Potential Of Agent-based Modeling Research Articles

Development of a radiological emergency evacuation model using agent-based modeling

In order to mitigate the damage caused by accidents in nuclear power plants (NPPs), evacuation strategies are usually managed on the basis of off-site effects such as the diffusion of radioactive materials and evacuee traffic simulations. However, the interactive behavior between evacuees and the accident environment has a significant effect on the consequential gap. Agent-based modeling (ABM) is a method that can control and observe such interactions by establishing agents (i.e., the evacuees) and patches (i.e., the accident environments). In this paper, a radiological emergency evacuation model is constructed to realistically check the effectiveness of an evacuation strategy using NetLogo, an ABM toolbox. Geographic layers such as radiation sources, roads, buildings, and shelters were downloaded from an official geographic information system (GIS) of Korea, and were modified into respective patches. The dispersion model adopted from the puff equation was also modified to fit the patches on the geographic layer. The evacuees were defined as vehicle agents and a traffic model was implemented by combining the shortest path search (determined by an A ∗ algorithm) and a traffic flow model incorporated in the Nagel-Schreckenberg cellular automata model. To evaluate the radiological harm to the evacuees due to the spread of radioactive materials, a simple exposure model was established to calculate the overlap fraction between the agents and the dispersion patches. This paper aims to demonstrate that the potential of ABM can handle disaster evacuation strategies more realistically than previous approaches.

Agent-based Simulation of Revenue Policy

The main research attention is focused on social interaction as a complex system of give-and-take actions with different ways to balance costs and rewards and on the problem of increasing the income inequality. The current investigation analyzes the influence of tax-and-transfer policy on the social structure of society. Different tax regimes were tested by means of computer experiments. The mathematical model of conflict interaction (Koshmanenko, 2014) is the theoretical basis of the agent-based simulation in NetLogo. It is shown that intervention into the process of conflict interactions aimed in support of those who are at the social “bottom” results in the emergence of fluctuations. Even a small value of external replenishment destroys the stable mechanism of internal redistribution and leads to an unpredictable chaotic behaviour. Moreover, the amplitude of these fluctuations is growing rapidly as the scope of assistance increases. The current research tries to give insight into the influence of rent-seeking practice on the social structure of society. Rent-seeking means the practice of obtaining wealth not through economically valuable activity but by extracting it from others (privileges, tax havens, offshores, etc.). The analysis of the results of computer simulation demonstrates qualitative changes in the social structure of society, namely: – the rich class includes only “selected” agents; – economically active agents of the middle class practically disappear while the number of economically passive agents increases; – the number of those who are under the poverty line is growing rapidly. This paper is also aimed at highlighting the potential of agent-based modelling as a research tool for analyzing complex social systems at the edge of chaos and drawing attention to the necessity of filling the gap between traditional social education and computer modelling.

Exploring resilience with agent-based models: State of the art, knowledge gaps and recommendations for coping with multidimensionality

Anthropogenic pressures increasingly alter natural systems. Therefore, understanding the resilience of agent-based complex systems such as ecosystems, i.e. their ability to absorb these pressures and sustain their functioning and services, is a major challenge. However, the mechanisms underlying resilience are still poorly understood. A main reason for this is the multidimensionality of both resilience, embracing the three fundamental stability properties recovery, resistance and persistence, and of the specific situations for which stability properties can be assessed. Agent-based models (ABM) complement empirical research which is, for logistic reasons, limited in coping with these multiple dimensions. Besides their ability to integrate multidimensionality through extensive manipulation in a fully controlled system, ABMs can capture the emergence of system resilience from individual interactions and feedbacks across different levels of organization. To assess the extent to which this potential of ABMs has already been exploited, we reviewed the state of the art in exploring resilience and its multidimensionality in ecological and socio-ecological systems with ABMs. We found that the potential of ABMs is not utilized in most models, as they typically focus on a single dimension of resilience by using variability as a proxy for persistence, and are limited to one reference state, disturbance type and scale. Moreover, only few studies explicitly test the ability of different mechanisms to support resilience. To overcome these limitations, we recommend to simultaneously assess multiple stability properties for different situations and under consideration of the mechanisms that are hypothesised to render a system resilient. This will help us to better exploit the potential of ABMs to understand and quantify resilience mechanisms, and hence support solving real-world problems related to the resilience of agent-based complex systems.

Demand for Agent-Based Transportation Models & Social Behavioral Challenges

Agent-Based modelling has been around us for quite some time now and has thus become a crucial factor for executing prediction-based planning, such as the transportation models for metropolitan cities. This paper undertakes the fundamental understanding of the agent-based modeling and simulation and its application to the transportation models while discussing the scope of its applications and advantages too. The paper then presents the concepts attributed to the social behaviors in conjunction with the agent-based modelling techniques applied so far. The literature review conducted in lieu of this work has resulted in agreement with the fact that the potential of agent-based modelling is by far greater than ever due to the ever-improving computing speeds and capabilities, while the understanding of complex human behavior will continue to be a challenge for simulations and automation techniques developed so far.

Agent-Based Modelling of the Evolution of the Russian Party System Based on Pareto and Hotelling Distributions. Part II

The given article presents research of the evolution of the Russian party system. The chosen methodology is based on the heuristic potential of agent-based modelling. The author analyzes various scenarios of parties’ competition (applying Pareto distribution) in connection with recent increase of the number of political parties. In addition, the author predicts the level of ideological diversity of the parties’ platforms (applying the principles of Hotelling distribution) in order to evaluate their potential competitiveness in the struggle for voters.

Trends in optimization models of sales force management

In the last half-century, significant advances have been made in directing sales force behavior with the use of optimization and decision models. The present paper both presents the current state-of-the art in sales force decision modeling, and also discusses key issues and trends in contemporary modeling of relevance to sales force researchers. The paper begins by exploring critical concepts regarding the estimation of the sales response function, and then discusses critical problems of endogeneity, heterogeneity, and temporal variation that are faced by modelers in this task. Modern approaches to dealing with these issues are presented. We then discuss areas of importance concerning finding model solutions, including closed form versus simulation, and optimization versus heuristic solutions. The paper next moves to areas of practical importance where models can help, including call planning, sales force size, territory allocation, and compensation design. Finally, we discuss trends that will likely impact on sales force modeling in coming years, including the use of big data and data mining, the possible breakdown of rationality, the rise of the Internet and social media, and the potential of agent-based modeling.

The economic entomologist: an interview with Alan Kirman

The Erasmus Journal for Philosophy and Economics was delighted to have the opportunity to interview Professor Kirman when he visited the Erasmus Institute for Philosophy and Economics (EIPE) in late November 2010 to present a paper on the state of macroeconomics. In this interview, Professor Kirman discusses his understanding of the relationship between individual behaviour and aggregate patterns, why it is essential to consider the interactions between agents, and what the study of ant’s behaviour can teach us about collective human actions. He explains the core concepts of his ‘interactionist’ approach, including microfoundations, rationality and emergence, and reflects on the potential of agent-based modelling, the limitations of game theory, the possibility of aggregate-level analysis, and the relevance of behavioural studies. The interview also ranges more widely, discussing the different goals of economics (for instance, explaining, predicting, and controlling), the role of mathematics in modern economics, and the state of macroeconomics.

An Agent Operationalization Approach for Context Specific Agent-Based Modeling

The potential of agent-based modeling (ABM) has been demonstrated in various research fields. However, three major concerns limit the full exploitation of ABM; (i) agents are too simple and behave unrealistically without any empirical basis, (ii) 'proof of concept' applications are too theoretical and (iii) too much value placed on operational validity instead of conceptual validity. This paper presents an operationalization approach to determine the key system agents, their interaction, decision-making and behavior for context specific ABM, thus addressing the above-mentioned shortcomings. The approach is embedded in the framework of Giddens' structuration theory and the structural agent analysis (SAA). The agents' individual decision-making (i.e. reflected decisions) is operationalized by adapting the analytical hierarchy process (AHP). The approach is supported by empirical system knowledge, allowing us to test empirically the presumed decision-making and behavioral assumptions. The output is an array of sample agents with realistic (i.e. empirically quantified) decision-making and behavior. Results from a Swiss mineral construction material case study illustrate the information which can be derived by applying the proposed approach and demonstrate its practicability for context specific agent-based model development.

OPEN PROBLEMS IN USING AGENT-BASED MODELS IN INDUSTRIAL AND LABOR DYNAMICS

The preceding papers have shown the impressive versatility and potential of agent-based modeling in developing an understanding of industrial and labor dynamics. The main attraction of agent-based models is that the actors — firms, workers, and networks — that are the objects of study in the 'real world,' can be represented directly in the model. This one-to-one correspondence between model agents and economic actors provides greater clarity and more opportunities for analysis than many alternative modeling approaches. However, the advantages of agent-based modeling have to be tempered by disadvantages and as yet unsolved methodological problems. In this brief summary drawn from the discussion at the closing session of WILD@ACE, we review three of these open problems in the context of the papers presented at the conference: How can agent-based models be empirically validated? What criteria should be used to evaluate the explanatory success of agent-based models? And how can the conclusions of research on similar topics be integrated?