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.

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 the Emergent Behavior of A System-of-Systems

2009 ◽  
Vol 19 (1) ◽  
pp. 1581-1590 ◽  
Author(s):  
John C. Hsu ◽  
John R. Clymer ◽  
Jose Garcia ◽  
Efrain Gonzalez
Keyword(s):  
System Of Systems ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Agent Based

scholarly journals Agent-based modeling and simulation of emergent behavior in air transportation

2013 ◽  
Vol 1 (1) ◽  
Author(s):  
Soufiane Bouarfa ◽  
Henk AP Blom ◽  
Richard Curran ◽  
Mariken HC Everdij
Keyword(s):  
Modeling And Simulation ◽  
Air Transportation ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Agent Based

Emergent Behavior in a Population of Thermostatically Controlled Loads With Peer-to-Peer Communication

2019 ◽  
Author(s):  
Ryan Schwartz ◽  
John F. Gardner
Keyword(s):  
Demand Response ◽  
Air Conditioning ◽  
Large Scale ◽  
Large Population ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Peer Communication ◽  
Agent Based ◽  
Thermostatically Controlled Loads ◽  
Residential Air Conditioning

Abstract Thermostatically controlled loads (TCLs) are often considered as a possible resource for demand response (DR) events. However, it is well understood that coordinated control of a large population of previously un-coordinated TCLs may result in load synchronization that results in higher peaks and large uncontrolled swings in aggregate load. In this paper we use agent based modeling to simulate a number of residential air conditioning loads and allow each to communicate a limited amount of information with their nearest neighbors. As a result, we document emergent behavior of this large scale, distributed and nonlinear system. Using the techniques described here, the population of TCLs experienced up to a 30% reduction in peak demand following the DR event. This behavior is shown to be beneficial to the goals of balancing the grid and integrating increasing penetration of variable generators.

Agent-based Modeling for Financial Markets

2018 ◽  
Author(s):  
Giulia Iori ◽  
James Porter
Keyword(s):  
Financial Markets ◽  
Data Gathering ◽  
Simple Zero ◽  
Agent Based Modeling ◽  
Policy Makers ◽  
Agent Based ◽  
Macro Scale ◽  
Learning Capabilities ◽  
Active Data ◽  
Zero Intelligence

This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.

Multi-Agent Based Modeling Simulation about VANET

2013 ◽  
Vol 760-762 ◽  
pp. 680-684
Author(s):  
Shan Shan Wan ◽  
Dong Liang Wang ◽  
Qing Cao
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Self Organization ◽  
Working Mechanism ◽  
Agent Based ◽  
Modeling Simulation ◽  
Large Numbers ◽  
Multi Agent

The self-organization characteristics and the interaction between a large numbers of self-organizing vehicles are complexity, to obtain a more accurate model of vehicular Ad-hoc network (VANET) and obtain a more profound comprehension of the complex behavior working mechanism of the vehicle in the VANET environment multi-agent based and bottom-up modeling approach is proposed here. It aims to describe the dynamics of VANET caused by the different behaviors of vehicular. The simulation tool for vehicular misbehaviors is developed with multi-agent. It aims to and be able to effectively reproduce the real VANET scene. Though the multi-agent based modeling the emergent behavior and sudden existing behaviors of VANET entities are well reflected.

scholarly journals IRREGULAR DYNAMICS IN THE AGENT-BASED MIGRATION MODEL

2021 ◽  
Vol 24 (1) ◽  
pp. 34-39
Author(s):  
M.Yu. Khavinson ◽  
A.N. Kolobov
Keyword(s):  
Complex Dynamics ◽  
Agent Based Modeling ◽  
Industrial Society ◽  
Migration Behavior ◽  
Modeling Tools ◽  
Agent Based ◽  
Migration Dynamics ◽  
Post Industrial ◽  
Environmental Level ◽  
Nonlinear Fluctuations

The article is devoted to modeling the dynamics of migration at the regional level. In the context of the transition to a post-industrial society, population migration becomes more dynamic, which requires improving approaches to its forecasting and makes significant the study of personal strategies for choosing the time of migration and the host region by agents. Different strategies of agents lead to the emergence of migrant strata with dynamically changing number, unevenly distributed among the receiving regions. As a result, it can observed nonlinear fluctuations in the number of migrants, for the study of which simulation modeling tools are relevant. This research is devoted to the study of the migration processes complex dynamics by the method of agent-based modeling. The simulation is based on the assumption that a migrant, when choosing a region, follows a strategy, characteristic of his age group, which in the long end directly affects the distribution of the number of migrants of various cohorts and the total number of migrants in the region. At this, the strategy choice is determined by socio-economic characteristics of the regions: different levels of economic, social and environmental attractiveness. The authors hypothesized that different strategies of migration behavior can lead to complex migration dynamics. To test the hypothesis, the authors built a basic agent-based model of migration for three regions, which takes into account various strategies of agents' migration behavior, including the choice of a region with the highest economic, social or environmental level of attractiveness. The result of numerical experiments shows that a combination of various strategies for choosing a region with a change in the age structure of migrants leads to periodic and complex regimes of migration dynamics. The authors have found the conditions under which complex dynamics in the model occurs in the short - and medium-term periods.

scholarly journals Enabling the Analysis of Emergent Behavior in Future Electrical Distribution Systems Using Agent-Based Modeling and Simulation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Sonja Kolen ◽  
Stefan Dähling ◽  
Timo Isermann ◽  
Antonello Monti
Keyword(s):  
Modeling And Simulation ◽  
Distribution Systems ◽  
Large Scale ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
System Control ◽  
Distribution Grid ◽  
Agent Based ◽  
Electrical Distribution Systems ◽  
Electrical Distribution

In future electrical distribution systems, component heterogeneity and their cyber-physical interactions through electrical lines and communication lead to emergent system behavior. As the distribution systems represent the largest part of an energy system with respect to the number of nodes and components, large-scale studies of their emergent behavior are vital for the development of decentralized control strategies. This paper presents and evaluates DistAIX, a novel agent-based modeling and simulation tool to conduct such studies. The major novelty is a parallelization of the entire model—including the power system, communication system, control, and all interactions—using processes instead of threads. Thereby, a distribution of the simulation to multiple computing nodes with a distributed memory architecture becomes possible. This makes DistAIX scalable and allows the inclusion of as many processing units in the simulation as desired. The scalability of DistAIX is demonstrated by simulations of large-scale scenarios. Additionally, the capability of observing emergent behavior is demonstrated for an exemplary distribution grid with a large number of interacting components.

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