This is a reply to a review in JASSS (Chattoe-Brown 2021) of (Dignum 2021).
Before responding to some of the specific concerns of Edmund I would like to thank him for the thorough review. I am especially happy with his conclusion that the book is solid enough to make it a valuable contribution to scientific progress in modelling crises. That was the main aim of the book and it seems that is achieved. I want to reiterate what we already remarked in the book; we do not claim that we have the best or only way of developing an Agent-Based Model (ABM) for crises. Nor do we claim that our simulations were without limitations. But we do think it is an extensive foundation from which others can start, either picking up some bits and pieces, deviating from it in specific ways or extending it in specific ways.
The concerns that are expressed by Edmund are certainly valid. I agree with some of them, but will nuance some others. First of all the concern about the fact that we seem to abandon the NetLogo implementation and move to Repast. This fact does not make the ABM itself any less valid! In itself it is also an important finding. It is not possible to scale such a complex model in NetLogo beyond around two thousand agents. This is not just a limitation of our particular implementation, but a more general limitation of the platform. It leads to the important challenge to get more computer scientists involved to develop platforms for social simulations that both support the modelers adequately and provide efficient and scalable implementations.
That the sheer size of the model and the results make it difficult to trace back the importance and validity of every factor on the results is completely true. We have tried our best to highlight the most important aspects every time. But, this leaves questions as to whether we make the right selection of highlighted aspects. As an illustration to this, we have been busy for two months to justify our results of the simulations of the effectiveness of the track and tracing apps. We basically concluded that we need much better integrated analysis tools in the simulation platform. NetLogo is geared towards creating one simulation scenario, running the simulation and analyzing the results based on a few parameters. This is no longer sufficient when we have a model with which we can create many scenarios and have many parameters that influence a result. We used R now to interpret the flood of data that was produced with every scenario. But, R is not really the most user friendly tool and also not specifically meant for analyzing the data from social simulations.
Let me jump to the third concern of Edmund and link it to the analysis of the results as well. While we tried to justify the results of our simulation on the effectiveness of the track and tracing app we compared our simulation with an epidemiological based model. This is described in chapter 12 of the book. Here we encountered the difference in assumed number of contacts per day a person has with other persons. One can take the results, as quoted by Edmund as well, of 8 or 13 from empirical work and use them in the model. However, the dispute is not about the number of contacts a person has per day, but what counts as a contact! For the COVID-19 simulations standing next to a person in the queue in a supermarket for five minutes can count as a contact, while such a contact is not a meaningful contact in the cited literature. Thus, we see that what we take as empirically validated numbers might not at all be the right ones for our purpose. We have tried to justify all the values of parameters and outcomes in the context for which the simulations were created. We have also done quite some sensitivity analyses, which we did not all report on just to keep the volume of the book to a reasonable size. Although we think we did a proper job in justifying all results, that does not mean that one can have different opinions on the value that some parameters should have. It would be very good to check the influence on the results of changes in these parameters. This would also progress scientific insights in the usefulness of complex models like the one we made!
I really think that an ABM crisis response should be institutional. That does not mean that one institution determines the best ABM, but rather that the ABM that is put forward by that institution is the result of a continuous debate among scientists working on ABM’s for that type of crisis. For us, one of the more important outcomes of the ASSOCC project is that we really need much better tools to support the types of simulations that are needed for a crisis situation. However, it is very difficult to develop these tools as a single group. A lot of the effort needed is not publishable and thus not valued in an academic environment. I really think that the efforts that have been put in platforms such as NetLogo and Repast are laudable. They have been made possible by some generous grants and institutional support. We argue that this continuous support is also needed in order to be well equipped for a next crisis. But we do not argue that an institution would by definition have the last word in which is the best ABM. In an ideal case it would accumulate all academic efforts as is done in the climate models, but even more restricted models would still be better than just having a thousand individuals all claiming to have a useable ABM while governments have to react quickly to a crisis.
The final concern of Edmund is about the empirical scale of our simulations. This is completely true! Given the scale and details of what we can incorporate we can only simulate some phenomena and certainly not everything around the COVID-19 crisis. We tried to be clear about this limitation. We had discussions about the Unity interface concerning this as well. It is in principle not very difficult to show people walking in the street, taking a car or a bus, etc. However, we decided to show a more abstract representation just to make clear that our model is not a complete model of a small town functioning in all aspects. We have very carefully chosen which scenarios we can realistically simulate and give some insights in reality from. Maybe we should also have discussed more explicitly all the scenarios that we did not run with the reasons why they would be difficult or unrealistic in our ABM. One never likes to discuss all the limitations of one’s labor, but it definitely can be very insightful. I have made up for this a little bit by submitting an to a special issue on predictions with ABM in which I explain in more detail, which should be the considerations to use a particular ABM to try to predict some state of affairs. Anyone interested to learn more about this can contact me.
To conclude this response to the review, I again express my gratitude for the good and thorough work done. The concerns that were raised are all very valuable to concern. What I tried to do in this response is to highlight that these concerns should be taken as a call to arms to put effort in social simulation platforms that give better support for creating simulations for a crisis.
Dignum, F. (Ed.) (2021) Social Simulation for a Crisis: Results and Lessons from Simulating the COVID-19 Crisis. Springer. DOI:10.1007/978-3-030-76397-8
Chattoe-Brown, E. (2021) A review of “Social Simulation for a Crisis: Results and Lessons from Simulating the COVID-19 Crisis”. Journal of Artificial Society and Social Simulation. 24(4). https://www.jasss.org/24/4/reviews/1.html
Dignum, F. (2020) Response to the review of Edmund Chattoe-Brown of the book “Social Simulations for a Crisis”. Review of Artificial Societies and Social Simulation, 4th Nov 2021. https://rofasss.org/2021/11/04/dignum-review-response/
© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)