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What more is needed for Democratically Accountable Modelling?

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By Bruce Edmonds

(A contribution to the: JASSS-Covid19-Thread)

In the context of the Covid19 outbreak, the (Squazzoni et al 2020) paper argued for the importance of making complex simulation models open (a call reiterated in Barton et al 2020) and that relevant data needs to be made available to modellers. These are important steps but, I argue, more is needed.

The Central Dilemma

The crux of the dilemma is as follows. Complex and urgent situations (such as the Covid19 pandemic) are beyond the human mind to encompass – there are just too many possible interactions and complexities. For this reason one needs complex models, to leverage some understanding of the situation as a guide for what to do. We can not directly understand the situation, but we can understand some of what a complex model tells us about the situation. The difficulty is that such models are, themselves, complex and difficult to understand. It is easy to deceive oneself using such a model. Professional modellers only just manage to get some understanding of such models (and then, usually, only with help and critique from many other modellers and having worked on it for some time: Edmonds 2020) – politicians and the public have no chance of doing so. Given this situation, any decision-makers or policy actors are in an invidious position – whether to trust what the expert modellers say if it contradicts their own judgement. They will be criticised either way if, in hindsight, that decision appears to have been wrong. Even if the advice supports their judgement there is the danger of giving false confidence.

What options does such a policy maker have? In authoritarian or secretive states there is no problem (for the policy makers) – they can listen to who they like (hiring or firing advisers until they get advice they are satisfied with), and then either claim credit if it turned out to be right or blame the advisers if it was not. However, such decisions are very often not value-free technocratic decisions, but ones that involve complex trade-offs that affect people’s lives. In these cases the democratic process is important for getting good (or at least accountable) decisions. However, democratic debate and scientific rigour often do not mix well [note 1].

A Cautionary Tale

As discussed in (Adoha & Edmonds 2019) Scientific modelling can make things worse, as in the case of the North Atlantic Cod Fisheries Collapse. In this case, the modellers became enmeshed within the standards and wishes of those managing the situation and ended up confirming their wishful thinking. An effect of technocratising the decision-making about how much it is safe to catch had the effect of narrowing down the debate to particular measurement and modelling processes (which turned out to be gravely mistaken). In doing so the modellers contributed to the collapse of the industry, with severe social and ecological consequences.

What to do?

How to best interface between scientific and policy processes is not clear, however some directions are becoming apparent.

  • That the process of developing and giving advice to policy actors should become more transparent, including who is giving advice and on what basis. In particular, any reservations or caveats that the experts add should be open to scrutiny so the line between advice (by the experts) and decision-making (by the politicians) is clearer.
  • That such experts are careful not to over-state or hype their own results. For example, implying that their model can predict (or forecast) the future of complex situations and so anticipate the effects of policy before implementation (de Matos Fernandes and Keijzer 2020). Often a reliable assessment of results only occurs after a period of academic scrutiny and debate.
  • Policy actors need to learn a little bit about modelling, in particular when and how modelling can be reliably used. This is discussed in (Government Office for Science 2018, Calder et al. 2018) which also includes a very useful checklist for policy actors who deal with modellers.
  • That the public learn some maturity about the uncertainties in scientific debate and conclusions. Preliminary results and critiques tend to be jumped on too early to support one side within polarised debate or models rejected simply on the grounds they are not 100% certain. We need to collectively develop ways of facing and living with uncertainty.
  • That the decision-making process is kept as open to input as possible. That the modelling (and its limitations) should not be used as an excuse to limit what the voices that are heard, or the debate to a purely technical one, excluding values (Aodha & Edmonds 2017).
  • That public funding bodies and journals should insist on researchers making their full code and documentation available to others for scrutiny, checking and further development (readers can help by signing the Open Modelling Foundation’s open letter and the campaign for Democratically Accountable Modelling’s manifesto).

Some Relevant Resources

  • CoMSeS.net — a collection of resources for computational model-based science, including a platform for publicly sharing simulation model code and documentation and forums for discussion of relevant issues (including one for covid19 models)
  • The Open Modelling Foundation — an international open science community that works to enable the next generation modelling of human and natural systems, including its standards and methodology.
  • The European Social Simulation Association — which is planning to launch some initiatives to encourage better modelling standards and facilitate access to data.
  • The Campaign for Democratic Modelling — which campaigns concerning the issues described in this article.

Notes

note1: As an example of this see accounts of the relationship between the UK scientific advisory committees and the Government in the Financial Times and BuzzFeed.

References

Barton et al. (2020) Call for transparency of COVID-19 models. Science, Vol. 368(6490), 482-483. doi:10.1126/science.abb8637

Aodha, L.Edmonds, B. (2017) Some pitfalls to beware when applying models to issues of policy relevance. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity – a handbook, 2nd edition. Springer, 801-822. (see also http://cfpm.org/discussionpapers/236)

Calder, M., Craig, C., Culley, D., de Cani, R., Donnelly, C.A., Douglas, R., Edmonds, B., Gascoigne, J., Gilbert, N. Hargrove, C., Hinds, D., Lane, D.C., Mitchell, D., Pavey, G., Robertson, D., Rosewell, B., Sherwin, S., Walport, M. & Wilson, A. (2018) Computational modelling for decision-making: where, why, what, who and how. Royal Society Open Science, DOI: 10.1098/rsos.172096.

Edmonds, B. (2020) Good Modelling Takes a Lot of Time and Many Eyes. Review of Artificial Societies and Social Simulation, 13th April 2020. https://rofasss.org/2020/04/13/a-lot-of-time-and-many-eyes/

de Matos Fernandes, C. A. and Keijzer, M. A. (2020) No one can predict the future: More than a semantic dispute. Review of Artificial Societies and Social Simulation, 15th April 2020. https://rofasss.org/2020/04/15/no-one-can-predict-the-future/

Government Office for Science (2018) Computational Modelling: Technological Futures. https://www.gov.uk/government/publications/computational-modelling-blackett-review

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (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

Edmonds, B. (2020) What more is needed for truly democratically accountable modelling? Review of Artificial Societies and Social Simulation, 2nd May 2020. https://rofasss.org/2020/05/02/democratically-accountable-modelling/

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

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Understanding the current COVID-19 epidemic: one question, one model

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By the CoVprehension Collective

(A contribution to the: JASSS-Covid19-Thread)

On the evening of 16th March 2020, the French president, Emmanuel Macron announced the start of a national lockdown, for a period of 15 days. It would be effective from noon the next day (17th March). On the 18th March 2020 at 01:11 pm, the first email circulated in the MicMac team, who had been working on the micro-macro modelling of the spread of a disease in a transportation network a few years. This email was the start of CoVprehension. After about a week of intense emulation, the website was launched, with three questions answered. A month later, there were about fifteen questions on the website, and the group was composed of nearly thirty members from French research institutions, in a varied pool of disciplines, all contributing as volunteers from their confined residence.

CoVprehension in principles

This rapid dynamic originates from a very singular context. It is tricky to analyse it given that the COVID-19 crisis is still developing. However, we can highlight a few fundamental principles leading the project.

The first principle is undeniably a principle of action. To become an actor of the situation first, but this invitation extends to readers of the website, allowing them to run the simulation and to change its parameters; but also more broadly by giving them suggestions on how to link their actions to this global phenomenon which is hard to comprehend. This empowerment also touches upon principles of social justice and, longer term, democracy in the face of this health crisis. By accompanying the process of social awareness, we aim to guide the audience towards a free and informed consent (cf. code of public health) in order to confront the disease. Our first principle is spelled out on theCoVprehension website in the form of a list of objectives that the CoVprehension collective set themselves:

  • Comprehension (the propagation of the virus, the actions put in place)
  • Objectification (giving a more concrete shape to this event which is bigger than us and can be overwhelming)
  • Visualisation (showing the mechanisms at play)
  • Identification (the essential principles and actions to put in place)
  • Do something (overcoming fears and anxieties to become actors in the epidemic)

The second founding principle is that of an interdisciplinary scientific collective formed on a voluntary basis. CoVprehension is self-organised and rests on three pillars: volunteering, collaborative work and the will to be useful during the crisis by offering a space for information, reflection and interaction with a large audience.

As a third principle, we have agility and reactivity. The main idea of the project is to answer questions that people ask, with short posts based on a model or data analysis. This can only be done if the delay between question and answer remains short, which is a real challenge given the complexity of the subject, the high frequency of scientific literature being produced since the beginning of the crisis, and the large number of unknowns and uncertainties which characterise it.

The fourth principle, finally, is the autonomy of groups which form to answer the questions. This allows a multiplicity of perspectives and points of view, sometimes divergent. This necessity draws on the acknowledgement by the European simulation community that a lack of pluralism is even more harmful to support public decision-making than a lack of transparency.

A collaborative organisation and an interactive website

The four principles have lead us, quite naturally, to favour a functioning organisation which exploits short and frequent retroactions and relies of adapted tools. The questions asked online through a Framasoft form are transferred to all CoVprehension members, while a moderator is in charge of replying to them quickly and personally. Each question is integrated into a Trello management board, which allows each member of the collective to pick the questions they want to contribute to and to follow their progression until publication. The collaboration and debate on each of the questions is done using VoIP application Discord. Model prototypes are mostly developed on the Netlogo platform (with some javascript exceptions). Finally, the whole project and website is hosted on GitHub.

The website itself (https://covprehension.org/en) is freely accessible online. Besides the posts answering questions, it contains a simulator to rerun and reproduce the simulations showcased in the posts, a page with scientific resources on the COVID-19 epidemic, a page presenting the project members and a link to the form allowing anyone to ask the collective a question.

On the 28th April 2020, the collective counted 29 members (including 10 women): medical doctors, researchers, engineers and specialists in the fields of computer science, geography, epidemiology, mathematics, economy, data analysis, medicine, architecture and digital media production. The professional statuses of the team members vary (from PhD student to full professor, from intern to engineer, from lecturer to freelancer) whereas their skills complement each other (although a majority of them are complex system modellers). The collective effort enables CoVprehension to scale up on information collection, sharing and updating. This is also fueled by debates during the first take on questions by small teams. Such scaling up would otherwise only be possible in large epidemiology laboratories with massive funding. To increase visibility, the content of the website, initially all in French, is being translated into English progressively as new questions are published.

Simple simulation models

When a question requires a model, especially so for the first questions, our choice has been to build simple models (cf. Question 0). Indeed, the objective of CoVprehension models is not to predict. It is rather to describe, to explain and to illustrate some aspects of the COVID-19 epidemic and its consequences on population. KISS models (“Keep It Simple, Stupid!” cf. Edmonds  & Moss 2004) for the opposition between simple and “descriptive” models) seem better suited to our project. They can unveil broad tendencies and help develop intuitions about potential strategies to deal with the crisis, which can then be also shared with a broad audience.

By choosing a KISS posture, we implicitly reject KIDS postures in such crisis circumstances. Indeed, if the conditions and processes modelled were better informed and known, we could simulate a precise dynamic and generate a series of predictions and forecasts. This is what N. Ferguson’s team did for instance, with a model initially developed with regards to the H5N1 flu in Asia (Ferguson et al., 2005). This model was used heavily to inform public decision-making in the first days of the epidemic in the United Kingdom. Building and calibrating such models takes an awfully long time (Ferguson’s project dates back from 2005) and requires teams and recurring funding which is almost impossible to get nowadays for most teams. At the moment, we think that uncertainty is too big, and that the crisis and the questions that people have do not always necessitate the modelling of complex processes. A large area of the space of social questions mobilised can be answered without describing the mechanisms in so much detail. It is possible that this situation will change as we get information from other scientific disciplines. For now, demonstrating that even simple models are very sensitive to many elements which remain uncertain shows that the scientific discourse could gain by remaining humble: the website reveals how little we know about the future consequences of the epidemic and the political decisions made to tackle it.

Feedback on the questions received and answered

At the end of April, twenty-seven questions have been asked to the CoVprehension collective, through the online form. Seven of them are not really questions (they are rather remarks and comments from people supporting the initiative). Some questions happen to have been asked by colleagues and relatives. The intended outreach has not been fully realised since the website seems to reach people who are already capable of looking for information on the internet. This was to be expected given the circumstances. Everyone who has done some scientific outreach knows how hard it is to reach populations who have not been been made aware of or are interested in scientific facts in the first place. Some successful initiatives (like “les petits débrouillards” or “la main à la pâte” in France) spread scientific knowledge related to recent publications in collaboration with researchers, but they are much better equipped for that (since they do not rely mostly on institutional portals like we do). This large selection bias in our audience (almost impossible to solve, unless we create some specific buzz… which we will then have to handle in terms of new question influx, which is not possible at the moment given the size of the collective and its organisation) means that our website has been protected from trolling. However, we can expect that it might be used within educational programs for example, where STEM teachers could make the students use the various simulators in a question and answer type of game.

Figure 1 shows that the majority of questions are taken by small interdisciplinary teams of two or three members. The most frequent collaborations are between geographers and computer scientists. They are often joined by epidemiologists and mathematicians, and recently by economists. Most topics require the team to build and analyse a simulation model in order to answer the question. The timing of team formations reflects the arrival of new team members in the early days of the project, leading to a large number of questions to be tackled simultaneously. Since April, the rhythm has slowed, reflecting also the increasing complexity of questions, models and answers, but also the marginal “cost” of this investment on the other projects and responsibilities of the researchers involved.

Visualisation of the questions tackled by Covprehension.

Figure 1. Visualisation of the questions tackled by Covprehension.

Initially, the website prioritised questions on simulation and aggregation effects specifically connected with the distribution models of diffusion. For instance, the first questions aimed essentially at showing the most tautological results: with simple interaction rules, we illustrated logically expected effects. These results are nevertheless interesting because while they are trivial to simulation practitioners, they also serve to convince profane readers that they are able to follow the logic:

  • Reducing the density of interactions reduces the spread of the virus and therefore: maybe the lockdown can alter the infection curve (cf. Question 2 and Question 3).
  • By simply adding a variable for the number of hospital beds, we can visualise the impact of lockdown on hospital congestion (cf. Question 7).

For more elaborate questions to be tackled (and to rationalise the debates):

  • Some alternative policies have been highlighted (the Swedish case: Question 13; the deconfinement: Question 9);
  • Some indicators with contradicting impacts have been discussed, which shows the complexity of political decisions and leads readers to question the relevance of some of these indicators (cf. Question 6);
  • The hypotheses (behavioural ones in particular) have been largely discussed, which highlights the way in which the model deviates from what it represents in a simplified way (cf. Question 15).

More than half of the questions asked could not be answered through modelling. In the first phase of the project, we personnally replied to these questions and directed the person towards robust scientific websites or articles where their question could be better answered. The current evolution of the project is more fundamental: new researchers from complementary disciplines have shown some interest in the work done so far and are now integrated into the team (including two medical doctors operating in COVID-19 centres for instance). This will broaden the scope of questions tackled by the team from now on.

Our work fits into a type of education to critical thinking about formal models, one that has long been known as necessary to a technical democracy (Stengers, 2017). At this point, the website can be considered both as a result by itself and as a pilot to function as a model for further initiatives.

Conclusion

Feedback on the CoVprehension project has mostly been positive, but not exempt from limits and weaknesses. Firstly, the necessity of a prompt response has been detrimental to our capacity to fully explore different models, to evaluate their robustness and look for unexpected results. Model validation is unglamorous, slow and hard to communicate. It is crucial nevertheless when assessing the credibility to be associated with models and results. We are now trying to explore our models in parallel. Secondly, the website may suggest a homogeneity of perspectives and a lack of debates regarding how questions are to be answered. These debates do take place during the assessment of questions but so far remain hidden from the readers. It shows indirectly in the way some themes appear in different answers treated from different angles by different teams (for example: the lockdown, treated in question 6, 7, 9 and 14). We consider the possibility of publishing alternative answers to a given question in order to show this possible divergence. Finally, the project is facing a significant challenge: that of continuing its existence in parallel with its members’ activities, with the number of members increasing. The efforts in management, research, editing, publishing and translation have to be maintained while the transaction costs are going up as the size and diversity of the collective increases, as the debates become more and more specific and happen on different platforms… and while new questions keep arriving!

References

Edmonds, B., & Moss, S. (2004). From KISS to KIDS–an ‘anti-simplistic’ modelling approach. In International workshop on multi-agent systems and agent-based simulation (pp. 130-144). Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-32243-6_11

Ferguson, N. M., Cummings, D. A., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A. & Burke, D. S. (2005). Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature, 437(7056), 209-214. doi:10.1038/nature04017

Stengers I. (2017). Civiliser la modernité ? Whitehead et les ruminations du sens commun, Dijon, Les presses du réel. https://www.lespressesdureel.com/EN/ouvrage.php?id=3497

the CoVprehension Collective (2020) Understanding the current COVID-19 epidemic: one question, one model. Review of Artificial Societies and Social Simulation, 30th April 2020. https://rofasss.org/2020/04/30/covprehension/

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

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What can and cannot be feasibly modelled of the Covid-19 Pandemic

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By Nick Gotts

(A contribution to the: JASSS-Covid19-Thread)

The place of modelling in informing policy has been highlighted by the Covid-19 pandemic. In the UK, a specific individual-based epidemiological model, that developed by Neil Ferguson of Imperial College London, has been credited with the government’s U-turn from pursuing a policy of building up “herd immunity” by allowing the Sars-CoV-2 virus to spread through the population in order to avoid a possible “second wave” next winter (while trying to limit the speed of spread so as to avoid overwhelming medical facilities, and to shield the most vulnerable), to a “lockdown” imposed in order to minimise the number of people infected. Ferguson’s model reportedly indicated several hundred thousand deaths if the original policy was followed, and this was judged unacceptable.

I do not doubt that the reversal of policy was correct – indeed, that the original policy should never have been considered – one prominent epidemiologist said he thought the report of it was “satire” when he first heard it (Hanage 2020). As Hanage says: “Vulnerable people should not be exposed to Covid-19 right now in the service of a hypothetical future”. But it has also been reported (Reynolds 2020) that Ferguson’s model is a rapid modification of one he built to study possible policy responses to a hypothetical influenza pandemic (Ferguson et al. 2006); and that (Ferguson himself says) this model consists of “thousands of lines of undocumented C”. That major policy decisions should be made on such a basis is both wrong in itself, and threatens to bring scientific modelling into disrepute – indeed, I have already seen the justified questioning of the UK government’s reliance on modelling used by climate change denialists in their ceaseless quest to attack climate science.

What can social simulation contribute in the Covid-19 crisis? I suggest that attempts to model the pandemic as a whole, or even in individual countries, are fundamentally misplaced at this stage: too little is known about the behaviour of the virus, and governments need to take decisions on a timescale that simply does not allow for responsible modelling practice. Where social simulation might be of immediate use is in relation to the local application of policies already decided on. To give one example, supermarkets in the UK (and I assume, elsewhere) are now limiting the number of shoppers in their stores at any one time, in an effort to apply the guidelines on maintaining physical distance between individuals from different households. But how many people should be permitted in a given store? Experience from traffic models suggests there may well be a critical point at which it rather suddenly becomes impossible to maintain distance as the number of shoppers increases – but where does it lie for a particular store? Could the goods on sale be rearranged in ways that allow larger numbers – for example, by distributing items in high demand across two or more aisles? Supermarkets collect a lot of information about what is bought, and which items tend to be bought together – could they shorten individual shoppers’ time in the store by improving their signage? (Under normal circumstances, of course, they are likely to want to retain shoppers as long as possible, and send them down as many aisles as possible, to encourage impulse buys.)

Agents in such a model could be assigned a list of desired purchases, speed of movement and of collecting items from shelves, and constraints on how close they come to other shoppers – probably with some individual variation. I would be interested to learn if any modelling teams have approached supermarket chains (or vice versa) with a proposal for such a model, which should be readily adaptable to different stores. Other possibilities include models of how police should be distributed over an area to best ensure they will see (and be seen by) individuals or groups disregarding constraints on gathering in groups, and of the “contagiousness” of such behaviour – which, unlike actual Covid-19 infection events, is readily observable. Social simulators, in summary, should look for things they can reasonably hope to do quickly and in conjunction with organisations that have or can readily collect the required data, not try to do what is way beyond what is possible in the time available.

References

Ferguson, N. M., Cummings, D. A., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006). Strategies for mitigating an influenza pandemic. Nature, 442(7101), 448-452. doi:10.1038/nature04795

Hanage, W. (2020) I’m an epidemiologist. When I heard about Britain’s ‘herd immunity’ coronavirus plan, I thought it was satire. The Guardian, 2020-03-15. https://www.theguardian.com/commentisfree/2020/mar/15/epidemiologist-britain-herd-immunity-coronavirus-covid-19

Reynolds, C. (2020) Big Tech Fights Back: From Pandemic Simulation Code, to Immune Response. Computer Business Review 2020-03-15. https://www.cbronline.com/news/pandemic-simulation-code.

Gotts, N. (2020) What can and cannot be feasibly modelled of the Covid-19 Pandemic. Review of Artificial Societies and Social Simulation, 29th April 2020. https://rofasss.org/2020/04/29/feasibility/

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

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The Danger of too much Compassion – how modellers can easily deceive themselves

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By Andreas Tolk

(A contribution to the: JASSS-Covid19-Thread)

In 2017, Shermer observed that in cases where moral and epistemological considerations are deeply intertwined, it is human nature to cherry-pick the results and data that support the current world view (Shermer 2017). In other words, we tend to look for data justifying our moral conviction. The same is an inherent challenge for simulations as well: we tend to favour our underlying assumptions and biases – often even unconsciously – when we implement our simulation systems. If now others use this simulation system in support of predictive analysis, we are in danger of philosophical regress: a series of statements in which a logical procedure is continually reapplied to its own result without approaching a useful conclusion. As stated in an earlier paper of mine (Tolk 2017):

“The danger of the simulationist’s regress is that such predictions are made by the theory, and then the implementation of the theory in form of the simulation system is used to conduct a simulation experiment that is then used as supporting evidence. This, however, is exactly the regress we wanted to avoid: we test a hypothesis by implementing it as a simulation, and then use the simulated data in lieu of empirical data as supporting evidence justifying the propositions: we create a series of statements – the theory, the simulation, and the resulting simulated data – in which a logical procedure is continually reapplied to its own result….

In particular in cases where moral and epistemological considerations are deeply intertwined, it is human nature to cherry-pick the results and data that support the current world view (Shermer 2017). Simulationists are not immune to this, and as they can implement their beliefs into a complex simulation system that now can be used by others to gain quasi-empirical numerical insight into the behavior of the described complex system, their implemented world view can easily be confused with a surrogate for real world experiments.“

I am afraid that we may have fallen into such a fallacy in some of our efforts to use simulation to better understand the Covid-19 crisis and what we can do. This is for sure a moral problem, as at the end of our recommendations this is about human lives! And we assumed that the recommendations of the medical community for social distancing and other non pharmaceutical interventions (NPI) is the best we can do, as it saves many lives. So we built our models to clearly demonstrate the benefits of social distancing and other NPIs, which leads to danger of regress: we assume that NPIs are the best action, so we write a simulation to show that NPIs are the best action, and then we use these simulations to prove that NPIs are the best action. But can we actually use empirical data to support these assumptions? Looking closely at the data, the correlation of success – measured as flattening the curves – and the amount and strictness of the NPIs is not always observable. So we may have missed something, as our model-based predictions are not supported as we hope for, which is a problem: do we just collect the wrong data and should use something else to validate the models, or are the models insufficient to explain the data? And how do we ensure that our passion doesn’t interfere with our scientific objectivity?

One way to address this issue is diversity of opinion implemented as a set of orchestrated models, to use a multitude of models instead of just one. In another comment, the idea of using exploratory analysis to support decision making under deep uncertainty is mentioned. I highly recommend to have a look at (Marchau, Bloemen & Popper 2019) Decision Making Under Deep Uncertainty: From Theory to Practice. I am optimistic that if we are inclusive of a diversity of ideas – even if we don’t like them – and allow for computational evaluation of ALL options using exploratory analysis, we may find a way for better supporting the community.

References

Marchau, V. A., Walker, W. E., Bloemen, P. J., & Popper, S. W. (2019). Decision making under deep uncertainty. Springer. doi:10.1007/978-3-030-05252-2

Tolk, A. (2017, April). Bias ex silico: observations on simulationist’s regress. In Proceedings of the 50th Annual Simulation Symposium. Society for Computer Simulation International. ANSS ’17: Proceedings of the 50th Annual Simulation Symposium, April 2017 Article No.: 15 Pages 1–9. https://dl.acm.org/citation.cfm?id=3106403

Shermer, M. (2017) How to Convince Someone When Facts Fail – Why worldview threats undermine evidence. Scientific American, 316, 1, 69 (January 2017). doi:10.1038/scientificamerican0117-69

Tolk, A. (2020) The Danger of too much Compassion - how modellers can easily deceive themselves. Review of Artificial Societies and Social Simulation, 28th April 2020. https://rofasss.org/2020/04/28/self-deception/

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

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Designing social simulation to (seriously) support decision-making: COMOKIT, an agent-based modelling toolkit to analyse and compare the impacts of public health interventions against COVID-19

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By Alexis Drogoul1, Patrick Taillandier2, Benoit Gaudou1,3, Marc Choisy4,8, Kevin Chapuis1,5,  Quang Nghi Huynh 1,6, Ngoc Doanh Nguyen1,7, Damien Philippon10, Arthur Brugière1, and Pierre Larmande8

1 UMI 209, UMMISCO, IRD, Sorbonne Université, Bondy, France. 2 UR 875, MIAT, INRAE, Toulouse University, Castanet Tolosan, France. 3 UMR 5505, IRIT, Université Toulouse 1 Capitole, Toulouse, France. 4 UMR 5290, MIVEGEC, IRD/CNRS/Univ. Montpellier, Montpellier, France. 5 UMR 228, ESPACE-DEV, IRD, Montpellier, France. 6 CICT, Can Tho University, Can Tho, Vietnam. 7 MSLab / WARM, Thuyloi University, Hanoi, Vietnam. 8 UMR 232, DIADE, IRD, Univ. Montpellier, Montpellier, France. 9 OUCRU, Centre for Tropical Medicine, Ho Chi Minh City, Viet Nam. 10 WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.

(A contribution to the: JASSS-Covid19-Thread)

In less than 4 months after its emergence in China, the COVID-19 pandemic has spread worldwide. In response to this health crisis, unprecedented in modern history, researchers have mobilized to produce knowledge and models in order to inform and support public decision-making, sometimes in real-time (Adam, D. 2020). However, the social modelling community is facing two challenges in this endeavour: the first one is its capacity to provide robust scientific knowledge and to translate it into evidences on concrete cases (and not only general principles) within a short time range; and the second one is to do it knowing (and anticipating the fact) that these evidences may have concrete social, economic or clinical impacts in the “real” world.

These two challenges require the design of realistic models that provide what B. Edmonds, in response to (Squazzoni & al. 2020), calls the “empirical grounding and validation needed to reliably support policy making” (Edmonds, 2020); in other words, spatially explicit, demographically realistic, data driven models that can be fed with both quantitative and qualitative (behavioural) data, and that can be easily experimented in huge numbers of scenarios so as to provide statistically sound results and evidences.

It is difficult to deny these requirements, but it is easier said than done. What we have witnessed, instead, these last 4 months, is an explosion of agent-based toy models representing, ad nauseam, the spread of the virus or similar dynamics within artificial populations without space, without behaviours, without friend nor family relations, without social networks, without even remotely realistic activities or mobility schemes; in short, populations of artificial agents devoid of everything that makes a human population slightly different from a mixture of homogeneous particles. How we, as a community, can claim to inform policy makers, in such a critical context, with such abstract and simplistic constructions is difficult to justify. Are public health decision makers really that interested, these days, in models that help them to understand the general principles, the inner mechanisms or hidden dynamics of this crisis? Or would they feel better supported if we could answer their questions on which interventions, at which place, at which spatial and temporal scale and on which populations, would have the best impact on the pandemic?

We tend to forget, however, that agent-based modelling (ABM), among other benefits, does not oppose these two objectives when building a model. And from the outset of the crisis, many of us were quick to advocate a modelling approach that would:

  • Be as close as possible to public decision making by having the possibility to answer to concrete, practical questions;
  • Be based on a detailed and realistic representation of space, as the spread of the epidemic is spatial and public health policies are also predominantly spatial (containment, social distancing, reduction of mobility, etc.);
  • Rely on spatial and social data that can be collected easily and, above all, quickly, and not be too dependent on the availability of large datasets (which may not be opened nor shared depending on the country of intervention);
  • Make it possible to represent as faithfully as possible the complexity of the social and ecological environments in which the pandemic is spreading;
  • Be generic, flexible and applicable to any case study, but also trustable as it relies on inner mechanisms that can be isolated and validated separately;
  • Be open and modular enough to support the cooperation of researchers across different disciplines while relying on rigorous scientific and computational principles;
  • Offer an easy access to large-scale experimentation and statistical validation by facilitating the exploration of its parameters;

This approach is currently being implemented by an interdisciplinary group of modellers, all signatories of this response, who have started to design and implement on the GAMA platform a generic model called COMOKIT, around which they now wish to gather the maximum number of modellers and researchers in epidemiology and social sciences. Being generic here means that COMOKIT is portable for almost any case study imaginable, from small towns to provinces or even countries, the only real limit to its application being the available RAM and computing power[1].

COMOKIT is an integrated model that, in its simplest incarnation, dynamically combines five sub-models:

  1. a sub-model of the individual clinical dynamics and epidemiological status of agents
  2. a sub-model of agent-to-agent direct transmission of the infection,
  3. a sub-model of environmental transmission through the built environment,
  4. a sub-model of policy design and implementation,
  5. an agenda-based model of people activities at a one-hour time step.

It allows, of course, to represent heterogeneity in individual characteristics (sex, age, household), agendas (depending on social structures, available services or age categories), social relationships and behaviours (e.g. respect of regulations).

COMOKIT has been designed as modular enough to allow modellers and users to represent different strategies and study their impacts in multiple scenarios. Using the experimental features provided by the underlying GAMA platform (Taillandier & al. 2019) (like advanced visualization, multi-simulation, batch experiments, easy large-scale explorations of parameters spaces on HPC infrastructures), it is made particularly easy and effective to compare the outcomes of these strategies. Modularity is also a key to facilitating its adoption by other modellers and users: COMOKIT is a basis that can be very easily extended (to new policies, people activities, actors, spatial features, etc.). For instance, more detailed socio-psychological models, like the ones described in ASSOCC (Ghorbani & al. 2020), could be interesting to test within realistic models. In that respect, COMOKIT is both a framework (for deriving new concrete models) and a model (that can be instantiated by itself on arbitrary datasets).

Finally, COMOKIT has been thought of as incrementally expandable: because of the urgency usually associated with its use, it can be instantiated on new case studies in a matter of minutes, by generating the built environment of an area and its synthetic population using a simple geolocalised boundary and reasonable defaults (which can of course be parametrized, or even, in the case of the population generation, be driven by a plugin called Gen* (Chapuis & al. 2018)). When more detailed data becomes available (about the population, peoples’ occupations, economic activities, public health policies, …) the same model can be fed with it in order to refine its initial outcomes.

A screenshot of the experiments’ UI in COMOKIT

Figure 1. A screenshot of the experiments’ UI in COMOKIT: six scenarios of partial confinement are being compared with respect to the number of cases during and after a 3 months-long period. Son Loi case study, 9988 inhabitants from the 2019 Vietnamese census.

Up to now, COMOKIT has been implemented and evaluated on two cases of city confinement in Vietnam (i.e. Son Loi (Thanh & al. 2020) and Thua Duc). In these cases, which have served as testbeds to verify the correctness of the individual sub-models and their interactions, we have compared the impacts of a number of social-distancing strategies (e.g. with a ratio of the population allowed to move outside, for various durations, to various geographical extents, by activities, and so on), and other non-pharmaceutical interventions such as advising the population to wear masks, or closing the schools and public places. These studies have shown in particular that the process of ending an intervention is as much impactful as the process of starting it, in particular to avoid a second epidemic wave

We need you: social scientists, epidemiologists, modellers, computer scientists, web designers…

As the epidemic moves to countries with more limited health infrastructure and economic space, it becomes critical to devise, test and compare original public interventions that are adapted to these constraints, for instance interventions that would be more geographically and socially targeted than an entire lockdown of the whole population. COMOKIT, which is used since the beginning of April 2020 within the Rapid Response Team of the Steering Committee against COVID-19 of the Ministry of Health in Vietnam, can become an invaluable help in this endeavour. However, it must become even more realistic, reliable and robust than it is at present, so that decision-makers can build a relationship of trust with this new tool and hopefully with agent-based modelling in general.

All the documentation (with a complete ODD description and UML diagrams), commented source code (of the models and utilities), as well as five example datasets, are made available on the project’s webpage and Github repository to be shared, reused and adapted to other case studies. We strongly encourage anyone interested to try COMOKIT, apply it on their own case studies, improve it by adding new policies, activities, agents or scenarios, and share their studies, proposals, and results. Any help will be appreciated to show that we can collectively contribute, as a community, to the fight against this pandemic (and maybe the next ones): analysing the sub-models, documenting them, proposing access to data, fixing bugs, adding new sub-models, testing their integration, proposing HPC infrastructures to run large-scale experiments, everything can be helpful!

Notes

[1] To give a very rough idea, it takes approximately 15mn and 800Mb of RAM on one core of a laptop to simulate 6 months of a town of 10.000 inhabitants, at a 1-hour step, while displaying a 3D view and charts.

References

Adam, D. (2020). Special report: The simulations driving the world’s response to COVID-19. Nature. doi:10.1038/d41586-020-01003-6

Chapuis, K., Taillandier, P., Renaud, M., & Drogoul, A. (2018). Gen*: a generic toolkit to generate spatially explicit synthetic populations. International Journal of Geographical Information Science, 32(6), 1194-1210. doi:10.1080/13658816.2018.1440563

Edmonds, B. (2020) Good Modelling Takes a Lot of Time and Many Eyes. Review of Artificial Societies and Social Simulation, 13rd April 2020. https://rofasss.org/2020/04/13/a-lot-of-time-and-many-eyes/

Ghorbani, A., Lorig, F., de Bruin, B., Davidsson, P., Dignum, F., Dignum, V., van der Hurk, M., Jensen, M., Kammler, C., Kreulen, K., Ludescher, L. G., Melchior, A., Mellema, R., Păstrăv, C., Vanhée, L. and Verhagen, H. (2020) The ASSOCC Simulation Model: A Response to the Community Call for the COVID-19 Pandemic. Review of Artificial Societies and Social Simulation, 25th April 2020. https://rofasss.org/2020/04/25/the-assocc-simulation-model/

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (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

Taillandier, P., Gaudou, P. Grignard, A. Huynh, Q.N., Marilleau, N., Caillou, P., Philippon, D., Drogoul, A. (2019) Building, Composing and Experimenting Complex Spatial Models with the GAMA Platform. GeoInformatica 23, 299-322. doi:10.1007/s10707-018-00339-6

Thanh, H. N., Van, T. N., Thu, H. N. T., Van, B. N., Thanh, B. D., Thu, H. P. T., … & Nguyen, T. A. (2020). Outbreak investigation for COVID-19 in northern Vietnam. The Lancet Infectious Diseases. DOI:10.1016/S1473-3099(20)30159-6

Drogoul, A., Taillandier, P., Gaudou, B., Choisy, M., Chapuis, K., Huynh, N. Q. , Nguyen, N. D., Philippon, D., Brugière, A., and Larmande, P. (2020) Designing social simulation to (seriously) support decision-making: COMOKIT, an agent-based modelling toolkit to analyze and compare the impacts of public health interventions against COVID-19 . Review of Artificial Societies and Social Simulation, 27th April 2020. https://rofasss.org/2020/04/27/comokit/

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

Content

The ASSOCC Simulation Model: A Response to the Community Call for the COVID-19 Pandemic

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Amineh Ghorbani1 , Fabian Lorig2 , Bart de Bruin1 , Paul Davidsson2, Frank Dignum3, Virginia Dignum3, Mijke van der Hurk4, Maarten Jensen3, Christian Kammler3, Kurt Kreulen1, Luis Gustavo Ludescher3, Alexander Melchior4, René Mellema3, Cezara Păstrăv3, Loïs Vanhée5, and Harko Verhagen6

1TU Delft, Netherlands, 
2Malmö University, Sweden, 
3Umeå University, Sweden, 
4Utrecht University, Netherlands, 
5University of Caen, France, 
6Stockholm University, Sweden
*

(A contribution to the: JASSS-Covid19-Thread)

Abstract: This article is a response to the call for action to the social simulation community to contribute to research on the COVID-19 pandemic crisis. We introduce the ASSOCC model (Agent-based Social Simulation for the COVID-19 Crisis), a model that has specifically been designed and implemented to address the societal challenges of this pandemic. We reflect on how the model addresses many of the challenges raised in the call for action. We conclude by pointing out that the focus of the efforts of the social simulation community should be less on the data and prediction-based simulations but rather on the explanation of mechanisms and exploration of social dependencies and impact of interventions.

Introduction

The COVID-19 crisis is a pandemic that is currently spreading all over the world. It has already had a dramatic toll on humanity affecting the daily life of billions of people and causing a global economic crisis resulting in deficits and unemployment rates never experienced before. Decision makers as well as the general public are in dire need of support to understand the mechanisms and connections in the ongoing crisis as well as support for potentially life-threatening and far-reaching decisions that are to be made with unknown consequences. Many countries and regions are struggling to deal with the impacts of the COVID-19 crisis on healthcare, economy and social well-being of communities, resulting in many different interventions. Examples are the complete lock-down of cities and countries, appeals to the individual responsibility of citizens, and suggestions to use digital technology for tracking and tracing of the disease spread. All these strategies require considerable behavioural changes by all individuals.

In such an unprecedented situation, agent-based social simulation seems to be a very suitable technique for achieving a better understanding of the situation and for providing decision-making support. Most of the available simulations for pandemics focus either on specific aspects of the crisis, such as epidemiology (Chang et al., 2020) or simplified general agglomerated mechanics (e.g., IndiaSIM). Many models, repurposing existing models that were originally developed for other pandemics such as influenza are mostly illustrative and intend to provide theory exposition (Squazzoni et al., 2020). Although current simulations are based on advanced statistical modelling that enables sound predictions of specific aspects of the disease, they use very limited models of human motives and cultural differences. Yet, understanding the possible consequences of drastic policy measures requires more than statistical analysis such as R0 factor (the basic reproduction number, which denotes the expected number of cases directly generated by one case in a population) or economic variables. Measures impact people and thus need to consider individuals’ needs (e.g., affiliation, control, or self-fulfilment), social networks (norms, relationships), and how these attributes and conditions can quickly change during difficult situations (e.g., need for job and food security, overloaded hospitals, loss of relatives).

In this context we have developed ASSOCC (Agent-based Social Simulation for the COVID-19 Crisis; see Figure 1) as a many-faceted observatory of scenarios. In ASSOCC, we connect the many involved aspects in a cohesive simulation, for helping stakeholders to raise their general awareness on all critical aspects of the problem and especially the dependencies between them. Of course, one can hardly aim to cover a large variety of aspects and have very complete models on each of them. Thus, we strike a balance between broadness of the model and accuracy on all aspects. This simulation delivers a complementary perspective to state of the art disciplinary models. Where most of other simulations offer sharp yet isolated pieces of the image, our approach is valuable for combining the pieces of the puzzle since a specific modelling focus can limit space for debate (ní Aodha & Edmonds, 2017).

The ASSOCC approach puts the human behaviour central as a linking pin between many disciplines and aspects: psychology (needs, values, beliefs, plans), social sensitivity (norms, social networks, work relationships), infrastructures (transportation, supplies), epidemiology (spreading), economy (transactions, bankruptcy), cultural influences and public measures (closing activities, lock-down, social distancing, testing). The already complex model is extended on a daily basis. This is done in a largely modular fashion such that specific aspects can be switched on and off during the runs. This leads to some limitations and also requires re-calibration of variables, but overall it seems worth the effort when looking at the first results of the scenarios we have simulated.

In this article, we aim to share our approach to simulating the COVID-19 pandemic, outline how the building and use of ASSOCC takes up a number of the challenges that were posed in (Squazzoni et al., 2020), and emphasize the potentials of agent-based simulation as method in mastering pandemics.

Figure 1: A screenshot of the Graphical User Interface of the ASSOCC simulation

Figure 1: A screen shot of the Graphical User Interface of the ASSOCC simulation

Introducing the ASSOCC Model

The goal of the ASSOCC simulation model is to integrate different parts of our daily life that are affected by the pandemic in order to support decision makers when trading off different policies against each other. It facilitates the identification of potential interdependencies that might exist and need to be addressed. This is important as different countries, cultures and populations affect the suitability and consequences of measures thus requiring local conditions to be taken into account. The model allows stakeholders to study individual and social reactions to different policies, to explore different scenarios, and to analyse their potential effects.

Figure 2: A screenshot of the base simulation model.

Figure 2: A screen shot of the base simulation model.

How it works

The ASSOCC simulation model is based on a synthetic population that consists of a set of artificial individuals (see Figure 1), each with given needs, demographic characteristics and attitude towards regulations and risks. By having all these agents decide over time what they should be doing, we can analyse their reactions to many different policies, such as total lock-down or voluntary isolation. Agents can move, perceive other agents, and decide on their actions based on their individual characteristics and their perception of the environment. The environment constrains the physical actions of the agents but can also impose norms and regulations on their behaviour. Through interaction, agents can take over characteristics from the other agents, such as becoming infected with COVID-19, or receiving information.

Agents

In the ASSOCC model, there are four types of agents: children, students, workers, and retirees. These types represent different age groups with different socio-demographic attributes, common activities, infection risks and behaviours. Each agent has a health status that represents being infected, symptomatic or asymptomatic contagiousness, and a critical state. Moreover, agents have needs and capabilities as well as personal characteristics such as risk aversion and the propensity to follow the law. Needs of the agent include health, wealth and belonging. They are modelled using the water tank model introduced by Dörner et al. (2006). Agent capabilities capture for instance their jobs or family situations. Agents need a minimum wealth value to survive which they receive by working or through subsidies (or by living together with a working agent). In shops and workplaces, agents trade wealth for products and services. Agents pay tax to a central government that then uses this money for subsidies and the maintenance of public services such as hospitals and schools.

Places

During the simulation, agents can move between different places according to their needs and obligations. Places represent homes, shops, hospitals, workplaces, schools, airports and stations. By assigning agents to homes, different households can be represented: single adults, families, retirement homes, and multi-generational households with children, adults and elderly people. The configuration of households is assumed to have an impact on the spreading of COVID-19 and great differences in household configurations exist between countries. Thus, the distribution of these households can be set in the simulation to analyse the situation in different cities 
or countries.

Policies

Policies describe interventions that can be taken by decision makers such as social distancing, infection and immunity testing or closing of schools and workplaces. Policies have complex effects on health, wealth and well-being of all agents. Policies can be extended in many different ways to provide an experimentation environment for decision makers. It is not only the decision of whether or not to implement certain policies but also the point in time when the policy is implemented that influences its success.

Conceptual Design

The ASSOCC model has been conceptualized based on many theories from various scientific disciplines, including psychology (basic motives and needs (McClelland, 1987; Jerome, 2013)), sociology (Schwartz value system (Schwartz, 2012)), culture (Hofstede’s cultural dimensions (Hofstede et al., 2010)), economy (circular flow of income (Murphy, 1993)), and epidemiology (the SEIR model (Cope et al., 2018)). For the disease model, we looked at the following sources: a case study of a corona time lapse (Xu et al., 2020), a cohort study showing the general time lapse of the disease with and without fatality (Zhou et al., 2020) and the incubation period determined by confirmed cases (Lauer et al., 2020). This theory-driven model, determines the reaction of agents to policies and their physical and social context.

A short description of the conceptual architecture of ASSOCC as well as an overview of the agent architecture are available at the project website.

Tools

The simulation is built in Netlogo (see Figure 2 with a visual interface in Unity (see Figure 1. The Netlogo model can be used as a standalone simulation model. For the scenarios, we use the Unity interface for better visualisation of the simulation. The complete source code is available on Github under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Note that at the time of publication of this article, this is still a beta version of the model, which we are continuously developing. The complete description of the agent-based model using the ODD protocol can as well be found on the ASSOCC website.

Addressing Key Challenges

Having explained the ASSOCC framework, in this section, we explain how our modelling effort addresses the 
challenges raised by Squazzoni et al. (2020).

Like any model, the ASSOCC model cannot be a complete representation of reality and has its own limitations. Yet, we believe that the dimensions of social complexity that we have included provide a promising ground to draw useful insights. As rightfully highlighted by Squazzoni et al. (2020), the quality of a model depends on its purpose, its theoretical assumptions, the level of abstraction, and the quality of data.

The purpose of the ASSOCC model is to illustrate and investigate mechanisms. Through the simulation of scenarios, ASSOCC shows dependencies between human behaviour and the spread of the virus, the economic incentives and the psychological needs of people.

In the next sections we aim to explain how the ASSOCC model addresses the main issues raised in (Squazzoni et al., 2020).

Social Complexity

In order to incorporate pre-existing behavioral attitudes, network effects, social norms and culture that influence people’s response to policy measure, we have built a cross-disciplinary extended team of researchers. We have spent extra time and effort to construct a complex model where social complexity is extensively taken into account. As an example, the Maslow theory for individual needs takes pre-existing behavioral attitudes of individuals into account (Jerome, 2013). By connecting this theory to Schwartz value dimensions (Schwartz, 2012) and connecting these dimensions to the cultural dimensions of Hofstede (Hofstede et al., 2010), we incorporate a whole spectrum of individual biological and social needs all the way to cultural diversity among nations.

Yet, the limitations of ASSOCC are in the richness of each of the societal dimensions. We use some rather simple models, for example, in the economic, culture, social network and transport aspects. We document which choices have been made to indicate which complexities we left out and why they were left out and why we think this does not affect the validity of our results. For example, in the transport dimension we do not distinguish between cars and bikes. We do not need that as we do not have large distances and both cars and bikes can be used as solo transport means. We are aware that there are differences in economic terms and also in values for choosing between the two means of transport, but these aspects are not very relevant for the spread of the virus.

Transparency

Although there is pressure on the community to respond to this crisis and to provide expert judgement, we have not sacrificed the complexity of our model, nor it’s transparency to provide rapid answers. In fact, we have aimed to make our modelling process as transparent as possible. Starting from low level programming code, ASSOCC uses Github repository to make the code publicly available. Besides code documentation, our large scale model makes use of the ODD protocol to make the model transparent at the conceptual level. Additionally, by building the Unity interface layer on the Netlogo model, we aim to connect policy scenarios to the parameter setup of the model, so that policy makers themselves can see how changes to scenarios leads to various outcomes.

By emphasizing that ASSOCC creates simulations of policy scenarios, we step away from giving a particular advice for a “best” policy. Rather we highlight the fundamental questions and priorities that have to be dealt with to choose among various policies. This is done by showing the consequences of the implementation of various scenarios and comparing them. This comparison can for example show how different groups of people are affected economically and health-wise by a policy. The most appropriate policy thus depends on the outcomes that are deemed more desirable.

Data

Given the short time since the outbreak, accurate data on the COVID-19 outbreak suitable for complex agent-based models is not yet available. It is not clear how various cases are defined and how the data is collected. However, in our view, this should not limit our modelling abilities for this much-needed rapid response.

In our view, detailed data is not required to build a useful model. In fact, our model is a ’SimCity’ to study various policy scenarios rather than actual data-driven representation of cities. While we have made sure that our model can show similar patterns to the ones observed in reality for overall validity, small fine-grain data is not included. The data used for the simulation comes from particular epidemiological models, from economic models and from calibration of the model against known, normal situations.

As illustrated in models that were described in (Squazzoni et al., 2020), even models that are calibrated with real-world data fail to capture important aspects such as network effects as these changes are still based on stochastic randomized processes. Therefore, being aware that the current data is not yet available nor reliable, 
we have built our model on strong theoretical basis in order to avoid oversimplification of factors that play important roles in this crisis.

Interface between modelling and policy

As highlighted by Squazzoni et al. (2020), “good pandemic models are not always good policy advice models”. We fully agree with this point, which is central to our modelling efforts. A user-interface has been especially developed in Unity (see Figure 1) to support comprehension of the model by policy makers and to facilitate experimentation. In the Unity interface, one can explore the different parameters of a scenario, see the results of the simulations in graph form and also follow several aspects live through the elements available in the spatial representation of the town. This spatial interface is meant purely for better understanding of the model. We believe that having clarity regarding our modelling goal increases policy makers trust in our insights.

In addition, we have been in close contact with policy makers around the world to, on the one hand, understand their needs and immediate and long-term concerns, and on the other hand, communicate our model’s capabilities in the most concise manner to support their decisions. To date, we have engaged with policy makers in the Netherlands, Italy and Sweden.

Predictive Power

In our interactions with policy makers and other users, we make clear that the ASSOCC platform is not meant for giving detailed predictions, but to support the generation of insights. Such a broad model is best used to indicate dependencies and trends between different aspects of the society. Due to the computing power needed for each agent running the complex reasoning, it is difficult to scale this type of model to more than a few thousand agents, at least in NetLogo. 
The validation of the model can be done through the causal chains that can be followed throughout the model. I.e. certain outcomes can be linked through agent states to certain causes in the environment or the actions of other agents. If these causal chains can be interpreted as plausible stories that can be confirmed by the theories of those respective aspects, it is possible to achieve a certain type of high level validation. So, this is not a validation on data, but validation based on expert opinion.

A second type of validation that can be done on this type of ABM is to make a detailed comparison with established epidemiological models. For instance, we are comparing our simulation with the one used for (Ferretti et al., 2020) in a particular scenario where the effect of using tracking and tracing apps is investigated. By translating the assumptions and parameters very carefully to ASSOCC parameters and comparing the resulting simulations, we can validate the underlying models against more traditional ones and also show possible deviations that might come up and that highlights advantages or lacunas in the ASSOCC model. The results of this comparison will be published jointly by the two groups. Finally, we are calibrating ASSOCC parameters by using statistical data, such as R0, number of deaths, and demographic data as means to improve validity.

Conclusion

In this article, we presented the ASSOCC model as a comprehensive modelling endeavour that aims to contribute to the efforts for managing the COVID-19 crisis. By modelling multiple aspects of the society and interrelating them, we provide insights into the underlying mechanisms in the society that are influenced both by the outbreak as well as policy measures that aim to control it.

Being aware of the challenges, we have aimed to include as much social complexity as possible in the model to avoid biases and oversimplification. At the same time, by being in close contact with policy makers around the world, we have taken the actual needs and considerations into account, while providing a traceable, usable and comprehensible user interface that brings the modelling insights within the reach of policy makers. In our modelling efforts, we have paid extra attention to transparency, providing well-documented and open-source code that can be used by the rest of the simulation community.

All the assumptions, underlying theories and the source code of ASSOCC are available on the project website and on Github. We invite people to use it, give feedback and based on this feedback we continuously improve the model and its parameters. According to the development of the pandemic and the state of discussion, new scenarios will be added as well.

We hope that the ASSOCC model can contribute to handling this crisis in a way that shows the capabilities and usefulness of agent-based modelling.

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Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L. et al. (2020). Pathological findings of covid-19 associated with acute respiratory distress syndrome. The Lancet respiratory medicine, 8(4), 420–422

Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X. et al. (2020). Clinical course and risk factors for mortality of adult inpatients with covid-19 in wuhan, china: a retrospective cohort study. The Lancet, 395(10229), 1054-1062. doi:10.1016/S0140-6736(20)30566-3

Ghorbani, A., Lorig, F., de Bruin, B., Davidsson, P., Dignum, F., Dignum, V., van der Hurk, M., Jensen, M., Kammler, C., Kreulen, K., Ludescher, L. G., Melchior, A., Mellema, R., Păstrăv, C., Vanhée, L. and Verhagen, H. (2020) The ASSOCC Simulation Model: A Response to the Community Call for the COVID-19 Pandemic. Review of Artificial Societies and Social Simulation, 25th April 2020. https://rofasss.org/2020/04/25/the-assocc-simulation-model/

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

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Sound behavioural theories, not data, is what makes computational models useful

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By Umberto Gostoli and Eric Silverman

(A contribution to the: JASSS-Covid19-Thread)

The paper “Computational Models that Matter During a Global Pandemic Outbreak: A Call to Action” by Squazzoni et al. (2020) is a valuable contribution to the ongoing self-reflection in the social simulation community regarding the role of ABM in the broader social-scientific enterprise. In this paper the authors try to assess the potential capacity of ABM to provide policy makers with a tool allowing them to predict the evolution of the pandemic and the effects of alternative policy responses. Their conclusions suggest a role for computational modelling during the pandemic, but also have implications regarding the position of ABM within the scientific and policy arenas, and its added value relative to other methodologies of scientific inquiry.

We agree with the authors that ABM has an important (and urgent) role to play to help policy makers to take more informed decisions, provided that the models are based on reliable and robust theories of human behaviour and social interaction. However, following in the footsteps of Joshua Epstein (2008), we claim that the importance and relevance of ABM goes beyond the capacity of the models to make point predictions (i.e. in the form of ‘There will be X infections/deaths in Y days time’). We propose that the ability of ABM to develop, inform, and test relevant theory is of particular relevance during this global crisis.

This does not mean that additional data allowing for the models’ calibration and validation are not important, as they can certainly help reduce the uncertainty associated with the models’ outputs, but in our view they are not essential to what agent-based models have to offer. With that in mind, the lack of these data should not prevent the ABM community from participating in the mass mobilization of the scientific community, which is working at unprecedented speed to develop models to inform the vital policy decisions being taken during this pandemic.

As we argue in a recent position paper (Silverman et al. 2020), it is precisely when we have limited data, or no data at all, that simulations provide greater value than traditional methodologies like statistical inference; indeed, the less data we have the more important is the role that agent-based (and other computational) simulations have to play. Computational models provide a way to say something about the evolution of complex systems by delimiting the set of possible outcomes through the constraints imposed by the theoretical framework which is encoded in the model. When we find ourselves in new situations such as the Covid-19 pandemic, where the data (i.e., our past experience) cannot give us any clue regarding the future evolution of the system, we find that theories become the only tool we have to make educated guesses about what could (and could not) possibly happen. Models of complex systems have typically hundreds, if not thousands, of parameters, many of which have unknown values, and some of which have values we cannot know. If we wait for the data we need to make point predictions, we would never have a say in the policy arena, and probably if these data were available other methodologies would serve the purpose better than computational models. Delimiting and quantifying the uncertainty associated with future scenarios in the face of limited data is where computational models can make a vital contribution, as they can give policy-makers useful information for risk management.

By no means are we saying that the development and effective deployment of computational models is without challenges. But we claim that the main challenge lies in the identification and inclusion of sound behavioural theories, as the outputs we get will depend upon the reliability of our models’ theoretical input. Identifying such theories is a significant challenge, requiring theoretical contributions from a number of different fields, ranging from epidemiology and urban studies to sociology and economics.

Further, putting scholars from those disciplines into the same room will not be sufficient; we must create a multidisciplinary community of people sharing the same conceptual framework, an endeavour that takes a lot of dedication, perseverance and, crucially, time. The lack of such multidisciplinary research groups strongly limits the ABM community’s capacity to develop an effective computational model of the pandemic, and we hope that at least this crisis will prove that developing such a community is necessary to improve our capacity for a timely response to the next one.

In relation to this challenge, we are aiming to develop and support a global community of agent-based modellers focused on population health concerns, via the PHASE Network project funded by the UK Prevention Research Partnership. We urge readers to join the network via our website at https://phasenetwork.org/, and help us build a multidisciplinary health modelling community that can contribute to global efforts in improving health both during and after the Covid-19 pandemic.

We must also remember that the current crisis is very unlikely to be over quickly, and its longer-term effects on society will be substantial. At the time of writing more than 80 separate groups and institutions are embarking on efforts to build a vaccine for the coronavirus, but even with such concerted efforts there are no guarantees that a vaccine will be found. As Kissler et al. have shown, even if the virus appears to abate, further waves of infections could arise years afterwards (Kissler et al. 2020). Because of the resources and time it takes to develop theoretically sound computational models, in our view this methodology is better suited to address these longer-term questions of how society can reorganize itself to increase resilience against future pandemics – and here the ability of computational models to implement and test behavioural theories is of paramount importance. The questions that must be asked in the years to come are numerous and profound: How can the world of work change to be more robust to future crises and global shut-downs? Can welfare policies like universal basic income help prevent widespread economic devastation in future crises? How must our health and care systems evolve to better protect the most vulnerable in society?

We propose that computational models can make a particularly valuable contribution in this area. At the present time there is ample evidence of the disastrous effects of delayed or insufficient policy responses to a pandemic. Economic projections already suggest we are due to enter a post-pandemic collapse to rival the Great Depression. We can, and should, begin to develop theories and models about how we may adjust society for the post-Covid world. Models could be valuable tools for testing and developing ambitious socio-economic policy ideas in silico, in order to prepare for this new reality.

To conclude, in principle we share with the authors of the paper the belief that computational models have an important role to play to inform policy makers during crisis (such as pandemics). However, we wish to emphasize the need for sound and robust theoretical frameworks ready to be included in these models, rather than on the existence and availability of data. In practice, the lack of such frameworks is more critical for ensuring that the computational modelling community can make a useful contribution during this pandemic.

References

Epstein, J. M. (2008) Why model? Journal of Artificial Societies and Social Simulation, 11(4):12. <http://jasss.soc.surrey.ac.uk/11/4/12.html>.

Kissler, S. M., Tedijanto, C., Goldstein, E. Grad, Y. H.  and Lipsitch, M. (2020) Projecting the transmission dynamics of sars-cov-2 through the postpandemic period. Science. doi:10.1126/science.abb5793. <https://science.sciencemag.org/content/early/2020/04/14/science.abb5793>

Silverman, E., Gostoli, U., Picascia, S., Almagor, J., McCann, M., Shaw, R., & Angione, C. (2020). Situating Agent-Based Modelling in Population Health Research. arXiv preprint arXiv:2002.02345. <https://arxiv.org/abs/2002.02345&gt;

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (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

Gostoli, U. and Silverman, E. (2020) Sound behavioural theories, not data, is what makes computational models useful. Review of Artificial Societies and Social Simulation, 22th April 2020. https://rofasss.org/2020/04/22/sound-behavioural-theories/

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

Content

Don’t try to predict COVID-19. If you must, use Deep Uncertainty methods

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By Patrick Steinmanna, Jason R. Wangb, George A. K. van Voorna, and Jan H. Kwakkelb

a Biometris, Wageningen University & Research, Wageningen, the Netherlands, b Delft University of Technology, Faculty of Technology, Policy & Management, Delft, the Netherlands

(A contribution to the: JASSS-Covid19-Thread)

Abstract

We respond to the recent JASSS article on COVID-19 and computational modelling. We disagree with the authors on one major point and note the lack of discussion of a second one. We believe that COVID-19 cannot be predicted numerically, and attempting to make decisions based on such predictions will cost human lives. Furthermore, we note that the original article only briefly comments on uncertainty. We urge those attempting to model COVID-19 for decision support to acknowledge the deep uncertainties surrounding the pandemic, and to employ Decision Making under Deep Uncertainty methods such as exploratory modelling, global sensitivity analysis, and robust decision-making in their analysis to account for these uncertainties.

Introduction

We read the recent article in the Journal of Artificial Societies and Social Simulation on predictive COVID-19 modelling (Squazzoni et al. 2020) with great interest. We agree with the authors on many general points, such as the need for rigorous and transparent modelling and documentation. However, we were dismayed that the authors focused solely on how to make predictive simulation models of COVID-19 without first discussing whether making such models is appropriate under the current circumstances. We believe this question is of greater importance, and that the answer will likely disappoint many in the community. We also note that the original piece does not engage substantively with methods of modelling and model analysis specifically designed for making time-critical decisions under uncertainty.

We respond to the call issued by the Review of Artificial Societies and Social Simulation for responses and opinions on predictive modelling of COVID-19. In doing so, we go above and beyond the recent RofASSS contribution by de Matos Fernandes & Keijzer (2020)—rather than saying that definite “predictions” should be replaced by probabilistic “expectations”, we contend that no probabilities whatsoever should be applied when modelling systems as uncertain as a global pandemic. This is presented in the first section. In the second section, we discuss how those with legitimate need for predictive epidemic modelling should approach their task, and which tools might be beneficial in the current context. In the last section, we summarize our opinions and issue our own challenges to the community.

To Model or Not to Model COVID-19, That Is the Question

The recent call attempts to lay out a path for using simulation modelling to forecast the COVID-19 epidemic. However, there is no critical reflection on the question of whether modelling is the appropriate tool for this, under the current circumstances. The authors argue that with sufficient methodological rigour, high-quality data and interdisciplinary collaboration, complex outcomes (such as the COVID-19 epidemic) can be predicted well and quickly enough to provide actionable decision support.

Computational modelling is difficult in the best of times. Even models with seemingly simple structure can have emergent behavior rendering them perfectly random (Wolfram 1983) or Turing complete (Cook 2004). Attempting to draw any kind of conclusions from a simulation model, especially in the life-and-death context of pandemic decision making, must be done carefully and with respect for uncertainty. If, for whatever reason, this cannot be done, then modelling is not the right tool to answer the question at hand (Thompson & Smith 2019). The numerical nature of models is seductive, but must be employed wisely to avoid “useless arithmetic” (Pilkey-Jarvis & Pilkey 2008) or statistical fallacies (Benessia et al. 2016).

Trying to skilfully predict how the COVID-19 outbreak will evolve regionally or globally is a fool’s errand. Epistemic uncertainties about key parameters and processes describing the disease abound. Human behaviour is changing in response to the outbreak. Research and development burgeon in many sciences with presently unknowable results. Anyone claiming to know where the world will be in even a few weeks is at best delusional. Uncertainty is aggravated by the problem of equifinality (Oreskes et al. 1994). For any simulation model of COVID-19, there will be a set of model parametrizations that has a similar quality of fit with the available data. Much of this is acknowledged by Squazzoni et al. (2020), yet inexplicably they still call for developing probabilistic forecasts of the outbreak using empirically validated models. We instead contend that “about these matters, there is no scientific basis on which to form any calculable probability” (Keynes 1937), and that validation should be based on usefulness in aiding time-urgent decision-making, rather than predictive accuracy (Pielke 2003). However, the capacity for such policy-oriented modelling must be built between pandemics, not during them (Rivers et al. 2019).

This call to abstain from predicting COVID-19 does not imply that the broader community should refrain from modelling completely. The illustrative power of simple models has been amply demonstrated in various media outlets. We do urge modellers not to frame their work as predictive (e.g. “How Pandemics Can End”, rather than “How COVID-19 Will End”), and to use watermarks where possible to indicate that the shown work is not predictive. There is also ample opportunity to use simulation modelling to solve ancillary problems. For example, established transport and logistics models could be adapted to ensure supply of critical healthcare equipment is timely and efficient. Similarly, agri-food models could explore how to secure food production and distribution under labour shortages. These can be vital, though less sensational, contributions of simulation modelling to the ongoing crisis.

Deep Uncertainty: How to Predict COVID-19, if(f) You Must

Deep Uncertainty (Lempert et al. 2003) is present when analysts cannot know, or stakeholders cannot agree on:

  1. The probability distributions relevant to unknown system variables,
  2. The relations and mechanisms present in the system, and/or
  3. The metrics by which future system states should be evaluated.

All three conditions are present in the case of the COVID-19 pandemic. To give a brief example of each, we know very little about asymptomatic infections, whether a vaccine will ever become available, and whether the socio-psychological and economic impacts of a “flattened curve” future are bearable (and by whom). The field of Decision Making under Deep Uncertainty has been working on problems of a similar nature for many years already, and developed a variety of tools to analyse such problems (Marchau et al. 2019). These methods may be beneficial for designing COVID-19 policies with simulation models—if, as discussed previously, this is appropriate. In the following, we present three such methods and their potential value for COVID-19 decision support: exploratory modelling, global sensitivity analysis, and robust decision-making.

Exploratory modelling (Bankes 1993) is a conceptual approach to using simulation models for policy analysis. It emerges as a response to the question how models that cannot be empirically validated can still be used to inform planning and decision-making (Hodges 1991, Hodges & Dewar 1992). Instead of consolidating increasing amounts of knowledge into “the” model of a system, exploratory modelling advocates using wide uncertainty ranges for unknown parameters to generate a large ensemble of plausible futures, with no predictive or probabilistic power attached or implied a priori (Shortridge & Zaitchik 2018). This ensemble may represent a variety of assumptions, theories, and system structures. It could even be generated using a multitude of models (Page 2018; Smaldino 2017) and metrics (Manheim 2018). By reasoning across such an ensemble, insights agnostic to specific assumptions may be reached, sidestepping a priori biases that are inherent in only examining a simple set of scenarios, as COVID-19 policy models observed by the authors do. Reasoning across such limited sets obscures policy-relevant futures which emerge as hybrids of pre-specified positive and negative narratives (Lamontagne et al. 2018). In the context of the COVID-19 pandemic, exploratory modelling could be used to contrast a variety of assumptions about disease transmission mechanisms (e.g., the role of schools, children, or asymptomatic cases in the speed of the outbreak), reinfection potential, or adherence to social distancing norms. Many ESSA members are already familiar with such methods—NetLogo’s BehaviorSpace function is a prime example. The Exploratory Modelling & Analysis Workbench (Kwakkel 2017) provides a similar, platform-agnostic functionality by means of a Python interface. We encourage all modellers to embrace such tools, and to be honest about which parameters and structural assumptions are uncertain, how uncertain they are, and how this affects the inferences that can and cannot be made based on the results from the model.

Global sensitivity analysis (Saltelli 2004) is a method of studying both the importance and interaction of uncertain parameters on the outputs of a simulation model. Many simulation modellers are already familiar with local sensitivity analysis, where parameters are varied one at a time to ascertain their individual effect on model output. This is insufficient for studying parameter interactions in non-linear systems (Saltelli et al. 2019; ten Broeke et al. 2016). In global sensitivity analysis, combinations of parameters are varied and studied simultaneously, illuminating their joint or interaction effects. This is critical for the rigorous study of complex system models, where parameters may have unexpected, non-linear interactions. In the context of the COVID-19 epidemic, we have seen at least two public health agencies perform local sensitivity analysis over small parameter ranges, which may blind decision makers to worst-case futures (Siegenfeld & Bar-Yam 2020). Global sensitivity analysis might reveal how different assumptions for e.g. duration of Intensive Care (IC) and age-related case severity may interact to create a “perfect storm” of IC need. A collection of global sensitivity analysis methods has been encoded for Python in the SALib package (Herman & Usher 2018), and how to use these with NetLogo is illustrated in Jaxa-Rozen & Kwakkel (2018).

Robust Decision Making (RDM) (Lempert et al. 2006) is a general analytic method for designing policies which are robust across uncertainties—they perform well regardless of which future actually materializes. Policies are designed by iteratively stress-testing them across ensembles of plausible futures representing different assumptions, theories, and input parameter combinations. This represents a departure from established, probabilistic risk management approaches, which are inappropriate for fat-tailed processes such as pandemics (Norman et al. 2020). More recently, RDM has been extended to Dynamic Adaptive Policy Pathways (DAPP) (Kwakkel et al. 2015) by incorporating adaptive policies conditioned on specific triggers or signposts identified in exploratory modeling runs. In the context of the COVID-19 epidemic, DAPP might be used to design policies which can adapt as the situation develops (Hamarat et al. 2012)—possibly representing a transparent and verifiable approach to implementing the “hammer and dance” epidemic suppression method which has been widely discussed in popular media. Thinking in terms of pathways conditional on how the outbreak evolves is also a more realistic way of preparing for the dance: Rather than giving a human time line, the virus determines the time line. All we can do is indicate the conditions under which certain types of actions will be taken.

Conclusions: Please Don’t. If You Must, Use Deep Uncertainty methods.

We have raised two points of importance which are not discussed in a recent article on COVID-19 predictive modelling in JASSS. In particular, we have proposed that the question of whether such models should be created must precede any discussion of how to do so. We found that complex outcomes such as epidemics cannot reliably be predicted using simulation models, as there are numerous uncertainties that significantly affect possible future system states. However, models may be still be useful in times of crisis, if created and used appropriately. Furthermore, we have noted that there exists an entire field of study focusing on Decision Making under Deep Uncertainty, and that model analysis methods for situations like this already exist. We have briefly highlighted three methods—exploratory modelling, global sensitivity analysis, and robust decision-making—and given examples for how they might be used in the present context.

Stemming from these two points, we issue our own challenges to the ESSA modelling community and the field of systems simulation in general:

  • COVID-19 prediction distancing challenge: Do not attempt to predict the COVID-19 epidemic.
  • COVID-19 deep uncertainty challenge: If you must predict the COVID-19 epidemic, embrace deep uncertainty principles, including transparent treatment of uncertainties, exploratory modeling, global sensitivity analysis, and robust decision-making.

References

Bankes, S. (1993). Exploratory Modeling for Policy Analysis. Operations Research, 41(3), 435–449. doi: 10.1287/opre.41.3.435

Benessia, A., Funtowicz, S., Giampietro, M., Guimarães Pereira, A., Ravetz, J. R., Saltelli, A., Strand, R., & van der Sluijs, J. P. (2016). Science on the Verge. Consortium for Science, Policy & Outcomes Tempe, AZ and Washington, DC.

Cook, M. (2004). Universality in Elementary Cellular Automata. Complex Systems, 15(1), 1–40.

de Matos Fernandes, C. A., & Keijzer, M. A. (2020). No one can predict the future: More than a semantic dispute. https://rofasss.org/2020/04/15/no-one-can-predict-the-future/

Hamarat, C., Kwakkel, J., & Pruyt, E. (2012). Adaptive Policymaking under Deep Uncertainty : Optimal Preparedness for the next pandemic. Proceedings of the 30th International Conference of the System Dynamics Society, Nrc 2009.

Herman, J., & Usher, W. (2018). SALib : Sensitivity Analysis Library in Python ( Numpy ). Contains Sobol , SALib : An open-source Python library for Sensitivity Analysis. 41(April), 2015–2017. doi:10.1016/S0010-1

Jaxa-Rozen, M., & Kwakkel, J. H. (2018). PyNetLogo: Linking NetLogo with Python. Journal of Artificial Societies and Social Simulation, 21(2). <http://jasss.soc.surrey.ac.uk/21/2/4.html> doi:10.18564/jasss.3668

Keynes, J. M. (1937). The General Theory of Employment. The Quarterly Journal of Economics, 51(2), 209. doi:10.2307/1882087

Kwakkel, J. H. (2017). The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making. Environmental Modelling & Software, 96, 239–250. doi:10.1016/j.envsoft.2017.06.054

Kwakkel, J. H., Haasnoot, M., & Walker, W. E. (2015). Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world. Climatic Change, 132(3), 373–386. doi:10.1007/s10584-014-1210-4

Lamontagne, J. R., Reed, P. M., Link, R., Calvin, K. V., Clarke, L. E., & Edmonds, J. A. (2018). Large Ensemble Analytic Framework for Consequence-Driven Discovery of Climate Change Scenarios. Earth’s Future, 6(3), 488–504. doi:10.1002/2017EF000701

Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A general, analytic method for generating robust strategies and narrative scenarios. Management Science, 52(4), 514–528. doi:10.1287/mnsc.1050.0472

Lempert, R. J., Popper, S., & Bankes, S. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. doi:10.7249/mr1626

Manheim, D. (2018). Building Less Flawed Metrics: Dodging Goodhart and Campbell’s Laws. In MPRA.

Marchau, V. A. W. J., Walker, W. E., Bloemen, P. J. T. M., & Popper, S. W. (Eds.). (2019). Decision Making under Deep Uncertainty. Springer International Publishing. doi:10.1007/978-3-030-05252-2

Norman, J., Bar-Yam, Y., & Taleb, N. N. (2020). Systemic Risk of Pandemic via Novel Pathogens – Coronavirus: A Note. New England Complex Systems Institute. http://arxiv.org/abs/1410.5787

Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, and confirmation of numerical models in the earth sciences. Science, 263(5147), 641–646. doi:10.1126/science.263.5147.641

Page, S. E. (2018). The model thinker: what you need to know to make data work for you. Hachette UK.

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Saltelli, A. (2004). Global sensitivity analysis: an introduction. Proc. 4th International Conference on Sensitivity Analysis of Model Output (SAMO’04), 27–43.

Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., Li, S., & Wu, Q. (2019). Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental Modelling and Software, 114(March 2018), 29–39. doi:10.1016/j.envsoft.2019.01.012

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

Content

No one can predict the future: More than a semantic dispute

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By Carlos A. de Matos Fernandes and Marijn A. Keijzer

(A contribution to the: JASSS-Covid19-Thread)

Models are pivotal to battle the current COVID-19 crisis. In their call to action, Squazzoni et al. (2020) convincingly put forward how social simulation researchers could and should respond in the short run by posing three challenges for the community among which is a COVID-19 prediction challenge. Although Squazzoni et al. (2020) stress the importance of transparent communication of model assumptions and conditions, we question the liberal use of the word ‘prediction’ for the outcomes of the broad arsenal of models used to mitigate the COVID-19 crisis by ours and other modelling communities. Four key arguments are provided that advocate using expectations derived from scenarios when explaining our models to a wider, possibly non-academic audience.

The current COVID-19 crisis necessitates that we implement life-changing policies that, to a large extent, build upon predictions from complex, quickly adapted, and sometimes poorly understood models. The examples of models spurring the news to produce catchphrase headlines are abundant (Imperial College, AceMod-Australian Census-based Epidemic Model, IndiaSIM, IHME, etc.). And even though most of these models will be useful to assess the comparative effectiveness of interventions in our aim to ‘flatten the curve’, the predictions that disseminate to news media are those of total cases or timing of the inflection point.

The current focus on predictive epidemiological and behavioural models brings back an important discussion about prediction in social systems. “[T]here is a lot of pressure for social scientists to predict” (Edmonds, Polhill & Hales, 2019), and we might add ‘especially nowadays’. But forecasting in human systems is often tricky (Hofman, Sharma & Watts, 2017). Approaches that take well-understood theories and simple mechanisms often fail to grasp the complexity of social systems, yet models that rely on complex supervised machine learning-like approaches may offer misleading levels of confidence (as was elegantly shown recently by Salganik et al., 2020). COVID-19 models appear to be no exception as a recent review concluded that “[…] their performance estimates are likely to be optimistic and misleading” (Wynants et al., 2020, p. 9). Squazzoni et al. describe these pitfalls too (2020: paragraph 3.3). In the crisis at hand, it may even be counter-productive to rely on complex models that combine well-understood mechanisms with many uncertain parameters (Elsenbroich & Badham, 2020).

Considering the level of confidence we can have about predictive models in general, we believe there is an issue with the way predictions are communicated by the community. Scientists often use ‘prediction’ to refer to some outcome of a (statistical) model where they ‘predict’ aspects of the data that are already known, but momentarily set aside. Edmonds et al. (2019: paragraph 2.4) state that “[b]y ‘prediction’, we mean the ability to reliably anticipate well-defined aspects of data that is not currently known to a useful degree of accuracy via computations using the model”. Predictive accuracy, in this case, can then be computed later on, by comparing the prediction to the truth. Scientists know that when talking about predictions of their models, they don’t claim to generalize to situations outside of the narrow scope of their study sample or their artificial society. We are not predicting the future, and wouldn’t claim we could. However, this is wildly different from how ‘prediction’ is commonly understood: As an estimation of some unknown thing in the future. Now that our models quickly disseminate to the general public, we need to be careful with the way we talk about their outcomes.

Predictions in the COVID-19 crisis will remain imperfect. In the current virus outbreak, society cannot afford to rely on the falsification of models for interventions against empirical data. As the virus remains to spread rapidly, our only option is to rely on models as a basis for policy, ceteris paribus. And it is precisely here – at ‘ceteris paribus’ – where the terminology ‘predictions’ miss the mark. All things will not be equal tomorrow, the next day, or the day after that (Van Bavel et al. [2020] note numerous topics that affect managing the COVID-19 pandemic and its impact on society). Policies around the globe are constantly being tweaked, and people’s behaviour changes dramatically as a consequence (Google, 2020). Relying on predictions too much may give a false sense of security.

We propose to avoid using the word ‘prediction’ too much and talk about scenarios or expectations instead where possible. We identify four reasons why you should avoid talking about prediction right now:

  1. Not everyone is acquainted with noise and emergence. Computational Social Scientists generally understand the effects of noise in social systems (Squazzoni et al., 2020: paragraph 1.8). Small behavioural irregularities can be reinforced in complex systems fostering unexpected outcomes. Yet, scientists not acquainted with studying complex social systems may be unfamiliar with the principles we have internalized by now, and put over-confidence in the median outputs of volatile models that enter the scientific sphere as predictions.
  2. Predictions do not convey uncertainty. The general public is usually unacquainted with academic esoteric concepts. For instance, showing a flatten-the-curve scenario generally builds upon mean or median approximation, oftentimes neglecting to include variability of different scenarios. Still, there are numerous other outcomes, building on different parameter values. We fear that by stating a prediction to an undisciplined public, they expect such a thing to occur for certain. If we forecast a sunny day, but there’s rain, people are upset. Talking about scenarios, expectations, and mechanisms may prevent confusion and opposition when the forecast does not occur.
  3. It’s a model, not a reality. The previous argument feeds into the third notion: Be honest about what you model. A model is a model. Even the most richly calibrated model is a model. That is not to say that such models are not informative (we reiterate: models are not a shot in the dark). Still, richly calibrated models based on poor data may be more misleading than less calibrated models (Elsenbroich & Badham, 2020). Empirically calibrated models may provide more confidence at face value, but it lies in the nature of complex systems that small measurement errors in the input data may lead to big deviations in outputs. Models present a scenario for our theoretical reasoning with a given set of parameter values. We can update a model with empirical data to increase reliability but it remains a scenario about a future state given an (often expansive) set of assumptions (recently beautifully visualized by Koerth, Bronner, & Mithani, 2020).
  4. Stop predicting, start communicating. Communication is pivotal during a crisis. An abundance of research shows that communicating clearly and honestly is a best practice during a crisis, generally comforting the general public (e.g., Seeger, 2006). Squazzoni et al. (2020) call for transparent communication. by stating that “[t]he limitations of models and the policy recommendations derived from them have to be openly communicated and transparently addressed”. We are united in our aim to avert the COVID-19 crisis but should be careful that overconfidence doesn’t erode society’s trust in science. Stating unequivocally that we hope – based on expectations – to avert a crisis by implementing some policy, does not preclude altering our course of action when an updated scenario about the future may require us to do so. Modellers should communicate clearly to policy-makers and the general public that this is the role of computational models that are being updated daily.

Squazzoni et al. (2020) set out the agenda for our community in the coming months and it is an important one. Let’s hope that the expectations from the scenarios in our well-informed models will not fall on deaf ears.

References

Edmonds, B., Polhill, G., & Hales, D. (2019). Predicting Social Systems – a Challenge. Review of Artificial Societies and Social Simulation, 4th June 2019. https://rofasss.org/2018/11/04/predicting-social-systems-a-challenge

Edmonds, B., Le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root, H., & Squazzoni, F. (2019). Different Modelling Purposes. Journal of Artificial Societies and Social Simulation, 22(3), 6. <http://jasss.soc.surrey.ac.uk/22/3/6.html> doi: 10.18564/jasss.3993

Elsenbroich, C., & Badham, J. (2020). Focussing on our Strengths. Review of Artificial Societies and Social Simulation, 12th April 2020.
https://rofasss.org/2020/04/12/focussing-on-our-strengths/

Google. (2020). COVID-19 Mobility Reports. https://www.google.com/covid19/mobility/ (Accessed 15th April 2020)

Hofman, J. M., Sharma, A., & Watts, D. J. (2017). Prediction and Explanation in Social Systems. Science, 355, 486–488. doi: 10.1126/science.aal3856

Koerth, M., Bronner, L., & Mithani, J. (2020, March 31). Why It’s So Freaking Hard To Make A Good COVID-19 Model. FiveThirtyEight. https://fivethirtyeight.com/

Salganik, M. J. et al. (2020). Measuring the Predictability of Life Outcomes with a Scientific Mass Collaboration. PNAS. 201915006. doi: 10.1073/pnas.1915006117

Seeger, M. W. (2006). Best Practices in Crisis Communication: An Expert Panel Process, Journal of Applied Communication Research, 34(3), 232-244.  doi: 10.1080/00909880600769944

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (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

Van Bavel, J. J. et al. (2020). Using social and behavioural science to support COVID-19 pandemic response. PsyArXiv. https://doi.org/10.31234/osf.io/y38m9

Wynants. L., et al. (2020). Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal. BMJ, 369, m1328. doi: 10.1136/bmj.m1328

de Matos Fernandes, C. A. and Keijzer, M. A. (2020) No one can predict the future: More than a semantic dispute. Review of Artificial Societies and Social Simulation, 15th April 2020. https://rofasss.org/2020/04/15/no-one-can-predict-the-future/

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

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Good Modelling Takes a Lot of Time and Many Eyes

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By Bruce Edmonds

(A contribution to the: JASSS-Covid19-Thread)

It is natural to want to help in a crisis (Squazzoni et al. 2020), but it is important to do something that is actually useful rather than just ‘adding to the noise’. Usefully modelling disease spread within complex societies is not easy to do – which essentially means there are two options:

  1. Model it in a fairly abstract manner to explore ideas and mechanisms, but without the empirical grounding and validation needed to reliably support policy making.
  2. Model it in an empirically testable manner with a view to answering some specific questions and possibly inform policy in a useful manner.

Which one does depends on the modelling purpose one has in mind (Edmonds et al. 2019). Both routes are legitimate as long as one is clear as to what it can and cannot do. The dangers come when there is confusion –  taking the first route whilst giving policy actors the impression one is doing the second risks deceiving people and giving false confidence (Edmonds & Adoha 2019, Elsenbroich & Badham 2020). Here I am only discussing the second, empirically ambitious route.

Some of the questions that policy-makers might want to ask, include, what might happen if we: close the urban parks, allow children of a specific range of ages go to school one day a week, cancel 75% of the intercity trains, allow people to go to beauty spots, visit sick relatives in hospital or test people as they recover and give them a certificate to allow them to go back to work?

To understand what might happen in these scenarios would require an agent-based model where agents made the kind of mundane, every-day decisions of where to go and who to meet, such that the patterns and outputs of the model were consistent with known data (possibly following the ‘Pattern-Oriented Modelling’ of Grimm & Railsback 2012). This is currently lacking. However this would require:

  1. A long-term, iterative development (Bithell 2018), with many cycles of model development followed by empirical comparison and data collection. This means that this kind of model might be more useful for the next epidemic rather than the current one.
  2. A collective approach rather than one based on individual modellers. In any very complex model it is impossible to understand it all – there are bound to be small errors and programmed mechanisms will subtly interaction with others. As (Siebers & Venkatesan 2020) pointed out this means collaborating with people from other disciplines (which always takes time to make work), but it also means an open approach where lots of modellers routinely inspect, replicate, pull apart, critique and play with other modellers’ work – without anyone getting upset or feeling criticised. This does involve an institutional and normative embedding of good modelling practice (as discussed in Squazzoni et al. 2020) but also requires a change in attitude – from individual to collective achievement.

Both are necessary if we are to build the modelling infrastructure that may allow us to model policy options for the next epidemic. We will need to start now if we are to be ready because it will not be easy.

References

Bithell, M. (2018) Continuous model development: a plea for persistent virtual worlds, Review of Artificial Societies and Social Simulation, 22nd August 2018. https://rofasss.org/2018/08/22/mb

Edmonds, B. & Adoha, L. (2019) Using agent-based simulation to inform policy – what could possibly go wrong? In Davidson, P. & Verhargen, H. (Eds.) (2019). Multi-Agent-Based Simulation XIX, 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers. Lecture Notes in AI, 11463, Springer, pp. 1-16. DOI: 10.1007/978-3-030-22270-3_1

Edmonds, B., Le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root, H., & Squazzoni, F. (2019). Different Modelling Purposes. Journal of Artificial Societies and Social Simulation, 22(3), 6. <http://jasss.soc.surrey.ac.uk/22/3/6.html> doi: 10.18564/jasss.3993

Elsenbroich, C. and Badham, J. (2020) Focussing on our Strengths. Review of Artificial Societies and Social Simulation, 12th April 2020. https://rofasss.org/2020/04/12/focussing-on-our-strengths/

Grimm, V., & Railsback, S. F. (2012). Pattern-oriented modelling: a ‘multi-scope’for predictive systems ecology. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1586), 298-310. doi:10.1098/rstb.2011.0180

Siebers, P-O. and Venkatesan, S. (2020) Get out of your silos and work together. Review of Artificial Societies and Social Simulation, 8th April 2020. https://rofasss.org/2020/0408/get-out-of-your-silos

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (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

Edmonds, B. (2020) Good Modelling Takes a Lot of Time and Many Eyes. Review of Artificial Societies and Social Simulation, 13th April 2020. https://rofasss.org/2020/04/13/a-lot-of-time-and-many-eyes/

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