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Modelling Deep Structural Change in Agent-Based Social Simulation

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By Thorid Wagenblast1, Nicholas Roxburgh2 and Alessandro Taberna3

1 Delft University of Technology, 0009-0003-5324-3778
2 The James Hutton Institute, 0000-0002-7821-1831
3 CMCC Foundation – Euro-Mediterranean Center on Climate Change, RFF-CMCC European Institute on Economics and the Environment, 0000-0002-0207-4148

Introduction

Most agent-based models (ABMs) are designed around the assumption of a broadly stable system architecture. Whether exploring emergent dynamics or testing the effects of external interventions or stressors, such models typically operate with a fixed ontology – predefined agent types, attribute classes, behavioural repertoires, processes, and social and institutional structures. While this can allow rich exploration of dynamics within the given configuration, it limits the model’s possibility space by excluding forms of change that would require the structure itself to evolve.

Some of the most consequential forms of real-world change involve shifts in the system architecture itself. These forms of change – what we refer to here as deep structural change – reconfigure the underlying logic and potentialities of the system. This may involve, for example, dramatic shifts in the environment in which agents operate, the introduction of novel technologies, or reshaping of the roles and categories through which agents understand and act in the world. Such transformations pose a fundamentally different challenge from those typically addressed in most agent-based modelling studies to date – one that pushes beyond parameter tuning or rule adjustment, and calls for new approaches to ontology design, model construction, and the conceptualisation of structural transformation and uncertainty in simulation.

Various theoretical lenses can be applied to this topic. The concepts of transformations or regime shifts seem particularly pertinent. Transformations, in contrast to incremental or minor changes, are changes that are large-scale and significant, but apart from that do not seem to consist of any specific features (Feola, 2015). The changes we explore here are more closely linked to regime shifts, which are characterised by structural changes, but with a notion of abruptness. Methods to detect and understand these regime shifts and the structural changes in relation to social simulation have been discussed for some time (Filatova, Polhill & van Ewijk, 2016). Nonetheless, there is still a lack of understanding around what this structural change entails and how this applies in social simulation, particularly ABMs.

To explore these issues, the European Social Simulation Association (ESSA) Special Interest Group on Modelling Transformative Change (SIG-MTC) organised a dedicated session at the Social Simulation Fest 2025. The session aimed to elicit experiences, ideas, and emerging practices from the modelling community around how deep structural change is understood and approached in agent-based simulation. Participants brought perspectives from a wide range of modelling contexts – including opinion dynamics, energy systems, climate adaptation, food systems, and pandemic response – with a shared interest in representing deep structural change. A majority of participants (~65%) reported that they were already actively working on, or thinking about, aspects of deep structural change in their modelling practice.

The session was framed as an opportunity to move beyond static ontologies and explore how models might incorporate adaptive structures or generative mechanisms capable of capturing deep structural shifts. As described in the session abstract:

“We will discuss what concepts related to deep structural change we observe and how models can incorporate adaptive ontologies or generative mechanisms to capture deep structural shifts. Furthermore, we want to facilitate discussion on the challenges we face when trying to model these deep changes and what practices are currently used to overcome these.”

This article reflects on key insights from that session, offering a synthesis of participant definitions, identified challenges, and promising directions for advancing the modelling of deep structural change in agent-based social simulation.

Defining deep structural change

Participant perspectives


To explore how participants understood deep structural change and its characteristics, we used both a pre-workshop survey (N=20) and live group discussion activities (N ≈ 20; divided into six discussion groups). The survey asked participants to define “deep structural change” in the context of social systems or simulations, and to explain how it differs from incremental change. During the workshop, groups expanded on these ideas using a collaborative Miro board, where they responded to three prompts: “What is deep structural change?”, “How does it differ from incremental change?”, and they were asked to come up with a “Group definition”. The exercises benefited from the conceptual and disciplinary diversity of participants. Individuals approached the prompts from different angles – shaped by their academic backgrounds and modelling traditions – resulting in a rich and multifaceted view of what deep structural change can entail.

Across the different exercises, a number of common themes emerged. One of the most consistent themes was the idea that deep structural change involves a reconfiguration of the system’s architecture – a shift in its underlying mechanisms, causal relationships, feedback loops, or rules of operation. This perspective goes beyond adjusting parameters; it points to transformations in what the system is, echoing the emphasis in our introductory framing on changes to the system’s underlying logic and potentialities. Participants described this in terms such as “change in causal graph”, “drastic shift in mechanisms and rules”, and “altering the whole architecture”. Some also emphasised the outcomes of such reconfigurations – the emergence of a new order, new dominant feedbacks, or a different equilibrium. As one participant put it, deep structural change is “something that brings out new structure”; others described “profound, systemic shifts that radically reshape underlying structures, processes and relationships”.

Another frequently discussed theme was the role of social and behavioural change in structural transformation – particularly shifts in values, norms, and decision-making. Several groups suggested that changes in attitudes, awareness, or shared meanings could contribute to or signal deeper structural shifts. In some cases, these were framed as indicators of transformation; in others, as contributing factors or intended outcomes of deliberate change efforts. Examples included evolving diets, institutional reform, and shifts in collective priorities. Participants referred to “behavioural change coming from a change in values and/or norms” and “a fundamental shift in values and priorities”.
Furthermore, participants discussed how deep structural change differs from incremental change. They described deep structural change as difficult to reverse and characterised by discontinuities or thresholds that shift the system into a new configuration, compared to slow, gradual incremental change. While some noted that incremental changes might accumulate and contribute to structural transformation, deep structural change was more commonly seen as involving a qualitative break from previous patterns. Several responses highlighted periods of instability or disruption as part of this process, in which the system may reorder around new structures or priorities.

Other topics emerging in passing included the distinction between scale and depth, the role of intentionality, and the extent to which a change must be profound or radical to qualify as deeply structural. This diversity of thought reflects both the complexity of deep structural change as a phenomenon and the range of domains in which it is seen as relevant. Rather than producing a single definition, the session surfaced multiple ways in which change can be considered structural, opening up productive space for further conceptual and methodological exploration.

A distilled definition

Drawing on both existing literature and the range of perspectives shared by participants, we propose the following working definition. It aims to clarify what is meant by deep structural change from the standpoint of agent-based modelling, while acknowledging its place within broader discussions of transformative change.

Deep structural change is a type of transformative change: From an agent-based modelling perspective, it entails an ontological reconfiguration. This reconfiguration is related to the emergence, disappearance, or transformation of entities, relationships, structures, and contextual features. While transformative change can occur within a fixed model ontology, deep structural change entails a revision of the ontology itself.

Challenges in modelling deep structural change

To understand the challenges modellers face when trying to incorporate deep structural change in ABMs or social simulations in general, we again asked participants in the pre-conference survey and had them brainstorm using a Miro board. We asked them about the “challenges [they] have encountered in this process” and “how [they] would overcome these challenges”. The points raised by the participants can roughly be grouped into: theory and data, model complexity, definition and detection.

The first challenge relates to availability of data on deep structural change and formalisation of related theory. Social simulations are increasingly based on empirical data to be able to model real-world phenomena more realistically. However, the data is often not good at capturing structural system changes, reflecting the status quo rather than the potential. While there are theories describing change, formalising this qualitative process comes with its own challenges, leading to hypothesising of the mechanisms and large uncertainties about model accuracy.

Second, a fine line has to be struck between keeping the model simple and understandable, while making it complex enough to allow for ontologies to shift and deep structural change to emerge. Participants highlighted the need for flexibility in the model structures, to allow new structures to develop. On the other hand, there is a risk of imposing transformation paths, so basically “telling” the model how to transform. In other words, it is often unclear how to make sure the necessary conditions for modelling deep structural change are there, without imposing the pathway of change.

The final challenge concerns the definition and detection of deep structural change. This article begins to address the question of definition, but detection remains difficult — even with greater conceptual clarity. How can one be confident that an observed change is genuinely deep and structural, and that the system has entered a new regime? This question touches on our ability to characterise system states, dominant feedbacks, necessary preconditions, and the timescales over which change occurs.

Closing remarks

Understanding transformative change in general, but increasingly so with the use of social simulation, is gaining attention to provide insights into complex issues. For social simulation modellers, it is therefore important to model deep structural changes. This workshop serves as a starting point for hopefully a wider discussion within the ESSA community on how to model transformative change. Bringing together social simulation researchers showed us that this is tackled from different angles. The definition provided above is a first attempt to combine these views, but key challenges remain. Thus far, people have approached this in a case-by-case manner; it would be useful to have a set of more systematic approaches.

The SIG-MTC will continue to examine questions around how we might effectively model deep structural change over the coming months and years, working with the ABM community to identify fruitful routes forward. We invite readers to comment  below on any further approaches to modelling deep structural change that they view as promising and to provide their own reflections on the topics discussed above. If you are interested in this topic and would like to engage further, please check out our ESSA Special Interest Group on Modelling Transformative Change or reach out to any one of us.

Acknowledgements

We would like to thank the participants of the SimSocFest 2025 Workshop on Modelling Deep Structural Change for their engagement in the workshop and the willingness to think along with us.

References

Feola, G. (2015). Societal transformation in response to global environmental change: A review of emerging concepts. Ambio, 44(5), 376–390. https://doi.org/10.1007/s13280-014-0582-z

Filatova, T., Polhill, J. G., & van Ewijk, S. (2016). Regime shifts in coupled socio-environmental systems: Review of modelling challenges and approaches. Environmental Modelling & Software, 75, 333–347. https://doi.org/10.1016/j.envsoft.2015.04.003

Wagenblast, T., Roxburgh, N. and Taberna, A. (2025) Modelling Deep Structural Change in Agent-Based Social Simulation. Review of Artificial Societies and Social Simulation, 8 Aug 2025. https://rofasss.org/2025/08/08/structch

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

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Nigel Gilbert

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By Corinna Elsenbroich & Petra Ahrweiler

The first piece on winners of the European Social Simulation Association’s Rosaria Conte Outstanding Contribution Award for Social Simulation.

“Gilbert, a former sociologist of science, has been one of the chief links in Britain between computer scientists and sociologists of science” [1, p. 294]

Nigel has always been and still is a sociologist – not only of science, but also of technology, innovation, methods and many other subfields of sociology with important contributions in theory, empirical research and sociological methods.

He has pioneered a range of sociological areas such as Sociology of Scientific Knowledge, Secondary Analysis of Government Datasets, Access to Social Security Information, Social Simulation, and Complexity Methods of Policy Evaluation.

Collins is right, however, that Nigel is one of the chief links between sociologists and computer scientists in the UK and beyond. This earned him to be elected as the first practising social scientist elected as a Fellow of the Royal Academy of Engineering (1999). As the principal founding father of agent-based modelling as a method for the social sciences in Europe, he initiated, promoted and institutionalised a completely novel way of doing social sciences through the Centre for Research in Social Simulation (CRESS) at the University of Surrey, the Journal of Artificial Societies and Social Simulation (JASSS), founded Sociological Research Online (1993) and Social Research Update. Nigel has 100s of publications on all aspects of social simulation and seminal books like: Simulating societies: the computer simulation of social phenomena (Gilbert & Doran 1994), Artificial Societies: The Computer Simulation of Social Phenomena (Gilbert & Conte 1995), Simulation for the Social Scientist (Gilbert &Troitzsch 2005), and Agent-based Models (Gilbert 2019). His entrepreneurial spirit and acumen resulted in over 25 large project grants (across the UK and Europe), often in close collaboration with policy and decision makers to ensure real life impact, a simulation platform on innovation networks called SKIN, and a spin off company CECAN Ltd, training practitioners in complexity methods and bringing their use to policy evaluation projects.

Nigel is a properly interdisciplinary person, turning to the sociology of scientific knowledge in his PhD under Michael Mulkay after graduating in Engineering from Cambridge’s Emmanuel College. He joined the Sociology Department at the University of Surrey in 1976 where he became professor of sociology in 1991. Nigel was appointed Commander of the Order of the British Empire (CBE) in 2016 for contributions to engineering and social sciences.

He was the second president of the European Social Simulation Association ESSA, the originator of the SIMSOC mailing list, launched and edited the Journal of Artificial Societies and Social Simulation from 1998-2014 and he was the first holder of the Rosaria Conte Outstanding Contribution Award for Social Simulation in 2016, an unanimous decision by the ESSA Management Committee.

Despite all of this, all these achievements and successes, Nigel is the most approachable, humble and kindest person you will ever meet. In any peril he is the person that will bring you a step forward when you need a helping hand. On asking him, after getting a CBE etc. what is the recognition that makes him most happy, he said, with the unique Nigel Gilbert twinkle in his eye, “my Rosaria Conte Award”.

References

Collins, H. (1995). Science studies and machine intelligence. In Handbook of Science and Technology Studies, Revised Edition (pp. 286-301). SAGE Publications, Inc., https://doi.org/10.4135/9781412990127

Gilbert, N., & Doran, R. (Eds.). (1994). Simulating societies: the computer simulation of social phenomena. Routledge.

Gilbert, N. & Conte, R. (1995) Artificial Societies: the computer simulation of social life. Routeledge. https://library.oapen.org/handle/20.500.12657/24305

Gilbert, N. (2019). Agent-based models. Sage Publications.

Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. Open University Press; 2nd edition.

Elsenbroich, C. & Ahrweiler, P. (2025) Nigel Gilbert. Review of Artificial Societies and Social Simulation, 3 Mar 2025. https://rofasss.org/2025/04/03/nigel-gilbert

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

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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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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

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

Content

ESSA@work: Reflections and looking ahead

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By Kavin Narasimhan, Silvia Leoni, Katharina Luckner, Dino Carpentras, and Natalie Davis*

essaatworkgroup@gmail.com

*All authors contributed equally – author order determined by a pseudo-random number generator and does not reflect their respective contributions.

Introduction

Since its inception in 2010, ESSA@work has been a mainstay at the annual Social Simulation Conference (SSC). It continues as a forum where beginners in individual- and agent-based modelling (hereon, ABM) present a work-in-progress model, along with specific problems and questions, to a community of practitioners to get feedback, suggestions, and tips for specific aspects of their modelling projects. During the session, participants present their model to an audience and two experts, the latter of whom are chosen for their constructive style of feedback and broad expertise. Participants are not required to answer questions or defend their work, as might be the case in a more traditional setting. Instead, experts enter into a dialogue with each other with the explicit goal of providing constructive feedback towards the progress of the project. After the expert discussion, the audience can also add constructive ideas and questions.

Each ESSA@work session is organised by a team of volunteers, who were often introduced to the format by being participants themselves. In the weeks prior to the SSC, this group drafts all necessary documents to elicit participation, selects participants, contacts experts, and distributes information via mailing lists and social media channels. During the sessions, they serve as chairs and provide outreach via social media. In between conferences and other events with ESSA@work sessions, organisers serve as points of contact for anyone who might want to organise a local ESSA@work session engage with the management of the broader European Social Simulation Association (ESSA), maintain information on the ESSA@work website (http://www.essa.eu.org/essawork/), and recruit the next generation of volunteers. Organisers typically stay on for a number of years, so that a continuity of knowledge on the processes is secured.

Over the years, a few themes that characterise ESSA@work have crystallised and indicate the importance of the track. In this contribution, we outline these themes: how ESSA@work provides a learning experience to participants and the audience, as well as the organisers; how it fosters interdisciplinarity; and how it builds upon a community of practice. We conclude with our wishes for its future.

Themes

Learning experience

The participants in ESSA@work tend to be early-career researchers, such as masters students, doctoral candidates, or post-doctoral researchers, but we have also had participants who are experienced academics, but new to ABM. For early-career researchers, participating holds additional benefits, as the SSC where they participate in ESSA@work may be their first (on-site) conference. For instance, this was the case for the SSC2022, which was held in a hybrid format after a long period of restrictions and uncertainty due to COVID-19. This deeply affected the career of young researchers: for some, most of their PhD has been spent online with no or little opportunity to participate in events such as annual conferences.

While the learning experience is focused on the participants and their contributions, it extends beyond them to include audience members and organisers as well, so that ESSA@work sessions present different learning channels. The first learning channel is focused on presentation and social skills. These general skills apply to any career path and are facilitated and supported by the friendly environment and specific format that ESSA@work implements. The practice of presenting unfinished work fosters collaboration, open conversations, and reflection, among peers and more senior academics alike, rather than an environment where participants must ‘defend’ their work from reviewers. Participants must adapt their presentation to a specific format, where they clearly address their doubts and issues. This requires them to put together a clear, concise presentation aligned with the non-standard focus of the track. We have one-page guidance, detailed guidance, and Frequently Asked Questions (FAQs) covering these aspects online (http://www.essa.eu.org/essawork/how-to-participate/ and http://www.essa.eu.org/essawork/faq/). The track then also facilitates and encourages the development of social skills by bringing together members of the ESSA community of all experience levels, allowing participants to develop their network of contacts and collaborations based on shared experiences and mentorship.

Secondly, there is the specific feedback from experts, including literature and data recommendations, and references to existing models or other contacts. This adds to or complements the feedback that participants (especially PhD students and postdocs) receive from their supervisors. Participants can find diverse, enriching suggestions with respect to the line of work that they were following, and new perspectives. For cases in which relationships with supervisors and mentors are proving to be difficult, or where supervisors are less familiar with ABM, this can be a crucial source of motivation and support for researchers who find themselves stuck in the process. This can also be a useful source of ideas for audience members with similar questions or challenges.

There is also the organiser’s experience. This usually starts with being involved as a participant in the track. As speakers, participants begin to familiarise themselves with the specific ESSA@work format, as well as with the steps, timing, and process that lead to the conference events. This is also a way to get in touch with former and current team members before officially joining the organisers’ team. After being introduced to ESSA@work as a participant or audience member, new members of the organising team receive training by current and/or former members in a process of knowledge transfer guided by prior experiences. This is put in place with the goal of sharing, improving from the past, and creating a community.

Once researchers have fully joined the team and start helping to prepare the next edition of ESSA@work, the learning opportunities are numerous. From building and strengthening their network of contacts across ESSA, to practising organisation and chairing (which would otherwise often come at a later career stage), reviewing submitted manuscripts, improving communication and coordination skills, and project and time management. Last but not least, organisers work in a team. This exercise of coordination is a fantastic occasion for learning-by-doing of how to adapt and organise heterogeneous skills, schedules, and expertise towards continuous improvement. On the one hand, this mimics co-authorship, and thus offers an opportunity to familiarise oneself with a frequent pattern in academic work; on the other hand, it emphasises and strengthens the feeling of community that characterises ESSA, and that is even stronger in the ESSA@work family.

Interdisciplinarity

Through our time as organisers, we have seen first-hand how diverse the ABM community is. The background of participants can include physics, ecology, computer science, economics, or psychology, just to name a few. This is a double-edged sword, as it both allows researchers to produce work connecting multiple disciplines, but can also result in work that is not accessible to the different audiences who may otherwise be interested in it.

For example, people from statistical physics may be very interested in solving the mean-field approximation of a model, while psychologists may be more interested in the qualitative interpretation of such a model. Similar problems also regard the use of technical terms. For example, terms like “experiment” are used by some to mean “computer simulation” and by others to mean “empirical experiment with real people.” Similarly, Edmund Chattoe-Brown found 5 different uses of the term “validation” (Chattoe-Brown, 2021). Therefore, while ABM can connect multiple different fields, research content can still be very hard to understand by multiple scientists. This can paradoxically result in more difficulty in reaching out or communicating results to some communities or fields (Carpentras, 2022).

ESSA@work can have a unique role in tackling this problem, as it allows people who have recently begun working with ABM to get an “inside view” of the ABM community. By presenting their work and research questions to experts in ABM, and receiving feedback from them, participants can have a smoother process to publishing their models, for example by avoiding common mistakes and pitfalls, and gain more insights on typical research questions, problems and jargon of ABM. This allows participants to get more acquainted with the ABM community and mindsets (as discussed in the next section), allowing for a better integration and long-term connection with the field.

Community building

When speaking of communities of practice, we ask how practitioners of a certain profession or discipline both shape and are shaped by their profession. ESSA@work has a role to play in both, but perhaps more heavily in the latter.

As a whole, ESSA seems to be actively shaping a community of practice in social simulation, more specifically ABM, through shared standards and protocols, regular exchanges, and collaborations across disciplines, all rallying around a specific method. For many members, this is a community separate to the one that they belong to on a day-to-day basis in their departments or organisations. There is active communal support in jointly shaping the rules that should govern the community and the method, as well as a continuous (and friendly) negotiation of who or what is included and excluded from the community and where overlaps with other communities might be (see recent discussion in the SIMSOC mailing list; https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2211&L=SIMSOC&O=D&P=19269). This is how the community shapes the practice – both actively and passively.

The other side of the coin is how the practice shapes the community. As discussed, ESSA@work sessions are often the place and time where new members are introduced to the community, and where their future outlook on the community and the method is significantly shaped. Through useful, tactful, and constructive feedback, new members are introduced to the core texts that at least partially constitute the collective imaginary of the community of practice, to the protocols that govern what constitutes good practice, and – perhaps most importantly – to the tone that the community uses in interacting with one another. ESSA@work therefore not only provides a forum for constructive feedback on work-in-progress, but also an experience which is useful to decide whether someone wants to be part of this community. With ‘alumni’ often coming back as organisers or panellists, and recommending the track to their peers and students, there is a sense that ESSA@work – and the attitude it embodies – is passed on through academic generations. It therefore becomes very much part of what we do, and how we do things, in the agent-based modelling community.

Future themes

As we look to the future for ESSA@work, we have considered both its continuing role in providing a multi-faceted learning experience and central point for the ESSA community, as well as how it can continue to contribute to the future of both ESSA and the field of agent-based modelling more broadly. Specifically, as agent-based modelling has become more accepted as a method for simulating and analysing complex systems, and therefore taken a more empirical turn, ESSA@work can have a unique role in fostering and maintaining the diversity of modelling purposes, which may otherwise become less valued in the rest of the scientific community.

Most participants have questions related to specific stages of their modelling journey. If you think of an ABM journey being roughly divided into the following stages: (1) conceptualisation and design, (2) development, (3) verification and calibration, (4) validation, and (5) simulations, uncertainty analysis and results, most ESSA@work participants are somewhere between steps 2 and 4 in their modelling journey. As step 1 presents several possibilities and needs longer for background work (like literature review, brainstorming, stakeholder consultation, etc.), we intentionally encourage participation in the forum from step 2 onwards, when the purpose, scope and objectives of models become clearer. This in turn enables specific modelling questions being put forth that can be usefully addressed within the time and space of an ESSA@work session. Over the years, we have received submissions from across disciplines and mostly focusing on issues in steps 2 to 4 of the modelling journey. More recently, we also started receiving submissions with questions about running simulation experiments, calibration and validation with empirical data, interpreting results, and conducting uncertainty analysis. We believe this speaks to ABM becoming more mainstream as a microsimulation approach during this period, enabled also by the availability and accessibility to powerful computing resources.

We find that when questions fall under modelling stages 2, 3 and 5, participants receive more direct answers as questions tend to be specific, which our practitioner community addresses based on their own work, or on wider references. On the other hand, questions about model validation (stage 4) could be quite broad and open-ended to attract a useful response in the time available. ‘How can I validate my model?’ – or the essence of this question worded differently – is a popular question in this category. A practical and straightforward answer to validate a model is to collect or use data on the modelled phenomenon, and use them as test data to check if the model replicates patterns of the test data. Often though, participants indicate that the test data do not exist or are difficult to obtain. This would then raise questions about the purpose of the model: specifically, whether it’s intended as a toy model to generate plausible explanations about an observed phenomenon (historically the realm of ABM), or as a specialised model to allow meaningful forecasts. Having the latter objective would mean that the model needs good quality data at every stage of model development, and lacking those data would raise concerns about the suitability of ABM in the first place to address the proposed research questions. Without validation, as robust as a model may be, it may not be trusted to generate valid predictions or forecasts.

On the other hand, where models are intended as ‘toy models’, lack of validation is less of a problem. These models are meant to inspire more informed research questions about observed phenomena, which can subsequently be explored through further targeted real-world experiments, data collection, modelling, or a combination thereof. These models also provide clear entry points to the discipline for someone just beginning to explore complex systems, ABM, or both – many of us can point to reading texts such as Growing Artificial Societies (Epstein and Axtell, 1996) as the first time we truly understood and connected with ABM. But somehow there appear to be fewer takers for developing toy models in recent years. This could be due to perceptions that toy models risk being dismissed as vague (or at least harder to publish), because practitioners are on tight timelines and thus experience a lack of time or room to experiment with toy models, or because of a need  to deliver model-based predictions (forecasts or projections) to satisfy specific project requirements.

We fear that any such bias against toy models might incur a cost in the form of compromised quality of models, or discourage new entrants and sponsors for ABM. The former is likely to occur when modellers try to build an overly complicated or specific model based on minimal, poor, or fragmented data, and thus possibly relying on too many assumptions that lack sound evidence. The latter could happen when ABM is solely intended as a means to an end rather than as a means to experiment. Reflecting on our journey and thinking ahead, we believe ESSA@work could avoid these outcomes by providing an unbiased, supportive, and well-connected incubatory forum to encourage the development and housing of toy models, which have sound methodological and modelling rigour, despite being unsuitable for prediction due to the lack of validation using empirical data. We could then expect that a growing bank of model examples and modellers would pave the way for ABM practice to flourish, alongside guiding data confidentiality, data collection, sharing, and management practices that allow turning toy models into specialised models in methodical, reusable, and reproducible ways. The prominence of ESSA@work in the ESSA network could allow us to take on such a role in the future if more ABM practitioners (at all stages of their modelling career) volunteer to support with running the forum and its activities.

Conclusion

ESSA@work offers a valuable learning experience for participants, audience members, and organisers alike. It has become an integral part of the SSC annual conference and especially of the ABM community. While this is the result of past efforts and activities, our current work looks to the future and aims at continuity with the past but also renovation and further development.

We strive to improve and make our team and community grow. For this reason, we always welcome new organisers to contribute in this joint effort to grow both the spectrum and the reach of our activities. To guarantee continuity of this track and continue to improve it, we believe that diversity in participation could play a major role in innovation and better identifying early career researchers’ and other participants’ needs in the coming years.

The COVID era has confronted us, among others, with different professional and academic challenges. We all transferred our work from on-site to remote or hybrid, and likewise we adapted to new formats to guarantee that the ESSA community could continue to meet. While originally the result of needs and adaptation, online and hybrid formats have proved to be effective in ensuring a wide reach and increased accessibility. The SSC2022 and SocSimFesT past editions showed the possibility and success of a plurality of formats and ways to meet, discuss, and progress our research. These formats are now integrated in our working life and they represent a possibility for ESSA@work to get in touch with new cohorts of international modellers.

ESSA@work is a friendly space for in-depth discussion and learning, and as such, it extends beyond the boundaries of the annual conference or on-site events. We aim to continue offering online or hybrid events in the hope that they will make participation more accessible and provide additional feedback to anyone who needs it. In addition, we encourage the organisation of local ESSA@work sessions. In order to do so, the ambition and priority of ESSA@work is preserving its function as a community-builder and ensuring that participants are supported and able to self-organise according to the challenges and needs arising from their research.

References

Carpentras, D. (2020) Challenges and opportunities in expanding ABM to other fields: the example of psychology. Review of Artificial Societies and Social Simulation, 20th December 2021. https://rofasss.org/2021/12/20/challenges/

Chattoe-Brown, E. (2022) Today We Have Naming Of Parts: A Possible Way Out Of Some Terminological Problems With ABM. Review of Artificial Societies and Social Simulation, 11th January 2022. https://rofasss.org/2022/01/11/naming-of-parts/

Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: social science from the bottom up. Brookings Institution Press.

Narasimhan, K., Leoni, S., Luckner, K., Carpentras, D. and Davis, N. (2022) ESSA@work: Reflections and looking ahead. Review of Artificial Societies and Social Simulation, 20 Feb 2023. https://rofasss.org/2022/02/20/essawork

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