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Predicting Social Systems – a Challenge

By Bruce Edmonds, Gary Polhill and David Hales

There is a lot of pressure on social scientists to predict. Not only is an ability to predict implicit in all requests to assess or optimise policy options before they are tried, but prediction is also the “gold standard” of science. However, there is a debate among modellers of complex social systems about whether this is possible to any meaningful extent. In this context, the aim of this paper is to issue the following challenge:

Are there any documented examples of models that predict useful aspects of complex social systems?

To do this the paper will:

  1. define prediction in a way that corresponds to what a wider audience might expect of it
  2. give some illustrative examples of prediction and non-prediction
  3. request examples where the successful prediction of social systems is claimed
  4. and outline the aspects on which these examples will be analysed

About Prediction

We start by defining prediction, taken from (Edmonds et al. 2019). This is a pragmatic definition designed to encapsulate common sense usage – what a wider public (e.g. policy makers or grant givers) might reasonably expect from “a prediction”.

By ‘prediction’, we mean the ability to reliably anticipate well-defined aspects of data that is not currently known to a useful degree of accuracy via computations using the model.

Let us clarify the language in this.

  • It has to be reliable. That is, one can rely upon its prediction as one makes this – a model that predicts erratically and only occasionally predicts is no help, since one does not whether to believe any particular prediction. This usually means that (a) it has made successful predictions for several independent cases and (b) the conditions under which it works is (roughly) known.
  • What is predicted has to be unknown at the time of prediction. That is, the prediction has to be made before the prediction is verified. Predicting known data (as when a model is checked on out-of-sample data) is not sufficient [1]. Nor is the practice of looking for phenomena that is consistent with the results of a model, after they have been generated (due to ignoring all the phenomena that is not consistent with the model in this process).
  • What is being predicted is well defined. That is, How to use the model to make a prediction about observed data is clear. An abstract model that is very suggestive – appears to predict phenomena but in a vague and undefined manner but where one has to invent the mapping between model and data to make this work – may be useful as a way of thinking about phenomena, but this is different from empirical prediction.
  • Which aspects of data about being predicted is open. As Watts (2014) points out, this is not restricted to point numerical predictions of some measurable value but could be a wider pattern. Examples of this include: a probabilistic prediction, a range of values, a negative prediction (this will not happen), or a second-order characteristic (such as the shape of a distribution or a correlation between variables). What is important is that (a) this is a useful characteristic to predict and (b) that this can be checked by an independent actor. Thus, for example, when predicting a value, the accuracy of that prediction depends on its use.
  • The prediction has to use the model in an essential manner. Claiming to predict something obviously inevitable which does not use the model is insufficient – the model has to distinguish which of the possible outcomes is being predicted at the time.

Thus, prediction is different from other kinds of scientific/empirical uses, such as description and explanation (Edmonds et al. 2019). Some modellers use “prediction” to mean any output from a model, regardless of its relationship to any observation of what is being modelled [2]. Others use “prediction” for any empirical fitting of data, regardless of whether that data is known before hand. However here we wish to be clearer and avoid any “post-truth” softening of the meaning of the word for two reasons (a) distinguishing different kinds of model use is crucial in matters of model checking or validation and (b) these “softer” kinds of empirical purpose will simply confuse the wider public when if talk to themabout “prediction”. One suspects that modellers have accepted these other meanings because it then allows them to claim they can predict (Edmonds 2017).

Some Examples

Nate Silver and his team aim to predict future social phenomena, such as the results of elections and the outcome of sports competitions. He correctly predicted the outcomes of all 50 electoral colleges in Obama’s election before it happened. This is a data-hungry approach, which involves the long-term development of simulations that carefully see what can be inferred from the available data, with repeated trial and error. The forecasts are probabilistic and repeated many times. As well as making predictions, his unit tries to establish the level of uncertainty in those predictions – being honest about the probability of those predictions coming about given the likely levels of error and bias in the data. These models are not agent-based in nature but tend to be of a mostly statistical nature, thus it is debatable whether these are treated as complex systems – it certainly does not use any theory from complexity science. His book (Silver 2012) describes his approach. Post hoc analysis of predictions – explaining why it worked or not – is kept distinct from the predictive models themselves – this analysis may inform changes to the predictive model but is not then incorporated into the model. The analysis is thus kept independent of the predictive model so it can be an effective check.

Many models in economics and ecology claim to “predict” but on inspection, this only means there is a fit to some empirical data. For example, (Meese & Rogoff 1983) looked at 40 econometric models where they claimed they were predicting some time-series. However, 37 out of 40 models failed completely when tested on newly available data from the same time series that they claimed to predict. Clearly, although presented as being predictive models, they could not predict unknown data. Although we do not know for sure, presumably what happened was that these models had been (explicitly or implicitly) fitted to the out-of-sample data, because the out-of-sample data was already known to the modeller. That is, if the model failed to fit the out-of-sample data when the model was tested, it was then adjusted until it did work, or alternatively, only those models that fitted the out-of-sample data were published.

The Challenge

The challenge is envisioned as happening like this.

  1. We publicise this paper requesting that people send us example of prediction or near-prediction on complex social systems with pointers to the appropriate documentation.
  2. We collect these and analyse them according to the characteristics and questions described below.
  3. We will post some interim results in January 2020 [3], in order to prompt more examples and to stimulate discussion. The final deadline for examples is the end of March 2020.
  4. We will publish the list of all the examples sent to us on the web, and present our summary and conclusions at Social Simulation 2020 in Milan and have a discussion there about the nature and prospects for the prediction of complex social systems. Anyone who contributed an example will be invited to be a co-author if they wish to be so-named.

How suggestions will be judged

For each suggestion, a number of answers will be sought – namely to the following questions:

  • What are the papers or documents that describe the model?
  • Is there an explicit claim that the model can predict (as opposed to might in the future)?
  • What kind of characteristics are being predicted (number, probabilistic, range…)?
  • Is there evidence of a prediction being made before the prediction was verified?
  • Is there evidence of the model being used for a series of independent predictions?
  • Were any of the predictions verified by a team that is independent of the one that made the prediction?
  • Is there evidence of the same team or similar models making failed predictions?
  • To what extent did the model need extensive calibration/adjustment before the prediction?
  • What role does theory play (if any) in the model?
  • Are the conditions under which predictive ability claimed described?

Of course, negative answers to any of the above about a particular model does not mean that the model cannot predict. What we are assessing is the evidence that a model can predict something meaningful about complex social systems. (Silver 2012) describes the method by which they attempt prediction, but this method might be different from that described in most theory-based academic papers.

Possible Outcomes

This exercise might shed some light of some interesting questions, such as:

  • What kind of prediction of complex social systems has been attempted?
  • Are there any examples where the reliable prediction of complex social systems has been achieved?
  • Are there certain kinds of social phenomena which seem to more amenable to prediction than others?
  • Does aiming to predict with a model entail any difference in method than projects with other aims?
  • Are there any commonalities among the projects that achieve reliable prediction?
  • Is there anything we could (collectively) do that would encourage or document good prediction?

It might well be that whether prediction is achievable might depend on exactly what is meant by the word.

Acknowledgements

This paper resulted from a “lively discussion” after Gary’s (Polhill et al. 2019) talk about prediction at the Social Simulation conference in Mainz. Many thanks to all those who joined in this. Of course, prior to this we have had many discussions about prediction. These have included Gary’s previous attempt at a prediction competition (Polhill 2018) and Scott Moss’s arguments about prediction in economics (which has many parallels with the debate here).

Notes

[1] This is sufficient for other empirical purposes, such as explanation (Edmonds et al. 2019)

[2] Confusingly they sometimes the word “forecasting” for what we mean by predict here.

[3] Assuming we have any submitted examples to talk about

References

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 (see also http://cfpm.org/discussionpapers/236)

Edmonds, B. (2017) The post-truth drift in social simulation. Social Simulation Conference (SSC2017), Dublin, Ireland. (http://cfpm.org/discussionpapers/195)

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.

Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke H-H, Weiner J, Wiegand T, DeAngelis DL (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310: 987-991.

Meese, R.A. & Rogoff, K. (1983) Empirical Exchange Rate models of the Seventies – do they fit out of sample? Journal of International Economics, 14:3-24.

Polhill, G. (2018) Why the social simulation community should tackle prediction, Review of Artificial Societies and Social Simulation, 6th August 2018. https://rofasss.org/2018/08/06/gp/

Polhill, G., Hare, H., Anzola, D., Bauermann, T., French, T., Post, H. and Salt, D. (2019) Using ABMs for prediction: Two thought experiments and a workshop. Social Simulation 2019, Mainz.

Silver, N. (2012). The signal and the noise: the art and science of prediction. Penguin UK.

Thorngate, W. & Edmonds, B. (2013) Measuring simulation-observation fit: An introduction to ordinal pattern analysis. Journal of Artificial Societies and Social Simulation, 16(2):14. http://jasss.soc.surrey.ac.uk/16/2/4.html

Watts, D. J. (2014). Common Sense and Sociological Explanations. American Journal of Sociology, 120(2), 313-351.


Edmonds, B., Polhill, G and Hales, D. (2019) Predicting Social Systems – a Challenge. Review of Artificial Societies and Social Simulation, 4th June 2019. https://rofasss.org/2018/11/04/predicting-social-systems-a-challenge

Some Philosophical Viewpoints on Social Simulation

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://roasss.wordpress.com/2019/07/02/phil-view/

A bad assumption: a simpler model is more general

By Bruce Edmonds

Thread6

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://roasss.wordpress.com/2018/08/28/be-2/

The “Formalist Fallacy”

By Bruce Edmonds

Thread3

This is the tendency to believe theories more if they are formalised (e.g. as sets of mathematical equations or computer simulations).

This can be simply an effect of Kuhn’s “Theoretical Spectacles” (1962) — due to the fact we can clearly see how a complex mechanism might result in some particular outcomes (due to the formal model) then we project this onto the world. That is, we fit our perception of some part of the world into the conception illustrated by the model. This is the opposite way to how science is supposed to work, where the model should be adjusted (or rejected) in light of the evidence.

Another reason for more readily accepting theories expressed in terms of mathematics is that maths has status. It used to be the case that mathematical models were the only practical formal technique, which is why science became associated with maths. Thus you are much more likely to be published in many journals if your paper is expressed mathematically, regardless of whether the formalism is used to prove or calculate anything.

If an idea is expressed in informal ways then we are freer to express doubt, as we have an instinctual idea of how slippery natural language statements can be. We know that humans are lazy and thus have a tendency to believe their own ideas, unless pretty well forced to change (e.g. by evidence). It should be the case that making ideas precise makes them easier to disprove (as in Popper 1963) but this is only the case if the mapping between the model and what it refers to is also precise. Otherwise one is free to imagine how a model could apply, giving the illusion of generality.

For example, Eckhart Arnold (2005) shows, in detail, how game theoretical models based on around the ‘Prisoner’s Dilemma’ (e.g. Axelrod 1984) fail to have empirical relevance. Other abstract models that have had many citations but do not seem to connect well to evidence include: (Schelling 1971), Hegselmann & Krause (2002) and Deffaunt et al (2002). Each of these is simple, formal but has interesting outcomes. As a result they seem apparently irresistible to other researchers with many citations and influence but no direct modelling relation with the observed world. This contrasts with modelling papers which compare simulated and real-world data (Chattoe-Brown 2018).

Do not mistake me – I think formalising ideas is very useful. It makes sharing the ideas without error or reinterpretation possible, allowing a community of researchers to critique, improve, check, and apply them (Edmonds 2000). It should also be easier to check if they actually work – for example if they do predict some unknown and measurable aspects of an observed system. It is just that formalism, of itself, does not make them more likely to be true (or the resulting models useful for anything that reliably relates to the observed world) but we are more likely to think they are, due to our tendency to project what we clearly understand.

References

Arnold, E. (2008). Explaining altruism: A simulation-based approach and its limits (Vol. 11). PhD Thesis. Walter de Gruyter. http://www.phil-fak.uni-duesseldorf.de/fileadmin/Redaktion/Institute/Philosophie/Theoretische_Philosophie/Allgemein/Hilfskraefte/Explaining_Altruism-colored_figures.pdf

Axelrod, Robert. 1984. The Evolution of Cooperation. Basic Books.

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://roasss.wordpress.com/2018/06/11/ecb/

Deffuant, G., Amblard, F., Weisbuch, G. and Faure, T. (2002) How can extremism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation 5(4), 1. http://jasss.soc.surrey.ac.uk/5/4/1.html

Edmonds, B. (2000) The Purpose and Place of Formal Systems in the Development of Science, CPM Report 00-75, MMU, UK. http://cfpm.org/cpmrep75.html

Hegselmann, R. and Krause, U. (2002). Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation. Journal of Artificial Societies and Social Simulation, 5(3), 2. http://jasss.soc.surrey.ac.uk/5/3/2.html

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

Popper, K. (1963). Conjectures and refutations: the growth of scientific knowledge. London: Routledge.

Schelling, T. C. (1971). Dynamic models of segregation. Journal of mathematical sociology, 1(2), 143-186.


Edmonds, B. (2018) The "formalist fallacy". Review of Artificial Societies and Social Simulation, 11th June 2018. https://roasss.wordpress.com/2018/07/20/be/