Dialogue on Complexity & Design 12 January 2005 Eve Mitleton-Kelly Director

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

2. Familiar terms Fractals Attractors Paradoxes Edge of chaos etc CHAOS THEORY

3. Complexity • Interrelationships • Connectivity & interdependence • Multiplicity • CREATION OF NEW ORDER

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

5. Theories Natural sciences Dissipative structures chemistry-physics (Prigogine) Autocatalytic sets evolutionary biology (Kauffman) Autopoiesis (self-generation) biology/cognition (Maturana) Chaos theory Social sciences Increasing returns economics (B. Arthur) self-organisation emergence connectivity interdependence feedback far from equilibrium space of possibilities co-evolution historicity & time path-dependence creation of new order Generic characteristics of complex co-evolving systems

6. 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

7. 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

8. Connectivity Diversity Density Intensity Quality of interactions between human agents Determine network of relationships

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

10. 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

11. 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

12. 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

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

14. 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

15. 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.

16. 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?

17. Cluster 2 • Co-evolution • Exploration-of-the-space-of possibilities

18. 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.

20. Co-evolution within an ecosystem

21. Co-evolution in a Social Ecosystem • A social ecosystem includes: • Social • Cultural • Technical • Geographic • Economic • Political dimensions

22. 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’.

23. Exaptation • Often not expensive R&D 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]

24. 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.

25. 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)

27. 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]

28. 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

29. 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 Creationof community

30. 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?

31. Cluster 3 • Far-from-equilibrium • Historicity & time • Path dependence • Creation of new order • Organisations and a different logic

32. Far-from-equilibrium& Dissipative Structures Ilya Prigogine

33. 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]

34. 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;

35. (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.

36. 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.

37. Ilya Prigogine’s 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.”

38. Ilya Prigogine’s 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]

39. 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]

40. 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

41. 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

42. 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

43. 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.

44. Change of emphasis from objects • to relationships between entities from control • to enabling infrastructures

45. Enabling Infrastructure Combination of cultural, social and technical conditions which facilitate ‘x’ Conditions enableinhibit

46. 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

47. 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 C&C) • Facilitates the emergence of new order - new ways of working and relating - new organisational forms - generation & sharing of knowledge

48. 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?