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An Introduction to Complex Adaptive System Theory & Key Concepts of Complexity Science

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Title: An Introduction to Complex Adaptive System Theory & Key Concepts of Complexity Science


1
An Introduction to Complex Adaptive System Theory
Key Concepts of Complexity Science
  • Dr Carol Webb
  • Manufacturing Dept, Bldg 50, RmF9b,
  • School of Applied Sciences

2
Why Complexity Science?
  • Problem with legacy of scientific management
  • Traces of scientific management in much
    management theory and discourse
  • Dominant metaphor mechanical, reductionist,
    linear
  • OK for target driven activities
  • But, something else needed for
  • What emerges between people
  • Non-linearity
  • Uncertainty unpredictability

3
Why Complexity Science?
  • 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.
  • 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

4
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
    phenomena and 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
  • Weicks concept of sensemaking can be
    summarized as an organisations 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).

5
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
6
Complex Adaptive Systems (CAS)?
  • Ever wondered how to describe

7
Complex Adaptive Systems
  • 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 2003a238).

8
Complex Adaptive Systems
9
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

10
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.

11
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
    produce orderly patterns of behaviour (or
    innovation)?
  • How do such living systems evolve to produce new
    orderly patterns of behaviour (or innovation)?

12
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

13
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 colonys 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 2001108).

14
Computer programmes to study CAS
  • Genetic algorithms
  • developed by John Holland of the Santa Fe
    Institute (Holland, 1992)
  • The Boids simulation
  • developed by Reynolds (1987) to simulate the
    flocking behaviour of birds
  • The Vants simulation
  • developed by Langton (1996) to simulate the
    trail-laying behaviour of ants
  • The Tierra simulation
  • developed by Ray (1992) using the analogy of
    biological evolution to evolve computer
    programmes.

15
Conversation in complexity science method
Analogies from the complexity sciences provide
insight into stabilising features of
communicative interaction.
Narrative and propositional themes that Stacey
describes as organising themselves into
conversation can take various forms (Stacey
2003a362) fantasies myths rituals ideology
culture gossip rumour discourses and speech
genres dialogues discussions debates and,
presentations.
These are responsible for organising the
experience of relating in different ways, by
e.g. selecting what is to be attended to
shaping how what is attended to is to be
described selecting who might describe it
accounting by one to another for their actions
articulating purpose in the form of themes
expressing intentions (Stacey 2003a 363)
Importance of acknowledging feelings,
reflection-in-action, and abstract thinking
(Stacey, 2001)
16
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17
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

18
Emergence
  • You cant 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.

19
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20
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.

21
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22
Diversity
  • If differences are 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.

23
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24
History Time
  • History and time irreversible you cant 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!

25
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26
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 and vice versa
  • Due to complicated interrelations, its 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

27
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28
Pattern Recognition
  • You cant always see direct and proportional
    links of cause and effect
  • People and groups dont 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.

29
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
  • Dont forget interconnectivity and the importance
    of networks!
  • Networks are the assumed context of CAS
  • (also see references in the bibliography for how
    CAS theory is applied to different contexts)

30
Linking theory and method
  • Systems practice as a way of managing in
    situations of complexity
  • Systems thinking shows there is no right answer
    when dealing with complexity
  • We avoid terms like manage and managed with
    deterministic overtones in favour of managing
    which is an active process associated with daily
    living
  • Need to see the parts in the context of the whole
  • Engaging with complexity entails
  • Engaging in situations of complexity
  • Using systems or complexity thinking to learn
  • Learning our way towards purposeful action that
    is situation improving

31
Conversation in complexity science method
  • Analogies from the complexity sciences provide
    insight into stabilising features of
    communicative interaction.
  • Narrative and propositional themes that Stacey
    describes as organising themselves into
    conversation can take various forms (Stacey
    2003a362)
  • fantasies myths rituals ideology culture
    gossip rumour discourses and speech genres
    dialogues discussions debates and,
    presentations.
  • These are responsible for organising the
    experience of relating in different ways, by
    e.g.
  • selecting what is to be attended to shaping how
    what is attended to is to be described selecting
    who might describe it accounting by one to
    another for their actions articulating purpose
    in the form of themes expressing intentions and,
    justifying actions in the form of themes that
    express ideology (Stacey 2003a 363).
  • Importance of acknowledging feelings,
    reflection-in-action, and abstract thinking
    (Stacey, 2001)

32
What Enables Self-Organising Behaviour in
Businesses?
  • Self-organising behaviour will naturally occur
    without addressing what causes it
  • Behaviour is self-organising when people (agents)
    are free to network with others and pursue their
    objectives
  • Even if this means crossing organisational
    boundaries created by formal structures
  • Self-organisation as the natural default
    behaviour
  • Organisation studies recognise barriers to such
    freedom in bureaucratic structure
  • Understand self-organising behaviour in
    adaptation to change by applying concepts of
    organisation theory and organisation behaviour

Coleman, H. J. (1999)
33
What Enables Self-Organising Behaviour in
Businesses?
  • Diversity seen as important in context of
    interconnected people translating ideas into
    innovation
  • Agents co-evolve with the environment of fitness
    landscapes through a process of self-organisation
    intended for both survival and growth from
    innovation
  • Impetus for creativity comes from shadow system
    of learning communities with enough diversity to
    provoke learning but not enough to overwhelm
    legitimate system and cause anarchy
  • Degree of connectivity between agents in a
    system necessary variety in behaviour depends on
    strength and number of ties
  • Few and strong ties producing stable behaviour
    too little for effective learning
  • Many and weak ties producing unstable behaviour
    too much variety for effective learning

Coleman, H. J. (1999)
34
What Enables Self-Organising Behaviour in
Businesses?
  • To operate at the edge of chaos, agents and
    systems balance canalisation and redundancy
  • Need for creative tension and experimentation
  • Space for creativity in an organisation
  • Tension between over-control (in legitimate
    system) and chaos (in shadow system)
  • Confident employees risk-takers and
    experimenters
  • Some organisational stability required and some
    order necessary for employees to recognise
    novelty
  • Organisations learn when there is new information
    combined with knowledge and applied to new
    opportunities provided by changes in the external
    environment
  • People in learning communities seize such
    opportunities to be innovative
  • If structure is flexible enough the firm can
    adapt and form new project teams or even new
    business units, or found new companies

Coleman, H. J. (1999), Eden and Ackermann, (1998)
35
What Enables Self-Organising Behaviour in
Businesses?
  • Organisational open systems assumed
  • Open to flows of data and information
    facilitating learning and construction of new
    knowledge
  • Goal is to encourage experimentation (planned or
    naturally occurring)
  • Some failure needs to be tolerated (e.g. Post-It
    notes developed from the failure of a search for
    an adhesive substance)
  • Judicious ignoring of local constraints helps
    avoid being trapped on poor local optima
  • Entrepreneurial behaviour is spontaneous in
    response to perceived opportunities to create an
    organisation

Coleman, H. J. (1999)
36
What Enables Self-Organising Behaviour in
Businesses?
  • Organisational theory and organisational
    behaviour
  • Need for innovation leads to particular emphasis
    on knowledge management
  • Adaptation in turbulent environments necessary
  • Small teams (or cells) pursue entrepreneurial
    opportunities and knowledge sharing among
    themselves (leads to a potent organisation)
  • Operating logic based on flexibility with
    knowledge sharing in place of hierarchical
    controls
  • Stability created for confident risk-taking and
    experimentation
  • New knowledge constructed in communities of
    practice (COPs)

37
What Enables Self-Organising Behaviour in
Businesses?
  • Organisation Design
  • Organisation design/structure can facilitate
    change by being flexible
  • Design org for purpose of evolution with the
    changing environment
  • Design for emergence by avoiding rigidities of
    bureaucratic hierarchy
  • Create org environments not inhibiting
    evolutionary change and accept discontinuous
    change
  • Leadership may be anywhere, and everyone is a
    champion of change
  • No need to bust bureaucracy because there is none
  • When an organisation is operating on the edge of
    chaos, not even its leaders can know its future
    direction
  • Becomes relevant to operate in a mode of inquiry,
    surfacing and questioning assumptions

Coleman, H. J. (1999)
38
What Enables Self-Organising Behaviour in
Businesses?
  • Loose-tight controls
  • Freedom of activity
  • Relative autonomy within boundaries
  • Management confidence and trust in employees to
    act according to shared values
  • Tension between empowerment and control reached
    through accountability
  • Satisfying human needs for interaction to obtain
    other needs
  • Computers and telecommunications increase
    interconnectedness of people and speed of sharing
    knowledge and information
  • Empowerment
  • Staff taking initiative - Intrinsic motivation in
    staff to contribute
  • Enabling feelings of meaning in work, autonomy,
    choice, and having an impact on outcomes
  • Releasing self-motivation of employees to take
    responsibility by trusting them to think,
    experiment and improve

Coleman, H. J. (1999)
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