23 May 2014

@cfpm #skin3_2014 Slides of talk in Budapest on "Towards Integrating Everything..."

Towards Integrating Everything (well at least: ABM, data-mining, qualitative and quantitative data, networks and complexity science)

Bruce Edmonds
Centre for Policy Modelling, Manchester Metropolitan University

Presented at: the 3rd SKIN workshop on "Joining Complexity Science and Social Simulation for Policy", May 2014, Budapest. (http://cress.soc.surrey.ac.uk/SKIN/events/third-skin-workshop)
Innovation or other policy-orientated research has tended to take one of two strategies: (a) work with high-level abstractions of macro-level variables or (b) focus on micro-level aspects/areas with simpler mechanisms.  Whilst (a) may provide some comfort in the form of forecasts, these are almost useless for policy since they can only be relied upon if nothing much has changed.  Although approach (b) may produce some interesting studies which show how complex even small aspects of the involved processes are, with maybe interesting emergent effects, it provides only a small part of the overall picture and little to guide decision making.

Rather, I (with others) suggest a different approach.  Instead of aiming to produce some kind of "adequate" theory (usually in the form of a model along with its interpretation), that instead we aim at integrating different kinds of evidence and find the best ways to present these to policy makers in order to help policy-makers 'drive' by providing views of what is happening.  Thus (1) utilising the greatest possible range of evidence and (2) providing rich, relevant but synthetic views of this evidence to the policy makers.  Any projections should be 'possibilistic' rather than 'probabilistic' - showing the different ways in which social processes might unfold, and help inform the analysis of risks.  The talk looks at some of the ways in which this might be done, to integrate micro-level narrative data, time-series data, survey data, network data, big data using a variety of techniques.  In this view, models do not disappear, but rather have a different purpose and hence be developed and checked differently.

This shift will involve a change in attitude and approach from both researchers and those in the policy world.  Researchers will have to give up the playing for general or abstract theory, satisfying themselves with more gentle and incremental abstraction, whilst also accepting and working with a greater variety of kinds of evidence.  They will also have to stop 'conning' the policy world with forecasts, and refuse to provide these as more dangerous than helpful.  The policy world will have to stop looking for a magic 'crutch' that will reduce uncertainty (or provide justification for chosen policies) and move towards greater openness with both data and models.  
Slides available at: http://www.slideshare.net/BruceEdmonds/towards-integrating-everything-well-at-least-abm-datamining-qualquant-data-networks-and-complexity-science

12 May 2014

#cfpm #mixedSNA my talk on "Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis"

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis

Bruce Edmonds

12 May 2014
BSA Social Network Analysis Group - Mixed Methods Approaches to Social Network Analysis Conference (organised with with University of Greenwich and Middlesex University)
Middlesex University, UK

Networks are an abstraction of complex social processes.  Albeit themselves formal, the social processes on which they are based can be researched using both quantitative and qualitative methods.  The problem in combining these approaches comes from the very different natures and levels on which they are based.  Here we describe an approach which uses agent-based modelling (ABM) as a stepping stone towards the more abstract network models.  These ABMs are more in the nature of complex and dynamic descriptions than general theories, and are ideally suited for integrating a variety of kinds of evidence into a coherent fashion - including quatitative evidence to inform the micro-level behaviours of agents, and quantitative evidence about the macro, aggregate levels.  The assumptions behind these kinds of ABM are relatively transparent, and the ABMs used to generate networks in a precise manner.  Thus this "staging" of the abstraction process allows a well-founded mixed-methods approach to social network research.  A worked example of this on voting behaviour is presented.

Slides at: http://www.slideshare.net/BruceEdmonds/using-agentbased-simulation-to-integrate-microqualitative-evidence-macroquantitative-data-and-network-analysis

5 May 2014

Paper, Slides and Model for presentation "Man on Earth – the challenge of discovering viable ecological survival strategies"

Man on Earth – the challenge of discovering viable ecological survival strategies 

A paper presented at the 15th International Workshop on Multi-Agent-Based Simulation, PAris, May 2014.
Abstract. Many previous societies have killed themselves off and, in the process, devastated their environments.  Perhaps the most famous of these is that of “Easter Island”.  This suggests a grand challenge: that of helping discover what kinds of rationality and/or coordination mechanisms might allow humans and the greatest possible variety of other species to coexist. As their contribution towards this, the agent community could investigate these questions within simulations to suggest hypotheses as to how this could be done.  The particular problem for our community is that of designing and releasing a society of plausible agents into a simulated ecology and assessing: (a) whether the agents survive and (b) if they do survive, what impact they have upon the diversity of other species in the simulation.  No other community is currently in a position to explore this problem as a whole. The simulated ecology needs to implement a suitably dynamic, complex and reactive environment for the test to be meaningful. In such a simulation, agents (as any other entity) would have to eat other entities to survive, but if they destroy the species they depend upon they are likely to die off themselves.  Up to now there has been a lack of simulations that combine a complex model of the ecology with a multi-agent model of society – there have been complex models of society but with simple ecological representations and complex ecological models but with little of human social complexity in them. In order for progress to be made with humanity’s challenge, we will have to move beyond simple ideas and solutions and embrace the complexity of the socio-ecological complex as a whole.  A suitable dynamic ecological model and simple tests with agents are described to illustrate this challenge, as the first steps towards a meaningful test bed to under pin the implied research programme.
Slides at: http://www.slideshare.net/BruceEdmonds/moe-mabsv1
Paper at: http://bruce.edmonds.name/cpmrep223.html
Model and ODD Description at: http://openabm.org/model/4204