HC’s – IBEM
HC’s – Innovation, Behaviour, Economy and Markets
HC 1 - Introduction
CAS is a system. It can be seen as something that is not always clear, there are often multiple actors.
A system is a set of connected things. If things are not connected, there is no system. If there are no
things, but just a connection, it is also not a system. There are 4 types of systems: simple,
complicated, non-linear (chaotic), and CAS. Both CAS and non-linear are dynamic. CAS theory should
be: quantitative (allow predictions, use metrics), generally applicable for many system types, allow
experimentation (simulations), and mathematically robust.
Even if you know exactly how the system is build up, you still wouldn’t know how to develop… CAS.
Simple systems are:
- E.g. bike - Input-output relations are simple
- Well-ordered, predictable cause-effect - Structure and functions are clear
- ‘Things’ are simple and few - ‘Things’ have no behaviour; they don’t
- Relations are simple and stable change their ‘mind’
- Easy to repair
Complicated systems are:
- E.g. airplane, mobile phone - Structures and functions are partly
- ‘Things’ are many and can be complex hidden; they have their own logic
- Relations are manyfold and diverse - Engineered
- Difficult to design and repair (you need - ‘things’ don’t change their ‘mind’
experts)
Non-linear systems are:
- E.g. atmosphere - Input-output relations unclear
- Continuously changing - Butterfly effect; a small change may cause
- Too complex to understand what is a large effect
happening - Difficult to control and change
- Unpredictable - (non-linear = no clear/stable cause-effect
- Many ‘things’, but no ‘thinking’ or relations)
adaptation
CAS systems (Complex Adaptive Systems) are:
- Many ‘things’; connected into a network
- Adaptive (at the ‘thing level’ and at the system level)
- Details are unpredictable, but general law exists
- There is a constant change, there is no fixed equilibrium. Instead it has multiple equilibria and
changing patterns
- Sometimes a small change may have a large effect (‘tipping point’). But it can also occur that
there is a change, but the system doesn’t react to that (‘resilient’)
- In CAS we see evolution, and specialization of the actors
- There is no single boss in CAS, no one has absolute power
- A CAS has no closed boundaries, they are connected to other systems; CAS is permeable
Pagina 1 van 28
,HC’s – IBEM
Examples of CAS are: ecosystem, health care system, city, organizations (e.g. hospital), market
(‘business ecosystem’), artificial systems (game of life), a masters course.
In CAS, ‘control’ and ‘management’ are distributed: it is the result of the interactions between actors,
governed by their internal models. So there is no boss, there is distributed control.
What are visible CAS properties?
- Diversity / specialization of actors
- Actors change their behaviour
- Flows in the system (e.g. food chains, information, water)
- Aggregation and cooperation
- Building blocks; ‘things’ that are successful can be copied, combined, and re-used (e.g. DNA
sequences, vaccines, a business model)
- Permeable boundaries
- Adaptation and behavioural change (learning)
- Tags: visible code to identify
- Competition between actors
- Reward mechanisms
- Strategies
- Adaptation, rewards and strategies result in:
o Selection (failure of the weak, success for the fittest)
o Inequality (CAS is unfair; e.g. difference in poor (most) and rich (few))
o Continuous change (a CAS is unpredictable)
There also exist invisible general CAS properties:
- A CAS can have several equilibrium points
- It can switch between these forms by passing through a ‘transition point’
- Perturbations (big or small, or critical events) may cause a jump to a new equilibrium point
(revolution, new organizational structure)
- In a CAS, cause-effect relations are non-linear; you cannot calculate the effect of a change, even
if you know everything about the individual actors
- CAS are resistant to change (resilient)
- CAS 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 (critical point), the system can jump to another stable situation
- CAS theory tries to understand;
o What makes a system stable?
o How to predict what is the critical point?
o How to change a system in the ‘right’ way?
There is no easy relation between the changes and how they affect the system: non-linearity. So a
small change might cause:
- No effect (stability)
- Unexpected effect (emergence)
- Large effect; across a transition point
And a large change may cause:
- No effect (resilience; stability, adaptation)
- A minor local effect
Pagina 2 van 28
, HC’s – IBEM
- Unexpected effect (emergence)
- Large effect; across a transition point → new
equilibrium
Because of the non-linearity of the CAS system, there is
no simple cause-effect relation.
Why would you study or use the CAS theory?
- To analyse how the system works as it does
- To understand what happens
- To find similarities and general laws
- To predict and forecast what will happen in a
system
- To improve it (small interventions; WHO)
- To be better prepared for the future
To study CAS, you need to find the hidden structure in networks.
Internal models
Internal models (also called Schemata) gives you a guideline on how to behave in a system. E.g. a
book, a computer programme. You can store interna models in; your brain (behaviour), DNA (coding
for protein structure), text (recipe, bible), software/algorithm (artificial intelligence). Internal models
cannot be stable. Internal models can change; by coincidence (mutation), by design (programming),
or by learning from experiences. If you have a good internal model, you are better able to react with
the circumstances (‘better change on survival’).
The internal models in your brain (neurons) are formed by; explicit knowledge/experience, cultural
norms, and instinct.
A ‘rich’ internal model allows more precise decisions and more diverse reactions, but you need more
‘memory space’.
Learning = changing and existing rule, adding and modifying exception rules, add exceptions,
transform all this to/from other actors.
In a CAS, we always see working internal models, not perfect internal models! We see a snapshot of
internal models in various stages at this moment.
According to John Holland, there are 2 adaptivity levels:
- First tier adaptivity: learning at actor level
- Second tier adaptivity: system level learning (the total of all internal models and their
interactions)
So, departments/organizations/markets have their own (collective) internal models. Even small local
changes in the actor’s internal model may create large changes at a higher level.
Pagina 3 van 28
HC’s – Innovation, Behaviour, Economy and Markets
HC 1 - Introduction
CAS is a system. It can be seen as something that is not always clear, there are often multiple actors.
A system is a set of connected things. If things are not connected, there is no system. If there are no
things, but just a connection, it is also not a system. There are 4 types of systems: simple,
complicated, non-linear (chaotic), and CAS. Both CAS and non-linear are dynamic. CAS theory should
be: quantitative (allow predictions, use metrics), generally applicable for many system types, allow
experimentation (simulations), and mathematically robust.
Even if you know exactly how the system is build up, you still wouldn’t know how to develop… CAS.
Simple systems are:
- E.g. bike - Input-output relations are simple
- Well-ordered, predictable cause-effect - Structure and functions are clear
- ‘Things’ are simple and few - ‘Things’ have no behaviour; they don’t
- Relations are simple and stable change their ‘mind’
- Easy to repair
Complicated systems are:
- E.g. airplane, mobile phone - Structures and functions are partly
- ‘Things’ are many and can be complex hidden; they have their own logic
- Relations are manyfold and diverse - Engineered
- Difficult to design and repair (you need - ‘things’ don’t change their ‘mind’
experts)
Non-linear systems are:
- E.g. atmosphere - Input-output relations unclear
- Continuously changing - Butterfly effect; a small change may cause
- Too complex to understand what is a large effect
happening - Difficult to control and change
- Unpredictable - (non-linear = no clear/stable cause-effect
- Many ‘things’, but no ‘thinking’ or relations)
adaptation
CAS systems (Complex Adaptive Systems) are:
- Many ‘things’; connected into a network
- Adaptive (at the ‘thing level’ and at the system level)
- Details are unpredictable, but general law exists
- There is a constant change, there is no fixed equilibrium. Instead it has multiple equilibria and
changing patterns
- Sometimes a small change may have a large effect (‘tipping point’). But it can also occur that
there is a change, but the system doesn’t react to that (‘resilient’)
- In CAS we see evolution, and specialization of the actors
- There is no single boss in CAS, no one has absolute power
- A CAS has no closed boundaries, they are connected to other systems; CAS is permeable
Pagina 1 van 28
,HC’s – IBEM
Examples of CAS are: ecosystem, health care system, city, organizations (e.g. hospital), market
(‘business ecosystem’), artificial systems (game of life), a masters course.
In CAS, ‘control’ and ‘management’ are distributed: it is the result of the interactions between actors,
governed by their internal models. So there is no boss, there is distributed control.
What are visible CAS properties?
- Diversity / specialization of actors
- Actors change their behaviour
- Flows in the system (e.g. food chains, information, water)
- Aggregation and cooperation
- Building blocks; ‘things’ that are successful can be copied, combined, and re-used (e.g. DNA
sequences, vaccines, a business model)
- Permeable boundaries
- Adaptation and behavioural change (learning)
- Tags: visible code to identify
- Competition between actors
- Reward mechanisms
- Strategies
- Adaptation, rewards and strategies result in:
o Selection (failure of the weak, success for the fittest)
o Inequality (CAS is unfair; e.g. difference in poor (most) and rich (few))
o Continuous change (a CAS is unpredictable)
There also exist invisible general CAS properties:
- A CAS can have several equilibrium points
- It can switch between these forms by passing through a ‘transition point’
- Perturbations (big or small, or critical events) may cause a jump to a new equilibrium point
(revolution, new organizational structure)
- In a CAS, cause-effect relations are non-linear; you cannot calculate the effect of a change, even
if you know everything about the individual actors
- CAS are resistant to change (resilient)
- CAS 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 (critical point), the system can jump to another stable situation
- CAS theory tries to understand;
o What makes a system stable?
o How to predict what is the critical point?
o How to change a system in the ‘right’ way?
There is no easy relation between the changes and how they affect the system: non-linearity. So a
small change might cause:
- No effect (stability)
- Unexpected effect (emergence)
- Large effect; across a transition point
And a large change may cause:
- No effect (resilience; stability, adaptation)
- A minor local effect
Pagina 2 van 28
, HC’s – IBEM
- Unexpected effect (emergence)
- Large effect; across a transition point → new
equilibrium
Because of the non-linearity of the CAS system, there is
no simple cause-effect relation.
Why would you study or use the CAS theory?
- To analyse how the system works as it does
- To understand what happens
- To find similarities and general laws
- To predict and forecast what will happen in a
system
- To improve it (small interventions; WHO)
- To be better prepared for the future
To study CAS, you need to find the hidden structure in networks.
Internal models
Internal models (also called Schemata) gives you a guideline on how to behave in a system. E.g. a
book, a computer programme. You can store interna models in; your brain (behaviour), DNA (coding
for protein structure), text (recipe, bible), software/algorithm (artificial intelligence). Internal models
cannot be stable. Internal models can change; by coincidence (mutation), by design (programming),
or by learning from experiences. If you have a good internal model, you are better able to react with
the circumstances (‘better change on survival’).
The internal models in your brain (neurons) are formed by; explicit knowledge/experience, cultural
norms, and instinct.
A ‘rich’ internal model allows more precise decisions and more diverse reactions, but you need more
‘memory space’.
Learning = changing and existing rule, adding and modifying exception rules, add exceptions,
transform all this to/from other actors.
In a CAS, we always see working internal models, not perfect internal models! We see a snapshot of
internal models in various stages at this moment.
According to John Holland, there are 2 adaptivity levels:
- First tier adaptivity: learning at actor level
- Second tier adaptivity: system level learning (the total of all internal models and their
interactions)
So, departments/organizations/markets have their own (collective) internal models. Even small local
changes in the actor’s internal model may create large changes at a higher level.
Pagina 3 van 28
