An article including quotes from me about the Turing test and the nature of intelligence in E&T

The Turing Test: brain-inspired computing's multiple-path approach
10th November 2014 By Edd Gent

About projects like the Human Brain project and the Turing Test for (so called) general intelligence.   In the Engineering and Techology magazine

Access it at: http://eandt.theiet.org/magazine/2014/11/imitation-brains.cfm
Relates to the paper:

Edmonds, B. and Gershenson, C (2012) Learning, Social Intelligence and the Turing Test – why an “out-of-the-box” Turing Machine will not pass the Turing Test. Lecture Notes in Computer Science, 7318, 182-192. http://link.springer.com/chapter/10.1007/978-3-642-30870-3_18 (previous version freely available at http://arxiv.org/abs/1203.3376)

Paper and Slides: "Analysing a Complex Agent-Based Model Using Data-Mining Techniques"

Analysing a Complex Agent-Based Model Using Data-Mining Techniques 
By Claire Little, Bruce Edmonds, Laurence Lessard-Phillips, and Ed Fieldhouse

Presented at Social Simulation 2014, Barcelona, September.

A complex “Data Integration Model” of voter behaviour is described. However it is very complex and hard to analyse. For such a model “thin” samples of the outcomes using classic parameter sweeps are inadequate. In order to get a more holistic picture of its behaviour data- mining techniques are applied to the data generated by many runs of the model, each with randomised parameter values.

Paper at: http://cfpm.org/aacabm/analysing a complex model-v3.4.pdf
Slides at:  http://www.slideshare.net/BruceEdmonds/analysing-a-complex-agentbased-model-using-datamining-techniques

Slides of my talk at the 2014 ESSA summer school: "Winter is coming! - how to survive the coming critical storm and demonstrate that social simulations work"

 A talk at the 2014 European Social Simulation Association summer school, at UAB in Barcelona 8th sept 2014

The talk covers some of the symptoms of hype in social simulation and argues that it needs to be more careful and rigourous. In particular that the (current) purpose of a simulation needs to be distinguished between theoretical, explanatory or predictive. Each having their own critieria.

http://www.slideshare.net/BruceEdmonds/winter-is-coming-38816017

Edited Book: "The Complexity of Social Norms"

The Complexity of Social Norms

Editors: Maria Xenitidou, and Bruce Edmonds 

Other Authors: Elinor Ostrom, Wesley Perkins, Cristina Bicchieri, Rosaria Conte,  Marco Janssen,  Flaminio Squazzoni Christine Horne, Brigitte Burgemeestre, Hugo Mercier, Chris Goldspink, Corinna Elsenbroich, Joris Hulstijn, Yao-Hua Tan, Giulia Andrighetto, and Daniel Villatoro

ISBN: 978-3-319-05307-3 (Print) 978-3-319-05308-0 (Online)

• Takes a fresh, fundamentally dynamic and complexity inspired approach to the study of social norms
• Presents a new methodological and theoretical perspective to study normative behavior
• Contributing authors provide a unique and varied perspective from departments such as philosophy, economics and political science

This book explores the view that normative behaviour is part of a complex of social mechanisms, processes and narratives that are constantly shifting. From this perspective, norms are not a kind of self-contained social object or fact, but rather an interplay of many things that we label as norms when we ‘take a snapshot’ of them at a particular instant. Further, this book pursues the hypothesis that considering the dynamic aspects of these phenomena sheds new light on them.

The sort of issues that this perspective opens to exploration include:

• Of what is this complex we call a "social norm" composed of?
• How do new social norms emerge and what kind of circumstances might facilitate such an appearance?
• How context-specific are the norms and patterns of normative behaviour that arise?
• How do the cognitive and the social aspects of norms interact over time?
• How do expectations, beliefs and individual rationality interact with social norm complexes to effect behaviour?
• How does our social embeddedness relate to social constraint upon behaviour?
• How might the socio-cognitive complexes that we call norms be usefully researched?

 http://www.springer.com/social+sciences/book/978-3-319-05307-3

Table of contents (11 chapters)

1. Front Matter

   Pages i-vi
   Download PDF (50KB)
2. 
   Book Chapter
   Pages 1-8
   The Conundrum of Social Norms
   Maria Xenitidou, Bruce Edmonds
   Look Inside Get Access

3. The Complex Roots of Social Norms

   1. Front Matter

      Pages 9-9
      Download PDF (21KB)
   2. Book Chapter
      Pages 11-36
      Misperception Is Reality: The “Reign of Error” About Peer Risk Behaviour Norms Among Youth and Young Adults
      H. Wesley Perkins Ph.D.
      Look Inside Get Access
   3. Book Chapter
      Pages 37-54
      Norms and Beliefs: How Change Occurs
      Cristina Bicchieri, Hugo Mercier
      Look Inside Get Access
   4. Book Chapter
      Pages 55-79
      Social Norms from the Perspective of Embodied Cognition
      Chris Goldspink
      Look Inside Get Access
   5. Book Chapter
      Pages 81-103
      It Takes Two to Tango: We-Intentionality and the Dynamics of Social Norms
      Corinna Elsenbroich
      Look Inside Get Access
   6. Book Chapter
      Pages 105-120
      The Relational Foundation of Norm Enforcement
      Christine Horne
      Look Inside Get Access

4. Methods and Epistemological Implications of Social Norm Complexity

   1. Front Matter

      Pages 121-121
      Download PDF (21KB)
   2. Book Chapter
      Pages 123-139
      Norm Emergence in Regulatory Compliance
      Brigitte Burgemeestre, Joris Hulstijn, Yao-Hua Tan
      Look Inside Get Access
   3. Book Chapter
      Pages 141-160
      Norm Dynamics Within the Mind
      Giulia Andrighetto, Daniel Villatoro, Rosaria Conte
      Look Inside Get Access
   4. Book Chapter
      Pages 161-173
      Vulnerability of Social Norms to Incomplete Information
      Marco A. Janssen, Elinor Ostrom
      Look Inside Get Access

5. Evaluating Complex Approaches to Norms

   1. Front Matter

      Pages 175-175
      Download PDF (21KB)
   2. Book Chapter
      Pages 177-188
      The “Reign of Mystery”: Have We Missed Something Crucial in Our Experimental and Computational Work on Social Norms?
      Flaminio Squazzoni
      Look Inside Get Access
   3. Book Chapter
      Pages 189-197
      Three Barriers to Understanding Norms: Levels, Dynamics and Context
      Bruce Edmonds
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6. Back Matter

   Pages 199-205
   Download PDF (63KB)
   

A Video of my talk "Towards Integrating everything..." about complexity science and policy @cfpm

Video is at: https://www.youtube.com/watch?v=OgDkngKELpE
Abstract at: http://bruceedmonds.blogspot.co.uk/2014/05/cfpm-skin32014-slides-of-talk-in.html
Slides at: http://www.slideshare.net/BruceEdmonds/towards-integrating-everything-well-at-least-abm-datamining-qualquant-data-networks-and-complexity-science

@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

#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

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

Slides from my talk "Integrating Microsimulation, Mathematics, and Network Models Using ABM – prospects and issues " #cfpm

A talk at the workshio on "Microsimulation of chronic disease: current methods & future directions", 27th Feb 2014, LSHTM, Tavistock place, London.

The talk looks at the different kinds of model, in more detail: equation-based, networks, agent-based simulation, and microsimulation.  The various trade-offs: advantages and disadvantages of each.  I then highlight the ability of gent-based models to (1) stage abstraction into more gentle steps that preserve reference and (2) integrate a variety of different kinds of evidence or results.  Some ways that microsimulation and agent-based modelling are outlined.  However it is suggested that the way forward is via 'packages' or 'chains' of highly related models rather than multi-purpose single models.

Slides available at: http://www.slideshare.net/BruceEdmonds/be-integmicrosimdis