On the Ontological Turn in Economics: The Promises of Agent-Based Computational Economics

2020 ◽  
Vol 50 (3) ◽  
pp. 238-259
Author(s):  
Shu-Heng Chen
Keyword(s):  
Social Interactions ◽  
Autonomous Agents ◽  
Critical Realism ◽  
Agent Based Modeling ◽  
Computational Economics ◽  
Agent Based ◽  
Methodological Implication ◽  
Ontological Turn

This article argues that agent-based modeling (ABM) is the methodological implication of Lawson’s championed ontological turn in economics. We single out three major properties of agent-based computational economics (ACE), namely, autonomous agents, social interactions, and the micro-macro links, which have been well accepted by the ACE community. We then argue that ACE does make a full commitment to the ontology of economics as proposed by Lawson, based on his prompted critical realism. Nevertheless, the article also points out the current limitations or constraints of ACE. Efforts to overcome them are deemed to be crucial before ACE can make itself more promising to the current ontological turn in economics.

scholarly journals Agent-Based Modeling

2016 ◽  
Vol 56 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Sarah Nicholls ◽  
Bas Amelung ◽  
Jillian Student
Keyword(s):  
Complex Systems ◽  
Autonomous Agents ◽  
Agent Based Modeling ◽  
Potential Outcomes ◽  
Tourism Planning ◽  
Agent Based ◽  
Modeling Techniques ◽  
Potential Applications ◽  
Planning Development

Agent-based modeling (ABM) is a way of representing complex systems of autonomous agents or actors, and of simulating the multiple potential outcomes of these agents’ behaviors and interactions in the form of a range of alternatives or futures. Despite the complexity of the tourism system, and the power and flexibility of ABM to overcome the assumptions such as homogeneity, linearity, equilibrium, and rationality typical of traditional modeling techniques, ABM has received little attention from tourism researchers and practitioners. The purpose of this paper is to introduce ABM to a wider tourism audience. Specifically, the appropriateness of tourism as a phenomenon to be subjected to ABM is established; the power and benefits of ABM as an alternative scientific mechanism are illuminated; the few existing applications of ABM in the tourism arena are summarized; and, a range of potential applications in the areas of tourism planning, development, marketing and management is proposed.

scholarly journals Agents.jl: a performant and feature-full agent-based modeling software of minimal code complexity

SIMULATION ◽  
2022 ◽  
pp. 003754972110688
Author(s):  
George Datseris ◽  
Ali R. Vahdati ◽  
Timothy C. DuBois
Keyword(s):  
Programming Languages ◽  
Autonomous Agents ◽  
Simulation Method ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Interactive Applications ◽  
Modeling Software ◽  
Mathematical Equations ◽  
Code Complexity ◽  
Analysis Platform

Agent-based modeling is a simulation method in which autonomous agents interact with their environment and one another, given a predefined set of rules. It is an integral method for modeling and simulating complex systems, such as socio-economic problems. Since agent-based models are not described by simple and concise mathematical equations, the code that generates them is typically complicated, large, and slow. Here we present Agents.jl, a Julia-based software that provides an ABM analysis platform with minimal code complexity. We compare our software with some of the most popular ABM software in other programming languages. We find that Agents.jl is not only the most performant but also the least complicated software, providing the same (and sometimes more) features as the competitors with less input required from the user. Agents.jl also integrates excellently with the entire Julia ecosystem, including interactive applications, differential equations, parameter optimization, and so on. This removes any “extensions library” requirement from Agents.jl, which is paramount in many other tools.

Hybrid fuzzy Monte Carlo agent-based modeling of workforce motivation and performance in construction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Raoufi ◽  
Aminah Robinson Fayek
Keyword(s):  
Monte Carlo ◽  
Social Interactions ◽  
Parametric Study ◽  
Agent Based Modeling ◽  
Practical Application ◽  
Content Type ◽  
Agent Based ◽  
Original Contribution ◽  
And Performance ◽  
Practical Implications

Purpose This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance. Design/methodology/approach The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables. Findings The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context. Research limitations/implications This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain. Practical implications This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties. Social implications This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance. Originality/value The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.

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.

Agent-based Simulation of Workers’ Behaviors, Productivity, and Safety around Construction Obstacles

2020 ◽  
Author(s):  
Omar Binhomaid ◽  
Tarek Hegazy
Keyword(s):  
Autonomous Agents ◽  
Productivity Loss ◽  
Agent Based Modeling ◽  
Sensitivity Analyses ◽  
Construction Sites ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Site Model ◽  
Behavioral Rules ◽  
Improvement Measures

Construction sites involve many hazardous areas and fixed/movable objects around which the workers interact. Unguided site policies or workers’ behaviors can lead to productivity loss and/or accidents. In this paper, an agent-based modeling and simulation (ABMS) framework has been developed to: (1) provide a detailed site model with temporary facilities and obstacles (danger zones and productivity-hindering spots); (2) model the workers’ natural behaviors towards safety while moving around the site; and (3) quantify the consequent site productivity and accident potential of any site configuration. The paper discusses the site model and the behavioral rules that autonomous agents follow to simulate the movement of aggressive and avoider workers around hazard areas. The results of two case studies and sensitivity analyses show that different simulations with different site improvement measures can quantify the effectiveness of, and optimize, site operational measures and establish rewards for positive worker’s behaviors that improve productivity and safety.

A Systematic Approach for Aligning ODE and Agent-Based Models: Application to Epidemiological Modeling

2008 ◽  
Author(s):  
Michael J. Leamy
Keyword(s):  
Autonomous Agents ◽  
Systematic Approach ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Ode Model ◽  
Epidemiological Modeling ◽  
Agent Based ◽  
Agent Behavior ◽  
Time Histories ◽  
Good Agreement

Recent researchers active in the field of agent-based modeling have called for the alignment, or ‘docking,’ of models which simulate the same system using different techniques. Addressing this need, the present article details a systematic approach for docking models described by (nonlinear) ordinary differential equations with analogous models employing autonomous agents — i.e., agent-based models (ABMs). In particular, the approach is demonstrated by example for an epidemiological SEIR (Susceptible, Exposed, Infectious, Recovered) ODE model with a newly-developed agent-based model. The ABM is designed such that the assumptions present in the ODE model are matched by the actions of the ABM agents and the model. In addition, less-than-transparent coefficients present in the ODE model are examined via difference equations and then mapped to appropriate agent behavior. The result is very good agreement in comparisons made between ODE and ABM model-generated time-histories — i.e., successful alignment. It is anticipated that the systematic alignment approach described herein should be useful for aligning ODE and ABM models in other fields of study — e.g., Lanchester ODE combat models vice ABM combat models.

Agent-Based Computational Economics

2007 ◽  
pp. 183-197
Author(s):  
C. Bruun
Keyword(s):  
Economic System ◽  
Autonomous Agents ◽  
Complex Adaptive System ◽  
Methodological Approach ◽  
Computational Economics ◽  
Economic Methodology ◽  
Economic Systems ◽  
New Approach ◽  
Agent Based ◽  
Complex Adaptive

This chapter argues that the economic system is best perceived as a complex adaptive system, and as such, the traditional analytical methods of economics are not optimal for its study. Agent-based computational economics (ACE) studies the economic system from the bottom up and recognizes interaction between autonomous agents as the central mechanism in generating the self-organizing features of economic systems. Besides a discussion of this new economic methodology, a short how-to introduction is given, and the problem of constraining economics as a science within the ACE approach is raised. It is argued that ACE should be perceived as a new methodological approach to the study of economic systems rather than a new approach to economics, and that the use of ACE should be anchored in existing economic theory.

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