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.
- 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.
- 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.
- 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)
- To see that this kind of conclusion is not necessary see (Hales & Edmonds 2019).
- 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)
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/