Lecture notes IBEM
Lecture 1 (It’s a jungle out there)
Definition: “a system is a set of connected things.”
Types of systems:
- Simple
- Complicated
- Non-linear (chaotic)
- CAS
Non-linear and CAS systems are DYNAMIC
Reductionism: you can understand a system completely if you know the properties of all its
“things” CAS systems are partly unpredictable, show emergence, are partly irrational, even if
you know all things…
Simple systems: (ex. bike)
- Well-ordered, predictable cause-effect
- “Things” are simple and few
- Relations are simple and stable
- Easily repairable
- Input-output relations are simple
- Structure and functions are clear
- “Things” have no behavior: they don’t change their “mind
Complicated systems: (ex. plane)
- “Things” are many and can be complex
- Relations are manyfold and diverse
- Difficult to design and repair (experts)
- Structures and functions are partly hidden; they have their own logic – but it’s logic
- Engineered
- “Things” don’t change their “mind”
Non-linear systems: (ex. atmosphere)
- Continuously changing
- Unpredictable
- Many “things”, but no “thinking” or “adaptation”
- Input-output relations unclear
- Butterfly effect: a small change may cause a large effect
- Difficult to control and change
“non-linear” explained later
CAS: (COMPLEX ADAPTIVE SYSTEMS)
- Many “things” (“actors”, “agents”); connected into a network
- Adaptive (at the “thing level” and at the system level)
, - Details unpredictable, but general laws exist
- Constant change: no fixed equilibrium… (instead it has multiple equilibria and
changing patterns)
- A CAS is non-linear too
- Butterfly effect (a small change may have a large effect)
- Resilience (system is resistant against disturbances)
- Evolution, specialization
- No a single “big boss”, but “distributed control”
- System has boundaries, but they are permeable
Examples of CAS: Ecosystem, Health care system, City, Organizations, Market, Artificial
systems, You, IBEM course, VU
Visible general CAS properties
- Diversity / specialization of actors
- Actors change behavior (genes and learning)
- Flows (food chains, information, water, cars…)
- Groups of animals etc.: aggregation and cooperation
- Building blocks: “things” that are successful can be copied, combined and re-used: a
business model, antibiotic, course slides, DNA sequences, vaccins…
- A CAS has boundaries, but they are permeable
- Adaptation and behavioral change (learning)
- Tags: a visible code to easily identify an actor (useful for other actors)
- Struggle and survival / competition between actors
- Reward mechanisms: determine outcomes/ behavior
- Adaptation + Rewards result in:
o Selection: failure of the weak - and success for the fittest
o Inequality: a CAS is unfair (some are rich, most are poor)
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 / 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 is 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
- 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?
- Key question: forecast critical points and new equilibrium state
,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
- A small change may cause:
a. No effect (stability)
b. Unexpected effect (emergence)
c. Large effect: across a transition point
- A large change may cause:
a. No effect (resilience: stability, adaptation)
b. Unexpected effect (emergence)
c. Large effect: across a transition point
- No simple cause-effect relation
Why would we study CAS?
• To analyze 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 system
• To improve it (smart interventions - WHO)
Lecture 2 (Hidden order)
Internal models:
- To be “adaptive”, you need something that remembers what you did and how it
worked (John Holland)
- You = [generalized] an actor in the CAS
An internal model is the carrier of adaptivity
- Forms of internal models
- Why internal models are useful
- Relation between survival and internal models
An internal model (also 'scheme') is an actor's model of its environment in a form that
describes how to behave
- An internal model can change:
o By coincidence (mutation)
o By design (“programming”)
o Purposeful (learning from experiences)
- A good internal model helps its owner to survive, because the actor reacts ‘better’
next time
- Internal models vary from very simple to very complicated
Internal models – how to store:
- Brain (behavior)
- DNA (coding for proteins)
- Text (recipe, business plan, Bible, surgery protocol)
- Software / algorithm (artificial intelligence)
Models can be copied and modified
- Brains (neurons):
, • Explicit knowledge / experience
• Cultural norms (explicit and implicit)
• Instinct (dogs, spiders, …)
Experience and learning = modifying the model. These models too can be copied and
modified (learning, copying, mutation, CRISPR, …)
How to act (medical protocol)
IF (Symptom1) THEN (Treatment1)
IF (S1 and S2) THEN (T2)
IF (S1 and S2 and age < 5) THEN (T1 + T3)
IF (S2 and low Hb) THEN (T2 + T4)
- Internal models have general and more specific rules
- Nested model (general rules - specific rules - exceptions)
- A “rich” internal model allows more precise decisions and more diverse reactions –
but you need more “memory space” (a large brain, or big computer memory, or a
big book)
Learning =
• Changing an existing rule
• Adding and modifying exception rules
• Add exceptions… and exceptions to exceptions (etc.)
• (Copy / transfer this to others actors)
Internal learning:
- Internal model is the basis for learning
- It makes an actor better equipped for survival
- Actors with a failing model have a competitive disadvantage
- Failing: too simple and/or wrong rules
- A better model will survive and spread (through its “owner”)
- A rich model helps its owner to do better
- In a CAS, we always see working internal models, not perfect internal models
- We see a snapshot of internal models in various stages
Social rules:
- Unwritten rules: how to behave and what is normal?
a. Talk, dressing, how we talk
b. “Loosing your face”
c. Interpersonal distance
- Distances vary by culture (Japan-Israel)
- Social rules in a university course?
Lecture 1 (It’s a jungle out there)
Definition: “a system is a set of connected things.”
Types of systems:
- Simple
- Complicated
- Non-linear (chaotic)
- CAS
Non-linear and CAS systems are DYNAMIC
Reductionism: you can understand a system completely if you know the properties of all its
“things” CAS systems are partly unpredictable, show emergence, are partly irrational, even if
you know all things…
Simple systems: (ex. bike)
- Well-ordered, predictable cause-effect
- “Things” are simple and few
- Relations are simple and stable
- Easily repairable
- Input-output relations are simple
- Structure and functions are clear
- “Things” have no behavior: they don’t change their “mind
Complicated systems: (ex. plane)
- “Things” are many and can be complex
- Relations are manyfold and diverse
- Difficult to design and repair (experts)
- Structures and functions are partly hidden; they have their own logic – but it’s logic
- Engineered
- “Things” don’t change their “mind”
Non-linear systems: (ex. atmosphere)
- Continuously changing
- Unpredictable
- Many “things”, but no “thinking” or “adaptation”
- Input-output relations unclear
- Butterfly effect: a small change may cause a large effect
- Difficult to control and change
“non-linear” explained later
CAS: (COMPLEX ADAPTIVE SYSTEMS)
- Many “things” (“actors”, “agents”); connected into a network
- Adaptive (at the “thing level” and at the system level)
, - Details unpredictable, but general laws exist
- Constant change: no fixed equilibrium… (instead it has multiple equilibria and
changing patterns)
- A CAS is non-linear too
- Butterfly effect (a small change may have a large effect)
- Resilience (system is resistant against disturbances)
- Evolution, specialization
- No a single “big boss”, but “distributed control”
- System has boundaries, but they are permeable
Examples of CAS: Ecosystem, Health care system, City, Organizations, Market, Artificial
systems, You, IBEM course, VU
Visible general CAS properties
- Diversity / specialization of actors
- Actors change behavior (genes and learning)
- Flows (food chains, information, water, cars…)
- Groups of animals etc.: aggregation and cooperation
- Building blocks: “things” that are successful can be copied, combined and re-used: a
business model, antibiotic, course slides, DNA sequences, vaccins…
- A CAS has boundaries, but they are permeable
- Adaptation and behavioral change (learning)
- Tags: a visible code to easily identify an actor (useful for other actors)
- Struggle and survival / competition between actors
- Reward mechanisms: determine outcomes/ behavior
- Adaptation + Rewards result in:
o Selection: failure of the weak - and success for the fittest
o Inequality: a CAS is unfair (some are rich, most are poor)
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 / 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 is 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
- 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?
- Key question: forecast critical points and new equilibrium state
,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
- A small change may cause:
a. No effect (stability)
b. Unexpected effect (emergence)
c. Large effect: across a transition point
- A large change may cause:
a. No effect (resilience: stability, adaptation)
b. Unexpected effect (emergence)
c. Large effect: across a transition point
- No simple cause-effect relation
Why would we study CAS?
• To analyze 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 system
• To improve it (smart interventions - WHO)
Lecture 2 (Hidden order)
Internal models:
- To be “adaptive”, you need something that remembers what you did and how it
worked (John Holland)
- You = [generalized] an actor in the CAS
An internal model is the carrier of adaptivity
- Forms of internal models
- Why internal models are useful
- Relation between survival and internal models
An internal model (also 'scheme') is an actor's model of its environment in a form that
describes how to behave
- An internal model can change:
o By coincidence (mutation)
o By design (“programming”)
o Purposeful (learning from experiences)
- A good internal model helps its owner to survive, because the actor reacts ‘better’
next time
- Internal models vary from very simple to very complicated
Internal models – how to store:
- Brain (behavior)
- DNA (coding for proteins)
- Text (recipe, business plan, Bible, surgery protocol)
- Software / algorithm (artificial intelligence)
Models can be copied and modified
- Brains (neurons):
, • Explicit knowledge / experience
• Cultural norms (explicit and implicit)
• Instinct (dogs, spiders, …)
Experience and learning = modifying the model. These models too can be copied and
modified (learning, copying, mutation, CRISPR, …)
How to act (medical protocol)
IF (Symptom1) THEN (Treatment1)
IF (S1 and S2) THEN (T2)
IF (S1 and S2 and age < 5) THEN (T1 + T3)
IF (S2 and low Hb) THEN (T2 + T4)
- Internal models have general and more specific rules
- Nested model (general rules - specific rules - exceptions)
- A “rich” internal model allows more precise decisions and more diverse reactions –
but you need more “memory space” (a large brain, or big computer memory, or a
big book)
Learning =
• Changing an existing rule
• Adding and modifying exception rules
• Add exceptions… and exceptions to exceptions (etc.)
• (Copy / transfer this to others actors)
Internal learning:
- Internal model is the basis for learning
- It makes an actor better equipped for survival
- Actors with a failing model have a competitive disadvantage
- Failing: too simple and/or wrong rules
- A better model will survive and spread (through its “owner”)
- A rich model helps its owner to do better
- In a CAS, we always see working internal models, not perfect internal models
- We see a snapshot of internal models in various stages
Social rules:
- Unwritten rules: how to behave and what is normal?
a. Talk, dressing, how we talk
b. “Loosing your face”
c. Interpersonal distance
- Distances vary by culture (Japan-Israel)
- Social rules in a university course?
