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)

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)