Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide

1998 ◽  
pp. 10-25 ◽  
Author(s):  
H. Van Dyke Parunak ◽  
Robert Savit ◽  
Rick L. Riolo
Keyword(s):  
Agent Based Modeling ◽  
Agent Based

An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland

2012 ◽  
Vol 79 (9) ◽  
pp. 1638-1653 ◽  
Author(s):  
Ehsan Shafiei ◽  
Hedinn Thorkelsson ◽  
Eyjólfur Ingi Ásgeirsson ◽  
Brynhildur Davidsdottir ◽  
Marco Raberto ◽  
...  
Keyword(s):  
Market Share ◽  
Electric Vehicles ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Modeling Approach

scholarly journals Approaches to Modeling the Gas-Turbine Maintenance Process

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Tai-Tuck Yu ◽  
James P. Scanlan ◽  
Richard M. Crowder ◽  
Gary B. Wills
Keyword(s):  
Decision Making ◽  
Gas Turbine ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Event Modeling ◽  
Human Decision ◽  
Event Model ◽  
Discrete Event Model

Discrete-event modeling has long been used for logistics and scheduling problems, while multi-agent modeling closely matches human decision-making process. In this paper, a metric-based comparison between the traditional discrete-event and the emerging agent-based modeling approaches is reported. The case study involved the implementation of two functionally identical models based on a realistic, nontrivial, civil aircraft gas turbine global repair operation. The size, structural complexity, and coupling metrics from the two models were used to gauge the benefits and drawbacks of each modeling paradigm. The agent-based model was significantly better than the discrete-event model in terms of execution times, scalability, understandability, modifiability, and structural flexibility. In contrast, and importantly in an engineering context, the discrete-event model guaranteed predictable and repeatable results and was comparatively easy to test because of its single-threaded operation. However, neither modeling approach on its own possesses all these characteristics nor can each handle the wide range of resolutions and scales frequently encountered in problems exemplified by the case study scenario. It is recognized that agent-based modeling can emulate high-level human decision-making and communication closely while discrete-event modeling provides a good fit for low-level sequential processes such as those found in manufacturing and logistics.

Facilitating organizational learning through agent-based modeling and simulation experimentation

2016 ◽  
Vol 23 (6) ◽  
pp. 429-443 ◽  
Author(s):  
Saša Baškarada ◽  
Arvind Chandran ◽  
Mina Shokr ◽  
Christopher Stewart
Keyword(s):  
Organizational Learning ◽  
Modeling And Simulation ◽  
Agent Based Modeling ◽  
Knowledge Generation ◽  
Generation Process ◽  
Complex Environments ◽  
Content Type ◽  
Agent Based ◽  
Internal Knowledge

Purpose In addition to requiring high absorptive capacity, contemporary organizations operating in highly dynamic and complex environments also require the ability to create knowledge internally, within the organization. While the organizational learning (OL) literature has produced a plethora of theories and frameworks, there has been relatively little empirical research on specific mechanisms for internal knowledge generation. Accordingly, this paper aims to answer calls for more research on mechanisms for internal generation of organizational knowledge. Design/methodology/approach This paper is an in-depth case study in the Australian Defence Organisation (ADO). Findings The paper presents a cyclical eight-stage knowledge generation process and demonstrates how agent-based modeling and simulation (ABMS) may be used to facilitate OL. Originality/value By detailing an in-depth case study of an ABMS mechanism for internal knowledge generation in the ADO, this paper provides a novel and relevant contribution to the OL literature.

scholarly journals Agent-Based Modeling, Scientific Reproducibility, and Taphonomy: A Successful Model Implementation Case Study

2020 ◽  
Author(s):  
Molly Carney ◽  
Benjamin Davies
Keyword(s):  
Social Sciences ◽  
Agent Based Modeling ◽  
Archaeological Research ◽  
Human Environment ◽  
Agent Based ◽  
Successful Model ◽  
Implementation Studies ◽  
Successful Replication ◽  
Access Data

There is a growing use of agent-based model (ABM) simulations to reconstruct past human-environment interactions. ABMs are useful in that they offer scientists the opportunity to model processes, phenomena, and study systems that may not be otherwise reproducible or testable. Replication or re-implementation studies of ABMs are, however, infrequently undertaken, and there are few examples within archaeology or other social sciences. This paper documents the process of a successful ABM replication study, as well as two additional modifications to the original model. Results corroborate the findings of the original geoarchaeological model and indicate that episodic geomorphic events significantly affect archaeological deposit formation and the inferences drawn from associated radiocarbon records. One revision of the model further demonstrates that episodic fluvial events can create highly varied radiocarbon distributions. The second modification illustrates that excavation data helps to fill in hiatuses in radiocarbon chronologies on depositional landforms, although there is no effect across landscapes subject to erosion. This successful replication exercise also illustrates the value of open access data and analyses in reproducing, testing, and expanding upon archaeological research and theory building.

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