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 Dynamics of the HIV Epidemics among Male Injecting Drug Users Using Agent-Based Modeling

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Le TT ◽  
◽  
Lim HJ ◽  
Shojaati N ◽  
◽  
...  
Keyword(s):  
Drug Users ◽  
Complex Dynamics ◽  
Hiv Infections ◽  
Injecting Drug Users ◽  
Agent Based Modeling ◽  
Cumulative Number ◽  
Needle Sharing ◽  
Agent Based ◽  
Hiv Prevention Intervention ◽  
Disproportionate Burden

Background: Although Injecting Drug Users (IDUs) carry a disproportionate burden of HIV, little is known about the dynamics of the HIV epidemics among IDUs. Objective: This study aimed to characterize the dynamics of the HIV epidemic among IDUs and the effects of alternative HIV prevention intervention strategies using Agent-Based Modeling (ABM). Methods: ABM was constructed using key behavioral risks. The HIV/STI Surveillance study was utilized to create datasets for simulation. Different intervention scenarios were simulated and compared. Results: Lowering needle sharing level among IDUs resulted in the largest reductions in both HIV prevalence and the cumulative number of HIV infections over time in all simulated populations. The majority of the reductions occurred when needle sharing declined from the baseline level to 40% and 30%, respectively. Conclusion: ABM may well complement traditional epidemiological regression-based analysis in providing important insights into the complex dynamics of the HIV epidemics among IDUs.

Introduction to the Theme Issue on Dynamics of Health Behavior: Revisiting Systems Science for Population Health

2020 ◽  
Vol 47 (2) ◽  
pp. 185-190
Author(s):  
Jessica G. Burke ◽  
Jessica R. Thompson ◽  
Patricia L. Mabry ◽  
Christina F. Mair
Keyword(s):  
Health Behavior ◽  
Health Behaviors ◽  
Population Health ◽  
Complex Dynamics ◽  
Agent Based Modeling ◽  
Systems Science ◽  
Science Methods ◽  
Factors Affecting ◽  
Agent Based ◽  
Public Health Professionals

Systems science can help public health professionals to better understand the complex dynamics between factors affecting health behaviors and outcomes and to identify intervention opportunities. Despite their demonstrated utility in addressing health topics such influenza, tobacco control, and obesity, the associated methods continue to be underutilized by researchers and practitioners addressing health behaviors. This article discusses the growth of systems science methods (e.g., system dynamics, social network analysis, and agent-based modeling) in health research, provides a frame for the articles included in this themed issue, and closes with recommendations for enhancing the future of systems science and health behavior research. We argue that integrating systems sciences methods into health behavior research and practice is essential for improved population health and look forward to supporting the evolution of the field.

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 Research of migration behavior of the population and assessment of the level of attractiveness of territories based on the methods of agent-based modeling

2020 ◽  
Vol 12 (6) ◽  
Author(s):  
Marsel Nizamutdinov ◽  
Alsu Atnabaeva ◽  
Miliausha Akhmetzianova
Keyword(s):  
Economic Development ◽  
Russian Federation ◽  
Agent Based Modeling ◽  
Demographic Model ◽  
Migration Behavior ◽  
Agent Based ◽  
Republic Of Bashkortostan ◽  
Socio Economic Development ◽  
The Russian Federation ◽  
The Republic

Migration processes play a significant role in the socio-economic and demographic development of the Russian Federation. According to Rosstat, in more than 50 % of the constituent entities of the Russian Federation in 2019, there is a migration decline in the population. It can also be noted that the most attractive for the population are large cities with a high level of socio-economic development. In this regard, the aim of the study is to assess the factors of mutual influence of the migration behavior of the population and the socio-economic development of the territory on the basis of agent-based modeling methods. The Republic of Bashkortostan was chosen as the object of the study, as the region with the highest level of migration loss in the Volga Federal District. According to the strategy of the Republic of Bashkortostan for the period up to 2030, the Government of the Republic of Belarus faces the difficult task of making effective management decisions that will lead to an increase in the socio-economic development of the territories and, consequently, to an increase in the level of migration growth. A distinctive feature of this study is the use of agent-based modeling (to take into account the individual characteristics of agents), fuzzy logic (to form the rules of behavior for agents) and GIS maps (to take into account the geographic location). A modular scheme for the functioning of the economic-demographic model has been developed, which describes the events, the agents that participate in them, and the methods by which they were implemented. An algorithm for the implementation of the "Migration" block has been developed, which describes the logic of the migration behavior of the "Man" agent and his reaction to changes in socio-economic indicators taken into account within the framework of the "Municipality" agent. According to the results of the study, scenarios for the development of municipal districts of the Republic of Bashkortostan were substantiated on the basis of data from the Ministry of Economic Development and Investment Policy of the Republic of Bashkortostan, as well as the program "Comprehensive development of single-industry towns in the Republic of Bashkortostan".

scholarly journals An agent-based modeling framework for cybersecurity in mobile tactical networks

2017 ◽  
Vol 15 (2) ◽  
pp. 205-218 ◽  
Author(s):  
Brian Thompson ◽  
James Morris-King
Keyword(s):  
Information Dissemination ◽  
Ad Hoc ◽  
Agent Based Modeling ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Agent Based ◽  
Tactical Networks ◽  
The Impact

Mobile tactical networks facilitate communication, coordination, and information dissemination between soldiers in the field. Their increasing use provides important benefits, yet also makes them a prime enemy target. Furthermore, their dynamic, distributed, and ad-hoc nature makes them particularly vulnerable to cyber attack. Unfortunately, most existing research on cybersecurity in mobile ad-hoc networks either uses simplistic mobility models that are easier to analyze mathematically or focuses on modeling the dynamics of civilian networks. In this work, we present an agent-based modeling framework to study malware spread in mobile tactical networks. Our framework includes military-inspired models of hierarchical command structure, unit movement, communication over short-range radio, self-propagating malware, and cyber defense mechanisms. We implement several example scenarios representing military units engaged in tactical operations on a synthetic battlefield. Finally, we conduct a case study, using agent-based simulation to analyze the impact of hierarchy and cybersecurity policies on malware spread. Our results support the claim that agent-based modeling is particularly well-suited for representing the complex organizational and spatial structures inherent to military operations, and we urge others to incorporate the key elements of our framework into existing modeling tools when performing studies of cyber attacks on mobile tactical networks and corresponding cybersecurity measures.

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.

Sign in / Sign up

Export Citation Format

Share Document