Dialogue on

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• Dialogue on
• Complexity Design
• 12 January 2005
• Eve Mitleton-Kelly
• Director
• Complexity Research Programme
• London School of Economics, UK
• E.Mitleton-Kelly_at_lse.ac.uk
• http//www.lse.ac.uk/complexity

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Familiar terms

• Fractals
• Attractors
• Paradoxes
• Edge of chaos
• etc
• CHAOS THEORY

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Complexity

• Interrelationships
• Connectivity interdependence
• Multiplicity
• CREATION OF NEW ORDER

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Complexity theory

• Context, time, history
• Process, meaning, politics, power
• Emergence, contingency, feedback
• Novelty, change, evolution, transition
• Continuity of identity over time

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Theories Natural sciences Dissipative
structures chemistry-physics (Prigogine) Autoca
talytic sets evolutionary biology
(Kauffman) Autopoiesis (self-generation) biolog
y/cognition (Maturana) Chaos theory Social
sciences Increasing returns economics (B.
Arthur)
self-organisation emergence connectivity interdep
endence feedback far from equilibrium space of
possibilities co-evolution historicity
time path-dependence creation of new order
Generic characteristics of complex co-evolving sys
tems
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• Clusters
• 1.
• Connectivity interdependence
• Self-organisation
• Emergence
• Feedback
• 2.
• Co-evolution
• Exploration of the space of possibilities
• 3.
• Far from equilibrium dissipative structures
• Historicity time
• Path dependence
• Creation of new order
• Organisations and a different logic

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Connectivity interdependence

• Networks of relationships with different degrees
  of connectivity
• strength of coupling
• epistatic interactions
• i.e. the fitness contribution made by one
  individual will depend upon related
  individuals
• Essential element of feedback

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Connectivity

• Diversity
• Density
• Intensity
• Quality
• of interactions
• between human agents
• Determine network of relationships

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Emergence

• Emergent properties or qualities or patterns
• Arise from interaction
• Cannot be predicted

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Self-organisation

• Spontaneous coming together
• Not directed or designed by someone outside the
  group
• The group decides what needs to be done, how,
  when
• Can be a source of innovation
• Consider what facilitates self-organisation

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Feedback

• 2 mechanisms
• Reinforcing (amplifying) a driver for change
  positive feedback
• Balancing (moderating or dampening) - creates
  stability negative feedback
• Processes not mechanisms
• need time dimension

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Feedback Process not
Mechanism

• to avoid the machine
  metaphor
• A machine is a system, which we can
• understand
• design
• plan its operation in detail
• predict its behaviour
• and
• control

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A machine

• Is a complicated system
• With many inter-related parts
• Relies on feedback
• Can be thought of as an object

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• Feedback in this context is taken to mean
  influence, which changes potential action and
  behaviour.
• Influence
• Not uniform
• It depends on the degree of connectivity
• Actions and behaviours vary with different
  individuals
• With time and context
• Reciprocal

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• Feedback links the micro and the macro processes
• The microscopic events and the macroscopic
  emergent structures or patterns change and evolve
  and in so doing influence each other through
  feedback processes.

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Small Groups

• Is your understanding of self-organisation and
  emergence different from that discussed? In what
  way? How do you think about them?
• How do they relate to design? Can you identify
  examples of self-organisation and emergence in
  design?
• What was the role of feedback?

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Cluster 2

• Co-evolution
• Exploration-of-the-space-of possibilities

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Co-evolution

• Reciprocal influence that changes the interacting
  entities
• Co-evolution within a social ecosystem
• not just adaptation to the environment
• One domain changes in the context of the other.

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Co-evolution within an ecosystem
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Co-evolution in a Social Ecosystem

• A social ecosystem includes
• Social
• Cultural
• Technical
• Geographic
• Economic
• Political dimensions

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Exploration of the space of possibilities

• Exploration of new options, different ways of
  working and relating
•  
• The search for a single 'optimum' strategy is
  neither possible nor desirable, in a turbulent
  environment multiple micro-strategies
  distributed strategies, power, intellectual cap.
• But variety alone is not enough. New connections
  or contributions also need to be seen.

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Exaptation

• Often not expensive RD which produces major
  innovations, but seeing a novel function, in a
  new light.
• Exaptation is the emergence of a novel function
  of a part in a new context. Major innovations
  in evolution are all exaptations. Exaptations
  are not predictable Kauffman, Complexity and
  Technology Conference, London, 11 March 1997

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Adjacent possible

• When searching the space of possibilities,
  whether for a new product or a different way of
  doing things
• It is not possible to explore all possibilities
• But it is possible to consider change one step
  away from what already exists.

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Fitness Landscape

• In the competition for survival, species attempt
  to alter their make-up by taking adaptive walks
  to move to higher fitness points, where their
  viability is enhanced.
• Adaptive walks are an optimisation technique for
  searching a space of possibilities.
• Powerful technique able to search many parts of
  the space in parallel (Kauffman)

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Fitness Landscape

• N number of entities or elements in a system
• K degree of connectivity between the entities
• Each entity N makes a fitness contribution which
  depends upon that entity and upon K other
  entities among the N
• K reflects the rich cross-coupling of the system
• K measures the richness of epistatic interactions
  among the components of the system.
• NK model, The Origins of Order, Kauffman,
  1993

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Organisational Fitness Landscapes

• Concept may be applied to evolutionary journey of
  an organisation.
• Consider multiple micro-strategies, exploring the
  space of possibilities.
• Success of strategies of an organisation is
  determined by the strategies of the other
  entities in the same ecosystem.
• Inter-coupling of landscapes richness of
  individual interactions alter the
  co-evolutionary dynamics

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• A complex co-evolving ecosystem is one of
  intricate and multiple intertwined interactions
  and relationships.
• Connectivity and interdependence propagate the
  effects of actions, decisions and behaviours
  throughout the ecosystem.
• Depend on degree of connectivity
• Creation of community

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Small Groups

• Can you identify examples of co-evolution,
• exploration of the space of possibilities,
  exaptation and the adjacent possible?
• How would they work as necessary conditions in
  the design process?
• How would you employ micro-strategies and use
  distributed intelligence?

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Cluster 3

• Far-from-equilibrium
• Historicity time
• Path dependence
• Creation of new order
• Organisations and a different logic

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Far-from-equilibrium Dissipative Structures

• Ilya Prigogine

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• Benard cell - example of a physico-chemical
  dissipative structure
• By applying an external constraint we do not
  permit the system to remain at equilibrium.
  Nicolis Prigogine 1989, p10

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• Several things have happened
• (a) self-organisation the water molecules have
  spontaneously organised themselves into
  right-handed and left-handed cells
• (b) from molecular chaos the system has created
  order and a structure has emerged
• (c) the handedness or direction of rotation can
  neither be predicted nor controlled although we
  can predict that the cells will appear

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• (d) the system was pushed far-from-equilibrium
  by an external constraint or perturbation
• (e) the homogeneity of the molecules at
  equilibrium was disturbed and their symmetry was
  broken.
• (f) the particles behaved in a coherent manner,
  despite the random thermal motion of each of
  them.
• This coherence at a macro level characterises
  emergent behaviour, which arises from micro-level
  interactions of individual elements.

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• In classical thermodynamics heat transfer or
  dissipation was considered as waste, but in the
  Benard cell it has created new order.
• It is this ability of complex systems to create
  new order and coherence, which is their
  distinctive feature.

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Ilya Prigogines contribution

• Reinterpretation of the Second Law of
  Thermodynamics.
• Time-irreversible processes are a source of order
• Arrow of time need not be associated with
  disorder
• Dissolution into entropy is not a necessary
  condition but under certain conditions,
• entropy itself becomes the progenitor of order.
  

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Ilya Prigogines contribution

• To be more specific, ... under non-equilibrium
  conditions, at least, entropy may produce, rather
  than degrade, order (and) organisation ... If
  this is so, then entropy, too, loses its
  either/or character. While certain systems run
  down, other systems simultaneously evolve and
  grow more coherent.
• Prigogine Stengers 1985, p. xxi

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• Bifurcation
• Splitting into alternative solutions.
• Several solutions are possible for the same
  parameter values.
• Chance alone will decide which of these solutions
  will be realized. The fact that only one among
  many possibilities occurred gives the system a
  historical dimension, some sort of memory of a
  past event that took place at a critical moment
  and which will affect its further evolution.
• Prigogine and Nicolis 1989

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Summary of characteristics

• Self-organisation
• Creation of order
• Emergence of structure
• Coherence
• Precise behaviour can neither be predicted nor
  controlled
• Far-from-equilibrium external constraint
• Symmetry breaking
• Bifurcation several possible solutions

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Complex Social Phenomena

• Historical dimension the role of time
• Chance events, unfolding in time, are intertwined
  to generate social phenomena
• Qualitative approach
• Narrative captures the historicity of social
  phenomena

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Path dependence

• Previous interactions bring about what we
  currently experience
• e.g. technological and economic changes are path
  dependent
• Increasing returns Brian Arthur
• The form and direction they take depend
• on the particular sequence of events that
  preceded them

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Why complexity thinking?

• Seeing organisations as complex co-evolving
  systems and by understanding their CCES
  characteristics we can facilitate learning and
  sustainability.
• We often inadvertently constrain these
  characteristics and limit innovation and the
  creation of new order.

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Change of emphasis

• from objects
• to relationships between entities
• from control
• to enabling infrastructures

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Enabling Infrastructure

• Combination of cultural, social and technical
  conditions which facilitate x
• Conditions
• enable
  inhibit

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A CCES organisation

• Facilitates (does not actively inhibit) emergence
  
• Encourages self-organisation
• Explores its space-of-possibilities
• Facilitates co-evolution
• Understands about degrees of connectivity
  interdependence
• Appreciates its distributed intellectual capital
• Fosters a collaborative culture

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A CCES organisation

• Creates variability large
  repertoire of responses
• Able to cope in an unpredictable environment
• Not too organised and not too random
• Emphasises Enabling Infrastructures (not CC)
• Facilitates the emergence of new order
• - new ways of working and relating
• - new organisational forms
• - generation sharing of knowledge

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Small Groups

• What does design mean from a complexity
  perspective?
• What difference does it make to our thinking
  about the design process
• Is it possible to design an organisation? How?