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>
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)
Review of Artificial Societies and Social Simulation 
What do you mean by “Sound Behavioural Theory”? Does this mean that the theory is empirically tested to a sufficient extent that we can rely upon its validity? Does it mean that we know when it can be relied upon and when not?
Broadly, by a ‘sound’ behavioural theory we mean a theory of behaviour which most members of the relevant community of scholars and researchers regard as a plausible model of human behaviour.
We are aware that the ‘degree of soundness’ of any theory is often a matter of debate, and that there can be alternative behavioural models that can be considered as ‘plausible’ by the relevant research community, so that results based on these alternative models may have to be considered.
So the obvious problems with this approach are (a) when such theories are continued by a community despite a lack of empirical evidence (or even, as in economics, despite the evidence) and (b) where there are almost an infinite number of possible mechanisms, so comparison is impossible. Maybe you can give an example where (a) and (b) do not hold and thus an academic consensus is good enough?