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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ABMs are often criticized for failing to capture the full spectrum of human behaviors; conventional agents within ABMs do not use language, interpret social contexts, or engage in conversations with each other.
“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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“Agents are usually defined (see Conte, 2009) as autonomous systems that operate transitions between states of the world, based on mechanisms and representations somehow incorporated into them.”
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ALTRUISM AMONG SIMPLE AND SMART VAMPIRES
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MethodOn How to Conduct Experimental Research with Self-Motivated Agents
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A Grid Aware Large Scale Agent-based Simulation System
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Can Generative AI improve social science?
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“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 214 · see citing works
“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 · see citing works
“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).”
BackgroundInfluentialMüller-Hansen, F., Schlüter, M., Mäs, M., Hegselmann, R., Donges, J. F., Kolb, J. J., Thonicke, K., & Heitzig, J. (2017) · Earth System Dynamics · doi:10.5194/esd-8-977-2017 · Cited by 117 · see citing works
“Agent-based modeling is widely used to study complex systems in computational social science (Conte and Paolucci, 2014), land-use science (Matthews et al.”
BackgroundZhang, W., Valencia, A., & Chang, N. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3106777 · Cited by 112 · see citing works
“Consequently, the necessity to combine learning logic, uncertainty, and induction is gaining increasing attention in various computational disciplines, such as knowledge discovery and representation, uncertainty reasoning, machine learning (ML), and data mining [7].”
BackgroundIn Silico Modeling of Immunotherapy and Stroma-Targeting Therapies in Human Colorectal Cancer
Kather, J. N., Poleszczuk, J., Suarez‐Carmona, M., Krisam, J., Charoentong, P., Valous, N. A., Weis, C., Tavernar, L., Leiß, F., Herpel, E., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-2006 · Cited by 100 · see citing works
A research agenda for the study of social norm change
Andrighetto, G., & Vriens, E. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0411 · Cited by 92 · see citing works
Advances in the agent-based modeling of economic and social behavior
Steinbacher, M., Raddant, M., Karimi, F., Camacho-Cuena, E., Alfarano, S., Iori, G., & Lux, T. (2021) · SN Business & Economics · doi:10.1007/s43546-021-00103-3 · Cited by 84 · see citing works
Behavioral models of climate change adaptation and mitigation in land‐based sectors
Brown, C., Alexander, P. M., Holzhauer, S., & Rounsevell, M. (2017) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.448 · Cited by 80 · see citing works
“In order for models to account for true adaptation— including in the nature, objectives, and forms of individual and social behaviors—they need to prioritize generative cognitive processes over more tractable optimisation functions.(112,113) Of course, it is not necessarily the case that ever-greater behavioral accura…”
BackgroundAgent-based simulation in management and organizational studies: a survey
Gómez-Cruz, N. A., Loaiza, I., & Hurtado, F. F. O. (2017) · European Journal of Management and Business Economics · doi:10.1108/ejmbe-10-2017-018 · Cited by 78 · see citing works
“It is also an experimental laboratory in which, through the manipulation of pre-specified parameters, it is possible to develop theories, models or new hypotheses about the world (Conte and Paolucci, 2014).”
BackgroundToward a methodology for explaining and theorizing about social-ecological phenomena
Schlüter, M., Orach, K., Lindkvist, E., Martin, R., Wijermans, N., Bodin, Ö., & Boonstra, W. J. (2019) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2019.06.011 · Cited by 77 · see citing works
Lindkvist, E. A. L., Wijermans, N., Daw, T., González-Mon, B., Giron‐Nava, A., Johnson, A. F., van Putten, I. V., Basurto, X., & Schlüter, M. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00733 · Cited by 60 · see citing works
“Also see, e.g., Conte and Paolucci (2014); Gilbert and Troitzsch (2005), or Railsback and Grimm (2011). or multi-stakeholder collaboration processes, which will also influence the purpose and characteristics of the models.”
BackgroundAgent‐Based Modeling in Molecular Systems Biology
Soheilypour, M., & Mofrad, M. R. K. (2018) · BioEssays · doi:10.1002/bies.201800020 · Cited by 61 · see citing works
Behavioural diversity in fishing—Towards a next generation of fishery models
Wijermans, N., Boonstra, W. J., Orach, K., Hentati‐Sundberg, J., & Schlüter, M. (2020) · Fish and Fisheries · doi:10.1111/faf.12466 · Cited by 66 · see citing works
“Agent-based modelling is presently the only modelling approach able to reflect heterogeneous agents and their behavioural diversity over time (Conte & Paolucci, 2014).”
BackgroundA Social Approach to Rule Dynamics Using an Agent‐Based Model
Cuskley, C. F., Loreto, V., & Kirby, S. (2018) · Topics in Cognitive Science · doi:10.1111/tops.12327 · Cited by 53 · see citing works
“These models provide a perspective is impossible to take with experimental or corpus methods alone (Conte & Paolucci, 2014), making population based models an ideal complement to a better understanding of language as a complex social system.”
MethodAgent‐based modeling to integrate elements from different disciplines for ambitious climate policy
Savin, I. V., Creutzig, F., Filatova, T., Foramitti, J., Konc, T., Niamir, L., Safarzynska, K., & van den Bergh, J. C. J. M. (2022) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.811 · Cited by 52 · see citing works
“The use of ABMs for social-science research and policy advice has proliferated over the last decade (Bianchi & Squazzoni, 2015; Conte & Paolucci, 2014; Dawid et al., 2018), thanks to advances in computing power, access to detailed household-and firm-level data, and availability of a multitude of…”
MethodInfluence of Narratives of Vision and Identity on Collective Behavior Change
Chabay, I., Koch, L., Martinez, G., & Scholz, G. (2019) · Sustainability · doi:10.3390/su11205680 · Cited by 49 · see citing works
“To explore an ABM, one can design experiments to test out different assumptions about parameters, rules, or structural aspects [48].”
MethodEberlen, J. C., Scholz, G., & Gagliolo, M. (2017) · International Review of Social Psychology · doi:10.5334/irsp.115 · Cited by 46 · see citing works
“Agentbased modeling allows for the development of testable theories and systematic experimentation through simulation (Conte & Paolucci, 2014).”
MethodModeling agent decision and behavior in the light of data science and artificial intelligence
An, L., Grimm, V., Bai, Y. D., Sullivan, A., Turner, B. L., Malleson, N., Heppenstall, A. J., Vincenot, C. E., Robinson, D. T., Ye, X., et al. (2023) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2023.105713 · Cited by 43 · see citing works
“For example, the Prisoner ’ s Dilemma can be related to several seemingly plausible mechanisms, including group selection (Di Tosto et al., 2007), strong reciprocity (Boyd et al., 2003), tit-for-tat retaliation (Axelrod, 1997), and others (Conte and Paolucci, 2014).”
Emerging Opportunities for Landscape Ecological Modelling
Synes, N. W., Brown, C., Watts, K., White, S. M., Gilbert, M. A., & Travis, J. M. J. (2016) · Current Landscape Ecology Reports · doi:10.1007/s40823-016-0016-7 · Cited by 42 · see citing works
“(cognitive) process accuracy is hard to achieve [153, 154].”
BackgroundHassan, E. M., & Mahmoud, H. N. (2021) · Scientific Reports · doi:10.1038/s41598-021-81130-w · Cited by 42 · see citing works
Opportunities for agent‐based modelling in human dimensions of fisheries
Burgess, M. G., Carrella, E., Drexler, M., Axtell, R. L., Bailey, R. M., Watson, J. R., Cabral, R. B., Clemence, M., Costello, C., Dorsett, C., et al. (2020) · Fish and Fisheries · doi:10.1111/faf.12447 · Cited by 35 · see citing works
“…with each other and their natural/social environments; (c) the agents follow decision rules that do not lend themselves well to analytical aggregation or abstraction (Axtell, 2000; Bonabeau, 2002; Conte & Paolucci, 2014; Gotts et al., 2019; Grimm, 1999; Heckbert et al., 2010; Schulze et al., 2017).”
BackgroundWhere Does Theory Have It Right? A Comparison of Theory-Driven and Empirical Agent Based Models
Taghikhah, F. R., Filatova, T., & Voinov, A. A. (2021) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.4573 · Cited by 35 · see citing works
Towards Bayesian Model-Based Demography
Bijak, J. (2021) · Methodos series · doi:10.1007/978-3-030-83039-7 · Cited by 33 · see citing works
Nearby outdoor recreation modelling: An agent-based approach
Мorelle, К., Buchecker, M., Kienast, F., & Tobias, S. (2018) · Urban forestry & urban greening · doi:10.1016/j.ufug.2018.07.007 · Cited by 32 · see citing works
Conrad, N. D., Helfmann, L., Zonker, J., Winkelmann, S., & Schütte, C. (2018) · EPJ Data Science · doi:10.1140/epjds/s13688-018-0153-9 · Cited by 32 · see citing works
Agent-based modeling for migration and modern slavery research: a systematic review
McAlpine, A., Kiss, L., Zimmerman, C., & Chalabi, Z. (2020) · Journal of Computational Social Science · doi:10.1007/s42001-020-00076-7 · Cited by 31 · see citing works
Gernigon, C., Hartigh, R. J. R. D., Vallacher, R. R., & van Geert, P. L. C. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231187324 · Cited by 32 · see citing works
“…perceptual and action tools, autonomous—although limited by the local nature of the usable information—in goal pursuit, and adaptable as a result of interactions with other agents and with the environment (Conte & Paolucci, 2014; Jackson et al., 2017; Smaldino et al., 2015; Smith & Conrey, 2007).”
Randomness in the evolution of cooperation
Hadzibeganovic, T., Stauffer, D., & Han, X.-P. (2015) · Behavioural Processes · doi:10.1016/j.beproc.2015.01.003 · Cited by 29 · see citing works
Ozawa, S., Evans, D. R., Higgins, C. R., Laing, S., & Awor, P. (2019) · Malaria Journal · doi:10.1186/s12936-018-2628-3 · Cited by 30 · see citing works
Pozzo, R., Filippetti, A., Paolucci, M., & Virgili, V. (2020) · Science and Public Policy · doi:10.1093/scipol/scaa023 · Cited by 30 · see citing works
Self-citationA philosophical case for process-based modelling of land use change
Brown, C., Brown, K., & Rounsevell, M. (2016) · Modeling Earth Systems and Environment · doi:10.1007/s40808-016-0102-1 · Cited by 29 · see citing works
“Consequently, social systems models have been characterised as ‘arbitrary, poorly comparable, competent in highly specific domains of knowledge and disarmingly inapt in any other’ (Conte and Paolucci 2014, p. 4).”
BackgroundInfluentialAn Argument Communication Model of Polarization and Ideological Alignment
Banisch, S., & Olbrich, E. (2021) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.4434 · Cited by 29 · see citing works
“…al., 2017) for more encompassing overviews. et al., 2016), structural representations of attitudes (Urbig and Malitz, 2005; Mäs and Flache, 2013) and cognitive networks (Van Overwalle and Heylighen, 2006; Wolf et al., 2012, 2015) is a relatively new development (see also Conte and Paolucci, 2014).”
BackgroundMechanism change in a simulation of peer review: from junk support to elitism
Paolucci, M., & Grimaldo, F. (2014) · Scientometrics · doi:10.1007/s11192-014-1239-1 · Cited by 28 · see citing works
“These include a tendency to build adhoc models (Conte and Paolucci 2011), the inability to do proper sensitivity analysis for changes in the process as opposed to changes in parameters, the risk of overfitting, and oversimplification.”
BackgroundSelf-citationAssessing model equifinality for robust policy analysis in complex socio-environmental systems
Williams, T. G., Guikema, S. D., Brown, D. G., & Agrawal, A. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104831 · Cited by 27 · see citing works
Ozawa, S., Haynie, D. G., Bessias, S., Laing, S., Ngamasana, E. L., Yemeke, T. T., & Evans, D. R. (2019) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.18-0334 · Cited by 26 · see citing works
“as patient care-seeking behavior and medicine supply chain processes that evolve over time.(21) We simulated the use of antimalarials in two regions in the DRC, Kinshasa province and the region formerly encompassing Katanga province, where antimalarial market data were available from ACTwatch.”
BackgroundHeslop, D. J., Chughtai, A. A., Bui, C. M., & MacIntyre, C. R. (2017) · Epidemics · doi:10.1016/j.epidem.2017.04.002 · Cited by 25 · see citing works
Larooij, M., & Törnberg, P. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11412-6 · Cited by 27 · see citing works
“These tensions have limited their adoption in mainstream social science, particularly as the rise of data-rich computational social science and machine learning in the 2010s shifted focus toward empirically grounded, predictive approaches (Conte and Paolucci 2014).”
InfluentialEmpirical characterisation of agents’ spatial behaviour in pedestrian movement simulation
Filomena, G., Kirsch, L., Schwering, A., & Verstegen, J. A. (2022) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2022.101807 · Cited by 23 · see citing works
An agent-based simulation of cooperation in the use of irrigation systems
Cai, J., & Xiong, H. (2017) · Complex Adaptive Systems Modeling · doi:10.1186/s40294-017-0047-x · Cited by 21 · see citing works
“In an agent-based model, one can make hypotheses about the behavioral rules or mechanisms, such as social learning (learning from others in the same social group), that are not, or are only indirectly, observable, and run simulations to test these hypotheses (Conte and Paolucci 2014).”
BackgroundConsidering the Elaboration Likelihood Model for simulating hate and counter speech on Facebook
Schieb, C., & Preuß, M. (2018) · Studies in Communication and Media · doi:10.5771/2192-4007-2018-4-580 · Cited by 21 · see citing works
“Agent-based models (Conte & Paolucci, 2014; Epstein, 1999; Waldherr, 2014) and computational simulations in general demonstrate complex interactions and effects in a defined system yielding “an optimal compromise between the model’s complexity and the complexity of the real world” (Voinea, 2016, p.…”
BackgroundMa, H., Li, M., Tong, X., & Dong, P. (2023) · Sustainability · doi:10.3390/su151310427 · Cited by 20 · see citing works
“ABM remains the only known approach that can simulate how a set of agents interact and communicate with each other, allowing the macro-level phenomenon disassembled into micro-level patterns [16].”
BackgroundSocial cognitive theory and women’s career choices: an agent—based model simulation
Mozahem, N. A. (2020) · Computational and Mathematical Organization Theory · doi:10.1007/s10588-020-09317-6 · Cited by 19 · see citing works
“ABMs allow researchers to convert theories into falsifiable hypotheses and to experiment with the theories by controlling certain elements of the theory while varying other elements (Conte and Paolucci 2014).”
BackgroundMethodological Challenges and Advances in Managerial and Organizational Cognition
Galavan, R., Sund, K. J., & Hodgkinson, G. P. (2017) · Emerald Group Publishing Limited eBooks · doi:10.1108/s2397-52102017 · Cited by 18 · see citing works
Chatterjee, D., & Rao, S. (2020) · ACM Computing Surveys · doi:10.1145/3409797 · Cited by 17 · see citing works
“Last, agent-based modeling (ABM) [Alonso-Betanzos et al. 2017; Conte and Paolucci 2014; Trivedi and Rao 2018] has been frequently used for modeling of dynamic systems [Bonabeau 2002] in computational sustainability and in computational social science.”
MethodUsing Neural Networks for a Universal Framework for Agent-based Models
Jäger, G. (2021) · Mathematical and Computer Modelling of Dynamical Systems · doi:10.1080/13873954.2021.1889609 · Cited by 15 · see citing works
“Especially in areas, where the agents represent human beings or entities influenced by human behaviour and decision making (corporations, governments), agent-based modelling is a promising technique that is getting more and more popular recently [15–17], but also faces unique challenges [18–20].”
BackgroundIn silico study of heterogeneous tumour-derived organoid response to CAR T-cell therapy
Luque, L. M., Carlevaro, C. M., Rodríguez‐Lomba, E., & Lomba, E. (2024) · Scientific Reports · doi:10.1038/s41598-024-63125-5 · Cited by 14 · see citing works
“Although these models simplify many aspects of reality, they have been shown to be extremely useful in a wide number of circumstances 32–34 .”
Simulating compliance behaviour for effective inspection strategies using agent based modelling
Asselt, E. D. V., Osinga, S., & Bremmers, H. J. (2016) · British Food Journal · doi:10.1108/bfj-05-2015-0175 · Cited by 14 · see citing works
Weisburd, D. L., Wolfowicz, M., Hasisi, B., Paolucci, M., & Andrighetto, G. (2022) · Criminology & Public Policy · doi:10.1111/1745-9133.12579 · Cited by 14 · see citing works
Self-citationWijermans, N., Schill, C., Lindahl, T., & Schlüter, M. (2022) · International Journal of Social Research Methodology · doi:10.1080/13645579.2022.2050120 · Cited by 14 · see citing works
“Core features that draw researchers to ABMs are: 1) the dynamics of interactions leading to emergent outcomes (multi-levelness), and 2) the explicit representation of dynamic behaviour of heterogeneous agents (Conte & Paolucci, 2014; Heckbert et al., 2010).”
BackgroundYou, Z.-Q., Han, X.-P., & Hadzibeganovic, T. (2015) · Physics Letters A · doi:10.1016/j.physleta.2015.12.022 · Cited by 12 · see citing works
Gameiro, D., Pérez‐Pérez, M., Rodríguez, G. P., Monteiro, G., Azevedo, N. F., & Lourenço, A. M. G. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv096 · Cited by 13 · see citing works
Picascia, S., & Mitchell, R. (2021) · Health & Place · doi:10.1016/j.healthplace.2021.102729 · Cited by 13 · see citing works
Towards Computational and Behavioral Social Science
Conte, R., & Giardini, F. (2016) · European Psychologist · doi:10.1027/1016-9040/a000257 · Cited by 13 · see citing works
Self-citationLettieri, N., Altamura, A., Malandrino, D., & Punzo, V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-65340-2_2 · Cited by 13 · see citing works
A survey on agent‐based modelling assisted by machine learning
Platas‐López, A., Guerra‐Hernández, A., Quiróz-Castellanos, M., & Cruz-Ramírez, N. (2023) · Expert Systems · doi:10.1111/exsy.13325 · Cited by 12 · see citing works
Daza, S., & Kreuger, L. (2019) · Sociological Methods & Research · doi:10.1177/0049124119826147 · Cited by 10 · see citing works
“…to formalize theory (Squazzoni 2012), to propose mechanisms able to recreate regularities, and to guide empirical research (Chattoe-Brown 2013; Conte and Paolucci 2014; Macy and Willer 2002), we are unaware of previous research that uses ABMs to assess the robustness of statistical methods.”
BackgroundA prospect for the geographical research of sport in the age of Big Data
Tian, E. (2018) · Sport in Society · doi:10.1080/17430437.2018.1555233 · Cited by 12 · see citing works
“But Conte and Mario (2014) point out that whether the datasets are large enough or not, the span of research is limited without sufficient theoretical explanations.”
BackgroundA social engineering model for poverty alleviation
Chattopadhyay, A. K., Kumar, T. K., & Rice, I. (2020) · Nature Communications · doi:10.1038/s41467-020-20201-4 · Cited by 12 · see citing works
Assessing the Impact of Substandard and Falsified Antimalarials in Benin
Bui, V., Higgins, C. R., Laing, S., & Ozawa, S. (2021) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.21-0450 · Cited by 11 · see citing works
Generative Agents in Agent-Based Modeling: Overview, Validation, and Emerging Challenges
Adornetto, C., Mora, A., Hu, K., Garcia, L. I., Atchade-Adelomou, P., Greco, G., Alonso, L., & Larson, K. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3566362 · Cited by 10 · see citing works
“According to the definitions provided by [17], models are considered generative of an observed social phenomenon when they describe it in terms of the external (environmental and social) and internal (behavioral) mechanisms that generate it, rather than inferring causes from observed…”
Kinetic equations for processes on co-evolving networks
Burger, M. (2022) · Kinetic and Related Models · doi:10.3934/krm.2021051 · Cited by 10 · see citing works
Sensemaking of causality in agent-based models
Antosz, P., Szczepanska, T., Bouman, L., Polhill, G. J., & Jager, W. (2022) · International Journal of Social Research Methodology · doi:10.1080/13645579.2022.2049510 · Cited by 10 · see citing works
“For example, Conte and Paolucci (2014) argue for including bi-directional causality when representing social phenomena, and Filatova et al. (2013) when representing socio-ecological systems.”
BackgroundOssewaarde, M., Filatova, T., Georgiadou, Y., Hartmann, A., Özerol, G., Pfeffer, K., Stegmaier, P., Torenvlied, R., van der Voort, M. C., Warmink, J. J., et al. (2021) · Natural hazards and earth system sciences · doi:10.5194/nhess-21-1119-2021 · Cited by 10 · see citing works
Explaining the Emerging Influence of Culture, from Individual Influences to Collective Phenomena
Vanhée, L., & Dignum, F. P. M. (2018) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.3881 · Cited by 9 · see citing works
“While this principle should not be blindly pursued, as argued by Conte & Paolucci (2014), no better alternative stands out.”
BackgroundMethodInfluentialThe GDPR beyond Privacy: Data-Driven Challenges for Social Scientists, Legislators and Policy-Makers
Vestoso, M. (2018) · Future Internet · doi:10.3390/fi10070062 · Cited by 9 · see citing works
“On the other hand, as highlighted by Conte et al. [36], the quantitative variant of CSS rooted in data statistical analysis is unable to explain mechanisms underlying social complexity and, thus, should not be considered as independent from a modelling need.”
BackgroundEmmert‐Streib, F., & Dehmer, M. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.591749 · Cited by 10 · see citing works
“An important aspect of methods from CSS are simulation-based approaches, e.g., agent-based modeling (Ciof fi -Revilla, 2010; Conte et al., 2012; Conte and Paolucci, 2014; Holme and Liljeros, 2015).”
MethodTucker, J., & Lorig, F. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1337151 · Cited by 8 · see citing works
“This type of experiment is also referred to as in silico experiment and is increasingly used in, for instance, biology, social sciences, and engineering (16, 17).”
Mark Levene, A., & Fenner, T. I. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.02.002 · Cited by 8 · see citing works
Ontological Foundation of Ecosystem Services and the Human Dimension of Agroecosystems
Martin‐Clouaire, R. (2018) · Agricultural Sciences · doi:10.4236/as.2018.95036 · Cited by 8 · see citing works
“The capacity for the described concepts to be incorporated as primitives in an agent-based modeling platform [44] is the challenging next step.”
BackgroundRodič, B. (2020) · International Scientific Conference ITEMA. Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture · doi:10.31410/itema.2020.231 · Cited by 7 · see citing works
“Conte and Paolucci (Conte & Paolucci, 2014) offer a similar view, and advise that model designer should strive to create a model system that captures the aspects of the real-world system in which she is interested, with enough complexity to create a credible analogy between the model and real world…”
BackgroundComplex adaptive systems science in the era of global sustainability crisis
An, L., Turner, B. L., Liu, J., Grimm, V., Zhang, Q., Wang, Z., & Huang, R. (2024) · Geography and sustainability · doi:10.1016/j.geosus.2024.09.011 · Cited by 7 · see citing works
Randomness, Emergence and Causation: A Historical Perspective of Simulation in the Social Sciences
Seri, R., Secchi, D., & Martinoli, M. (2022) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-030-95703-2_7 · Cited by 7 · see citing works
History of Philosophy and the Reflective Society
Pozzo, R. (2021) · De Gruyter eBooks · doi:10.1515/9783110709292 · Cited by 7 · see citing works
Using a Modeling-Based Approach to Assess and Optimize HIV Linkage to Care Services
McKay, V., Cambey, C., Combs, T., Stubbs, A. W., Pichon, L. C., & Gaur, A. H. (2020) · AIDS and Behavior · doi:10.1007/s10461-020-03051-5 · Cited by 7 · see citing works
“Once developed, the model can be used as a simulated laboratory to test interventions and understand the subsequent impact on other aspects of the system over time [20, 21].”
MethodAgent-Based Models Assisted by Supervised Learning: A Proposal for Model Specification
Platas‐López, A., Guerra‐Hernández, A., Quiróz-Castellanos, M., & Cruz-Ramírez, N. (2023) · Electronics · doi:10.3390/electronics12030495 · Cited by 7 · see citing works
Balke, T., Cranefield, S., Tosto, G. D., Mahmoud, S., Paolucci, M., Savarimuthu, B. T. R., & Verhagen, H. (2013) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/dfu.vol4.12111.171 · Cited by 7 · see citing works
Self-citationNorms in Social Simulation: Balancing Between Realism and Scalability
Păstrăv, C., & Dignum, F. P. M. (2020) · Springer proceedings in complexity · doi:10.1007/978-3-030-34127-5_32 · Cited by 7 · see citing works
An agent-based modeling approach for simulating solar PV adoption: A case study of Irish dairy farms
Faiud, I., Schukat, M., & Mason, K. (2024) · Renewable energy focus · doi:10.1016/j.ref.2024.100653 · Cited by 6 · see citing works
SimSpark: Interactive Simulation of Social Media Behaviors
Lin, Z., Shan, Y., Gao, L., Jia, X., & Chen, S. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711066 · Cited by 6 · see citing works
“Additionally, extracting the data needed to address specific research questions from raw datasets requires significant computational resources and a multidisciplinary approach to ensure the reliability and validity of the extracted data [12, 62].”
Luan, T. (2024) · International Journal of Emerging Technologies and Advanced Applications · doi:10.62677/ijetaa.2405119 · Cited by 6 · see citing works
“In recent years, social computing, a new interdisciplinary field integrating social science and computer science, has emerged, aiming to understand and tackle complex social problems with the help of computational simulation methods [30].”
BackgroundSileno, G., Boer, A., & van Engers, T. (2014) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0004909606220631 · Cited by 6 · see citing works
A stochastic differential equation approach to the analysis of the UK 2016 EU referendum polls
Fenner, T. I., Mark Levene, A., & Loizou, G. D. (2018) · Journal of Physics Communications · doi:10.1088/2399-6528/aac347 · Cited by 6 · see citing works
“Generative models, arising from agent-based modelling [CP14], have played an important role in the sociophysics literature in the context of opinion dynamics [CFL09, SLST17].”
MethodDistributed Agent-Based Simulation and GIS: An Experiment with the Dynamics of Social Norms
Lettieri, N., Spagnuolo, C., & Vicidomini, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27308-2_31 · Cited by 6 · see citing works
Agent-Based Spatiotemporal Simulation of Biomolecular Systems within the Open Source MASON Framework
Rodríguez, G. P., Pérez‐Pérez, M., Glez‐Peña, D., Fdez‐Riverola, F., Azevedo, N. F., & Lourenço, A. M. G. (2015) · BioMed Research International · doi:10.1155/2015/769471 · Cited by 6 · see citing works
“Agent-based modelling (ABM) is as a relatively new paradigm for engineering complex and distributed intelligent systems [17].”
BackgroundTemporal dynamics of angiogenesis: the emerging role of mechanoregulated pathways
Zarin-Bal, S., Passier, M., Bentley, K., & Ristori, T. (2025) · Biochemical Society Transactions · doi:10.1042/bst20253048 · Cited by 6 · see citing works
“This approach allows the analysis of phenomena at the collective level that result from interactions at smaller scales [73,74].”
BharatSim: An agent-based modelling framework for India
Cherian, P., Kshirsagar, J., Neekhra, B., Deshkar, G., Hayatnagarkar, H. G., Kapoor, K., Kaski, C., Kathar, G., Khandekar, S., Mookherjee, S., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012682 · Cited by 5 · see citing works
Schlüter, M., Wijermans, N., González‐Mon, B., Lindkvist, E., Orach, K., Prawitz, H., Martin, R., Martínez-Peña, R., Pellowe, K. E., & Sanga, U. (2025) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2025.106444 · Cited by 5 · see citing works
“…and ecology have long argued for the potential of agent-based modelling (ABM) to support theory development in complex systems (Antosz et al., 2023; Conte and Paolucci, 2014; Dilaver and Gilbert, 2023; Grimm et al., 2024; Lorscheid et al., 2019; O ’ Sullivan et al., 2015; Smaldino et al., 2015).”
Yang, Y. (2024) · Journal for the Theory of Social Behaviour · doi:10.1111/jtsb.12412 · Cited by 4 · see citing works
High performance computing for three-dimensional agent-based molecular models
Rodríguez, G. P., Pérez‐Pérez, M., Fdez‐Riverola, F., & Lourenço, A. M. G. (2016) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2016.06.001 · Cited by 5 · see citing works
Modelling Adaptive and Anticipatory Human Decision-Making in Complex Human-Environment Systems
Madsen, J. K., Powers, B. R., Bailey, R. M., Carrella, E., Payette, N., & Pilditch, T. D. (2024) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.5214 · Cited by 5 · see citing works
Computational Social and Behavioral Science
Conte, R. (2016) · Springer eBooks · doi:10.1007/978-3-319-23938-5_1 · Cited by 4 · see citing works
Self-citationTesting the Social Function of Metacognition for Common‐Pool Resource Use
Fischer, H., Wijermans, N., & Schlüter, M. (2023) · Cognitive Science · doi:10.1111/cogs.13212 · Cited by 4 · see citing works
Computational Social Science and Big Data: A Quick SWOT Analysis
Leiber, T. (2017) · Springer eBooks · doi:10.1007/978-3-658-12153-2_14 · Cited by 5 · see citing works
Analyzing the Effects of Memory Biases and Mood Disorders on Social Performance
Sreenivas, N. K., & Rao, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-77715-6 · Cited by 4 · see citing works
Developing political-ecological theory: The need for many-task computing
Haas, T. C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226861 · Cited by 4 · see citing works
“This separation of concerns allows the two elements to be managed 752 and developed independently [19].”
BackgroundInfluentialXiao, B., Yin, Z., & Shan, Z. (2023) · arXiv · doi:10.48550/arxiv.2311.06957 · Cited by 4 · see citing works
“To address these challenges, researchers have proposed using agents with diverse personalities and heterogeneous system construction [35, 36], even retaining individual radical views.”
BackgroundGimmelberg, D., & Ludviga, I. (2025) · International Journal of Financial Studies · doi:10.3390/ijfs13040210 · Cited by 5 · see citing works
- 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 · 2013: 3
- 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 and History of Science
- Keywords
- agent-based modeling, computational social science, generative theory, generative power, cognitive models, interdisciplinary research, quantitative social science
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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