scholarly journals A research paper of Hossein Sabzian (2019), Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners, ArXiv, 54p

2020 ◽  
Author(s):  
Dai-Long Ngo-Hoang
Keyword(s):  
Differential Equations ◽  
Network Effects ◽  
New Technologies ◽  
Complex Adaptive System ◽  
Agent Based Modeling ◽  
Housing Bubble ◽  
Agent Based ◽  
Economic Agents ◽  
Macro Scale ◽  
Housing Bubbles

Nowadays, we are surrounded by a large number of complex phenomena such as virus epidemic, rumor spreading, social norms formation, emergence of new technologies, rise of new economic trends and disruption of traditional businesses. To deal with such phenomena, social scientists often apply reductionism approach where they reduce such phenomena to some lower-lever variables and model the relationships among them through a scheme of equations (e.g. Partial differential equations and ordinary differential equations). This reductionism approach which is often called equation based modeling (EBM) has some fundamental weaknesses in dealing with real world complex systems, for example in modeling how a housing bubble arises from a housing market, the whole market is reduced into some factors (i.e. economic agents) with unbounded rationality and often perfect information, and the model built from the relationships among such factors is used to explain the housing bubble while adaptability and the evolutionary nature of all engaged economic agents along with network effects go unaddressed. In tackling deficiencies of reductionism approach, in the past two decades, the Complex Adaptive System (CAS) framework has been found very influential. In contrast to reductionism approach, under this framework, the socio-economic phenomena such as housing bubbles are studied in an organic manner where the economic agents are supposed to be both boundedly rational and adaptive. According to CAS framework, the socio-economic aggregates such as housing bubbles emerge out of the ways agents of a socio-economic system interact and decide. As the most powerful methodology of CAS modeling, Agent-based modeling (ABM) has gained a growing application among academicians and practitioners. ABMs show how simple behavioral rules of agents and local interactions among them at micro-scale can generate surprisingly complex patterns at macro-scale. Despite a growing number of ABM publications, those researchers unfamiliar with this methodology have to study a number of works to understand (1) the why and what of ABMs and (2) the ways they are rigorously developed. Therefore, the major focus of this paper is to help social sciences researchers get a big picture of ABMs and know how to develop them both systematically and rigorously.

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.

scholarly journals System Dynamics versus Agent-Based Modeling: A Review of Complexity Simulation in Construction Waste Management

2018 ◽  
Vol 10 (7) ◽  
pp. 2484 ◽  
Author(s):  
Zhikun Ding ◽  
Wenyan Gong ◽  
Shenghan Li ◽  
Zezhou Wu
Keyword(s):  
Waste Management ◽  
System Dynamics ◽  
Complex Adaptive System ◽  
Agent Based Modeling ◽  
Effective Management ◽  
Construction Waste ◽  
Agent Based ◽  
Complex Adaptive ◽  
Research Findings ◽  
Novel Model

The environmental impacts caused by construction waste have attracted increasing attention in recent years. The effective management of construction waste is essential in order to reduce negative environmental influences. Construction waste management (CWM) can be viewed as a complex adaptive system, as it involves not only various factors (e.g., social, economic, and environmental), but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. System dynamics (SD) and agent-based modeling (ABM) are the two most popular approaches to deal with the complexity in CWM systems. However, the two approaches have their own advantages and drawbacks. The aim of this research is to conduct a comprehensive review and develop a novel model for combining the advantages of both SD and ABM. The research findings revealed that two options can be considered when combining SD with ABM; the two options are discussed.

Collective Behavior Under the Umbrella of Blockchain

2020 ◽  
pp. 425-445
Author(s):  
Kanak Saxena ◽  
Umesh Banodha
Keyword(s):  
Social Psychology ◽  
Artificial Intelligence ◽  
Collective Behavior ◽  
New Technologies ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Digital World ◽  
Current Scenario ◽  
Future Potential ◽  
Or Organization

Any social or organization system will fetch the properties from economics, sociology, and social psychology. In the digital world everyone is trying to cope with the new technologies for the survival. The dynamics of such a system are very multifarious due to the complexity in the convergence of the digital, physical, and biological realms. The dynamics of the society and organization are rapidly changing due to the imparting of the new technologies, such as artificial intelligence, internet of things, virtual reality, etc. The resultant is revolutionizing of opportunities and expectations due to the changes in the values, norms, identities, and future potential. The collective behavior (CB) plays an important role in predicting the various dynamics which are not only coherent but also paying attention. Blockchain will not only help in detecting but also help in finding the major causes and challenges for current scenario dynamics. The chapter describes the agent-based modeling and ant colony optimization components of the CB.

scholarly journals Quantifying the driving factors for language shift in a bilingual region

2017 ◽  
Vol 114 (17) ◽  
pp. 4365-4369 ◽  
Author(s):  
Katharina Prochazka ◽  
Gero Vogl
Keyword(s):  
Cellular Automata ◽  
Differential Equations ◽  
Large Scale ◽  
Language Shift ◽  
Reaction Diffusion ◽  
Agent Based Modeling ◽  
Reaction Diffusion Equations ◽  
Agent Based ◽  
Using Data ◽  
A Minor

Many of the world’s around 6,000 languages are in danger of disappearing as people give up use of a minority language in favor of the majority language in a process called language shift. Language shift can be monitored on a large scale through the use of mathematical models by way of differential equations, for example, reaction–diffusion equations. Here, we use a different approach: we propose a model for language dynamics based on the principles of cellular automata/agent-based modeling and combine it with very detailed empirical data. Our model makes it possible to follow language dynamics over space and time, whereas existing models based on differential equations average over space and consequently provide no information on local changes in language use. Additionally, cellular automata models can be used even in cases where models based on differential equations are not applicable, for example, in situations where one language has become dispersed and retreated to language islands. Using data from a bilingual region in Austria, we show that the most important factor in determining the spread and retreat of a language is the interaction with speakers of the same language. External factors like bilingual schools or parish language have only a minor influence.

scholarly journals Agent-based Computational Economics in Management Accounting Research: Opportunities and Difficulties

2020 ◽  
Author(s):  
Friederike Wall ◽  
Stephan Leitner
Keyword(s):  
Research Methods ◽  
Management Accounting ◽  
Agent Based Modeling ◽  
Computational Economics ◽  
Agent Based ◽  
Research Opportunities ◽  
Economic Agents ◽  
Accounting Research ◽  
Theoretical Assumptions ◽  
Research Endeavor

Agent-based computational economics (ACE) - while adopted comparably widely in other domains of managerial science - is a rather novel paradigm for management accounting research (MAR). This paper provides an overview of opportunities and difficulties that ACE may have for research in management accounting and, in particular, introduces a framework that researchers in management accounting may employ when considering ACE as a paradigm for their particular research endeavor. The framework builds on the two interrelated paradigmatic elements of ACE: a set of theoretical assumptions on economic agents and the approach of agent-based modeling. Particular focus is put on contrasting opportunities and difficulties of ACE in comparison to other research methods employed in MAR.

Agent-Based Models

2018 ◽  
Author(s):  
Brenda Heaton ◽  
Abdulrahman El-Sayed ◽  
Sandro Galea
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Complex Adaptive System ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Or Groups ◽  
Complex Adaptive ◽  
Adaptive Processes ◽  
Neighborhoods And Health

Agent-based modeling is a newer approach to the study of neighborhoods and health. In brief, an agent-based model is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities, such as organizations or groups) with a view to assessing their effects on the system as a whole. Neighborhood characteristics and resources evolve and adapt as the individuals living within them change and vice versa. In this way, neighborhoods reflect a complex adaptive system. In this chapter, we introduce agent-based models as a tool for modeling these interactive and adaptive processes that occur within a system, such as a neighborhood. The chapter provides a basic introduction to this method, drawing on examples from the neighborhoods and health literature.

scholarly journals Study of Network Migration to New Technologies Using Agent-Based Modeling Techniques

2014 ◽  
Vol 23 (4) ◽  
pp. 920-949 ◽  
Author(s):  
Tamal Das ◽  
Marek Drogon ◽  
Admela Jukan ◽  
Marco Hoffmann
Keyword(s):  
New Technologies ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Modeling Techniques
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