedded in conventional modeling approaches being relatively coarse, aggregate, static, normative, and inflexible across scales with a reductionist viewpoint (Batty 2005 cited under Application: Urban Systems.” With complexity science, including complex systems, complex adaptive systems, and artificial life, providing theoretical foundations and rationales, ABM is a computational methodology for simulating dynamic processes of nature and human systems driven by disaggregated, heterogeneous, and autonomous entities, i.e., agents, that interact among themselves and their environments. A key fundamental concept of the ABM framework is that a system emerges from the dynamic individual-level interactions from bottom-up, where the simulated outcome is more than the sum of its components. This bottom-up approach enables ABM to exhibit complex system dynamics, properties of which could include feedback effect, path-dependence, phase shift, non-linearity, adaptation, self-organization, tipping points, and emergence. Three key components of ABM are agents, their environment, and their decision rules. Agents are the crucial component in ABM where each individual agent has its own characteristics and agenda, assesses its surrounded situation, and makes decisions. Agents reside in an environment, which can represent a geographic space in case for spatially explicit agent-based models. Agents’ behavioral decisions and interactions within their environment are defined based on a set of rules, which can alter their status and location over time. The purpose of ABM research can be classified into theoretical exploration and empirical investigation as well as the combination of two. In the latter case, ABM can be used as an artificial laboratory experiment to explore what-if scenarios and to investigate how changes in agents, environments, and/or rules affect the macro-level outcomes. ABM has been applied to represent a wide variety of geographic processes and behaviors including but not limited to urban system, land-use/land-cover change, ecology, transportation, animal/human movement, behavioral geography, spatial cognition, transportation, and disease epidemiology. While the growing interest in ABM as a modeling methodology to simulate complex systems is remarkable, there exist various conceptual, methodological, and technical challenges.

Agent-Based Modeling

2017 ◽  
pp. 36-51
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complexity Science ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Linear Behavior ◽  
Complex Adaptive ◽  
Economic Markets ◽  
The Common ◽  
Policy Problems

Agent based modeling is one of many tools, from the complexity sciences, available to investigate complex policy problems. Complexity science investigates the non-linear behavior of complex adaptive systems. Complex adaptive systems can be found across a broad spectrum of the natural and human created world. Examples of complex adaptive systems include various ecosystems, economic markets, immune response, and most importantly for this research, human social organization and competition / cooperation. The common thread among these types of systems is that they do not behave in a mechanistic way which has led to problems in utilizing traditional methods for studying them. Complex adaptive systems do not follow the Newtonian paradigm of systems that behave like a clock works whereby understanding the workings of each of the parts provides an understanding of the whole. By understanding the workings of the parts and a few external rules, predictions can be made about the behavior of the system as a whole under varying circumstances. Such systems are labeled deterministic (Zimmerman, Lindberg, & Plsek, 1998).

Spatial Units as Agents: Making the Landscape an Equal Player in Agent-Based Simulations

2002 ◽  
Author(s):  
Paul Box
Keyword(s):  
Cell Structure ◽  
Natural Extension ◽  
Data Gathering ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Modeling Tools ◽  
Bottom Up ◽  
Agent Based ◽  
Landscape Processes ◽  
Dynamic Landscape

Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the availability of software packages, such as Swarm and StarLogo, that allow for relatively complex simulations to be constructed by researchers with limited computer-programming backgrounds. A typical use of agent-based models is to simulate scenarios where large numbers of individuals are inhabiting a landscape, interacting with their landscape and each other by relatively simple rules, and observing the emergent behavior of the system (population) over time. It has been a natural extension in this sort of a study to create a landscape from a “real world” example, typically imported through a geographic information system (GIS). In most cases, the landscape is represented either as a static object, or a “stage” upon which the agents act (see Briggs et al. , Girnblett et al., and Remm). In some cases, an approximation of a dynamic landscape has been added to the simulation in a way that is completely exogenous to the population being simulated; the dynamic conditions are read from historical records, in effect “playing a tape” of conditions, to which the population reacts through time (such as Dean et al. and Kohler et al. ). There has also been many simulations where dynamic landscape processes have been modeled through “bottom up” processes, where localized processes in landscapes are simulated, and the global emergent processes are observed. Topmodel is a Fortran-based implementation of this concept for hydrologic processes; and PCRaster has used similar software constructs to simulate a variety of landscape processes, with sophisticated visualization and data-gathering tools. In both of these examples, the landscape is represented as a regular lattice or cell structure. There are also many examples of “home grown” tools (simulations created for a specific project), applying cellular automata (CA) rules to landscapes to simulate urban growth, wildfire , lava flows, and groundwater flow. There are also examples of how agent-based modeling tools were employed to model dynamic landscape processes such as forest dynamics, i.e., Arborgames. In these models the landscape was the object of the simulation, and free-roaming agents were not considered as part of the model.

scholarly journals Emergence of a Snake-Like Structure in Mobile Distributed Agents: An Exploratory Agent-Based Modeling Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Muaz A. Niazi
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Dynamic Structure ◽  
Agent Based Modeling ◽  
The Body ◽  
Agent Based ◽  
Practical Applications ◽  
Computer Animations ◽  
Computationally Intensive ◽  
Self Organizing

The body structure of snakes is composed of numerous natural components thereby making it resilient, flexible, adaptive, and dynamic. In contrast, current computer animations as well as physical implementations of snake-like autonomous structures are typically designed to use either a single or a relatively smaller number of components. As a result, not only these artificial structures are constrained by the dimensions of the constituent components but often also require relatively more computationally intensive algorithms to model and animate. Still, these animations often lack life-like resilience and adaptation. This paper presents a solution to the problem of modeling snake-like structures by proposing an agent-based, self-organizing algorithm resulting in an emergent and surprisingly resilient dynamic structure involving a minimal of interagent communication. Extensive simulation experiments demonstrate the effectiveness as well as resilience of the proposed approach. The ideas originating from the proposed algorithm can not only be used for developing self-organizing animations but can also have practical applications such as in the form of complex, autonomous, evolvable robots with self-organizing, mobile components with minimal individual computational capabilities. The work also demonstrates the utility of exploratory agent-based modeling (EABM) in the engineering of artificial life-like complex adaptive systems.

scholarly journals A Novel Formal Agent-Based Simulation Modeling Framework of an AIDS Complex Adaptive System

2013 ◽  
Vol 5 (3) ◽  
pp. 33-53 ◽  
Author(s):  
Amnah Siddiqa ◽  
Muaz Niazi
Keyword(s):  
Complex Adaptive Systems ◽  
Formal Specification ◽  
Simulation Modeling ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Modeling Framework ◽  
Agent Based ◽  
Complex Adaptive

HIV/AIDS spread depends upon complex patterns of interaction among various subsets emerging at population level. This added complexity makes it difficult to study and model AIDS and its dynamics. AIDS is therefore a natural candidate to be modeled using agent-based modeling, a paradigm well-known for modeling Complex Adaptive Systems (CAS). While agent-based models are well-known to effectively model CAS, often times models can tend to be ambiguous and using only using text-based specifications (such as ODD) making models difficult to be replicated. Previous work has shown how formal specification may be used in conjunction with agent-based modeling to develop models of various CAS. However, to the best of the authors’ knowledge, no such model has been developed in conjunction with AIDS. In this paper, we present a Formal Agent-Based Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS. FABS-AIDS employs the use of a formal specification model in conjunction with an agent-based model to reduce ambiguity as well as improve clarity in the model definition. The proposed model demonstrates the effectiveness of using formal specification in conjunction with agent-based simulation for developing models of CAS in general and, social network-based agent-based models, in particular.

scholarly journals Socio-hydrology from the bottom up: A template for agent-based modeling in irrigation systems

2017 ◽  
Author(s):  
Dimitrios Bouziotas ◽  
Maurits Ertsen
Keyword(s):  
Irrigation Management ◽  
Water System ◽  
Agent Based Modeling ◽  
Proof Of Concept ◽  
Management Game ◽  
Irrigation Systems ◽  
Individual Agent ◽  
Bottom Up ◽  
Agent Based ◽  
Agent Behavior

Abstract. Based on a review of key concepts in agent-based modeling for irrigation systems and coupled human-water systems in general, this study presents a proof of concept of an agent-based model based on the existing Irrigation Management Game. After the modeling philosophy and main characteristics are outlined, a number of pilot applications are presented and evaluated. Following the evaluation of the results, future steps that could be incorporated in the model are discussed. The proposed template offers a bottom-up approach to socio-hydrological modelling design, as individual agent behavior explicitly co-shapes the response of the water system, which allows the discovery of emergent dynamics and the conditions under which these are produced. The concepts explained and modeled at a proof-of-concept level in this work serve as a call to the socio-hydrological community to expand its modeling efforts to the agent level.

Agent-Based Modeling and Simulation

AI Magazine ◽  
2012 ◽  
Vol 33 (3) ◽  
pp. 29 ◽  
Author(s):  
Franziska Klügl ◽  
Ana L. C. Bazzan
Keyword(s):  
Complex Systems ◽  
Modeling And Simulation ◽  
Basic Concept ◽  
Agent Based Modeling ◽  
Bottom Up ◽  
Agent Based ◽  
Future Challenges

This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.

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