Monthly Archives: May 2020

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Basic Modelling Hygiene – keep descriptions about models and what they model clearly distinct

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By Bruce Edmonds

The essence of a model is that it relates to something else – what it models – even if this is only a vague or implicit mapping. Otherwise a model would be indistinguishable from any other computer code, set of equations etc (Hesse 1964; Wartofsky 1966). The centrality of this essence makes it unsurprising that many modellers seem to conflate the two.

This is made worse by three factors.

  1. A strong version of Kuhn’s “Spectacles” (Kuhn 1962) where the researcher goes beyond using the model as a way of thinking about the world to projecting their model onto the world, so they see the world only through that “lens”. This effect seems to be much stronger for simulation modelling due to the intimate interaction that occurs over a period of time between modellers and their model.
  2. It is a natural modelling heuristic to make the model more like what it models (Edmonds & al. 2019), introducing more elements of realism. This is especially strong with agent-based modelling which lends itself to complication and descriptive realism.
  3. It is advantageous to stress the potential connections between a model (however abstract) and possible application areas. It is common to start an academic paper with a description of a real-world issue to motivate the work being reported on; then (even if the work is entirely abstract and unvalidated) to suggest conclusions for what is observed. A lack of substantiated connections between model and any empirical data can be covered up by slick passing from the world to the model and back again and a lack of clarity as to what their research achieves (Edmonds & al. 2019).

Whatever the reasons the result is similar – that the language used to describe entities, processes and outcomes in the model is the same as that used for its descriptions of what is intended to be modelled.

Such conflation is common in academic papers (albeit to different degrees). Expert modellers will not usually be confused by such language because they understand the modelling process and know what to look for in a paper. Thus one might ask, what is the harm of a little rhetoric and hype in the reporting of models? After all, we want modellers to be motivated and should thus be tolerant of their enthusiasm. To show the danger I will thus look at an example that talks about modelling aspects of ethnocentrism.

In their paper, entitled “The Evolutionary Dominance of Ethnocentric Cooperation“, Hartshorn, Kaznatcheev & Shultz (2013) further analyse the model described in (Hammond & Axelrod 2006). The authors have reimplemented the original model and extensively analysed it especially the temporal dynamics. The paper is solely about the original model and its properties, there is no pretence of any validation or calibration with respect to any data. The problem is in the language used, because it the language could equally well refer to the model and the real world.

Take the first sentence of its abstract: “Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution“. This sounds like the simulation suggests something about the world we live in – that, as the title suggests, Ethnocentric cooperation naturally dominates other strategies (e.g. humanitarianism) and so it is natural. The rest of the abstract then goes on in the same sort of language which could equally apply to the model and the real world.

Expert modellers will understand that they were talking about the purely abstract properties of the model, but this will not be clear to other readers. However, in this case there is evidence that it is a problem. This paper has, in recent years, shot to the top of page requests from the JASSS website (22nd May 2020) at 162,469 requests over a 7-day period, but is nowhere in the top 50 articles in terms of JASSS-JASSS citations. Tracing where these requests come from, results in many alt-right and Russian web sites. It seems that many on the far right see this paper as confirmation of their Nationalist and Racist viewpoints. This is far more attention than a technical paper just about a model would get, so presumably they took it as confirmation about real-world conclusions (or were using it to fool others about the scientific support for their viewpoints) – namely that Ethnocentrism does beat Humanitarianism and this is an evolutionary inevitability [note 1].

This is an extreme example of the confusion that occurs when non-expert modellers read many papers on modelling. Modellers too often imply a degree of real-world relevance when this is not justified by their research. They often imply real-world conclusions before any meaningful validation has been done. As agent-based simulation reaches a less specialised audience, this will become more important.

Some suggestions to avoid this kind of confusion:

  • After the motivation section, carefully outline what part this research will play in the broader programme – do not leave this implicit or imply a larger role than is justified
  • Add in the phrase “in the model” frequently in the text, even if this is a bit repetitive [note 2]
  • Keep  discussions about the real world in a different sections from those that discuss the model
  • Have an explicit statement of what the model can reliably say about the real world
  • Use different terms when referring to parts of the model and part of the real world (e.g. actors for real world individuals, agents in the model)
  • Be clear about the intended purpose of the model – what can be achieved as a result of this research (Edmonds et al. 2019) – for example, do not imply the model will be able to predict future real world properties until this has been demonstrated (de Matos Fernandes & Keijzer 2020)
  • Be very cautious in what you conclude from your model – make sure this is what has been already achieved rather than a reflection of your aspirations (in fact it might be better to not mention such hopes at all until they are realised)

Notes

  1. To see that this kind of conclusion is not necessary see (Hales & Edmonds 2019).
  2. This is similar to a campaign to add the words “in mice” in reports about medical “breakthroughs”, (https://www.statnews.com/2019/04/15/in-mice-twitter-account-hype-science-reporting)

Acknowledgements

Bruce Edmonds is supported as part of the ESRC-funded, UK part of the “ToRealSim” project, grant number ES/S015159/1.

References

Edmonds, B., et al. (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

Hammond, R. A., N. D. and Axelrod, R. (2006). The Evolution of Ethnocentrism. Journal of Conflict Resolution, 50(6), 926–936. doi:10.1177/0022002706293470

Hartshorn, Max, Kaznatcheev, Artem and Shultz, Thomas (2013) The Evolutionary Dominance of Ethnocentric Cooperation, Journal of Artificial Societies and Social Simulation 16(3), 7. <http://jasss.soc.surrey.ac.uk/16/3/7.html>. doi:10.18564/jasss.2176

Hesse, M. (1964). Analogy and confirmation theory. Philosophy of Science, 31(4), 319-327.

Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Univ. of Chicago Press.

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/

Wartofsky, M. (1966). the Model Muddle – Proposals for an Immodest Realism. Journal Of Philosophy, 63(19), 589-589.

Edmonds, B. (2020) Basic Modelling Hygiene - keep descriptions about models and what they model clearly distinct. Review of Artificial Societies and Social Simulation, 22nd May 2020. https://rofasss.org/2020/05/22/modelling-hygiene/

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

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The Policy Context of Covid19 Agent-Based Modelling

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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/

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

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What more is needed for Democratically Accountable Modelling?

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By Bruce Edmonds

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

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

The Central Dilemma

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

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

A Cautionary Tale

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

What to do?

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

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

Some Relevant Resources

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

Notes

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

References

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

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

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

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

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

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

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

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

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