27 Jun 2017

Slides for talk and draft paper on: Modelling Purposes

A talk at the 2017 ESSA SiLiCo Summer school in Wageningen.

Slides at: https://www.slideshare.net/BruceEdmonds/model-purpose-and-complexity

This discusses some different purposes for a simulation model and the consequences of this in terms of its development, checking and justification. It also looks at how complex one's model should be.

Connected to this is a draft of a paper:

How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model is used for different purposes, which means that a model built for one purpose but now used for another may need to be re-checked, re-validated and maybe even rebuilt in a different way. Here we review some of the different purposes for building a simulation model of complex social phenomena, focussing on five in particular: theoretical exposition, prediction, explanation, description and illustration. The chapter looks at some of the implications in terms of the ways in which the intended purpose might fail. In particular, it looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated.

This is a draft of the text that will be in the next edition of, due this year 2017.

Edmonds, B. & Meyer, R. (2013) Simulating Social Complexity – a handbook. Springer. (Publisher's Page)

The text of this draft is at: http://cfpm.org/file_download/178/Five+Different+Modelling+Purposes.pdf

9 Jun 2017

Wide variation in number of votes needed to get elected

As usual, there is a very wide variation in the number of votes needed to get each seat in the UK parliament.  Provincial parties have it relatively easy (in NI, Wales and Scotland), minority parties spread over the UK have it hard (Green, LibDem, UKIP).

2 Jun 2017

Slides for talk on: Modelling Innovation – some options from probabilistic to radical

Given at the European Academy of Technology and Innovation Assessment, see notice about the talk at: https://www.ea-aw.de/service/news/2017/05/22/ea-kolloquium-prof-bruce-edmonds-vom-centre-forpolicy-modelling-cfpm-quotmodellier.html


In general, most modelling of innovation bypasses the creativity involved. Here I look at some of the different options. This loosely follows (but expands) Margaret Boden's analysis of creativity. Four ways are presented and their implementation discussed (a) probabilistic, where the 'innovation' simply corresponds to an unlikely event within a known distribution (b) combinatorial, where innovation is a process of finding the right combination of existing components or aspects (c) complex path-dependency where the path to any particular product is a complex set of decisions or steps and not deducible before it is discovered and (d) radical, where the innovation causes us to think of things in a new way or introduces a novel dimension in which to evaluate. A model of making things that introduces complex path-dependency will be exhibited. Some ways of moving towards (d) the most radical option are discussed and a future possible research agenda outlined.

Slides are at: https://www.slideshare.net/BruceEdmonds/modelling-innovation-some-options-from-probabilistic-to-radical