An Introduction to Complex Adaptive System Theory & Key Concepts of Complexity Science Dr Carol Webb Manufacturing Dept, Bldg 50, RmF9b, School of Applied Sciences
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
Why Complexity Science? “Complexity refers to the condition of the universe which is integrated and yet toorich 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]
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] “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).]
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
Complex Adaptive Systems (CAS)? • Ever wondered how to describe…
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 2003a:238).
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
Think of a flock of birds as a complex adaptive system • Complexity science seeks to: • identify common features of the dynamicsof 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.
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 dosuch living systemsevolve to produce neworderly patterns of behaviour (or innovation)?
CAS – Methodological considerations • No search for an overall blueprint for the whole system; • modelagent 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”
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 activityof individuals following two very basic rules: lay pheromone and follow the trails of others” (Bonabeau and Meyer 2001:108).
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.
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 2003a:362): • 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)
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
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.
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.
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.
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!
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, 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
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.
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 • (also see references in the bibliography for how CAS theory is applied to different contexts)
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
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 2003a:362): • 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)
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)
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)
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)
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)
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)
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)
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)