IBEM lecture 1
Complex adaptive system
CAS is defined as a group of semi-autonomous agents who interact in interdependent ways
to produce system-wide patterns such that those patterns then influence the behaviours of
the agents.
It informs our understandings of how some of the patterns emerge as dominant over others
and how other patterns may be diminished and/or eradicated (uitgeroeid).
What constitute a Complex Adaptive System?
A system in which many independent elements or agents interact, leading to emergent
outcomes that are often difficult (or impossible) to predict simply by looking at the individual
interactions
Complex = difficult to understand
Dynamic = moving, changing
Adaptive = changing to adapt to an environment or condition
Types of systems
Definition: a system is a set of connected things.
- Simple
- Complicated
- Non-linear – ‘dynamic’
- CAS – ‘dynamic’
Reductionism: you can understand a system completely if you know the properties of all its
‘’things’’. CAS are partly unpredictable, show emergence, are partly irrational, even if you
know all things..
Simple
- Well ordered, predictable cause-effect
- ‘things’ are simple and few
- Easy to repair
- Relations are simple and stable
- Input-output relations are simple
- Structure and functions are clear
Complicated
- Aeroplane is an example
- Engineered
- Relations are manifold and diverse
- Difficult to design and repair
- Structures and functions are partly hidden
Non-linear
- Continuously changing
- Atmosphere is an example
- Unpredictable
, - Many things but no thinking or adaption
- Input/output relation unclear
- Butterfly effect: a small change may cause a large effect
- Difficult to control and change
- ‘’non-linear’’: no clear/stable cause effect relations
Characteristics of CAS
- Leaderless – without a leader
- Emergent patterns – patterns that form even though the agents were not ‘directed’
to make a pattern
- Self-organising – a system in which a pattern emerges because of the agents
following simple rules without external control or a leader is called a ‘’self-
organizing’’ system
- Feedback loop – a closed system that contains a circular process in which the
system’s output is returned or ‘’fed back’’ to the system as input
- Adaptive – reacts to changes
- Chaotic behaviour of a system – small changes in initial conditions can generate large
changes in the system’s outcome
- Stochastic (willekeurige kansverdeling) – governed by change. The behaviour of a
complex adaptive system can be inherently stochastic as elements of the system, the
agents, can have randomness in their movement, and thus, in their interactions
CAS:
- Contain many diverse and specialized agents, components, or parts (>3) in an
intricate arrangement, which are the building blocks
- CAS are adaptive as they have the capacity to change under influence of feedback or
memory and thus evolve, giving it resilience in the face of perturbation
- Are usually open systems: a system which continuously interacts with its
environment, and this permits feedback property
- Show emerges: the whole is more than the sum of the components and the very
specific connectivity creates property
- Operates far from equilibrium: there must be a constant input of energy to maintain
the organization of the system, and this is essential for emergence.
How does a CAS react to change?
Sometimes a small change may have a large effect, or the system is resistant against a
disturbance.
Evolution, specialization of the actors.
Examples CAS
- Ecosystem
- Health care system
- City
- Organizations (like hospital)
- Market
- Artificial systems (game of life)
- Gut
- A master courses
, Visible general CAS properties
- Diversity/specialization of actors
- Actors change behaviour: genes and learning
- Flows (food chains)
- Groups of actors, they cooperate
- Building blocks: ‘things’ that are successful can be copied, combined, and re-used: a
business model, antibiotic, DNA sequences…
- CAS has boundaries, but they are permeable
- Adaption and behaviour change (learning)
- Tags: a visible code to easily identify an actor
- Adaption: struggle and survival
- Reward mechanism: they determine how the actor behaves
- Strategies: actors think how they can do better
- Adaptation + rewards + strategies =
o selection failure of the weak, survival of the fittest
o inequality a CAS is unfair (some are rich, and some are poor)
o continuous change (a CAS is unpredictable)
Invisible general CAS properties
- A CAS can have several equilibrium points
- It can switch between these forms by passing
through a ‘’transition point’’
- ‘’perturbations’’ may cause a jump to a new
equilibrium point
- Cause effect relations are non-linear. You cannot
calculate the effect of a change, even if you know
everything about the individual actors
- Resistant to change (resilient)
- Are usually in a stable form; small changes do not
disturb the system; the system adapts and stays
close to the equilibrium with minor variations
- At a certain level of perturbation (verstoring)
(‘’critical point’’), the system van jump to another
stable situation
Non-linearity
Because of the complexity of a CAS and the emergent nature of phenomena
There is no simple relationship between a change and the reaction of the system (no simple
cause-effect relation)
A small change can cause:
- No effect (stability)
- Unexpected effect (emergence)
- Large effect: across a transition point
A large change can cause:
- No effect (resilience: stability, adaption)
- A minor local effect
- Unexpected effect (emergence)
Complex adaptive system
CAS is defined as a group of semi-autonomous agents who interact in interdependent ways
to produce system-wide patterns such that those patterns then influence the behaviours of
the agents.
It informs our understandings of how some of the patterns emerge as dominant over others
and how other patterns may be diminished and/or eradicated (uitgeroeid).
What constitute a Complex Adaptive System?
A system in which many independent elements or agents interact, leading to emergent
outcomes that are often difficult (or impossible) to predict simply by looking at the individual
interactions
Complex = difficult to understand
Dynamic = moving, changing
Adaptive = changing to adapt to an environment or condition
Types of systems
Definition: a system is a set of connected things.
- Simple
- Complicated
- Non-linear – ‘dynamic’
- CAS – ‘dynamic’
Reductionism: you can understand a system completely if you know the properties of all its
‘’things’’. CAS are partly unpredictable, show emergence, are partly irrational, even if you
know all things..
Simple
- Well ordered, predictable cause-effect
- ‘things’ are simple and few
- Easy to repair
- Relations are simple and stable
- Input-output relations are simple
- Structure and functions are clear
Complicated
- Aeroplane is an example
- Engineered
- Relations are manifold and diverse
- Difficult to design and repair
- Structures and functions are partly hidden
Non-linear
- Continuously changing
- Atmosphere is an example
- Unpredictable
, - Many things but no thinking or adaption
- Input/output relation unclear
- Butterfly effect: a small change may cause a large effect
- Difficult to control and change
- ‘’non-linear’’: no clear/stable cause effect relations
Characteristics of CAS
- Leaderless – without a leader
- Emergent patterns – patterns that form even though the agents were not ‘directed’
to make a pattern
- Self-organising – a system in which a pattern emerges because of the agents
following simple rules without external control or a leader is called a ‘’self-
organizing’’ system
- Feedback loop – a closed system that contains a circular process in which the
system’s output is returned or ‘’fed back’’ to the system as input
- Adaptive – reacts to changes
- Chaotic behaviour of a system – small changes in initial conditions can generate large
changes in the system’s outcome
- Stochastic (willekeurige kansverdeling) – governed by change. The behaviour of a
complex adaptive system can be inherently stochastic as elements of the system, the
agents, can have randomness in their movement, and thus, in their interactions
CAS:
- Contain many diverse and specialized agents, components, or parts (>3) in an
intricate arrangement, which are the building blocks
- CAS are adaptive as they have the capacity to change under influence of feedback or
memory and thus evolve, giving it resilience in the face of perturbation
- Are usually open systems: a system which continuously interacts with its
environment, and this permits feedback property
- Show emerges: the whole is more than the sum of the components and the very
specific connectivity creates property
- Operates far from equilibrium: there must be a constant input of energy to maintain
the organization of the system, and this is essential for emergence.
How does a CAS react to change?
Sometimes a small change may have a large effect, or the system is resistant against a
disturbance.
Evolution, specialization of the actors.
Examples CAS
- Ecosystem
- Health care system
- City
- Organizations (like hospital)
- Market
- Artificial systems (game of life)
- Gut
- A master courses
, Visible general CAS properties
- Diversity/specialization of actors
- Actors change behaviour: genes and learning
- Flows (food chains)
- Groups of actors, they cooperate
- Building blocks: ‘things’ that are successful can be copied, combined, and re-used: a
business model, antibiotic, DNA sequences…
- CAS has boundaries, but they are permeable
- Adaption and behaviour change (learning)
- Tags: a visible code to easily identify an actor
- Adaption: struggle and survival
- Reward mechanism: they determine how the actor behaves
- Strategies: actors think how they can do better
- Adaptation + rewards + strategies =
o selection failure of the weak, survival of the fittest
o inequality a CAS is unfair (some are rich, and some are poor)
o continuous change (a CAS is unpredictable)
Invisible general CAS properties
- A CAS can have several equilibrium points
- It can switch between these forms by passing
through a ‘’transition point’’
- ‘’perturbations’’ may cause a jump to a new
equilibrium point
- Cause effect relations are non-linear. You cannot
calculate the effect of a change, even if you know
everything about the individual actors
- Resistant to change (resilient)
- Are usually in a stable form; small changes do not
disturb the system; the system adapts and stays
close to the equilibrium with minor variations
- At a certain level of perturbation (verstoring)
(‘’critical point’’), the system van jump to another
stable situation
Non-linearity
Because of the complexity of a CAS and the emergent nature of phenomena
There is no simple relationship between a change and the reaction of the system (no simple
cause-effect relation)
A small change can cause:
- No effect (stability)
- Unexpected effect (emergence)
- Large effect: across a transition point
A large change can cause:
- No effect (resilience: stability, adaption)
- A minor local effect
- Unexpected effect (emergence)
