Tag Archives: social science

Continuous model development: a plea for persistent virtual worlds

By Mike Bithell


Consider the following scenario:-

A policy maker has a new idea and wishes to know what might be the effect of implementing the associated policy or regulation. They ask an agent-based modeller for help. The modeller replies that the situation looks interesting. They will start a project to develop a new model from scratch and it will take three years. The policy maker replies they want the results tomorrow afternoon. On being informed that this is not possible (or that the model will of necessity be bad) the policy maker looks elsewhere.

Clearly this will not do. Yet it seems, at present that every new problem leads to the development of a new “hero” ABM developed from the ground up. I would like to argue that for practical policy problems we need a different approach., one in which persistent models are developed that outlast the life of individual research projects, and are continuously developed, updated and challenged against the kinds of multiple data streams that are now becoming available in the social realm.

By way of comparison consider the case of global weather and climate models. These are large models developed over many years. They are typically hundreds of thousands of lines of code, and are difficult for any single individual to fully comprehend. Their history goes back to the early 20th century, when Richardson made the first numerical weather forecast for Europe, doing all the calculations by hand. Despite the forecast being incorrect (a better understanding of how to set up initial conditions was needed) he was not deterred: His vision of future forecasts involved a large room full of “computers” (i.e. people) each calculating the numerics for their part of the globe and pooling the results to enable forecasting in real time (Richardson 1922). With the advent of digital computing in the 1950s these models began to be developed systematically, and their skill at representing the weather and climate has undergone continuous improvement (see e.g. Lynch 2006). At the present time there are perhaps a few tens of such models that operate globally, with various strengths and weaknesses,. Their development is very far from complete: The systems they represent are complex, and the models very complicated, but they gain their effectiveness through being run continually, tested and re-tested against data,, with new components being repeatedly improved and developed by multiple teams over the last 50 years. They are not simple to set up and run, but they persist over time and remain close to the state-of-the –art and to the research community.

I suggest that we need something like this in agent-based modelling. A suite of communally developed models that are not abstract, but that represent substantial real systems, such as large cities, countries or regions,; that are persistent and continually developed, on a code base that is largely stable; and more importantly undergo continual testing and validation. At the moment this last part of the loop is not typically closed: models are developed and scenarios proposed, but the model is not then updated in the light of new evidence, and then re-used and extended: the PhD has finished, or the project ended, and the next new problem leads to a new model. Persistent models, being repeatedly run by many, would gradually have bugs and inconsistencies discovered and corrected(although new ones would also inevitably be introduced), could be very complicated because continually tested, and continually available for interpretation and development of understanding, and become steadily better documented. Accumulated sets of results would show their strengths and weaknesses for particular kinds of issues, and where more work was most urgently needed.

In this way when, say ,the mayor London wanted to know the effect of a given policy, a set of state-of the-art models of London would already exist which could be used to test out the policy given the best available current knowledge. The city model would be embedded in a lager model or models of the UK, or even the EU, so as to be sure that boundary conditions would not be a problem, and to see what the wider unanticipated consequences might be. The output from such models might be very uncertain: “forecasts” (saying what will happen, as opposed to what kind of things might happen) would not be the goal, but the history of repeated testing and output would demonstrate what level of confidence was warranted in the types of behaviour displayed by the results: preferably this would at least be better than random guesswork. Nor would such a set of models rule out or substitute for other kinds of model: idealised, theoretical, abstract and applied case studies would still be needed to develop understanding and new ideas.

The kind of development of models for policy is already taking place in to an extent (see e.g. Waldrop 2018), but is currently very limited. However, in the face of current urgent and pressing problems, such as climate change, eco-system destruction, global financial insecurity, continuing widespread poverty and failure to approach sustainable development goals in any meaningful way, the ad-hoc make-a-new-model every time approach is inadequate. To build confidence in ABM as a tool that can be relied on for real world policy we need persistent virtual worlds.


Lynch, P. (2006). The Emergence of Numerical Weather Prediction: Richardson’s Dream. Cambridge: Cambridge University Press.

Richardson, L. F. (1922). Weather Prediction by Numerical Process (reprinted 2007). Cambridge: Cambridge University Press.

Waldrop, M. (2018). Free Agents. Science, 13, 360, 144-147. DOI: 10.1126/science.360.6385.144

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

Query: 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?

By Edmund Chattoe-Brown


On one level this is a straightforward request. The earliest convincing example I have found is Hägerstrand (1965, p. 381) an article that seems to be undeservedly neglected because it is also the earliest example of a simulation I have been able to identify that demonstrates independent calibration and validation (Gilbert and Troitzsch 2005, p. 17).1

However, my attempts to find the earliest examples are motivated two more substantive issues (which may help to focus the search for earlier candidates). Firstly, what is the value of a canon (and giving due intellectual credit) for the success of ABM? The Schelling model is widely known and taught but it is not calibrated and validated. If a calibrated and validated model already existed in 1965, should it not be more widely cited? If we mostly cite a non-empirical model, might we give the impression that this is all that ABM can do? Also, failing to cite an article means that it cannot form the basis for debate. Is the Hägerstrand model in some sense “better” or “more important” than the Schelling model? This is a discussion we cannot have without awareness of the Hägerstrand model in the first place.

The second (and related) point regards the progress made by ABM and how those outside the community might judge it. Looking at ABM research now, the great majority of models appear to be non-empirical (Angus and Hassani-Mahmooei 2015, Table 5 in section 4.5). Without citations of articles like Hägerstrand (and even Clarkson and Meltzer), the non-expert reader of ABM might be led to conclude that it is too early (or too difficult) to produce such calibrated and validated models. But if this was done 50 years ago, and is not being much publicised, might we be using up our credibility as a “new” field still finding its feet?) If there are reasons for not doing, or not wanting to do, what Hägerstrand managed, let us be obliged to be clear what they are and not simply hide behind widespread neglect of such examples2.)


  1. I have excluded an even earlier example of considerable interest (Clarkson and Meltzer 1960 which also includes an attempt at calibration and validation but has never been cited in JASSS) for two reasons. Firstly, it deals with the modelling of a single agent and therefore involves no interaction. Secondly, it appears that the validation may effectively be using the “same” data as the calibration in that protocols elicited from an investment officer regarding portfolio selection are then tested against choices made by that same investment officer.
  2. And, of course, this is a vicious circle because in our increasingly pressurised academic world, people only tend to read and cite what is already cited.


Angus, Simon D. and Hassani-Mahmooei, Behrooz (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), October, article 16, .

Clarkson, Geoffrey P. and Meltzer, Allan H. (1960) ‘Portfolio Selection: A Heuristic Approach, The Journal of Finance, 15(4), December, pp. 465-480.

Gilbert, Nigel and Troitzsch, Klaus G. (2005) Simulation for the Social Scientist, 2nd edition (Buckingham: Open University Press).

Hägerstrand, Torsten (1965) ‘A Monte Carlo Approach to Diffusion’, Archives Européennes de Sociologie, 6(1), May, Special Issue on Simulation in Sociology, pp. 43-67.

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/