Employing Machine Learning to Advance Agent-based Modeling in Information Systems Research
Type
conference paper
Date Issued
2023
Author(s)
Abstract
In recent years, computationally intensive theory construction, leveraging big data and machine learning (ML), has gained significant interest in the information systems (IS) community. The integration of computational methods can generate novel methodological paradigms or enhance existing methods. Agent-based modeling (ABM) is one of the computational methods that has recently proliferated in IS research to generate computationally intensive theories. However, ABM is still in nascent state of adoption in IS research and entails some pathological challenges that limit its applicability and robustness. With the goal of advancing ABM in IS research, this article proposes a methodological framework that integrates ML within relevant steps of ABM. The framework is demonstrated in an exemplary IS study, showing its potential for addressing the pathological challenges of ABM. We finally discuss the implications of applying the proposed methodological framework in IS research.
Language
English
Keywords
Agent-based modeling (ABM)
machine learning (ML)
simulation
computational methods
HSG Classification
contribution to scientific community
Refereed
Yes
Event Title
International Conference on Information Systems (ICIS)
Event Location
Hyderabad, India
Event Date
December 10-13, 2023
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
ICIS-2023-Revision-final-version.pdf
Type
Main Article
Size
485.25 KB
Format
Adobe PDF
Checksum (MD5)
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