Experimenting with Agent-Based Model Simulation Tools
Source abstract
Abstract
Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent an interdisciplinary approach to examining complex systems, and the heterogeneous background of ABM users demands comprehensive, easy-to-use, and efficient environments to develop ABM simulations. Currently, many tools, frameworks, and libraries exist, each with its characteristics and objectives. This article aims to guide newcomers in the jungle of ABM tools toward choosing the right tool for their skills and needs. This work proposes a thorough overview of open-source general-purpose ABM tools and offers a comparison from a two-fold perspective. We first describe an off-the-shelf evaluation by considering each ABM tool’s features, ease of use, and efficiency according to its authors. Then, we provide a hands-on evaluation of some ABM tools by judging the effort required in developing and running four ABM models and the obtained performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovation Diffusion and Forecasting · Transportation Planning and Optimization · Simulation Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Dipartimento di Informatica, Università degli Studi di Salerno, 84084 Fisciano, Italy
Dipartimento di Psicologia, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
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