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Rosaria Conte (1952–2016)

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By Mario Paolucci

This is the “header piece” for a short series on those who have been awarded the “Rosaria Conte Outstanding Award for Social Simulation” awarded by the European Social Simulation Association every two years. It makes no sense to describe those who have got this award without information about the person which it is named after, so this is about her.

Rosaria Conte was one of the first researchers in Europe to recognize and champion agent-based social simulation. She became a leader of what would later become the ESSA community in the 1990s, chairing the 1997 ICCS&SS – First International Conference on Computer Simulation and the Social Sciences in Cortona, Italy, and co-editing with Nigel Gilbert the book Artificial Societies (Gilbert & Conte, 1995). With her unique approach, her open approach to interdisciplinarity, and her charisma, she inspired and united a generation of researchers who still pursue her scientific endeavour.

Known as a relentless advocate for cognitive agents in the agent-based modeling community, Conte stood firmly against the keep-it-simple principle. Instead, she argued that plausible agents—those capable of explaining complex social phenomena where immergence (Castelfranchi, 1998; Conte et al., 2009) is as critical as emergence—require explicit, theory-backed representations of cognitive artifacts (Conte & Paolucci, 2011).

Born in Foggia, Italy, Rosaria graduated in philosophy at the University of Rome La Sapienza in 1976, to later join the Italian National Research Council (Consiglio Nazionale delle Ricerche, CNR). In the ‘90s, she founded and directed the Laboratory of Agent-Based Social Simulation (LABSS) at the Institute of Cognitive Sciences and Technologies (ISTC-CNR). Under her leadership, LABSS became an internationally renowned hub for research on agent-based modeling and social simulation. Conte’s work at LABSS focused on the development of computational models to study complex social phenomena, including cooperation, reputation, and social norms.

Influenced by collaborators such as Cristiano Castelfranchi and Domenico Parisi, whose guidance helped shape her studies of social behavior through computational models, she proposed the integration of cognitive and social theories into agent-based models. Unlike approaches that treated agents as simple rule-followers, Rosaria emphasized the importance of incorporating cognitive and emotional processes into simulations. Her 1995 book, Cognitive and Social Action (Conte & Castelfranchi, 1995), became a landmark text in the field. The book employed their characteristic pre-formal approach—using logic formulas in order to illustrate relationships between concepts, without a fully developed system of postulates or theorem-proving tools. The reason for this approach was, as they noted, that “formalism sometimes disrupts implicit knowledge and theories” (p. 14). The ideas in the book, together with her attention to the dependance relations between agents (Sichman et al., 1998) would go on to inspire Rosaria’s approach to simulation throughout her career.

Rosaria’s research extended to the study of reputation and social norms. For reputation (Conte & Paolucci, 2002), an attempt to create a specific, cognitive-based model has been made with the Repage approach (Sabater et al., 2006). Regarding social norms (Andrighetto et al., 2007), she explored how norms emerge, spread, and influence individual and collective behavior. This work had practical implications for a range of fields, including organizational behavior, policy design, and conflict resolution. She had a key role in the largest recent attempt to create a center for complexity and social sciences, the FuturICT project (Conte et al., 2012).

Rosaria Conte held several leadership positions. She served as President of the European Social Simulation Society (ESSA) from 2010 to 2012. Additionally, she was President of the Italian Cognitive Science Association (AISC) from 2008 to 2009, member of the Italian Bioethics Committee (CNB) from 2013 to 2016, and Vice President of the Italian CNR Scientific Council.

You can watch an interview with Rosaria about FuturICT here: https://www.youtube.com/watch?v=ghgzt5zgGP8

References

Andrighetto, G., Campenni, M., Conte, R., & Paolucci, M. (2007). On the immergence of norms: A normative agent architecture. Proceedings of AAAI Symposium, Social and Organizational Aspects of Intelligence. http://www.aaai.org/Library/Symposia/Fall/fs07-04.php

Castelfranchi, C. (1998). Simulating with Cognitive Agents: The Importance of Cognitive Emergence. Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation, 26–44. http://portal.acm.org/citation.cfm?id=665578

Conte, R., Andrighetto, G., & Campennì, M. (2009). The Immergence of Norms in Agent Worlds. In H. Aldewereld, V. Dignum, & G. Picard (Eds.), Engineering Societies in the Agents World X< (pp. 1–14). Springer. https://doi.org/10.1007/978-3-642-10203-5_1

Conte, R., & Castelfranchi, C. (1995). Cognitive Social Action. London: UCL Press.

Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., Loreto, V., Moat, S., Nadal, J.-P., Sanchez, A., Nowak, A., Flache, A., San Miguel, M., & Helbing, D. (2012). Manifesto of computational social science. The European Physical Journal Special Topics, 214(1), 325–346. https://doi.org/10.1140/epjst/e2012-01697-8

Conte, R., & Paolucci, M. (2002). Reputation in Artificial Societies—Social Beliefs for Social Order. Springer. https://iris.unibs.it/retrieve/ddc633e2-a83d-4e2e-e053-3705fe0a4c80/Review%20of%20Conte%2C%20Rosaria%20and%20Paolucci%2C%20Mario_%20Reputation%20in%20Artificial%20Socie.pdf

Conte, R., & Paolucci, M. (2011). On Agent Based Modelling and Computational Social Science. Social Science Research Network Working Paper Series. https://doi.org/10.3389/fpsyg.2014.00668

Gilbert, N., & Conte, R. (Eds.). (1995). Artificial Societies: The Computer Simulation of Social Life. Taylor & Francis, Inc. https://library.oapen.org/bitstream/handle/20.500.12657/24305/1005826.pdf

Sabater, J., Paolucci, M., & Conte, R. (2006). Repage: REPutation and ImAGE Among Limited Autonomous Partners. Journal of Artificial Societies and Social Simulation, 9<(2). http://jasss.soc.surrey.ac.uk/9/2/3.html

Sichman, J. S., Conte, R., Demazeau, Y., & Castelfranchi, C. (1998). A social reasoning mechanism based on dependence networks. 416–420.

Paolucci, M. (2023) Rosaria Conte (1952-2016). Review of Artificial Societies and Social Simulation, 11 Feb 2023. https://rofasss.org/2025/02/11/rosariaconte/

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

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Outlining some requirements for synthetic populations to initialise agent-based models

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By Nick Roxburgh1, Rocco Paolillo2, Tatiana Filatova3, Clémentine Cottineau3, Mario Paolucci2 and Gary Polhill1

1  The James Hutton Institute, Aberdeen AB15 8QH, United Kingdom {nick.roxburgh,gary.polhill}@hutton.ac.uk

2  Institute for Research on Population and Social Policies, Rome, Italy {rocco.paolillo,mario.paolucci}@cnr.it

3 Delft University of Technology, Delft, The Netherlands {c.cottineau,t.filatova}@tudelft.nl

Abstract. We propose a wish list of features that would greatly enhance population synthesis methods from the perspective of agent-based modelling. The challenge of synthesising appropriate populations is heightened in agent-based modelling by the emphasis on complexity, which requires accounting for a wide array of features. These often include, but are not limited to: attributes of agents, their location in space, the ways they make decisions and their behavioural dynamics. In the real-world, these aspects of everyday human life can be deeply interconnected, with these associations being highly consequential in shaping outcomes. Initialising synthetic populations in ways that fail to respect these covariances can therefore compromise model efficacy, potentially leading to biased and inaccurate simulation outcomes.

1 Introduction

With agent-based models (ABMs), the rationale for creating ever more empirically informed, attribute-rich synthetic populations is clear: the closer agents and their collectives mimic their  real-world counterparts, the more accurate the models can be and the wider the range of questions they can be used to address (Zhou et al., 2022). However, while many ABMs would benefit from synthetic populations that more fully capture the complexity and richness of real-world populations – including their demographic and psychological attributes, social networks, spatial realms, decision making, and behavioural dynamics – most efforts are stymied by methodological and data limitations. One reason for this is that population synthesis methods have predominantly been developed with microsimulation applications in mind (see review by Chapuis et al. (2022)), rather than ABM. We therefore argue that there is a need for improved population synthesis methods, attuned to support the specific requirements of the ABM community, as well as commonly encountered data constraints. We propose a wish list of features for population synthesis methods that could significantly enhance the capability and performance of ABMs across a wide range of application domains, and we highlight several promising approaches that could help realise these ambitions. Particular attention is paid to methods that prioritise accounting for covariance of characteristics and attributes.

2 The interrelationships among aspects of daily life

2.1 Demographic and psychological attributes

To effectively replicate real-world dynamics, ABMs must realistically depict demographic and psychological attributes at both individual and collective levels. A critical aspect of this realism is accounting for the covariance of such attributes. For instance, interactions between race and income levels significantly influence spatial segregation patterns in the USA, as demonstrated in studies like Bruch (2014).

Several approaches to population synthesis have been developed over the years, often with a specific focus on assignment of demographic attributes. That said, where psychological attributes are collected in surveys alongside demographic data, they can be incorporated into synthetic populations just like other demographic attributes (e.g., Wu et al. (2022)). Among the most established methods is Iterative Proportional Fitting (IPF). While capable of accounting for covariances, it does have significant limitations. One of these is that it “matches distributions only at one demographic level (i.e., either household or individual)” (Zhou et al., 2022 p.2). Other approaches have sought to overcome this – such as Iterative Proportional Updating, Combinatorial Optimisation, and deep learning methods – but they invariably have their own limitations and downsides, though the extent to which these will matter depends on the application. In their overview of the existing population synthesis landscape, Zhou et al., (2022) suggest that deep learning methods appear particularly promising for high-dimensional cases. Such approaches tend to be data hungry, though – a potentially significant barrier to exploitation given many studies already face challenges with survey availability and sample size.

2.2 Social networks

Integrating realistic social networks into ABMs during population synthesis is crucial for effectively mimicking real-world social interactions, such as those underlying epidemic spread, opinion dynamics, and economic transactions (Amblard et al., 2015). In practice, this means generating networks that link agents by edges that represent particular associations between them. These networks may need to be weighted, directional, or multiplex, and potentially need to account for co-dependencies and correlations between layers. Real-world social networks emerge from distinct processes and tendencies. For example, homophily preferences strongly influence the likelihood of friendship formation, with connections more likely to have developed in cases where agents share attributes like age, gender, socio-economic context, and location (McPherson et al., 2001). Another example is personality which can strongly influence the size and nature of an individual’s social network (Zell et al., 2014). For models where social interactions play an important role, it is therefore critical that consideration be given to the underlying factors and mechanisms that are likely to have influenced the development of social networks historically, if synthetic networks are to have any chance of reasonably depicting real world network structures.

Generating synthetic social networks is challenging due to often limited or unavailable data. Consequently, researchers tend to use simple models like regular lattices, random graphs, small-world networks, scale-free networks, and models based on spatial proximity. These models capture basic elements of real-world social networks but can fall short in complex scenarios. For instance, Jiang et al. (2022) describes a model where agents, already assigned to households and workplaces, form small-world networks based on employment or educational ties. While this approach accounts for spatial and occupational similarities, it overlooks other factors, limiting its applicability for networks like friendships that rely on personal history and intangible attributes.

To address these limitations, more sophisticated methods have been proposed, including Exponential Random Graph Models (ERGM) (Robins et al., 2007) and Yet Another Network Generator (YANG) (Amblard et al., 2015). However, they also come with their own challenges; for example, ERGMs sometimes misrepresent the likelihood of certain network structures, deviating from real-world observations.

2.3 Spatial locations

The places where people live, work, take their leisure and go to school are critically interlinked and interrelated with social networks and demographics. Spatial location also affects options open to people, including transport, access to services, job opportunities and social encounters. ABMs’ capabilities in representing space explicitly and naturally is a key attraction for geographers interested in social simulation and population synthesis (Cottineau et al., 2018). Ignoring the spatial concentration of agents with common traits, or failing to account for the effects that space has on other aspects of everyday human existence, risks overlooking a critical factor that influences a wide range of social dynamics and outcomes.

Spatial microsimulation generates synthetic populations tailored to defined geographic zones, such as census tracts (Lovelace and Dumont, 2017). However, many ABM applications require agents to be assigned to specific dwellings and workplaces, not just aggregated zones. While approaches to dealing with this have been proposed, agreement on best practice is yet to cohere. Certain agent-location assignments can be implemented using straightforward heuristic methods without greatly compromising fidelity, if heuristics align well with real-world practices. For example, children might be allocated to schools simply based on proximity, such as in Jiang et al., (2022). Others use rule-based or stochastic methods to account for observed nuances and random variability, though these often take the form of crude approximations. One of the more well-rounded examples is detailed by Zhou et al. (2022). They start by generating a synthetic population, which they then assign to specific dwellings and jobs using a combination of rule-based matching heuristic and probabilistic models. Dwellings are assigned to households by considering factors like household size, income, and dwelling type jointly. Meanwhile, jobs are assigned to workers using a destination choice model that predicts the probability of selecting locations based on factors such as sector-specific employment opportunities, commuting costs, and interactions between commuting costs and individual worker attributes. In this way, spatial location choices are more closely aligned with the diverse attributes of agents. The challenge with such an approach is to obtain sufficient microdata to inform the rules and probabilities.

2.4 Decision-making and behavioural dynamics

In practice, peoples’ decision-making and behaviours are influenced by an array of factors, including their individual characteristics such as wealth, health, education, gender, and age, their social network, and their geographical circumstances. These factors shape – among other things – the information agents’ are exposed to, the choices open to them, the expectations placed on them, and their personal beliefs and desires (Lobo et al., 2023). Consequently, accurately initialising such factors is important for ensuring that agents are predisposed to make decisions and take actions in ways that reflect how their real world counterparts might behave. Furthermore, the assignment of psychographic attributes to agents necessitates the prior establishment of these foundational characteristics as they are often closely entwined.

Numerous agent decision-making architectures have been proposed (see Wijermans et al. (2023)). Many suggest that a range of agent state attributes could, or even should, be taken into consideration when evaluating information and selecting behaviours. For example, the MoHub Framework (Schlüter et al., 2017) proposes four classes of attributes as potentially influential in the decision-making process: needs/goals, knowledge, assets, and social. In practice, however, the factors taken into consideration in decision-making procedures tend to be much narrower. This is understandable given the higher data demands that richer decision-making procedures entail. However, it is also regrettable given we know that decision-making often draws on many more factors than are currently accounted for, and the ABM community has worked hard to develop the tools needed to depict these richer processes.

3 Practicalities

Our wish list of features for synthetic population algorithms far exceeds their current capabilities. Perhaps the main issue today is data scarcity, especially concerning less tangible aspects of populations, such as psychological attributes and social networks, where systematic data collection is often more limited. Another significant challenge is that existing algorithms struggle to manage the numerous conditional probabilities involved in creating realistic populations, excelling on niche measures of performance but not from a holistic perspective. Moreover, there are accessibility issues with population synthesis tools. The next generation of methods need to be made more accessible to non-specialists through developing easy to use stand-alone tools or plugins for widely used platforms like NetLogo, else they risk not having their potential exploited.

Collectively, these issues may necessitate a fundamental rethink of how synthetic populations are generated. The potential benefits of successfully addressing these challenges are immense. By enhancing the capabilities of synthetic population tools to meet the wish list set out here, we can significantly improve model realism and expand the potential applications of social simulation, as well as strengthen credibility with stakeholders. More than this, though, such advancements would enhance our ability to draw meaningful insights, respecting the complexities of real-world dynamics. Most critically, better representation of the diversity of actors and circumstances reduces the risk of overlooking factors that might adversely impact segments of the population – something there is arguably a moral imperative to strive for.

Acknowledgements

MP & RP were supported by FOSSR (Fostering Open Science in Social Science Research), funded by the European Union – NextGenerationEU under NPRR Grant agreement n. MUR IR0000008. CC was supported by the ERC starting Grant SEGUE (101039455).

References

Amblard, F., Bouadjio-Boulic, A., Gutiérrez, C.S. and Gaudou, B. 2015, December. Which models are used in social simulation to generate social networks? A review of 17 years of publications in JASSS. In 2015 Winter Simulation Conference (WSC) (pp. 4021-4032). IEEE. https://doi.org/10.1109/WSC.2015.7408556

Bruch, E.E., 2014. How population structure shapes neighborhood segregation. American Journal of Sociology, 119(5), pp.1221-1278. https://doi.org/10.1086/675411

Chapuis, K., Taillandier, P. and Drogoul, A., 2022. Generation of synthetic populations in social simulations: a review of methods and practices. Journal of Artificial Societies and Social Simulation, 25(2). https://doi.org/10.18564/jasss.4762

Cottineau, C., Perret, J., Reuillon, R., Rey-Coyrehourcq, S. and Vallée, J., 2018, March. An agent-based model to investigate the effects of social segregation around the clock on social disparities in dietary behaviour. In CIST2018-Représenter les territoires/Representing territories (pp. 584-589). https://hal.science/hal-01854398v1

Jiang, N., Crooks, A.T., Kavak, H., Burger, A. and Kennedy, W.G., 2022. A method to create a synthetic population with social networks for geographically-explicit agent-based models. Computational Urban Science, 2(1), p.7. https://doi.org/10.1007/s43762-022-00034-1

Lobo, I., Dimas, J., Mascarenhas, S., Rato, D. and Prada, R., 2023. When “I” becomes “We”: Modelling dynamic identity on autonomous agents. Journal of Artificial Societies and Social Simulation, 26(3). https://doi.org/10.18564/jasss.5146

Lovelace, R. and Dumont, M., 2017. Spatial microsimulation with R. Chapman and Hall/CRC. https://spatial-microsim-book.robinlovelace.net

McPherson, M., Smith-Lovin, L. and Cook, J.M., 2001. Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), pp.415-444. https://doi.org/10.1146/annurev.soc.27.1.415

Robins, G., Pattison, P., Kalish, Y. and Lusher, D., 2007. An introduction to exponential random graph (p*) models for social networks. Social networks, 29(2), pp.173-191. https://doi.org/10.1016/j.socnet.2006.08.002

Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M.A., McAllister, R.R., Müller, B., Orach, K. and Schwarz, N., 2017. A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecological economics, 131, pp.21-35. https://doi.org/10.1016/j.ecolecon.2016.08.008

Wijermans, N., Scholz, G., Chappin, É., Heppenstall, A., Filatova, T., Polhill, J.G., Semeniuk, C. and Stöppler, F., 2023. Agent decision-making: The Elephant in the Room-Enabling the justification of decision model fit in social-ecological models. Environmental Modelling & Software, 170, p.105850. https://doi.org/10.1016/j.envsoft.2023.105850

Wu, G., Heppenstall, A., Meier, P., Purshouse, R. and Lomax, N., 2022. A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain. Scientific Data, 9(1), p.19. https://doi.org/10.1038/s41597-022-01124-9

Zell, D., McGrath, C. and Vance, C.M., 2014. Examining the interaction of extroversion and network structure in the formation of effective informal support networks. Journal of Behavioral and Applied Management, 15(2), pp.59-81. https://jbam.scholasticahq.com/article/17938.pdf

Zhou, M., Li, J., Basu, R. and Ferreira, J., 2022. Creating spatially-detailed heterogeneous synthetic populations for agent-based microsimulation. Computers, Environment and Urban Systems, 91, p.101717. https://doi.org/10.1016/j.compenvurbsys.2021.101717

Roxburgh, N., Paolillo, R., Filatova, T., Cottineau, C., Paolucci, M. and Polhill, G. (2025) Outlining some requirements for synthetic populations to initialise agent-based models. Review of Artificial Societies and Social Simulation, 27 Jan 2025. https://rofasss.org/2025/01/29/popsynth

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

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An Institute for Crisis Modelling (ICM) – Towards a resilience center for sustained crisis modeling capability

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By Fabian Lorig1*, Bart de Bruin2, Melania Borit3, Frank Dignum4, Bruce Edmonds5, Sinéad M. Madden6, Mario Paolucci7, Nicolas Payette8, Loïs Vanhée4

*Corresponding author
1 Internet of Things and People Research Center, Malmö University, Sweden
2 Delft University of Technology, Netherlands
3 CRAFT Lab, Arctic University of Norway, Tromsø, Norway
4 Department of Computing Science, Umeå University, Sweden
5 Centre for Policy Modelling, Manchester Metropolitan University Business School, UK
6 School of Engineering, University of Limerick, Ireland
7 Laboratory of Agent Based Social Simulation, ISTC/CNR, Italy
8 Complex Human-Environmental Systems Simulation Laboratory, University of Oxford, UK

The Need for an ICM

Most crises and disasters do occur suddenly and hit the society while it is unprepared. This makes it particularly challenging to react quick to their occurrence, to adapt to the resulting new situation, to minimize the societal impact, and to recover from the disturbance. A recent example was the Covid-19 crisis, which revealed weak points of our crisis preparedness. Governments were trying to put restrictions in place to limit the spread of the virus while ensuring the well-being of the population and at the same time preserving economic stability. It quickly became clear that interventions which worked well in some countries did not seem to have the intended effect in other countries and the reason for this is that the success of interventions to a great extent depends on individual human behavior.

Agent-based Social Simulations (ABSS) explicitly model the behavior of the individuals and their interactions in the population and allow us to better understand social phenomena. Thus, ABSS are perfectly suited for investigating how our society might be affected by different crisis scenarios and how policies might affect the societal impact and consequences of these disturbances. Particularly during the Covid-19 crisis, a great number of ABSS have been developed to inform policy making around the globe (e.g., Dignum et al. 2020, Balkely et al. 2021, Lorig et al. 2021). However, weaknesses in creating useful and explainable simulations in a short time also became apparent and there is still a lack of consistency to be better prepared for the next crisis (Squazzoni et al. 2020). Especially, ABSS development approaches are, at this moment, more geared towards simulating one particular situation and validating the simulation using data from that situation. In order to be prepared for a crisis, instead, one needs to simulate many different scenarios for which data might not yet be available. They also typically need a more interactive interface where stake holders can experiment with different settings, policies, etc.

For ABSS to become an established, reliable, and well-esteemed method for supporting crisis management, we need to organize and consolidate the available competences and resources. It is not sufficient to react once a crisis occurs but instead, we need to proactively make sure that we are prepared for future disturbances and disasters. For this purpose, we also need to systematically address more fundamental problems of ABSS as a method of inquiry and particularly consider the specific requirements for the use of ABSS to support policy making, which may differ from the use of ABSS in academic research. We therefore see the need for establishing an Institute for Crisis Modelling (ICM), a resilience center to ensure sustained crisis modeling capability.

The vision of starting an Institute for Crisis Modelling was the result of the discussions and working groups at the Lorentz Center workshop on “Agent Based Simulations for Societal Resilience in Crisis Situations” that took place in Leiden, Netherlands from 27 February to 3 March 2023**.

Vision of the ICM

“To have tools suitable to support policy actors in situations that are of
big uncertainty, large consequences, and dependent on human behavior.”

The ICM consists of a taskforce for quickly and efficiently supporting policy actors (e.g., decision makers, policy makers, policy analysts) in situations that are of big uncertainty, large consequences, and dependent on human behavior. For this purpose, the taskforce consists of a larger (informal) network of associates that contribute with their knowledge, skills, models, tools, and networks. The group of associates is composed of a core group of multidisciplinary modeling experts (ranging from social scientists and formal modelers to programmers) as well as of partners that can contribute to specific focus areas (like epidemiology, water management, etc.). The vision of ICM is to consolidate and institutionalize the use of ABSS as a method for crisis management. Although physically ABSS competences may be distributed over a variety of universities, research centers, and other institutions, the ICM serves as a virtual location that coordinates research developments and provides a basic level of funding and communication channel for ABSS for crisis management. This does not only provide policy actors with a single point of contact, making it easier for them to identify who to reach when simulation expertise is needed and to develop long-term trust relationships. It also enables us to jointly and systematically evolve ABSS to become a valuable and established tool for crisis response. The center combines all necessary resources, competences, and tools to quickly develop new models, to adapt existing models, and to efficiently react to new situations.

To achieve this goal and to evolve and establish ABSS as a valuable tool for policy makers in crisis situations, research is needed in different areas. This includes the collection, development, critical analysis, and review of fundamental principles, theories, methods, and tools used in agent-based modeling. This also includes research on data handling (analysis, sharing, access, protection, visualization), data repositories, ontologies, user-interfaces, methodologies, documentation, and ethical principles. Some of these points are concisely described in (Dignum, 2021, Ch. 14 and 15).

The ICM shall be able to provide a wide portfolio of models, methods, techniques, design patterns, and components required to quickly and effectively facilitate the work of policy actors in crisis situations by providing them with adequate simulation models. For the purpose of being able to provide specialized support, the institute will coordinate the human effort (e.g., the modelers) and have specific focus areas for which expertise and models are available. This might be, for instance, pandemics, natural disasters, or financial crises. For each of these focus areas, the center will develop different use cases, which ensures and facilitates rapid responses due to the availability of models, knowledge, and networks.

Objectives of the ICM

To achieve this vision, there are a series of objectives that a resilience center for sustained crisis modeling capability in crisis situations needs to address:

1) Coordinate and promote research

Providing quick and appropriate support for policy actors in crisis situations requires not only a profound knowledge on existing models, methods, tools, and theories but also the systematic development of new approaches and methodologies. This is to advance and evolve ABSS for being better prepared for future crises and will serve as a beacon for organizing the ABSS research oriented towards practical applications.

2) Enable trusted connections with policy actors

Sustainable collaborations and interactions with decision-makers and policy analysts as well as other relevant stakeholders is a great challenge in ABSS. Getting in contact with the right actors, “speaking the same language”, and having realistic expectations are only some of the common problems that need to be addressed. Thus, the ICM should not only connect to policy actors in times of crises, but have continuous interactions, provide sample simulations, develop use cases, and train the policy actors wherever possible.

3) Enable sustainability of the institute itself

Classic funding schemes are unfit for responding in crises, which require fast responses with always-available resources as well as the continuous build-up of knowledge, skills, network, and technological buildup requires long-term. Sustainable funding is needed that for enabling such a continuity, for which the IBM provides a demarked, unifying frame.

4) Actively maintain the network of associates

Maintaining a network of experts is challenging because it requires different competences and experiences. PhD candidates, for instance, might have a great practical experience in using different simulation frameworks, however, after their graduation, some might leave academia and others might continue to other positions where they do not have the opportunity to use their simulation expertise. Thus, new experts need to be acquired continuously to form a resilient and balanced network.

5) Inform policy actors

The most advanced and profound models cannot do any good in crisis situations in case of a lacking demand from policy actors. Many modelers perceive a certain hesitation from policy actors regarding the use of ABSS which might be due to them being unfamiliar with the potential benefits and use-cases of ABSS, lacking trust in the method itself, or simply due to a lack of awareness that ABSS actually exists. Hence, the center needs to educate policy makers and raise awareness as well as improve trust in ABSS.

6) Train the next generation of experts

To quickly develop suitable ABSS models in critical situations requires a variety of expertise. In addition to objective 4, the acquisition of associates, it is also of great importance to educate and train the next generation of experts. ABSS research is still a niche and not taught as an inherent part of the spectrum of methods of most disciplines. The center shall promote and strengthen ABSS education to ensure the training of the next generation of experts.

7) Engage the general public

Finally, the success of ABSS does not only depend on the trust of policy actors but also on how it is perceived by the general public. When developing interventions during the Covid-19 crisis and giving recommendations, the trust in the method was a crucial success factor. Also, developing realistic models requires the active participation of the general public.

Next steps

For ABSS to become a valuable and established tool for supporting policy actors in crisis situations, we are convinced that our efforts need to be institutionalized. This allows us to consolidate available competences, models, and tools as well as to coordinate research endeavors and the development of new approaches required to ensure a sustained crisis modeling capability.

To further pursue this vision, a Special Interest Group (SIG) on Building ResilienCe with Social Simulations (BRICSS) was established at the European Social Simulation Association (ESSA). Moreover, Special Tracks will be organized at the 2023 Social Simulation Conference (SSC) to bring together interested experts.

However, for this vision to become reality, the next steps towards establishing an Institute for Crisis Modelling consist of bringing together ambitious and competent associates as well as identifying core funding opportunities for the center. If the readers feel motivated to contribute in any way to this topic, they are encouraged to contact Frank Dignum, Umeå University, Sweden or any of the authors of this article.

Acknowledgements

This piece is a result of discussions at the Lorentz workshop on “Agent Based Simulations for Societal Resilience in Crisis Situations” at Leiden, NL in earlier this year! We are grateful to the organisers of the workshop and to the Lorentz Center as funders and hosts for such a productive enterprise. The final report of the workshop as well as more information can be found on the webpage of the Lorentz Center: https://www.lorentzcenter.nl/agent-based-simulations-for-societal-resilience-in-crisis-situations.html

References

Blakely, T., Thompson, J., Bablani, L., Andersen, P., Ouakrim, D. A., Carvalho, N., Abraham, P., Boujaoude, M.A., Katar, A., Akpan, E., Wilson, N. & Stevenson, M. (2021). Determining the optimal COVID-19 policy response using agent-based modelling linked to health and cost modelling: Case study for Victoria, Australia. Medrxiv, 2021-01. doi: 10.1101/2021.01.11.21249630

Dignum, F., Dignum, V., Davidsson, P., Ghorbani, A., van der Hurk, M., Jensen, M., Kammler C., Lorig, F., Ludescher, L.G., Melchior, A., Mellema, R., Pastrav, C., Vanhee, L. & Verhagen, H. (2020). Analysing the combined health, social and economic impacts of the coronavirus pandemic using agent-based social simulation. Minds and Machines, 30, 177-194. doi: 10.1007/s11023-020-09527-6

Dignum, F. (ed.). (2021) Social Simulation for a Crisis; Results and Lessons from Simulating the COVID-19 Crisis. Springer.

Lorig, Fabian, Johansson, Emil and Davidsson, Paul (2021) ‘Agent-Based Social Simulation of the Covid-19 Pandemic: A Systematic Review’ Journal of Artificial Societies and Social Simulation 24(3), 5. http://jasss.soc.surrey.ac.uk/24/3/5.html. doi: 10.18564/jasss.4601

Squazzoni, F. et al. (2020) ‘Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action‘ Journal of Artificial Societies and Social Simulation 23(2), 10. http://jasss.soc.surrey.ac.uk/23/2/10.html. doi: 10.18564/jasss.4298

Lorig, F., de Bruin, B., Borit, M., Dignum, F., Edmonds, B., Madden, S.M., Paolucci, M., Payette, N. and Vanhée, L. (2023) An Institute for Crisis Modelling (ICM) –
Towards a resilience center for sustained crisis modeling capability. Review of Artificial Societies and Social Simulation, 22 May 2023. https://rofasss.org/2023/05/22/icm

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