22 Jun 2015

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

11 May 2015

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

12 Nov 2014

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)

9 Sept 2014

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

8 Sept 2014

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

3 Jul 2014

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
  2. No Access
    Book Chapter
    Pages 1-8
  3. The Complex Roots of Social Norms

    1. Front Matter

      Pages 9-9
    2. Book Chapter
      Pages 11-36
    3. Book Chapter
      Pages 37-54
    4. Book Chapter
      Pages 55-79
    5. Book Chapter
      Pages 81-103
    6. Book Chapter
      Pages 105-120
  4. Methods and Epistemological Implications of Social Norm Complexity

    1. Front Matter

      Pages 121-121
    2. Book Chapter
      Pages 123-139
    3. Book Chapter
      Pages 141-160
    4. Book Chapter
      Pages 161-173
  5. Evaluating Complex Approaches to Norms

    1. Front Matter

      Pages 175-175
    2. Book Chapter
      Pages 177-188
    3. Book Chapter
      Pages 189-197
  6. Back Matter

    Pages 199-205

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

27 Feb 2014

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

19 Dec 2013

Am invited speaker in workshop on "Joining Complexity Science and Social Simulation for Policy", Budapest, May 2013

Call for Papers: SKIN 3 Workshop Joining Complexity Science and Social Simulation for Policy

A workshop at Eötvös Loránd University, Budapest, Hungary, 22–23 May 2014 Workshop URL: http://cress.soc.surrey.ac.uk/skin/events/third-skin-workshop
This 2-days workshop organised by the EA European Academy of Technology and Innovation Assessment (www.ea-aw.org) as its annual conference with two co-organisers and one local host will bring together two scientific communities to join forces in research on innovation policy modelling. Innovation intersects the concerns of complexity models and social simulation. The intention of the workshop is to explore how complexity models and simulation can be used to improve and inform the innovation policy making process. The workshop will take place at Eötvös Loránd University, Budapest (Hungary), from 22 to 23 May 2014 and is supported by the EGovPoliNet project (http://www.policy-community.eu ).

Guest Speakers:

  • Prof. Erik Johnston (Centre for Policy Informatics, Arizona State University, USA)
  • Prof. Bruce Edmonds (Centre for Policy Modelling, Manchester Metropolitan University Business School, UK)
It will focus on three key overlapping themes:
  • Modelling, understanding and managing innovation policy using the SKIN model
  • Large scale data and scalability for research and innovation policy modelling
  • SKIN between complexity science and social science: mechanisms and components
Places are limited and priority will be given to those offering presentations or posters.
A more detailed account of these themes can be found at: http://www.policy-community.eu. Further information about the SKIN model is at http://cress.soc.surrey.ac.uk/skin/

Abstract Submission

Talk abstracts should be submitted by 6th January 2014 in text, Word or pdf format to skin3@ea-aw.de. Posters from PhD students, planning to attend, describing their research designs, issues and any results are greatly encouraged. Please also email skin3@ea-aw.de if you plan to do this.

SKIN Book Launch

The workshop will use the opportunity to launch the SKIN book Simulating the Knowledge Dynamics of Innovation Networks that will have been just published by Springer. There will be a book launch event on the evening of the first workshop day.

Key Dates

  • Abstract submission: 6th January 2014
  • Notification of acceptance: 17th February 2014
  • Workshop: 22nd and 23rd May 2014

Organisation

SKIN Organisers:

  • Prof. Petra Ahrweiler (EA European Academy of Technology and Innovation Assessment, Germany, EgovPoliNet partner)
  • Prof. Nigel Gilbert (Centre for Research on Social Simulation CRESS, University of Surrey, UK)
  • Prof. Andreas Pyka (Innovation Economics, University of Hohenheim, Germany)
  • Local Organiser:
  • Prof. George Kampis (Eötvös Loránd University, Budapest, Hungary)

Programme Committee (to be confirmed):

Dirk Helbing, Wander Jager, Jeff Johnson, Paul Ormerod, Andrea Scharnhorst, Flaminio Squazzoni, Klaus G. Troitzsch, Matthias Weber, Maria Wimmer

Contact

For organisational queries, contact skin3@ea-aw.de and for general queries, contact Petra Ahrweiler at petra.ahrweiler@ea-aw.de.

13 Dec 2013

Special issue of "Foundations of Science" on "Philosophy and Complexity"

Foundations of Science
There is a special issue/section of the journal Foundations of Science on the topic "Philosophy and Complexity" (Volume 18, Issue 4, November 2013).  This follows the track on this topic at the ECCS 2010 conference in Lisbon.  The introduction to this is the paper "Philosophy and Complexity" by Gil Santos, the contributed papers follow this in this volume sequence (the first half is on a different topic).

  1.  
  2.  

8 Dec 2013

Survey Paper: Squazzoni, Jager and Edmonds "Social Simulation in the Social Sciences: A Brief Overview"

Squazzoni, F., Jager, W. & Edmonds, B. (online first), Social Simulation in the Social Sciences: A Brief Overview. Social Science Computer Review. DOI:10.1177/0894439313512975 

This is an overview of agent-based social simulation that resulted (loosely) from the 2013 ECMS track on Simulating Social Interaction.  It is written very much from the point of view of showing agent-based modelling can address issues of interest to social scientists.

3 Nov 2013

Three social simulation lectures from different perspectives

Three invited lectures from the wonderful ESSA 2013 conference in Warsaw.  The first by Rob Axtell is a too-scale model of employment in the US, which validates against many different sets of data.  In contrast the physics-type models of Dirk Helbing try to explain more with simpler models.  Andre Nowak gives a more psychological perspective to simulation modelling.

http://www.youtube.com/channel/UCQ7VYOFQpvXeDIHGBv9ulqQ/videos

28 Aug 2013

New Paper and Slides of: "Capturing the Implicit – an iterative approach to enculturing artificial agents "

A paper presented at the workshop on "Computers as Social Agents" at IVA@2013

Available from: http://cfpm.org/cpmrep221.html


Capturing the Implicit – an iterative approach to enculturing artificial agents
Peter Wallis and Bruce Edmonds
Abstract. Artificial agents of many kinds increasingly intrude into the human sphere. SatNavs, help systems, automatic telephone answering systems, and even robotic vacuum cleaners are positioned to do more than exist on the side-lines as potential tools. These devices, intentionally or not, often act in a way that in- trudes into our social life. Virtual assistants pop up offering help when an error is encountered, the robot vacuum cleaner starts to clean while one is having tea with the vicar, and automated call handling systems refuse to let you do what you want until you have answered a list of questions. This paper addresses the problem of how to produce artificial agents that are less socially inept. A distinction is drawn between things which are operationally available to us as human conversational- ists and the things that are available to a third party (e.g. a scientists or engineer) in terms of an explicit explanation or representation. The former implies a de- tailed skill at recognising and negotiating the subtle and context-dependent rules of human social interaction, but this skill is largely unconscious – we do not know how we do it, in the sense of the later kind of understanding. The paper proposes a process that bootstraps an incomplete formal functional understanding of hu- man social interaction via an iterative approach using interaction with a native. Each cycle of this iteration entering and correcting a narrative summary of what is happening in recordings of interactions with the automatic agent. This interac- tion is managed and guided through an “annotators’ work bench” that uses the current functional understanding to highlight when user input is not consistent with the current understanding, suggesting alternatives and accepting new sug- gestions via a structured dialogue. This relies on the fact that people are much better at noticing when dialogue is ”wrong” and in making alternate suggestions than theorising about social language use. This, we argue, would allow the itera- tive process to build up understanding and hence CA scripts that fit better within the human social world. Some preliminary work in this direction is described.

13 Aug 2013

A new blog critiquing universities...

... and exploring how the production, transmission and certification of knowledge might be done better in the internet age.

      http://afteruniversities.blogspot.co.uk/

21 Jun 2013

Slides from talk on "Context-dependency and the development of social institutions"

Presented at the 1st "Constructed Complexities" workshop on "Institutions as social constructs and social construction through institutions", at the University of Surrey, 21st July 2013. (http://constructedcomplexities.wordpress.com/workshops/workshop-1/)

Slides at:

http://slideshare.net/BruceEdmonds/context-dependency-and-the-development-of-social-institutions

-------------------------------
It is well established that many aspects of human cognition are context-dependent, including: memory, preferences, language, perception, reasoning and emotion.  What seems to occur is that the kind of situation is recognised and information stored with respect to that.  This means that when faced with a similar situation, beliefs, expectations, habits, defaults, norms, procedures etc. that are relevant to the context can be brought to bear.  I will call this mental correlate of the kind of situation the “context”. Thus the mental context frames conscious thinking by preferentially providing the relevant information making learning and reasoning practical, as well as allowing relatively “crisp” and logical thought within this frame.  This is the “context heuristic” that seems to have been built into us by the process of evolution.
This recognition seems to occur in a rich, fuzzy and largely unconscious manner, which means that it can be hard to give distinct identities and talk about these contexts.  It can thus be problematic to talk about “the” context in many cases, and indeed one cannot assume that different people are thinking about the same situation as (effectively) the same context from a third party perspective.  Indeed one of the powerful aspects of the context heuristic is that it allows us flip between mental contexts allowing us to thing about a situation or problem from different contextual frames.  Due to our facility at automatically identifying context and the indefinable way it is recognised it is hard for people to retrieve what is or signals a context (in contrast to what is relevant when recognised). However, they do seem to be sensitive to when they have the wrong context.
Thus learning is not just a matter of recording beliefs, expectations, habits, defaults, norms, procedures etc. but also a matter of learning to recognise the kinds of situation to organise their remembrance.  A large part of our world is humanly constructed, or common (e.g. shared human emotions or a shared environment).  Our classification of these kinds of situation is thus heavily coordinated among people of the same society – we learn to recognise situations in effectively the same way and hence remember the relevant beliefs, expectations, habits, defaults, norms, procedures etc. for the same kinds of situation.  A shared body of knowledge (in its wisest sense) that constitutes a culture does not only include the foreground beliefs, norms etc. but also how the world is divided into kinds of situation.  Some of these contexts will have universal roots, such as the emotion of fear or being hungry, and thus might be approximately the same across cultures (without transmission), others will be specific to cultures. 
The power of the context heuristic comes from the ability it gives us to socially coordinate.  It allows for contexts to be socially co-developed and the beliefs, expectations, habits, defaults, norms, procedures etc. that are associated with these.  Thus different kinds and bases for coordination can be developed for different kinds of situation and be appropriate to that situation.  Indeed over time such shared contexts can become deeply entrenched. If a kind of situation is readily recognisable then it is more likely that specific norms, protocols, signals, infrastructure etc. is developed to facilitate coordination in that kind of situation.  However, equally if specific norms, protocols, signals, infrastructure are developed for a kind of situation then the more recognisable it becomes.  In this way a kind of situation is instituted.  Courts, social parties, lectures, and board meetings are examples of such. The institutionalisation of social contexts not only ensures its consistent recognition and treatment by members of a society but also allows for it to be reified with a specific label so that it can be reasoned about.  Thus institutionalisation is usually a process originating in shared context but which makes its recognition explicit, thus allowing for it to be talked about and debated.
The ability to coordinate in specific ways for different kinds of situation has obvious evolutionary advantage for homo sapiens.  This is coherent with the “Social Intelligence Hypothesis” (SIH) that suggests that the evolutionary advantage of our intelligence is not our general problem solving ability but the social abilities it allows.  The ability to coordinate in groups, and develop a culture of knowledge, coordination and techniques that enables a group to inhabit an ecological niche allows homo sapiens to inhabit a wide range of niches (and not just one like most species).  Examples include even extreme environments such as the Kalahari Desert and the Artic Tundra.  Being spread over a number of very different niches gives homo sapiens a considerable resistance to unpredictable catastrophes that wipe out particular niches.  This resilience to the species (not the particular groups which might well be very susceptible to such disasters) gives a very distinct evolutionary advantage. The ability to reliably co-recognise the same context as others and hence apply the same beliefs, expectations, habits, defaults, norms, procedures etc. as others is a strongly social ability.
The institutionalisation of context will not be restricted to a mental alignment, but often also involve a considerable development of physical, educational and legal infrastructure.  For example to facilitate the institution of a lecture, we have built special rooms, equipment and software in addition to long training to familiarise children to the institution.  In other words much social signalling and entrenchment is via stimergic means – changing the environment to flag and facilitate the institution.  It is by no means restricted to purely mental structures: norms, beliefs, habits etc. but is marked by other changes.  Thus, once established, many institutions will not be limited to the coordinated mental constructs of the society’s members, but marked out in the environment.  Such traces might well be distinguishable by a future archaeologist – even one not familiar with our culture – just as the statues of Easter Island are recognisable now.
Where does that leave the nature of such institutions from an epistemological view?   Firstly note that a truth being context-dependent is not necessarily the same as it being relative – if a context is reliably co-recognisable then any knowledge specific to that context can be checked by an independent person (first by recognising the correct context then seeing if the knowledge holds therein).  Secondly that, although institutions might originate in ineffable contexts it may become institutionalised in a way that leaves considerable traces in the environment, and thus its existence goes beyond being a purely social construct (although its origin remains so).  Thirdly, although the form of any particular institution might be specific to a particular culture and socially determined, the roots of institutions in general might be deeply rooted in our shared biological evolution.

3 Jun 2013

New Paper: "Multi-Patch Cooperative Specialists With Tags Can Resist Strong Cheaters", ECMS 2013, Alesund Norway

The paper, slides and model are at: http://cfpm.org/cpmrep220.html.  ECMS 2013 is at http://www.scs-europe.net/conf/ecms2013/ in beautiful Alesund, Norway (http://www.youtube.com/watch?v=b6vCUhCFY_I).

Published as:
Edmonds, B. (2013) Multi-Patch Cooperative Specialists With Tags Can Resist Strong Cheaters. In Rekdalsbakken, W., Bye, R.T. and Zhang, H. (eds), Proceedings of the 27th European Conference on Modelling and Simulation (ECMS 2013), May 2013, Alesund, Norway. European Council for Modelling and Simulation, 900-906.
Keywords: Symbiosis, meta-population, multi-patch, cooperation, agent-based simulation, tags, defection.

Abstract.

The paper looks at tag-based cooperation within abstract simulation models. Previous models of this kind have been shown to either have ‘programmed in’ cooperation or to be vulnerable to “strong cheaters”.  Previous work by the author included a model of social specialisation and cooperation, but where only a single dominant tag-group arose at any one time and where cooperation eventually collapsed.  Here a multi-patch version of this model is explored and show to not to collapse but seed itself indefinitely.  Furthermore, the model seems to be resistant to significant levels of strong cheaters.