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Good Modelling Takes a Lot of Time and Many Eyes

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By Bruce Edmonds

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (2020) Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation, 23(2):10. <http://jasss.soc.surrey.ac.uk/23/2/10.html>. doi: 10.18564/jasss.4298

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

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

Content

Go for DATA

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By Gérard Weisbuch

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

I totally share the view on the importance of DATA. What we need is data driven models and the reference to weather forecasting and data assimilation is very appropriate. This probably implies the establishment of a center for epidemics forecasting similar to Reading in the UK or Météo-France in Toulouse. The persistence of such an institution in “normal times” would be hard to warrant, but its operation could be organised as the military reserve.

Let me stress three points.

  1. Models are needed not only by National Policy makers but by a wide range of decision makers such as hospitals and even households. These meso-scales units face hard problems of supplies: hospitals have to manage the supplies of material, consumables, personnel to face hard to predict demand from patients. The same holds true for households: e.g. how to program errands in view of the dynamics of the epidemics? All the supply chain issues also exist for firms, including the chain of deliveries of consumables to hospitals. Hence the importance of available data provided by a center for epidemics forecasting.
  2. The JASSS call (Flaminio et al. 2020) stresses the importance DATA, but does not provide many clues about how to get them. One can hope that some institutions would provide them, but my limited experience is that you have to dig for them. Do It Yourself is a leitmotiv of the Big Data industry. I am thinking of processing patient records to build models of the disease, or private diaries and tweets to model individual behaviour. One then needs collaboration from the NLP (Natural Language Processing) community.
  3. The public and even the media have a very low understanding of dynamical systems and of exponential growth. We know since D. Kahneman book “Thinking, Fast and Slow” (2011) that we have a hard time reasoning on probabilities for instance, but this also applies to dynamics and exponential. We face situations that mandate different actions at different stage of the epidemics such as doing errands or moving to the country-side for town dwellers. The issue is even more difficult for firms, who have to manage employment. Simple models and experimental cognitive science results should be brought to journalists and the general public concerning these issues, in the style of Kahneman if possible.

References

Kahneman, D., & Patrick, E. (2011). Thinking, fast and slow. Allen Lane.

Squazzoni, Flaminio, Polhill, J. Gareth, Edmonds, Bruce, Ahrweiler, Petra, Antosz, Patrycja, Scholz, Geeske, Chappin, Émile, Borit, Melania, Verhagen, Harko, Giardini, Francesca and Gilbert, Nigel (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

Weisbuch, G. (2020) Go for DATA. Review of Artificial Societies and Social Simulation, 7th April 2020. https://rofasss.org/2020/04/07/go-for-data/

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

Meta

Call for responses to the JASSS Covid19 position paper

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In the recent position paper in JASSS, entitled “Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action” the authors suggest some collective actions we, as social simulators, could take.

We are asking for submissions that present serious comments on this paper. This  could include:

  • To discuss other points of view
  • To talk about possible modelling approaches
  • To review simulation modelling of covid19 that includes social aspects
  • To point out some of the difficulties of interpretation and the interface with the policy/political world
  • To discuss or suggest other possible collective actions that could be taken.

All such contributions will form the the: JASSS-Covid19-Thread

Content, Questions & Answers

Query: What is the earliest example of a social science simulation (that is nonetheless arguably an ABM) and shows real and simulated data in the same figure or table?

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By Edmund Chattoe-Brown

On one level this is a straightforward request. The earliest convincing example I have found is Hägerstrand (1965, p. 381) an article that seems to be undeservedly neglected because it is also the earliest example of a simulation I have been able to identify that demonstrates independent calibration and validation (Gilbert and Troitzsch 2005, p. 17).1

However, my attempts to find the earliest examples are motivated two more substantive issues (which may help to focus the search for earlier candidates). Firstly, what is the value of a canon (and giving due intellectual credit) for the success of ABM? The Schelling model is widely known and taught but it is not calibrated and validated. If a calibrated and validated model already existed in 1965, should it not be more widely cited? If we mostly cite a non-empirical model, might we give the impression that this is all that ABM can do? Also, failing to cite an article means that it cannot form the basis for debate. Is the Hägerstrand model in some sense “better” or “more important” than the Schelling model? This is a discussion we cannot have without awareness of the Hägerstrand model in the first place.

The second (and related) point regards the progress made by ABM and how those outside the community might judge it. Looking at ABM research now, the great majority of models appear to be non-empirical (Angus and Hassani-Mahmooei 2015, Table 5 in section 4.5). Without citations of articles like Hägerstrand (and even Clarkson and Meltzer), the non-expert reader of ABM might be led to conclude that it is too early (or too difficult) to produce such calibrated and validated models. But if this was done 50 years ago, and is not being much publicised, might we be using up our credibility as a “new” field still finding its feet?) If there are reasons for not doing, or not wanting to do, what Hägerstrand managed, let us be obliged to be clear what they are and not simply hide behind widespread neglect of such examples2.)

Notes

  1. I have excluded an even earlier example of considerable interest (Clarkson and Meltzer 1960 which also includes an attempt at calibration and validation but has never been cited in JASSS) for two reasons. Firstly, it deals with the modelling of a single agent and therefore involves no interaction. Secondly, it appears that the validation may effectively be using the “same” data as the calibration in that protocols elicited from an investment officer regarding portfolio selection are then tested against choices made by that same investment officer.
  2. And, of course, this is a vicious circle because in our increasingly pressurised academic world, people only tend to read and cite what is already cited.

References

Angus, Simon D. and Hassani-Mahmooei, Behrooz (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), October, article 16, .

Clarkson, Geoffrey P. and Meltzer, Allan H. (1960) ‘Portfolio Selection: A Heuristic Approach, The Journal of Finance, 15(4), December, pp. 465-480.

Gilbert, Nigel and Troitzsch, Klaus G. (2005) Simulation for the Social Scientist, 2nd edition (Buckingham: Open University Press).

Hägerstrand, Torsten (1965) ‘A Monte Carlo Approach to Diffusion’, Archives Européennes de Sociologie, 6(1), May, Special Issue on Simulation in Sociology, pp. 43-67.

Chattoe-Brown, E. (2018) What is the earliest example of a social science simulation (that is nonetheless arguably an ABM) and shows real and simulated data in the same figure or table? Review of Artificial Societies and Social Simulation, 11th June 2018. https://rofasss.org/2018/06/11/ecb/

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