Complexity Science – Some key concepts and principles

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Why Complexity Science?

http://www.youtube.com/watch?v=A4NpOcpPcZA

“Complexity refers to the condition of the universe which is integrated and yet too rich and varied for us to understand in simple, mechanistic

or linear ways.

We can understand many parts of the universe in these ways but the larger and more intricately related phenomena can only be understood

by principles and patterns – not in detail.”[Lissack, M. (1997). “Mind your Metaphors: Lessons from Complexity Science” in Long Range Planning, Vol. 30/2

pp294]

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Why Complexity Science?

“Complexity deals with the nature of emergence, innovation, learning and adaptation”

[Lissack, M. (1997). “Mind your Metaphors: Lessons from Complexity Science” in Long Range Planning, Vol. 30/2 pp294]

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Complexity Science: changing the way we think

“Complexity theory deals with systems which show complex structures in time or space, often hiding simple deterministic rules. Complexity theory research has allowed for new insights into many phenomenaand for the development of a new language. The use of complexity theory metaphors can change the way managers think about the problems they face. Instead of competing in a game or a war, they are trying to find their way on an ever changing, ever turbulent landscape”

[Lissack, M. (1997). “Mind your Metaphors: Lessons from Complexity Science” in Long Range Planning, Vol. 30/2 pp294]

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“Weick’s concept of ‘sensemaking’ can be summarized as an organisation’s need to interpret and make sense of the environment around it if it is to survive”

[K. E. Weick and K. H. Roberts, Collective Mind in Organisations: Heedful Interrelating on Decks, “Administrative Science Quarterly, September (1993), And: K. E. Weick, Sensemaking in Organisations, Sage Press, Thousand Oaks, CA (1995).]

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Complexity Science: Changing what we do

"Complexity science offers a way of going beyond the limits of reductionism, because it understands that much of the world is not machine-like and comprehensible through a cataloguing of its parts; but consists instead mostly of organic and holistic systems that are difficult to comprehend by traditional scientific analysis.

[…] it remains very much a science - that is, a body of observation and analysis of natural phenomena - rather than being deep theory"

(Lewin, R., 1999)

However, let us consider some of the theory generated by this body of observation

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Complex Adaptive Systems (CAS)?

• Ever wondered how to describe…

• Watch some of these and think:• Brain Cells:

– http://www.youtube.com/watch?v=KpJXBmiik-g• Inside a bee hive:

– http://www.youtube.com/watch?v=qjieOEI-wlU• High School Crowd:

– http://www.youtube.com/watch?v=nCn6_MgL1s4• Thunderstorm:

– http://www.youtube.com/watch?v=_cl0aw87LqA• Time Lapse Reel (artistic!):

– http://www.youtube.com/watch?v=Bn_MuUQbOUs• Termites – Life’s Ultimate Architects:

– http://www.youtube.com/watch?v=0m7odGafpQU

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“A flock of birds might be thought of as a complex adaptive system. It consists of many agents, perhaps thousands, who might be following simple rules to do with adapting to the behaviour of neighbours so as to fly in formation without crashing into each other.

A human being might be seen as a network of 100,000 genes interacting with each other. An ecology could be thought of as a network of vast numbers of species relating to each other. A brain could be considered as a system of ten billion neurones interacting with each other.

In much the same way, an organisation might be thought of in terms of a network of people relating to each other. Complexity science seeks to identify common features of the dynamics of such systems or networks in general”

(Stacey 2003a:238).

Complex Adaptive Systems

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Complex Adaptive Systems

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Complex Adaptive Systems• A Complex Adaptive System

(CAS) consists of a large number of agents, each of which behaves according to some set of rules;

• These rules require the agents to adjust their behaviour to that of other agents;

• In other words, agents interact with, and adapt to, each other;

•• Out of these interactions,

novelty, spontaneity and creativity emerge – sometimes in unpredictable ways

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Think of a flock of birds as a complex adaptive system

• See some videos of flocking birds online:– http://www.youtube.com/watch?v=YouLl-hlRDo– http://www.youtube.com/watch?v=GFefta0b7Xs– http://www.youtube.com/watch?v=XrUTLveVVvs– http://www.youtube.com/watch?v=fPN01xePmM0– http://www.youtube.com/watch?v=MuY9hJ6gKeI– http://www.youtube.com/watch?v=XH-groCeKbE

• And a complexity science simulation of flocking birds:– http://www.red3d.com/cwr/boids/

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Think of a flock of birds as a complex adaptive system

• Complexity science seeks to: – identify common features of

the dynamics of such systems or networks in general;

• The emergent outcome in the case of the self-organisation of the birds is the order present in the formation of the flock.

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Innovation as an emergent outcome of system-wide self-organisation – how?

• Key questions: – How do such complex non-

linear systems with their vast numbers of interacting agents function to produceorderly patterns of behaviour(or innovation)?

– How do such living systems evolve to produce neworderly patterns of behaviour (or innovation)?

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CAS – Methodological considerations

• No search for an overall blueprint for the whole system;

– model agent interaction;

– each agent behaving according to their own principles of local interaction;

– No individual agent, or group, determines the patterns of behaviour;

– “bottom-up emergence”

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Ants as an analogy to convey the meaning & potential of self-organisation to solve business problems“To understand the power of self-organisation, consider how certain species

of ants are able to find the shortest path to a food source merely by laying and following chemical trails. Individual ants emit a chemical substance – a pheromone – which then attracts other ants. In a simple case, two ants leave the nest at the same time and take different paths to a food source, marking their trails with pheromone.

The ant that took the shorter path will return first, and this trail will now be marked with twice as much pheromone (from the nest to the food and back) as the path taken by the second ant, which has yet to return.

Their nest mates will be attracted to the shorter path because of its higher concentration of pheromone. As more and more ants take that route, they too lay pheromone, further amplifying the attractiveness of the shorter trail.

The colony’s efficient behaviour emerges from the collective activity of individuals following two very basic rules: lay pheromone and follow the trails of others” (Bonabeau and Meyer 2001:108).

See http://www.youtube.com/watch?v=A85TskjfGxo for a vid’ of ants in action!

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Self-Organisation

• No single person absolutely in command or control of the situation• No-one really planning and managing the situation – even though they might

think they are• Obvious hierarchy in complex systems are not immediately noticeable • Agents continuously organising themselves without a ‘leader’• Agents interacting with each other in simple ways• Complex systems structure themselves out of themselves• Interacting elements act according to simple rules• Order is created out of chaos

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Emergence

• You can’t easily predict what is going to happen next• The way people are interacting appears to be random• You see new things emerging from interactions • If you were to look on a wide scale there might be some patterns emerging• Patterns emerge from interactions• Patterns inform the behaviour of a system• New qualities arise through particular types of networks• Higher complexity is produced out of many simple components• Each individual component outgrows usual capabilities – e.g. people outgrow

their competencies.

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The ‘edge of chaos’

• Not a fixed state – a transitional phase!• Lots of creative activity going on • Lots of transitions and changes from one state to another• Living networks reside in a critical phase between chaos and order where

networks find creativity and stability in an optimal balance• Living systems are most creative, with the greatest potential for discovering

order that expresses an emergent property for the whole system, when they are living near the ‘edge of chaos’

• Living systems naturally undergo transitions from current order to chaos, from which emerges new order.

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Diversity

• Differences not flattened out or levelled• Change happens easily• Interaction and change appears flexible• The ‘system’ seems strong in these cases• Networks combine the most different variants, characters, functions• High diversity creates more possibilities to react flexibly, on environmental

changes• The greater the variety within the system the stronger it is• Ambiguity and paradox abound• Contradiction is used to create new possibilities to co-evolve with their

environment.

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History & Time

• History and time irreversible – you can’t go back in time and change things • Some specific decisions brought you to where you ended – some you were aware of,

others you were not (what might have been???)• In a social context, the series of decisions which an individual makes from a number

of alternatives partly determine the subsequent path of the individual• Before a decision is made there are a number of alternatives – after, it becomes part

of history and influences the subsequent options open to the individual• Unique histories mean every decision the organisation makes is context specific

(therefore questions the idea of ‘best practice’ and ‘one size fits all’ treatments)• Also, think about path dependency – e.g. technological path dependency – systems

are locked into using dominant tools and processes because of historical factors• Think about our present day road systems – these often date back to Roman times!

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Unpredictability

• Detail and order of outcomes not determined by an elite group• Not really possible to forecast or control behaviour in details• No actions isolated• Interlinked groups or networks with lots of people acting and reacting among each

other• Things happening in one place create consequences elsewhere• When one thing changes everything else changes – maybe not immediately but there

would definitely be some consequence somewhere.• Due to complicated interrelations, it’s very difficult to foresee or to control behaviour

of the nodes of the network, when reacting to impulses (from outside or inside the network).

• Emergent order is holistic – a consequence of interactions between elements of the system

• All systems exist within their own environment and they are also part of that environment

• As their environment changes they need to ensure best fit• When they change, they change their environment too

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Pattern Recognition

• You can’t always see direct and proportional links of cause and effect• People and groups don’t really link in random ways• Small numbers of people are loosely coupled to others• Small changes are amplified - You can see big effects coming from small changes• You see patterns of activity being repeated over and over again• The ways agents in a system connect or relate to each other is critical to the survival

of the system • From these connections patterns are formed and feedback disseminated• Relationships between agents are more important than agents themselves• Self-organised, living networks always show similar patterns.• Feedback is the systems way of staying constantly tuned to its environment and

landscape and enables the system to re-adjust its behaviour. • In far from equilibrium conditions change is non-linear, so small changes can be

amplified, and produce exponential change• Novel, emergent order arises through cycles of iteration in which a pattern of activity,

defined by rules or regularities, is repeated over and over again, giving rise in coherent order.

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6 Properties of Complex Adaptive Systems (CAS)

• Self-Organisation & Emergence• Diversity• The Edge of Chaos• History & Time• Unpredictability• Pattern Recognition

• … there are more – these are just some basic principles• Don’t forget interconnectivity and the importance of networks! • Networks are the assumed context of CAS• You will learn more over the course of the week… (also see references in the

bibliography for how CAS theory is applied to different contexts)