2 book chapters on complexity and policy modelling

@cfpm_org
Edmonds. B. & Gershenson, C. (2015). Modelling Complexity for Policy: opportunities and challenges. In Geyer, R. & Cairney, P. (eds.) Handbook on Complexity and Public Policy. Edward Elgar, pp. 205-220.

Introduction

For policy and decision-making, models can be an essential component, as models allow the description of a situation, the exploration of future scenarios, the valuation of different outcomes and the establishment of possible explanations for what is observed. The principle problem with this is the sheer complexity of what is being modelled.  A response to this is to use more expressive modelling approaches, drawn from the “sciences of complexity”— to use more complex models to try and get a hold on the complexity we face.  However, this approach has potential pitfalls as well as opportunities, and it is these that this chapter will attempt to make clear.  Thus, we hope to show that more complex modelling approaches can be useful, but also to help people avoid “fooling themselves” in the process.

      The chapter is basically in three parts: a general discussion about models and their characteristics that will inform the subsequent decision and help the reader understand their potential and difficulties, then a brief review of some of the available techniques, and ending with a review of some models used in a policy context.  It thus starts with an examination of the different kinds of model that exist, so that these kinds might be clearly distinguished and not confused.  In particular it looks at what it means for a model to be formal.  A section follows on the kinds of uses to which such models can be put. Then we look at some of the consequences of the fact that what we are modelling is complex and the kinds of compromises this forces us into, followed by some examples of models applied to policy issues.  We conclude by summarising some of the key danger and opportunities for using complex modelling for policy analysis.

http://www.e-elgar.com/shop/handbook-on-complexity-and-public-policy

Jager, W. & Edmonds, B. (2015) Policy Making and Modelling in a Complex world. In Janssen, M., Wimmer, M. and Deljoo, A. (eds.) Policy Practice anbd Digitial Science. Springer, pp. 57-74.

Abstract

In this chapter we discuss the consequences of complexity in the real world together with some meaningful ways of understanding and managing such situations.  The implications of such complexity are that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions). We shortly discuss the problems arising from a too narrow focus on quantification in managing complex systems. We criticise some of the approaches that ignore these difficulties and pretend to prediction using simplistic models.  However, lack of predictability does not automatically imply a lack of managerial possibilities. We will discuss how some insights and tools from "Complexity Science" can help with such management.  To manage a complex systems requires a good understanding of the dynamics of the system in question - to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. Agent based simulation will be discussed as a tool that is suitable for this task, and its particular strengths and weaknesses for this are discussed.

http://www.springer.com/gb/book/9783319127835

Slides from talk on: "Possibilistic prediction and risk analyses"

Arguing for an approach for complexity scientists/modellers to interact with those making decisions (policy, business etc) in situations, in a way that does not deprive those decision makers of responsibility and which leaves them in control, whilst informing them.

A talk given at the EA Conference, Bonn, May 2015.

Abstract:

It is in the nature of complex systems that predictions that give a probability are not possible.

Indeed I argue that giving "the most likely" or "rough" prediction is more harmful than useful.

Rather an approach which maps out some of the possible outcomes is outlined. 

Agent-based modelling is ideal for producing these - including, crucially, possibilities that could not have been conceived just by thinking about it (due to the fact that events can combine in ways that are more complex than the human brain can cope with directly).

A characterisation of the real future possibilities and their nature allows some positive responses to events:
* putting in place 'early warning indicators' for the emergence of identified possibilities
* contingency planning for when they are indicated. 

Such an approach would allow policy makers to better 'drive' their decision making, without abnegating responsibility to experts

 Slides availabe at: http://www.slideshare.net/BruceEdmonds/be-eatalkfinal