The DOI matches its Crossref record; 80 of the 83 references it cites were found in a registry or an index.
On agent-based modeling and computational social science
Open accessAbstract
In the first part of the paper, the field of agent-based modeling (ABM) is discussed focusing on the role of generative theories, aiming at explaining phenomena by growing them. After a brief analysis of the major strengths of the field some crucial weaknesses are analyzed. In particular, the generative power of ABM is found to have been underexploited, as the pressure for simple recipes has prevailed and shadowed the application of rich cognitive models. In the second part of the paper, the renewal of interest for Computational Social Science (CSS) is focused upon, and several of its variants, such as deductive, generative, and complex CSS, are identified and described. In the concluding remarks, an interdisciplinary variant, which takes after ABM, reconciling it with the quantitative one, is proposed as a fundamental requirement for a new program of the CSS.
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“Yet ABMs are often criticized for failing to capture the full spectrum of human behaviors (38). For example, conventional agents within ABMs do not use language, interpret social contexts, or engage in conversations with each other (39).”
From Can Generative AI improve social science? (2024) · cites this paper as [38]
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Cites
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“Agents also differ in their level of complexity: according to a classic distinction introduced by Wooldridge and Jennings in their influential work (Wooldridge and Jennings, 1995), agents in a “strong” sense are capable to manipulate and reason upon mental representations; otherwise they are…”
BackgroundEpidemic Spreading in Scale-Free Networks
Pastor‐Satorras, R., & Vespignani, A. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.3200 · Cited by 5,785 · cited 4×
“, 2010) and that of epidemics (Pastor-Satorras and Vespignani, 2001).”
BackgroundInfluentialSimulating dynamical features of escape panic
Helbing, D., Farkas, I. J., & Vicsek, T. (2000) · Nature · doi:10.1038/35035023 · Cited by 5,005 · cited 1×
“The popularity of sociophysics grew even more under the influence of success stories, especially concerning the domain of pedestrians’ crowd (Helbing et al., 2000) and that of epidemics (Pastor-Satorras and Vespignani, 2001).”
BackgroundSchelling, T. (1971) · Journal of Mathematical Sociology · doi:10.1080/0022250x.1971.9989794 · Cited by 4,966 · cited 1×
“Examples of this application of ABM abound and are among the best cited works so far worked out in this field. phenomena trace back to as early as 1971, when the famous (Schelling, 1971) model of segregation was published in the Journal of Mathematical Sociology.”
BackgroundAgent-based modeling: Methods and techniques for simulating human systems
Bonabeau, E. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082080899 · Cited by 4,500 · cited 2×
“…acknowledgment, when the proceedings of a Sackler Colloqium of the National Academy of Sciences, under the title “Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling,” held in October 2001, were published on PNAS (Bonabeau, 2002).”
BackgroundStatistical physics of social dynamics
Castellano, C., Fortunato, S., & Loreto, V. (2009) · Reviews of Modern Physics · doi:10.1103/revmodphys.81.591 · Cited by 4,119 · cited 2×
“Again, current models (Deffuant et al., 2001; Galam, 2002; Hegselmann and Krause, 2002; Castellano et al., 2009), find that the structure of the network of communication affects opinion dynamics.”
BackgroundMethodLazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Evolution of indirect reciprocity by image scoring
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“…to emerge from a set of heterogeneous strategies, from TIT-FOR-TAT (Axelrod, 1997) to strong reciprocity (Boyd et al., 2003), from image-scoring (Nowak and Sigmund, 1998) to reputation-building (Pinyol et al., 2012), and finally group selection (Di Tosto et al., 2007); social control is found…”
BackgroundOpinion Dynamics and Bounded Confidence Models, Analysis and Simulation
Hegselmann, R., & Krause, U. (2002) · RePEc: Research Papers in Economics · Cited by 2,366
Computer science as empirical inquiry
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“Inductive computational science is certainly not new (Newell and Simon, 1976).”
Background
Research works printed but not linked: 3
- Epstein, 2008, Why model?, JASSS, 11
- 2010, Computational Social Science
- Helbing, 2011b, How to do agent-based simulations in the future: from modeling social mechanisms to emergent phenomena and interactive systems design
Cited by
Sorted by citations
Everything you need to know about agent-based modelling and simulation
Macal, C. M. (2016) · Journal of Simulation · doi:10.1057/jos.2016.7 · Cited by 469
Can Generative AI improve social science?
Bail, C. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314021121 · Cited by 369
Large language models empowered agent-based modeling and simulation: a survey and perspectives
Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03611-3 · Cited by 294
“Speci fi cally, for social sciences (Conte and Paolucci, 2014; Gilbert, 2007b; Gilbert and Terna, 2000; Ternaet al., 1998), agent-based modeling and simulation are widely used to model social phenomena such as crowd behavior (Kountouriotis et al., 2014; Luo et al., 2008), opinion dynamics (Banisch…”
Challenges, tasks, and opportunities in modeling agent-based complex systems
An, L., Grimm, V., Sullivan, A., Turner, B. L., Malleson, N., Heppenstall, A. J., Vincenot, C. E., Robinson, D. T., Ye, X., Liu, J., et al. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109685 · Cited by 241
“S4A), which features a concurrent, parallel investigation of the same phenomena or subject from many relevant disciplinary perspectives or an integration of them (Conte and Paolucci, 2014).”
BackgroundMethodInfluentialSimple or complicated agent-based models? A complicated issue
Sun, Z., Lorscheid, I., Millington, J. D. A., Lauf, S., Magliocca, N. R., Groeneveld, J., Balbi, S., Nolzen, H., Müller, B. H., Schulze, J., et al. (2016) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2016.09.006 · Cited by 196
“Ironically, ABMs are often criticized for simultaneously being too simple (regarding the rules and specifications) and too “complex” (mainly with respect to the model behaviours) (Conte and Paolucci, 2014).”
BackgroundInfluential
Metadata
- Authors
- Conte, R., & Paolucci, M.
- Affiliations
- National Research Council (IT)
- Corresponding author
- Paolucci, Mario
- ORCID
- Paolucci, Mario
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2014-07-14
- Volume
- 5
- Pages
- 668-668
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC4094840
- OpenAlex
- W3124521395
- Cited by
- 160
- References
- 83
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 17 · 2023: 15 · 2022: 12 · 2021: 17 · 2020: 20 · 2019: 9 · 2018: 12 · 2017: 11 · 2016: 8 · 2015: 7 · 2014: 2
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (284709)
- Programma Operativo Nazionale Ricerca e Competitività (2007-2013)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- agent-based modeling, computational social science, generative theory, generative power, cognitive models, interdisciplinary research, quantitative social science
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