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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

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

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Making Models FAIR: An educational initiative to build good ABM practices

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By Marco A. Janssen1, Kelly Claborn1, Bruce Edmonds2, Mohsen Shahbaznezhadfard1 and Manuela Vanegas-Ferro1

  1. Arizona State University, USA
  2. Manchester Metropolitan University, UK

Imagine a world where models are available to build upon. You do not have to build from scratch and painstakingly try to figure out how published papers are getting the published results. To achieve this utopian world, models have to be findable, accessible, interoperable, and reusable (FAIR). With the “Making Models FAIR” initiative, we seek to contribute to moving towards this world.

The initiative – Making Models FAIR – aims to provide capacity building opportunities to improve the skills, practices, and protocols to make computational models findable, accessible, interoperable and reusable (FAIR). You can find detailed information about the project on the website (tobefair.org), but here we will present the motivations behind the initiative and a brief outline of the activities.

There is increasing interest to make data and model code FAIR, and there is quite a lot of discussion on standards (https://www.openmodelingfoundation.org/ ). What is lacking are opportunities to gain skills for how to do this in practice. We have selected a list of highly cited publications from different domains and developed a protocol for making those models FAIR. The protocol may be adapted over time when we learn what works well.

This list of model publications provides opportunities to learn the skills needed to make models FAIR. The current list is a starting point, and you can suggest alternative model publications as desired. The main goal is to provide the modeling community a place to build capacity in making models FAIR. How do you use Github, code a model in a language or platform of your choice, and write good model documentation? These are necessary skills for collaboration and developing FAIR models. A suggested way of participating is for an instructor to have student groups participate in this activity, selecting a model publication that is of interest to their research.

To make a model FAIR, we focus on five activities:

  1. If the code is not available with the publication, find out whether the code is available (contact the authors) or replicate the model based on the model documentation. It might also happen that the code is available in programming language X, but you want to have it available in another language.
  2. If the code does not have a license, make sure an appropriate license is selected to make it available.
  3. Get a DOI, which is a permanent link to the model code and documentation. You could use comses.net or zenodo.org or similar services.
  4. Can you improve the model documentation? There is typically a form of documentation in a publication, in the article or an appendix, but is this detailed enough to understand how and why certain model choices have been made? Could you replicate the model from the information provided in the model documentation?
  5. What is the state of the model code? We know that most of us are not professional programmers and might be hesitant to share our code. Good practice is to provide comments on what different procedures are doing, defining variables, and not leave all kinds of wild ideas commented out left in the code base.

Most of the models listed do not have code available with the publication, which will require participants to contact the original others to obtain the code and/or to reproduce the code from the model documentation.

We are eager to learn what challenges people experience to make models FAIR. This could help to improve the protocols we provide. We also hope that those who made a model FAIR publish a contribution in RofASSS or relevant modeling journals. For publishing contributions in journals, it would be interesting to use a FAIR model to explore the robustness of the model results, especially for models that have been published many years ago and for which there were less computational resources available.

The tobefair.org website contains a lot of detailed information and educational opportunities. Below is a diagram from the site that aims to illustrate the road map of making models FAIR, so you can easily find the relevant information. Learn more by navigating to the About page and clicking through the diagram.

Making simulation models findable, accessible, interoperable and reusable is an important part of good scientific practice for simulation research. If important models fail to reach this standard, then this makes it hard for others to reproduce, check and extend them. If you want to be involved – to improve the listed models, or to learn the skills to make models FAIR – we hope you will participate in the project by going to tobefair.org and contributing.

Janssen, M.A., Claborn, K., Edmonds, B., Shahbaznezhadfard, M. and Vanegas-Ferro, M. (2023) Making Models FAIR: An educational initiative to build good ABM practices. Review of Artificial Societies and Social Simulation, 8 May 2023. https://rofasss.org/2023/05/11/fair/

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

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Teaching highly intelligent primary school kids energy system complexity

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By Emile Chappin

An energy system complexity lecture for kids?

I was invited to open the ‘energy theme’ at a primary school with a lecture on energy and wanted to give it a complexity and modelling flavour. And I wondered… can you teach this to a large group of 7-to-12-year-old children, all highly intelligent but so far apart in their development? What works in this setting, and what doesn’t? How long should I make such a lecture? Can I explain and let them feel what research is? Can I do some experiments? Can I share what modelling is? What concepts should I include? What are such kids interested in? What do they know? What would they expect? Many of these questions haunted me for some time, and I thought it would be nice to share my observations from simply going for it.

I outline my learning goals, observations from the first few minutes, approach, some later observations, and main takeaways. I end with a plea for teaching social simulation at primary schools. This initiative is part of the Special Interest Group on Education (http://www.essa.eu.org/sig/sig-education/) of the European Social Simulation Association.

Learning goals

I wanted to provide the following insights to these kids:

  • Energy is everywhere; you can feel, hear, and see it all around you. Even from outer space, you can see where cities are when you look at the earth. All activities you do require some form of energy.
  • Energy comes in different forms and can be converted into other forms.
  • Everyone likes to play games, and we can use games even to do research and perform experiments.
  • Doing research/being a researcher involves asking (sometimes odd) questions, looking very carefully at things, studying how the world works and why and solving problems.
  • You can use computers to perform social simulations that help us think. Not necessarily to answer questions but as tools that help us think about the world, do many experiments and study their implications.

First observations

It is easy to notice that this is a rather ambitious plan. Nevertheless, I learnt very quickly that these kids knew a lot! And that they (may) question everything from every angle. They are keen to understand and eager to share what they know. I was happy I could connect with them quickly by helping them get seated, chit chatting before the start.

My approach

I used symbols/analogies to explain deep concepts and layered the meaning, deepening the understanding layer by layer. I came back to and connected all these layers. This enables kids from different age groups to understand the lecture on their level. An example is that I mentioned early on how I was interested in as a kid in black holes. I explained that black holes were discovered by thinking carefully about how the universe works and that theoretical physicists concluded there might be something like a black hole. It was decades later before a real black hole was photographed. The fact that you can imagine and reason how something may exist that you cannot (yet) observe… that much later has been proven to exist. This is what research can be; it is incredible how this happened. Much later in the talk, I connected this to how you can use the computer to imagine, dream up, and test ideas because, in many cases, it is tough to do in real life.

I asked many questions and listened carefully to the answers. Some answers are way off-topic, and it is essential to guide these kids enough so the story continues, but at the same time, the kids stay on board. An early question was… do you like to play games? It is so lovely to have a group of kids cheering that they want to play games! It provides a connection. Another question I asked was, what is the similarity between a wind turbine and a sheep? Kids laughed at the funny question and picture but also came up with the desired answer (they both need/convert energy). Other creative solutions were that the colours were similar, and the shape had similarities. These are fun answers and also correct!

Because of these questions, kids came up with many great insights and good observations. This was astonishing. Research is looking at something carefully, like a snail. A black hole comes from a collapsing star, and our sun will collapse at some point in time. One kid knew that the object I brought was a kazoo… so I invited him to try imitating the sound of Max Verstappen’s Formula One car. And, of course, I had a few more kazoos, so we made a few reasonable attempts. I went back to 5+ times during the next hour to some of these kids’ great remarks: it helped to keep connected to the kids.

I played the ‘heroes and cowards’ game (similar to the ‘heroes and cowards’ model from the Netlogo library). This was a game as well as an experiment. I announced that it only works if we all follow the rules carefully. I made the kids silently think about what would happen. It worked reasonably well: they could observe the emergent phenomenon of the group cluttering and exploding, although it went somewhat rough.

A fantastic moment was to explain the concept of validity to young kids simply by experiencing it. I pressed on the fact that following the rules was crucial for our experiment to be valid and that stumbling and running was problematic for our outcomes. It was amazing that this landed so well; I was fortunate that the circumstances allowed this.

After playing this game a couple of times, with hilarious moments and chaos, I showed how you could replicate what happened in a simulation in Netlogo. I showed that you could repeat it rapidly and do variations that would be hard to do with the kids. I even showed the essential piece of code. And I remarked that the kids on the computer did listen better to me.

Later observations

We planned to take 60 minutes, observe how far we could go, and adapt. I noticed I could stretch it to 75 minutes, far longer than I thought was possible. I used less material than I thought I would use for 60 minutes. I started relatively slow and with a personal touch. I was happy I had flexible material and could adapt to what the kids shared. I used my intuition and picked up objects that were around that I could use to tell the story.

Some sweet things happened. When I first arrived, one kid played the piano in the general area. He played with much possess, small but intense. I said in the lecture that I heard him play and that I was also into music. Raised hand: ‘Will you play something for us at the end’? Of course, I promised this! During the lecture… I repeatedly promised I would; the question came back many times. I played a song the young piano player liked to hear.

These children were very open and direct. I had expected that but was still surprised by how honest and straightforward. ‘Ow, now I lost my question, this happens to me all the time’. I said: do you know I also have this quite often? It is perfectly normal. It doesn’t matter. If the question comes back, you can raise your hand again. If it doesn’t, then that is also just fine.

My takeaways

  • Do fun things, even if it is not perfectly connected. It helps with the attention span and provides a connection. Using humour helps us all to be open to learning.
  • Ask many questions, and use your intuition when asking questions. Listen to the answers, remember important ones (and who gave them), and refer back to them. If something is off-topic, you can ‘park’ that question and remark or answer it politely without dismissing it.
  • Act things out very dramatically. I acted very brave and very cowardly when introducing the game. I used two kids to show the rules and kept referring to them using their names.
  • Don’t overprepare but make the lecture flexible. Where can you expand? What do you need to do to make the connection, to make it stick?
  • I was happy that the class teachers helped me by asking a crucial question at the end, allowing me to close a couple of circles. Keep the teacher active and involved in the lecture. Invite them beforehand to do so.
  • A helpful hint I received afterwards was to use a whiteboard (or something similar) to develop a visual record of concepts and keywords raised by the kids, e.g., in the form of a mind map.
  • Kids keep surprising you all the way. One asked about NetLogo: ‘Can you install this software on Windows 8?’ It is free. You can try it out yourselves, I said. ‘Can you upgrade windows 8 to windows 10’. Well, this depends on your computer, I said. These kids keep surprising you!
  • You can teach complexity, emergence, and agent-based modelling without using words. But if kids use a term, acknowledge it. In this case: ‘But with AI….’ This is AI. It is worth exploring how to reach and teach children crucial complexity insights at a young age.

Teaching social simulation in primary schools

I plea that it is worth the effort to inspire children at a young age with crucial insights into what research is, into complexity, and into using social simulation. In this specific lecture, I only briefly touched on the use of social simulation (right at the end). It is a fantastic gift to help someone see complexity unfold before their eyes and to catch a glimpse of the tools that show the ingredients of this complexity. And it is a relatively small step towards unravelling social behaviour through social simulations. I’m tempted to conclude that you could teach young children a basic understanding of social simulation with relatively small educational modules. Even if it is implicit through games and examples, they may work effectively if placed carefully in the social environment that the different age groups typically face. Showing social structures emerging from behavioural rules. Illustrating different patterns emerging due to stochasticity and changes in assumptions. Dreaming up basic (but distinct) codified decision rules about actual (social) behaviour you see around you. If this becomes an immersive experience, such educational modules have the potential to contribute to an intuitive understanding of what social simulations are and how they can be used. Children may be inspired to learn to see and understand emergent phenomena around them from an early age; they may become the thinkers of tomorrow. And for the kids I met on this occasion: I’d be amazed if none of them became researchers one day. I hope that if you get the chance, you also give it a go and share your experience! I’m keen to hear and learn!

Chappin, E. (2023) Teaching highly intelligent primary school kids energy system complexity. Review of Artificial Societies and Social Simulation, 19 Apr 2023. https://rofasss.org/2023/04/19/teachcomplex

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