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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)

Content

A Tale of Three Pandemic Models: Lessons Learned for Engagement with Policy Makers Before, During, and After a Crisis

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By Emil Johansson1,2, Vittorio Nespeca3, Mikhail Sirenko4, Mijke van den Hurk5, Jason Thompson6, Kavin Narasimhan7, Michael Belfrage1, 2, Francesca Giardini8, and Alexander Melchior5,9

  1. Department of Computer Science and Media Technology, Malmö University, Sweden
  2. Internet of Things and People Research Center, Malmö University, Sweden
  3. Computational Science Lab, University of Amsterdam, The Netherlands
  4. Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands
  5. Department of Information and Computing Sciences, Utrecht University, The Netherlands
  6. Transport, Health and Urban Design Research Lab, The University of Melbourne, Australia
  7. Centre for Research in Social Simulation, University of Surrey, United Kingdom
  8. Department of Sociology & Agricola School for Sustainable Development, University of Groningen, The Netherlands
  9. Ministry of Economic Affairs and Climate Policy and Ministry of Agriculture, Nature and Food Quality, The Netherlands

Motivation

Pervasive and interconnected crises such as the COVID-19 pandemic, global energy shortages, geopolitical conflicts, and climate change have shown how a stronger collaboration between science, policy, and crisis management is essential to foster societal resilience. As modellers and computational social scientists we want to help. Several cases of model-based policy support have shown the potential of using modelling and simulation as tools to prepare for, learn from (Adam and Gaudou, 2017), and respond to crises (Badham et al., 2021). At the same time, engaging with policy-makers to establish effective crisis-management solutions remains a challenge for many modellers due to lacking forums that promote and help develop sustained science-policy collaborations. Equally challenging is to find ways to provide effective solutions under changing circumstances, as it is often the case with crises.

Despite the existing guidance regarding how modellers can engage with policy makers e.g. (Vennix, 1996; Voinov and Bousquet, 2010), this guidance often does not account for the urgency that characterizes crisis response. In this article, we tell the stories of three different models developed during the COVID-19 pandemic in different parts of the world. For each of the models, we draw key lessons for modellers regarding how to engage with policy makers before, during, and after crises. Our goal is to communicate the findings from our experiences to  modellers and computational scientists who, like us, want to engage with policy makers to provide model-based policy and crisis management support. We use selected examples from Kurt Vonnegut’s 2004 lecture on ‘shapes of stories’ alongside analogy with Lewis Carroll’s Alice In Wonderland as inspiration for these stories.

Boy Meets Girl (Too Late)

A Social Simulation On the Corona Crisis’ (ASSOCC) tale

The perfect love story between social modellers and stakeholders would be they meet (pre-crisis), build a trusting foundation and then, when a crisis hits, they work together as a team, maybe have some fight, but overcome the crisis together and have a happily ever after.

In the case of the ASSOCC project, we as modellers met our stakeholders too late, (i.e., while we were already in the middle of the COVID-19 crisis). The stakeholders we aimed for had already met their ‘boy’: Epidemiological modellers. For them, we were just one of the many scientists showing new models and telling them that ours should be looked at. Although, for example, our model showed that using a track and tracing-app would not help reduce the rate of new COVID-19 infections (as turned out to be the case), our psychological and social approach was novel for them. It was not the right time to explain the importance of integrating these kinds of concepts in epidemiological models, so without this basic trust, they were reluctant to work with us.

The moral of our story is that not only should we invest in a (working) relationship during non-crisis times to get the stakeholders on board during a crisis, such an approach would be helpful for us modelers too. For example, we integrated both social and epidemiological models within the ASSOCC project. We wanted to validate our model with that used by Oxford University. However, our model choices were not compatible with this type of validation. Had we been working with these types of researchers before a pandemic, we could have built a proper foundation for validation.

So, our biggest lesson learned is the importance of having a good relationship with stakeholders before a crisis hits, when there is time to get into social models and show the advantages of using these. When you invest in building and consolidating this relationship over time, we promise a happily ever after for every social modeler and stakeholder (until the next crisis hits).

Modeller’s Adventures in Wonderland

A Health Emergency Response in Interconnected Systems (HERoS) tale

If you are a modeler, you are likely to be curious and imaginative, like Alice from “Alice’s Adventures in Wonderland.” You like to think about how the world works and make models that can capture these sometimes weird mechanisms. We are the same. When Covid came, we made a model of a city to understand how its citizens would behave.

But there is more. When Alice first saw the White Rabbit, she found him fascinating. A rabbit with a pocket watch which is too late, what could be more interesting? Similarly, our attention got caught by policymakers who wear waistcoats, who are always busy but can bring change. They must need a model that we made! But why are they running away? Our model is so helpful, just let us explain! Or maybe our model is not good enough?

Yes, we fell down deep into a rabbit hole. Our first encounter with a policymaker didn’t result in a happy “yes, let’s try your model out.” However, we kept knocking on doors. How many did Alice try? But alright, there is one. It seems too tiny. We met with a group of policymakers but had only 10 minutes to explain our large-scale data-driven agent-based-like model. How can we possibly do that? Drink from a “Drink me” bottle, which will make our presentation smaller! Well, that didn’t help. We rushed over all the model complexities too fast and got applause, but that’s it. Ok, we have the next one, which will last 1 hour. Quickly! Eat an “Eat me” cake that will make the presentation longer! Oh, too many unnecessary details this time. To the next venue!

We are in the garden. The garden of crisis response. And it is full of policymakers: Caterpillar, Duchess, Cheshire Cat and Mad Hatter. They talk riddles: “We need to consult with the Head of Paperclip Optimization and Supply Management,” want different things: “Can you tell us what will be the impact of a curfew. Hmm, yesterday?” and shift responsibility from one to another. Thankfully there is no Queen of Hearts who would order to behead us.

If the world of policymaking is complex, then the world of policymaking during the crisis is a wonderland. And we all live in it. We must overgrow our obsession with building better models, learn about its fuzzy inhabitants, and find a way to instead work together. Constant interaction and a better understanding of each other’s needs must be at the centre of modeler-policymaker relations.

“But I don’t want to go among mad people,” Alice remarked.

“Oh, you can’t help that,” said the Cat: “we’re all mad here. I’m mad. You’re mad.”

“How do you know I’m mad?” said Alice.

“You must be,” said the Cat, “or you wouldn’t have come here.”

Lewis Carroll, Alice in Wonderland

Cinderella – A city’s tale

Everyone thought Melbourne was just too ugly to go to the ball…..until a little magic happened.

Once upon a time, the bustling Antipodean city of Melbourne, Victoria found itself in the midst of a dark and disturbing period. While all other territories in the great continent of Australia had ridded themselves of the dreaded COVID-19 virus, it was itself, besieged. Illness and death coursed through the land.

Shunned, the city faced scorn and derision. It was dirty. Its sisters called it a “plague state” and the people felt great shame and sadness as their family, friends and colleagues continued to fall to the virus. All they wanted was a chance to rejoin their families and countryfolk at the ball. What could they do?

Though downtrodden, the kind-hearted and resilient residents of Melbourne were determined to regain control over their lives. They longed for a glimmer of sunshine on these long, gloomy days – a touch of magic, perhaps? They turned to their embattled leaders for answers. Where was their Fairy Godmother now?

In this moment of despair, a group of scientists offered a gift in the form of a powerful agent-based model that was running on a supercomputer. This model, the scientists said, might just hold the key to transforming the fate of the city from vanquished to victor (Blakely et al., 2020). What was this strange new science? This magical black box?

Other states and scientists scoffed. “You can never achieve this!”, they said. “What evidence do you have? These models are not to be trusted. Such a feat as to eliminate COVID-19 at this scale has never been done in the history of the world!” But what of it? Why should history matter? Quietly and determinedly, the citizens of Melbourne persisted. They doggedly followed the plan.

Deep down, even the scientists knew it was risky. People’s patience and enchantment with the mystical model would not last forever. Still, this was Melbourne’s only chance. They needed to eliminate the virus so it would no longer have a grip on their lives. The people bravely stuck to the plan and each day – even when schools and businesses began to re-open – the COVID numbers dwindled from what seemed like impossible heights. Each day they edged down…

and down…

and down…until…

Finally! As the clock struck midnight, the people of Melbourne achieved the impossible: they had defeated COVID-19 by eliminating transmission. With the help of the computer model’s magic, illness and death from the virus stopped. Melbourne had triumphed, emerging stronger and more united than ever before (Thompson et al., 2022a).

From that day forth, Melbourne was internationally celebrated as a shining example of resilience, determination, and the transformative power of hope. Tens of thousands of lives were saved – and after enduring great personal and community sacrifice, its people could once again dance at the ball.

But what was the fate of the scientists and the model? Did such an experience change the way agent-based social simulation was used in public health? Not really. The scientists went back to their normal jobs and the magic of the model remained just that – magic. Its influence vanished like fairy dust on a warm Summer’s evening.

Even to this day the model and its impact largely remains a mystery (despite over 10,000 words of ODD documentation). Occasionally, policy-makers or researchers going about their ordinary business might be heard to say, “Oh yes, the model. The one that kept us inside and ruined the economy. Or perhaps it was the other way around? I really can’t recall – it was all such a blur. Anyway, back to this new social problem – Shall we attack it with some big data and ML techniques?”.

The fairy dust has vanished but the concrete remains.

And in fairness, while agent-based social simulation remains mystical and our descriptions opaque, we cannot begrudge others for ever choosing concrete over dust (Thompson et al, 2022b).

Conclusions

So what is the moral of these tales? We consolidate our experiences into these main conclusions:

  • No connection means no impact. If modellers wish for their models to be useful before, during or after a crisis, then it is up to them to start establishing a connection and building trust with policymakers.
  • The window of opportunity for policy modelling during crises can be narrow, perhaps only a matter of days. Capturing it requires both that we can supply a model within the timeframe (impossible as it may appear) and that our relationship with stakeholders is already established.
  • Engagement with stakeholders requires knowledge and skills that might be too much to ask of modelers alone, including project management, communication with individuals without a technical background, and insight into the policymaking process.
  • Being useful only sometimes means being excellent. A good model is one that is useful. By investing more in building relationships with policymakers and learning about each other, we have a bigger chance of providing the needed insight. Such a shift, however, is radical and requires us to give up our obsession with the models and engage with the fuzziness of the world around us.
  • If we cannot communicate our models effectively, we cannot expect to build trust with end-users over the long term, whether they be policy-makers or researchers. Individual models – and agent-based social simulation in general – needs better understanding that can only be achieved through greater transparency and communication, however that is achieved.

As taxing, time-consuming and complex as the process of making policy impact with simulation models might be, it is very much a fight worth fighting; perhaps even more so during crises. Assuming our models would have a positive impact on the world, not striving to make this impact could be considered admitting defeat. Making models useful to policymakers starts with admitting the complexity of their environment and willingness to dedicate time and effort to learn about it and work together. That is how we can pave the way for many more stories with happy endings.

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.

References

Adam, C. and Gaudou, B. (2017) ‘Modelling Human Behaviours in Disasters from Interviews: Application to Melbourne Bushfires’ Journal of Artificial Societies and Social Simulation 20(3), 12. http://jasss.soc.surrey.ac.uk/20/3/12.html. doi: 10.18564/jasss.3395

Badham, J., Barbrook-Johnson, P., Caiado, C. and Castellani, B. (2021) ‘Justified Stories with Agent-Based Modelling for Local COVID-19 Planning’ Journal of Artificial Societies and Social Simulation 24 (1) 8 http://jasss.soc.surrey.ac.uk/24/1/8.html. doi: 10.18564/jasss.4532

Crammond, B. R., & Kishore, V. (2021). The probability of the 6‐week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS‐CoV‐2. The Medical Journal of Australia, 215(2), 95-95. doi:10.5694/mja2.51146

Thompson, J., McClure, R., Blakely, T., Wilson, N., Baker, M. G., Wijnands, J. S., … & Stevenson, M. (2022). Modelling SARS‐CoV‐2 disease progression in Australia and New Zealand: an account of an agent‐based approach to support public health decision‐making. Australian and New Zealand Journal of Public Health, 46(3), 292-303. doi:10.1111/1753-6405.13221

Thompson, J., McClure, R., Scott, N., Hellard, M., Abeysuriya, R., Vidanaarachchi, R., … & Sundararajan, V. (2022). A framework for considering the utility of models when facing tough decisions in public health: a guideline for policy-makers. Health Research Policy and Systems, 20(1), 1-7. doi:10.1186/s12961-022-00902-6

Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmental modelling & software, 25(11), 1268-1281. doi:10.1016/j.envsoft.2010.03.007

Vennix, J.A.M. (1996). Group Model Building: Facilitating Team Learning Using System Dynamics. Wiley.

Vonnegut, K. (2004). Lecture to Case College. https://www.youtube.com/watch?v=4_RUgnC1lm8

Johansson,E., Nespeca, V., Sirenko, M., van den Hurk, M., Thompson, J., Narasimhan, K., Belfrage, M., Giardini, F. and Melchior, A. (2023) A Tale of Three Pandemic Models: Lessons Learned for Engagement with Policy Makers Before, During, and After a Crisis. Review of Artificial Societies and Social Simulation, 15 Mar 2023. https://rofasss.org/2023/05/15/threepandemic

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