Abstract
We outline a simulation development process, backed by a software framework, which focuses on developing and using a partial conceptual model as a ‘lens’ to compare and possibly re-implement existing models in a chosen problem domain (as well as to design new models). To make this feasible for existing models of arbitrary structure and background social theory, we construct our (partial) conceptual model in a way that acknowledges that it is a base representation which any individual model will typically add detail to, and abstract away from, in various ways which we argue can be formalised. A given model’s design is fitted to the conceptual model to capture how its structural architecture (and selected aspects of the system’s state and driving processes) map to the conceptual model. This fit can be used to produce incomplete skeleton code which can then be extended to produce a simulation. Along the way, we use robust decision-making to provide a useful frame and discuss how our approach differs from others. This is inevitably a preliminary approach to a broad and difficult problem, which we explore in the conclusions.
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Notes
- 1.
The authors also term it computer-assisted reasoning (CAR), but we feel this is too general a term to be useful, and prefer ‘robust decision-making’.
- 2.
Boundaries between a model and models can begin to blur where, for example, a model has parameters which act as switches to turn on and off different structural alternatives (e.g., alternative decision-making algorithms).
- 3.
Indeed, the ultimate aim of this understanding is often to inform policy, and thus there is the same idea of designing policy robust to a range of theories.
- 4.
They use the XLRM analysis framework. The ‘relationships’ (R) comprise the model, which takes into account externalities (X) and the policy levers (L) in place to produce outcomes, where we are interested in particular measures (M) of scenario desirability.
- 5.
Theory also drives system scope to some degree, and we explain why this is less problematic than it might seem later.
- 6.
That is, there will not be any changes over the course of the simulation which are fundamental and disruptive enough to change these ‘structural goalposts’. This assumption is implicitly embedded in all simulation models, unless they are explicitly trying to model such change. (Even then, they can only cover a set of possibilities which the modeller can conceive of.)
- 7.
See Sect. 21.4 for some discussion on the use of the word ‘theory’.
- 8.
In software design pattern terminology, this wrapper code acts as a gateway [8, p. 466].
- 9.
The dependencies specify only what interactions of information and possible action occur (i.e., that a dependency exists), not when and how these occur (i.e., not how the dependency should play out in implementation terms). Dependencies also have types, but we do not discuss that here.
- 10.
This does not necessarily mean that they are the main drivers of system-level patterns: that is a question of sensitivity analysis.
- 11.
This idea has analogues with concepts in theory, such as Adam Smith’s ‘invisible hand’.
- 12.
They may have small amounts of generation, such as a household with photovoltaic panels, but they are a consumer on balance.
- 13.
This convention is appropriate here because the process governing changes in consumer instances is largely independent from that governing electricity demand per consumer. If it were not, there is an alternative convention.
- 14.
There is always some subjectivity in this fitting process. We assume here, for example, that there are no other external-market-related factors which could be folded into this fitted constant.
References
Axelrod R (1997) Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P (eds) Simulating social phenomena. Lecture notes in economics and mathematical systems, vol 456. Springer, New York, pp 21–40
Axtell R, Axelrod R, Epstein J, Cohen M (1996) Aligning simulation models: a case study and results. Comput Math Organ Theory 1(2):123–141
Bankes S (2003) Improving the utility and the rigor of agent-based modeling through ensembles of models. In: Proceedings of agent 2003: challenges in social simulation, pp 155–168
Chappin EJL, Dijkema GPJ (2010) Agent-based modelling of energy infrastructure transitions. Int J Crit Infrastruct 6(2):106–130
DeRemer F, Kron H (1975) Programming-in-the large versus programming-in-the-small. In: Proceedings of the international conference on Reliable software, pp 114–121
Edmonds B, Hales D (2003) Replication, replication and replication: some hard lessons from model alignment. J Artif Soc Soc Simul 6(4):11. http://jasss.soc.surrey.ac.uk/6/4/11.html
Evans E (2004) Domain-driven design: tackling complexity in the heart of software. Addison-Wesley, Boston
Fowler M, Rice D, Foemmel M, Hieatt E, Mee R, Stafford R (2003) Patterns of enterprise application architecture. Addison-Wesley, Boston
Lempert R (2002) A new decision sciences for complex systems. Proc Natl Acad Sci 99: 7309–7313
Lempert R, Popper S, Bankes S (2003) Shaping the next one hundred years: new methods for quantitative, long-term policy analysis. Technical Report MR-1626, RAND Corporation
Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) MASON: a multiagent simulation environment. Simulation 81(7):517–527
Moss S (1999) Relevance, realism and rigour: a third way for social and economic research. Technical Report 99, Centre for Policy Modelling (CfPM), Manchester Metropolitan University
Moss S (2008) Alternative approaches to the empirical validation of agent-based models. J Artif Soc Soc Simul 11(1):5. http://jasss.soc.surrey.ac.uk/11/1/5.html
Parker DC, Brown DG, Polhill JG, Deadman PJ, Manson SM (2008) Illustrating a new “conceptual design pattern” for agent-based models of land use via five case studies—the MR POTATOHEAD framework, Chap. 2. INSISOC, Spain, pp 23–51
Polhill JG, Parker D, Brown D, Grimm V (2008) Using the ODD protocol for describing three agent-based social simulation models of land-use change. J Artif Soc Soc Simul 11(2):3. http://jasss.soc.surrey.ac.uk/11/2/3.html
Richiardi M, Leombruni R, Saam N, Sonnessa M (2006) A common protocol for agent-based social simulation. J Artif Soc Soc Simul 9(1):15. http://jasss.soc.surrey.ac.uk/9/1/15.html
Rouchier J, Cioffi-Revilla C, Polhill J, Takadama K (2008) Progress in model-to-model analysis. J Artif Soc Soc Simul 11(2):8. http://jasss.soc.surrey.ac.uk/11/2/8.html
Sterman J (2000) Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill, Boston
Sun J, Tesfatsion L (2007) Dynamic testing of wholesale power market designs: an open-source agent-based framework. Comput Econ 30(3):291–327
ter Hofstede A, van der Aalst W, Adams M, Russell N (2010) Modern business process automation: YAWL and its support environment. Springer, Heidelberg
van der Aalst W, van Hee K (2002) Workflow management models, methods, and systems. MIT Press, Cambridge. http://www.worldcat.org/isbn/9780262720465
Zeigler BP, Gon Kim T, Praehofer H (2000) Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems, 2nd edn. Academic, San Diego
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Rossiter, S., Noble, J., Bell, K.R.W. (2014). Social Simulation Comparison in Arbitrary Problem Domains: First Steps Towards a More Principled Approach. In: Chen, SH., Terano, T., Yamamoto, R., Tai, CC. (eds) Advances in Computational Social Science. Agent-Based Social Systems, vol 11. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54847-8_21
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