Tag Archives: Computational models

The Policy Context of Covid19 Agent-Based Modelling

By Edmund Chattoe-Brown

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

In the recent discussions about the role of ABM and COVID, there seems to be an emphasis on the purely technical dimensions of modelling. This obviously involves us “playing to our strengths” but unfortunately it may reduce the effectiveness that our potential policy contributions can make. Here are three contextual aspects of policy for consideration to provide a contrast/corrective.

What is “Good” Policy?

Obviously from a modelling perspective good policy involves achieving stated goals. So a model that suggests a lower death rate (or less taxing of critical care facilities) under one intervention rather than another is a potential argument for that intervention. (Though of course how forceful the argument is depends on the quality of the model.) But the problem is that policy is predominantly a political and not a technical process (related arguments are made by Edmonds 2020). The actual goals by which a policy is evaluated may not be limited to the obvious technical ones (even if that is what we hear most about in the public sphere) and, most problematically, there may be goals which policy makers are unwilling to disclose. Since we do not know what these goals are, we cannot tell whether their ends are legitimate (having to negotiate privately with the powerful to achieve anything) or less so (getting re-elected as an end in itself).

Of course, by its nature (being based on both power and secrecy), this problem may be unfixable but even awareness of it may change our modelling perspective in useful ways. Firstly, when academic advice is accused of irrelevance, the academics can only ever be partly to blame. You can only design good policy to the extent that the policy maker is willing to tell you the full evaluation function (to the extent that they know it of course). Obviously, if policy is being measured by things you can’t know about, your advice is at risk of being of limited value. Secondly, with this is mind, we may be able to gain some insight into the hidden agenda of policy by looking at what kind of suggestions tend to be accepted and rejected. Thirdly, once we recognise that there may be “unknown unknowns” we can start to conjecture intelligently about what these could be and take some account of them in our modelling strategies. For example, how many epidemic models consider the financial costs of interventions even approximately? Is the idea that we can and will afford whatever it takes to reduce deaths a blind spot of the “medical model?”

When and How to Intervene

There used to be an (actually rather odd) saying: “You can’t get a baby in a month by making nine women pregnant”. There has been a huge upsurge in interest regarding modelling and its relationship to policy since start of the COVID crisis (of which this theme is just one example) but realising the value of this interest currently faces significant practical problems. Data collection is even harder than usual (as is scholarship in general), there is a limit to how fast good research can ever be done, peer review takes time and so on. The question here is whether any amount of rushing around at the present moment will compensate for neglected activities when scholarship was easier and had more time (an argument also supported by Bithell 2018). The classic example is the muttering in the ABM community about the Ferguson model being many thousands of lines of undocumented C code. Now we are in a crisis, even making the model available was a big ask, let alone making it easier to read so that people might “heckle” it. But what stopped it being available, documented, externally validated and so on before COVID? What do we need to do so that next time there is a pandemic crisis, which there surely will be, “we” (the modelling community very broadly defined) are able to offer the government a “ready” model that has the best features of various modelling techniques, evidence of unfudgeable quality against data, relevant policy scenarios and so on? (Specifically, how will ABM make sure it deserves to play a fit part in this effort?) Apart from the models themselves, what infrastructures, modelling practices, publishing requirements and so on do we need to set up and get working well while we have the time? In practice, given the challenges of making effective contributions right now (and the proliferation of research that has been made available without time for peer review may be actively harmful), this perspective may be the most important thing we can realistically carry into the “post lockdown” world.

What Happens Afterwards?

ABM has taken such a long time to “get to” policy based on data that looking further than the giving of such advice simply seems to have been beyond us. But since policy is what actually happens, we have a serious problem with counterfactuals. If the government decides to “flatten the curve” rather than seek “herd immunity” then we know how the policy implemented relates to the model “findings” (for good or ill) but not how the policy that was not implemented does. Perhaps the outturn of the policy that looked worse in the model would actually have been better had it been implemented?

Unfortunately (this is not a typo), we are about to have an unprecedently large social data set of comparative experiments in the nature and timing of epidemiological interventions, but ABM needs to be ready and willing to engage with this data. I think that ABM probably has a unique contribution to make in “endogenising” the effects of policy implementation and compliance (rather than seeing these, from a “model fitting” perspective, as structural changes to parameter values) but to make this work, we need to show much more interest in data than we have to date.

In 1971, Dutton and Starbuck, in a worryingly neglected article (cited only once in JASSS since 1998 and even then not in respect of model empirics) reported that 81% of the models they surveyed up to 1969 could not achieve even qualitative measurement in both calibration and validation (with only 4% achieving quantitative measurement in both). As a very rough comparison (but still the best available), Angus and Hassani-Mahmooei (2015) showed that just 13% of articles in JASSS published between 2010 and 2012 displayed “results elements” both from the simulation and using empirical material (but the reader cannot tell whether these are qualitative or quantitative elements or whether their joint presence involves comparison as ABM methodology would indicate). It would be hard to make the case that the situation in respect to ABM and data has therefore improved significantly in 4 decades and it is at least possible that it has got worse!

For the purposes of policy making (in the light of the comments above), what matters of course is not whether the ABM community believes that models without data continue to make a useful contribution but whether policy makers do.

References

Angus, S. D. and Hassani-Mahmooei, B. (2015) “Anarchy” Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012, Journal of Artificial Societies and Social Simulation, 18(4), 16. doi:10.18564/jasss.2952

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

Dutton, John M. and Starbuck, William H. (1971) Computer Simulation Models of Human Behavior: A History of an Intellectual Technology. IEEE Transactions on Systems, Man, and Cybernetics, SMC-1(2), 128–171. doi:10.1109/tsmc.1971.4308269

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/


Chattoe-Brown, E. (2020) The Policy Context of Covid19 Agent-Based Modelling. Review of Artificial Societies and Social Simulation, 4th May 2020. https://rofasss.org/2020/05/04/policy-context/


 

What more is needed for Democratically Accountable Modelling?

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,

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/


 

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

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/


 

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

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

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/


 

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

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

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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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/