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The inevitable “layering” of models to extend the reach of our understanding

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By Bruce Edmonds

“Just as physical tools and machines extend our physical abilities, models extend our mental abilities, enabling us to understand and control systems beyond our direct intellectual reach” (Calder  & al. 2018)

Motivation

There is a modelling norm that one should be able to completely understand one’s own model. Whilst acknowledging there is a trade-off between a model’s representational adequacy and its simplicity of formulation, this tradition assumes there will be a “sweet spot” where the model is just tractable but also good enough to be usefully informative about the target of modelling – in the words attributed to Einstein, “Everything should be made as simple as possible, but no simpler”1. But what do we do about all the phenomena where to get an adequate model2 one has to settle for a complex one (where by “complex” I mean a model that we do not completely understand)? Despite the tradition in Physics to the contrary, it would be an incredibly strong assumption that there are no such phenomena, i.e. that an adequate simple model is always possible (Edmonds 2013).

There are three options in these difficult cases.

  • Do not model the phenomena at all until we can find an adequate model we can fully understand. Given the complexity of much around us this would mean to not model these for the foreseeable future and maybe never.
  • Accept inadequate simpler models and simply hope that these are somehow approximately right3. This option would allow us to get answers but with no idea whether they were at all reliable. There are many cases of overly simplistic models leading policy astray (Adoha & Edmonds 2017; Thompson 2022), so this is dangerous if such models influence decisions with real consequences.
  • Use models that are good for our purpose but that we only partially understand. This is the option examined in this paper.

When the purpose is empirical the last option is equivalent to preferring empirical grounding over model simplicity (Edmonds & Moss 2005).

Partially Understood Models

In practice this argument has already been won – we do not completely understand many computer simulations that we use and rely on. For example, due to the chaotic nature of the dynamics of the weather, forecasting models are run multiple times with slightly randomised inputs and the “ensemble” of forecasts inspected to get an idea of the range of different outcomes that could result (some of which might be qualitatively different from the others)4. Working out the outcomes in each case requires the computational tracking of a huge numbers of entities in a way that is far beyond what the human mind can do5. In fact, the whole of “Complexity Science” can be seen as different ways to get some understanding of systems for which there is no analytic solution6.

Of course, this raises the question of what is meant by “understand” a model, for this is not something that is formally defined. This could involve many things, including the following.

  1. That the micro-level – the individual calculations or actions done by the model each time step – is understood. This is equivalent to understanding each line of the computer code.
  2. That some of the macro-level outcomes that result from the computation of the whole model is understood in terms of partial theories or “rules of thumb”.
  3. That all the relevant macro-level outcomes can be determined to a high degree of accuracy without simulating the model (e.g. by a mathematical model).

Clearly, level (1) is necessary for most modelling purposes in order to know the model is behaving as intended. The specification of this micro-level is usually how such models are made, so if this differs from what was intended then this would be a bug. Thus this level would be expected of most models7. However, this does not necessarily mean that this is at the finest level of detail possible – for example, we usually do not bother about how random number generators work, but simply rely on its operation, but in this case we have very good level (3) of understanding for these sub-routines.

At the other extreme, a level (3) understanding is quite rare outside the realm of physics. In a sense, having this level of understanding makes the model redundant, so would probably not be the case for most working models (those used regularly)8. As discussed above, there will be many kinds of phenomena for which this level of understanding is not feasible.

Clearly, what many modelers find useful is a combination of levels (1) & (2) – that is, the detailed, micro-level steps that the model takes are well understood and the outcomes understood well enough for the intended task. For example, when using a model to establish a complex explanation9 (of some observed pattern in data using certain mechanisms or structures) then one might understand the implementation of the candidate mechanisms and verify that the outcomes fit the target pattern for a range of parameters, but not completely understand the detail of the causation involved. There might well be some understanding, for example how robust this is to minor variations in the initial conditions or the working of the mechanisms involved (e.g. by adding some noise to the processes). A complete understanding might not be accessible but this does not stop an explanation being established (although a  better understanding is an obvious goal for future research or avenue for critiques of the explanation).

Of course, any lack of a complete, formal understanding leaves some room for error. The argument here is not deriding the desirability of formal understanding, but is against prioritising that over model adequacy. Also the lack of a formal, level (3), understanding of a model does not mean we cannot take more pragmatic routes to checking it. For example: performing a series of well-designed simulation experiments that intend to potentially refute the stated conclusions, systematically comparing to other models, doing a thorough sensitivity analysis and independently reproducing models can help ensure their reliability. These can be compared with engineering methods – one may not have a proof that a certain bridge design is solid over all possible dynamics, but practical measures and partial modelling can ensure that any risk is so low as to be negligible. If we had to wait until bridge designs were proven beyond doubt, we would simply have to do without them.

Layering Models to Leverage some Understanding

As a modeller, if I do not understand something my instinct is to model it. This instinct does not change if what I do not understand is, itself, a model. The result is a model of the original model – a meta-model. This is, in fact, common practice. I may select certain statistics summarising the outcomes and put these on a graph; I might analyse the networks that have emerged during model runs; I may use maths to approximate or capture some aspect of the dynamics; I might cluster and visualise the outcomes using Machine Learning techniques; I might make a simpler version of the original and compare them. All of these might give me insights into the behaviour of the original model. Many of these are so normal we do not think of this as meta-modelling. Indeed, empirically-based models are already, in a sense, meta-models, since the data that they represent are themselves a kind of descriptive model of reality (gained via measurement processes).

This meta-modelling strategy can be iterated to produce meta-meta-models etc. resulting in “layers” of models, with each layer modelling some aspect of the one “below” until one reaches the data and then what the data measures. Each layer should be able to be compared and checked with the layer “below”, and analysed by the layer “above”.

An extended example of such layering was built during the SCID (Social Complexity of Immigration and Diversity) project10 and illustrated in Figure 1. In this a complicated simulation (Model 1) was built to incorporate some available data and what was known concerning the social and behavioural processes that lead people to bother to vote (or not). This simulation was used as a counter-example to show how assumptions about the chaining effect of interventions might be misplaced (Fieldhouse et al. 2016). A much simpler simulation was then built by theoretical physicists (Model 2), so that it produced the same selected outcomes over time and aa range of parameter values. This allowed us to show that some of the features in the original (such as dynamic networks) were essential to get the observed dynamics in it (Lafuerza et al. 2016a). This simpler model was in turn modelled by an even simpler model (Model 3) that was amenable to an analytic model (Model 4) that allowed us to obtain some results concerning the origin of a region of bistability in the dynamics (Lafuerza et al. 2016b).

Layering fig 1

Figure 1. The Layering of models that were developed in part of the SCID project

Although there are dangers in such layering – each layer could introduce a new weakness – there are also methodological advantages, including the following. (A) Each model in the chain (except model 4) is compared and checked against both the layer below and that above. Such multiple model comparisons are excellent for revealing hidden assumptions and unanticipated effects. (B) Whilst previously what might have happened was a “heroic” leap of abstraction from evidence and understanding straight to Model 3 or 4, here abstraction happens over a series of more modest steps, each of which is more amenable to checking and analysis. When you stage abstraction the introduced assumptions are more obvious and easier to analyse.

One can imagine such “layering” developing in many directions to leverage useful (but indirect) understanding, for example the following.

  • Using an AI algorithm to learn patterns in some data (e.g. medical data for disease diagnosis) but then modelling its working to obtain some human-accessible understanding of how it is doing it.
  • Using a machine learning model to automatically identify the different “phase spaces” in model results where qualitatively different model behaviour is exhibited, so one can then try to simplify the model within each phase.
  • Automatically identifying the processes and structures that are common to a given set of models to facilitate the construction of a more general, ‘umbrella’ model that approximates all the outcomes that would have resulted from the set, but within a narrower range of conditions.

As the quote at the top implies, we are used to settling for partial control of what machines do because it allows us to extend our physical abilities in useful ways. Each time we make their control more indirect, we need to check that this is safe and adequate for purpose. In the cars we drive there are ever more layers of electronic control between us and the physical reality it drives through which we adjust to – we are currently adjusting to more self-drive abilities. Of course, the testing and monitoring of these systems is very important but that will not stop the introduction of layers that will make them safer and more pleasant to drive.

The same is true of our modelling, which we will need to apply in ever more layers in order to leverage useful understanding which would not be accessible otherwise. Yes, we will need to use practical methods to test their fitness for purpose and reliability, and this might include the complete verification of some components (where this is feasible), but we cannot constrain ourselves to only models we completely understand.

Concluding Discussion

If the above seems obvious, then why am I bothering to write this? I think for a few reasons. Firstly, to answer the presumption that understanding one’s model must have priority over all other considerations (such as empirical adequacy) so that sometimes we must accept and use partially understood models. Secondly, to point out that such layering has benefits as well as difficulties – especially if it can stage abstraction into more verifiable steps and thus avoid huge leaps to simple but empirically-isolated models. Thirdly, because such layering will become increasingly common and necessary.

In order to extend our mental reach further, we will need to develop increasingly complicated and layered modelling. To do this we will need to accept that our understanding is leveraged via partially understood models, but also to develop the practical methods to ensure their adequacy for purpose.

Notes

[1] These are a compressed version of his actual words during a 1933 lecture, which were: “It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.” (Robinson 2018)
[2] Adequate for whatever our purpose for it is (Edmonds & al. 2019).
[3]The weasel words I once heard from a Mathematician excusing an analytic model he knew to be simplistic were: that, although he knew it was wrong, it was useful for “capturing core dynamics” (though how he knew that they were not completely wrong eludes me).
[4] For an introduction to this approach read the European Centre for Medium-Range Weather Forecasts’ fact sheet on “Ensemble weather forecasting” at: https://www.ecmwf.int/en/about/media-centre/focus/2017/fact-sheet-ensemble-weather-forecasting
[5] In principle, a person could do all the calculations involved in a forecast but only with the aid of exterior tools such as pencil and paper to keep track of it all so it is arguable whether the person doing the individual calculations has an “understanding” of the complete picture. Lewis Fry Richardson, who pioneered the idea of numerical forecasting of weather in the 1920s, did a 1-day forecast by hand to illustrate his method (Lynch 2008), but this does not change the argument.
[6] An analytic solution is when one can obtain a closed-form equation that characterises all the outcomes by manipulating the mathematical symbols in a proof. If one has to numerically calculate outcomes for different initial conditions and parameters this is a computational solution.
[7] For purely predictive models, whose purpose is only to anticipate an unknown value to a useful level of accuracy, this is not strictly necessary. For example, how some AI/Machine learning models work may not clear at the micro-level, but as long as it works (successfully predicts) this does not matter – even if its predictive ability is due to a bug.
[8] Models may still be useful in this case, for example to check the assumptions made in the matching mathematical or other understanding.
[9] For more on this use see (Edmonds et al. 2019).
[10] For more about this project see http://cfpm.org/scid

Acknowledgements

Bruce Edmonds is supported as part of the ESRC-funded, UK part of the “ToRealSim” project, 2019-2023, grant number ES/S015159/1 and was supported as part of the EPSRC-funded “SCID” project 2010-2016, grant number EP/H02171X/1.

References

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. and 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. (2013) Complexity and Context-dependency. Foundations of Science, 18(4):745-755. DOI:10.1007/s10699-012-9303-x

Edmonds, B. and Moss, S. (2005) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In P. Davidsson et al. (Eds.): Multi Agent Based Simulation 2004. Springer, Lecture Notes in Artificial Intelligence, 3415:130–144. DOI:10.1007/978-3-540-32243-6_11

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. DOI:10.18564/jasss.3993

Fieldhouse, E., Lessard-Phillips, L. & Edmonds, B. (2016) Cascade or echo chamber? A complex agent-based simulation of voter turnout. Party Politics. 22(2):241-256.  DOI:10.1177/1354068815605671

Lafuerza, LF, Dyson, L, Edmonds, B & McKane, AJ (2016a) Simplification and analysis of a model of social interaction in voting, European Physical Journal B, 89:159. DOI:10.1140/epjb/e2016-70062-2

Lafuerza L.F., Dyson L., Edmonds B., & McKane A.J. (2016b) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261

Lynch, P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, 227(7), 3431-3444. DOI:10.1016/j.jcp.2007.02.034

Robinson, A. (2018) Did Einstein really say that? Nature, 557, 30. DOI:10.1038/d41586-018-05004-4

Thompson, E. (2022) Escape from Model Land. Basic Books. ISBN-13: 9781529364873

Edmonds, B. (2023) The inevitable “layering” of models to extend the reach of our understanding. Review of Artificial Societies and Social Simulation, 9 Feb 2023. https://rofasss.org/2023/02/09/layering

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

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The Poverty of Suggestivism – the dangers of “suggests that” modelling

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By Bruce Edmonds

Vagueness and refutation

A model[1] is basically composed of two parts (Zeigler 1976, Wartofsky 1979):

  1. A set of entities (such as mathematical equations, logical rules, computer code etc.) which can be used to make some inferences as to the consequences of that set (usually in conjunction with some data and parameter values)
  2. A mapping from this set to what it aims to represent – what the bits mean

Whilst a lot of attention has been paid to the internal rigour of the set of entities and the inferences that are made from them (1), the mapping to what that represents (2) has often been left as implicit or incompletely described – sometimes only indicated by the labels given to its parts. The result is a model that vaguely relates to its target, suggesting its properties analogically. There is not a well-defined way that the model is to be applied to anything observed, but a new map is invented each time it is used to think about a particular case. I call this way of modelling “Suggestivism”, because the model “suggests” things about what is being modelled.

This is partly a recapitulation of Popper’s critique of vague theories in his book “The Poverty of Historicism” (1957). He characterised such theories as “irrefutable”, because whatever the facts, these theories could be made to fit them. Irrefutability is an indicator of a lack of precise mapping to reality – such vagueness makes refutation very hard. However, it is only an indicator; there may be other reasons than vagueness for it not being possible to test a theory – it is their disconnection from well-defined empirical reference that is the issue here.

Some might go as far as suggesting that any model or theory that is not refutable is “unscientific”, but this goes too far, implying a very restricted definition of what ‘science’ is. We need analogies to think about what we are doing and to gain insight into what we are studying, e.g. (Hartman 1997) – for humans they are unavoidable, ‘baked’ into the way language works (Lakoff 1987). A model might make a set of ideas clear and help map out the consequences of a set of assumptions/structures/processes. Many of these suggestivist models relate to a set of ideas and it is the ideas that relate to what is observed (albeit informally) (Edmonds 2001). However, such models do not capture anything reliable about what they refer to, and in that sense are not part of the set of the established statements and theories that is at the core of science  (Arnold 2014).

The dangers of suggestivist modelling

As above, there are valid uses of abstract or theoretical modelling where this is explicitly acknowledged and where no conclusions about observed phenomena are made. So what are the dangers of suggestivist modelling – why am I making such a fuss about it?

Firstly, that people often seem to confuse a model as an analogy – a way of thinking about stuff – and a model that tells us reliably about what we are studying. Thus they give undue weight to the analyses of abstract models that are, in fact, just thought experiments. Making models is a very intimate way of theorising – one spends an extended period of time interacting with one’s model: developing, checking, analysing etc. The result is a particularly strong version of “Kuhnian Spectacles” (Kuhn 1962) causing us to see the world though our model for weeks after. Under this strong influence it is natural to confuse what we can reliably infer about the world and how we are currently perceiving/thinking about it. Good scientists should then pause and wait for this effect to wear off so that they can effectively critique what they have done, its limitations and what its implications are. However, often in the rush to get their work out, modellers often do not do this, resulting in a sloppy set of suggestive interpretations of their modelling.

Secondly, empirical modelling is hard. It is far easier (and, frankly, more fun) to play with non-empirical models. A scientific culture that treats suggestivist modelling as substantial progress and significantly rewards modellers that do it, will effectively divert a lot of modelling effort in this direction. Chattoe-Brown (2018) displayed evidence of this in his survey of opinion dynamics models – abstract, suggestivist modelling got far more reward (in terms of citations) than those that tried to relate their model to empirical data in a direct manner. Abstract modelling has a role in science, but if it is easier and more rewarding then the field will become unbalanced. It may give the impression of progress but not deliver on this impression. In a more mature science, researchers working on measurement methods (steps from observation to models) and collecting good data are as important as the theorists (Moss 1998).

Thirdly, it is hard to judge suggestivist models. Given their connection to the modelling target is vague there cannot be any decisive test of its success. Good modellers should declare the exact purpose of their model, e.g. that is analogical or merely exploring the consequences of theory (Edmonds et al. 2019), but then accept the consequences of this choice – namely, that it excludes  making conclusions about the observed world. If it is for a theoretical exploration then the comprehensiveness of the exploration, the scope of the exploration and the applicability of the model can be judged, but if the model is analogical or illustrative then this is harder. Whilst one model may suggest X, another may suggest the opposite. It is quite easy to fix a model to get the outcomes one wants. Clearly, if a model makes startling suggestions – illustrating totally new ideas or making a counter-example to widely held assumptions – then this helps science by widening the pool of theories or hypotheses that are considered. However most suggestivist modelling does not do this.

Fourthly, their sheer flexibility of as to application causes problems – if one works hard enough one can invent mappings to a wide range of cases, the limits are only those of our imagination. In effect, having a vague mapping from model to what it models adds in huge flexibility in a similar way to having a large number of free (non-empirical) parameters. This flexibility gives an impression of generality, and many desire simple and general models for complex phenomena. However, this is illusory because a different mapping is needed for each case, to make it apply. Given the above (1)+(2) definition of a model this means that, in fact, it is a different model for each case – what a model refers to, is part of the model. The same flexibility makes such models impossible to refute, since one can just adjust the mapping to save them. The apparent generality and lack of refutation means that such models hang around in the literature, due to their surface attractiveness.

Finally, these kinds of model are hugely influential beyond the community of modellers to the wider public including policy actors. Narratives that start in abstract models make their way out and can be very influential (Vranckx 1999). Despite the lack of rigorous mapping from model to reality, suggestivist models look impressive, look scientific. For example, very abstract models from the Neo-Classical ‘Chicago School’ of economists supported narratives about the optimal efficiency of markets, leading to a reluctance to regulate them (Krugman 2009). A lack of regulation seemed to be one of the factors behind the 2007/8 economic crash (Baily et al 2008). Modellers may understand that other modellers get over-enthusiastic and over-interpret their models, but others may not. It is the duty of modellers to give an accurate impression of the reliability of any modelling results and not to over-hype them.

How to recognise a suggestivist model

It can be hard to detangle how empirically vague a model is, because many descriptions about modelling work do not focus on making the mapping to what it represents precise. The reasons for this are various, for example: the modeller might be conflating reality and what is in the model in their minds, the researcher is new to modelling and has not really decided what the purpose of their model is, the modeller might be over-keen to establish the importance of their work and so is hyping the motivation and conclusions, they might simply not got around to thinking enough about the relationship between their model and what it might represent, or they might not have bothered to make the relationship explicit in their description. Whatever the reason the reader of any description of such work is often left with an archaeological problem: trying to unearth what the relationship might be, based on indirect clues only. The only way to know for certain is to take a case one knows about and try and apply the model to it, but this is a time consuming process and relies upon having a case with suitable data available. However, there are some indicators, albeit fallible ones, including the following.

  • A relatively simple model is interpreted as explaining a wide range of observed, complex phenomena
  • No data from an observed case study is compared to data from the model (often no data is brought in at all, merely abstract observations) – despite this, conclusions about some observed phenomena are made
  • The purpose of the model is not explicitly declared
  • The language of the paper seems to conflate talking about the model with what is being modelled
  • In the paper there are sudden abstraction ‘jumps’ between the motivation and the description of the model and back again to the interpretation of the results in terms of that motivation. The abstraction jumps involved are large and justified by some a priori theory or modelling precedents rather than evidence.

How to avoid suggestivist modelling

How to avoid the dangers of suggestivist modelling should be clear from the above discussion, but I will make them explicit here.

  • Be clear about the model purpose – that is does the model aim to achieve, which indicates how it should be judged by others (Edmonds et al 2019)
  • Do not make any conclusions about the real world if you have not related the model to any data
  • Do not make any policy conclusions – things that might affect other people’s lives – without at least some independent validation of the model outcomes
  • Document how a model relates (or should relate) to data, the nature of that data and maybe even the process whereby that data should be obtained (Achter et al 2019)
  • Be explicit as possible about what kinds of phenomena the model applies to – the limits of its scope
  • Keep the language about the model and what is being modelled distinct – for any statement it should be clear whether it is talking about the model or what it models (Edmonds 2020)
  • Highlight any bold assumptions in the specification of the model or describe what empirical foundation there is for them – be honest about these

Conclusion

Models can serve many different purposes (Epstein 2008). This is fine as long as the purpose of models are always made clear, and model results are not interpreted further than their established purpose allows. Research which gives the impression that analogical, illustrative or theoretical modelling can tell us anything reliable about observed complex phenomena is not only sloppy science, but can have a deleterious impact – giving an impression of progress whilst diverting attention from empirically reliable work. Like a bad investment: if it looks too good and too easy to be true, it probably isn’t.

Notes

[1] We often use the word “model” in a lazy way to indicate (1) rather than (1)+(2) in this definition, but a set of entities without any meaning or mapping to anything else is not a model, as it does not represent anything. For example, a random set of equations or program instructions does not make a model.

Acknowledgements

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

References

Achter, S., Borit, M., Chattoe-Brown, E., Palaretti, C. & Siebers, P.-O. (2019) Cherchez Le RAT: A Proposed Plan for Augmenting Rigour and Transparency of Data Use in ABM. Review of Artificial Societies and Social Simulation, 4th June 2019. https://rofasss.org/2019/06/04/rat/

Arnold, E. (2014). What’s wrong with social simulations?. The Monist, 97(3), 359-377. DOI:10.5840/monist201497323

Baily, M. N., Litan, R. E., & Johnson, M. S. (2008). The origins of the financial crisis. Fixing Finance Series – Paper 3, The Brookings Institution. https://www.brookings.edu/wp-content/uploads/2016/06/11_origins_crisis_baily_litan.pdf

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/

Edmonds, B. (2001) The Use of Models – making MABS actually work. In. Moss, S. and Davidsson, P. (eds.), Multi Agent Based Simulation, Lecture Notes in Artificial Intelligence, 1979:15-32. http://cfpm.org/cpmrep74.html

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/

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.

Epstein, J. M. (2008). Why model?. Journal of artificial societies and social simulation, 11(4), 12. https://jasss.soc.surrey.ac.uk/11/4/12.html

Hartmann, S. (1997): Modelling and the Aims of Science. In: Weingartner, P. et al (ed.) : The Role of Pragmatics in Contemporary Philosophy: Contributions of the Austrian Ludwig Wittgenstein Society. Vol. 5. Wien und Kirchberg: Digi-Buch. pp. 380-385. https://epub.ub.uni-muenchen.de/25393/

Krugman, P. (2009) How Did Economists Get It So Wrong? New York Times, Sept. 2nd 2009. https://www.nytimes.com/2009/09/06/magazine/06Economic-t.html

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

Lakoff, G. (1987) Women, fire, and dangerous things. University of Chicago Press, Chicago.

Morgan, M. S., & Morrison, M. (1999). Models as mediators. Cambridge: Cambridge University Press.

Moss, S. (1998) Social Simulation Models and Reality: Three Approaches. Centre for Policy Modelling  Discussion Paper: CPM-98-35, http://cfpm.org/cpmrep35.html

Popper, K. (1957). The poverty of historicism. Routledge.

Vranckx, An. (1999) Science, Fiction & the Appeal of Complexity. In Aerts, Diederik, Serge Gutwirth, Sonja Smets, and Luk Van Langehove, (eds.) Science, Technology, and Social Change: The Orange Book of “Einstein Meets Magritte.” Brussels: Vrije Universiteit Brussel; Dordrecht: Kluwer., pp. 283–301.

Wartofsky, M. W. (1979). The model muddle: Proposals for an immodest realism. In Models (pp. 1-11). Springer, Dordrecht.

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Edmonds, B. (2022) The Poverty of Suggestivism – the dangers of "suggests that" modelling. Review of Artificial Societies and Social Simulation, 28th Feb 2022. https://rofasss.org/2022/02/28/poverty-suggestivism

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

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Some Philosophical Viewpoints on Social Simulation

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By Bruce Edmonds

How one thinks about knowledge can have a significant impact on how one develops models as well as how one might judge a good model.

  • Pragmatism. Under this view a simulation is a tool for a particular purpose. Different purposes will imply different tests for a good model. What is useful for one purpose might well not be good for another – different kinds of models and modelling processes might be good for each purpose. A simulation whose purpose is to explore the theoretical implications of some assumptions might well be very different from one aiming to explain some observed data. An example of this approach is (Edmonds & al. 2019).
  • Social Constructivism. Here knowledge about social phenomena (including simulation models) are collectively constructed. There is no other kind of knowledge than this. Each simulation is a way of thinking about social reality and plays a part in constructing it so. What is a suitable construction may vary over time and between cultures etc. What a group of people construct is not necessarily limited to simulations that are related to empirical data. (Ahrweiler & Gilbert 2005) seem to take this view but this is more explicit in some of the participatory modelling work, where the aim is to construct a simulation that is acceptable to a group of people, e.g. (Etienne 2014).
  • Relativism. There are no bad models, only different ways of mediating between your thought and reality (Morgan 1999). If you work hard on developing your model, you do not get a better model, only a different one. This might be a consequence of holding to an Epistemological Constructivist position.
  • Descriptive Realism. A simulation is a picture of some aspect of reality (albeit at a much lower ‘resolution’ and imperfectly). If one obtains a faithful representation of some aspect of reality as a model, one can use it for many different purposes. Could imply very complicated models (depending on what one observes and decides is relevant), which might themselves be difficult to understand. I suspect that many people have this in mind as they develop models, but few explicitly take this approach. Maybe an example is (Fieldhouse et al. 2016).
  • Classic Positivism. Here, the empirical fit and the analytic understanding of the simulation is all that matters, nothing else. Models should be tested against data and discarded if inadequate (or they compete and one is currently ahead empirically). Also they should be simple enough that they can be thoroughly understood. There is no obligation to be descriptively realistic. Many physics approaches to social phenomena follow this path (e.g Helbing 2010, Galam 2012).

Of course, few authors make their philosophical position explicit – usually one has to infer it from their text and modelling style.

References

Ahrweiler, P. and Gilbert, N. (2005). Caffè Nero: the Evaluation of Social Simulation. Journal of Artificial Societies and Social Simulation 8(4):14. http://jasss.soc.surrey.ac.uk/8/4/14.html

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

Etienne, M. (ed.) (2014) Companion Modelling: A Participatory Approach to Support Sustainable Development. Springer

Fieldhouse, E., Lessard-Phillips, L. and Edmonds, B. (2016) Cascade or echo chamber? A complex agent-based simulation of voter turnout. Party Politics. 22(2):241-256. DOI:10.1177/1354068815605671

Galam, S. (2012) Sociophysics: A Physicist’s modeling of psycho-political phenomena. Springer.

Helbing, D. (2010). Quantitative sociodynamics: stochastic methods and models of social interaction processes. Springer.

Morgan, M. S., Morrison, M., & Skinner, Q. (Eds.). (1999). Models as mediators: Perspectives on natural and social science (Vol. 52). Cambridge University Press.

Edmonds, B. (2019) Some Philosophical Viewpoints on Social Simulation. Review of Artificial Societies and Social Simulation, 2nd July 2019. https://rofasss.org/2019/07/02/phil-view/

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

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A bad assumption: a simpler model is more general

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By Bruce Edmonds

If one adds in some extra detail to a general model it can become more specific — that is it then only applies to those cases where that particular detail held. However the reverse is not true: simplifying a model will not make it more general – it is just you can imagine it would be more general.

To see why this is, consider an accurate linear equation, then eliminate the variable leaving just a constant. The equation is now simpler, but now will only be true at only one point (and only be approximately right in a small region around that point) – it is much less general than the original, because it is true for far fewer cases.

This is not very surprising – a claim that a model has general validity is a very strong claim – it is unlikely to be achieved by arm-chair reflection or by merely leaving out most of the observed processes.

Only under some special conditions does simplification result in greater generality:

  • When what is simplified away is essentially irrelevant to the outcomes of interest (e.g. when there is some averaging process over a lot of random deviations)
  • When what is simplified away happens to be constant for all the situations considered (e.g. gravity is always 9.8m/s^2 downwards)
  • When you loosen your criteria for being approximately right hugely as you simplify (e.g. mover from a requirement that results match some concrete data to using the model as a vague analogy for what is happening)

In other cases, where you compare like with like (i.e. you don’t move the goalposts such as in (3) above) then it only works if you happen to know what can be safely simplified away.

Why people think that simplification might lead to generality is somewhat of a mystery. Maybe they assume that the universe has to obey ultimately laws so that simplification is the right direction (but of course, even if this were true, we would not know which way to safely simplify). Maybe they are really thinking about the other direction, slowly becoming more accurate by making the model mirror the target more. Maybe this is just a justification for laziness, an excuse for avoiding messy complicated models. Maybe they just associate simple models with physics. Maybe they just hope their simple model is more general.

References

Aodha, L. and 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.

Edmonds, B. (2007) Simplicity is Not Truth-Indicative. In Gershenson, C.et al. (2007) Philosophy and Complexity. World Scientific, 65-80.

Edmonds, B. (2017) Different Modelling Purposes. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity – a handbook, 2nd edition. Springer, 39-58.

Edmonds, B. and Moss, S. (2005) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In P. Davidsson et al. (Eds.): Multi Agent Based Simulation 2004. Springer, Lecture Notes in Artificial Intelligence, 3415:130–144.

Edmonds, B. (2018) A bad assumption: a simpler model is more general. Review of Artificial Societies and Social Simulation, 28th August 2018. https://rofasss.org/2018/08/28/be-2/

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