Lectures Innovation Behaviour
Emergence & Markets
VU Amsterdam
AM_1052
2025-2026
Table of Contents
Lecture complex adaptive systems (CAS) – Dirk Essink .................................................... 2
Complex adaptive system (CAS) ........................................................................................... 3
Lecture a CAS look at innovation - Anne van der Geest ..................................................... 6
Innovation models................................................................................................................... 6
Entrepreneurship .................................................................................................................... 9
Innovations in healthcare ..................................................................................................... 10
Lecture 3 Why markets are a CAS – Linda van de Burgwal ............................................. 13
Classical economic theory and the CAS view on economy/markets .................................. 13
Market imperfections in health care ..................................................................................... 14
Forms of "irrational" behaviour and how to make use of that ("nudging") ........................... 15
Influence of market abnormalities and irrational behaviours on the health care sector ...... 18
Lecture 4 Network approach, emergence and change – Eduardo Urias ......................... 20
Network theory and types ..................................................................................................... 20
Network analysis and metrics............................................................................................... 23
Micro-macro link ................................................................................................................... 24
Emergence............................................................................................................................ 25
Leverage points .................................................................................................................... 26
Case study: Ozempic ........................................................................................................... 26
Lecture 5 lessons for policy makers in CAS – Kristiaan Kok ........................................... 27
Two underlying models ......................................................................................................... 28
Part 2 Eduardo Urias ............................................................................................................ 30
,Lecture complex adaptive systems (CAS) – Dirk
Essink
Systems
Have a goal. This goal is outside the system and is not really part of the system. The outcome is
often separate from the system. Only the system itself creates meaning and value to the goal
Some elements that are successful, remain (just like with evolution). Examples of this are
- Hospital based systems → people often correlate healthcare immediately with hospitals
and doctors, while this wasn’t the normal way in the past
- Fossil fuel systems → our need for energy was met by fossil fuels. Even though we know
the bad side effects, it is now hard to break up that system
Objectives
- To understand the various types of systems, among them CAS
- To identify the general properties of CAS
- To explain the underlying mechanisms and levels of adaptivity in CAS
Reductionism = a philosophical position which holds that a ‘complex system’ is
nothing but the sum of its parts. Solving a systemic problem is breaking it up to
the part that is broken. Innovation is a ‘rational’ add-on to this system.
- Everything functions as a machine and we can break it down into parts
and fix them.
Simple systems
(a combination of parts to make a whole)
- Well-ordered, predictable cause-effect
- Relations are simple and stable
- Input-output relations are simple
- ‘things’ are simple and few
- Easy to repair
- Structure and functions are clear
Complicated systems
- Things are many and can be complex
- Relations are manyfold and diverse
- Difficult to design and repair (experts)
- Structures and functions are partly hidden For example, the climate is
- Engineered unpredictable, the weather itself
has no agency and no thinking.
Non-linear systems
However, this is non-linear but not
- Continuously changing & unpredictable
a complex adaptive system.
- Input-output relations unclear ‘non-linear’: no clear/stable
cause → effect relations
- Butterfly effect: a small change may cause a large effect
- Thus, difficult to control
- But... many ‘things’, but no ‘thinking’, no ideas, no agency
2
,Critical theory, systems thinking, CAS
understanding the (non-)linear interactions of actors
enabled and constrained by system hardware and
software
- In health systems there are system hardware’s,
the human resources, medicines, IDs, software
= culture
- We use the ‘culture’ to actually act in the
system, to have agency.
- What happened is that actors see policy
changes happen and therefore they will
organize themselves around these changes in
(non-)linear ways.
Agents produce actions. Interacting with each other and
through that they create patterns. Because those patterns
are there, they in turn influence how the system works
again. The system then becomes more stable. Actors
reproduce the system to gain structure
Complex adaptive system (CAS)
Complex Adaptive System (CAS) = a system in which many semi-autonomous 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 or difficult to predict
- Dynamic = moving, changing
- Adaptive = changing to adapt to an environment or condition
Examples
- Ecosystem
- Health care system
- City
- Organizations
- Market
- Artificial systems
- Gut
- A master course
3
,Features of CAS
What are CAS?
- The agents and the system are adaptive Imagine a hospital (part of the healthcare
- Non-linearity & unpredictability CAS) facing a perturbation such as a
- (Leaderless; but influential agents) sudden government budget cut. Because
- Fuzzy boundaries = the boundaries of a CAS often overlap the system's boundaries are fuzzy, you
with other systems see that the response doesn't just
Why are they rigid? happen within the hospital; local
- Actions based on internalized rules neighbourhood initiatives or technology
(culture/structure/practice) companies (which fell 'just' outside the
o People are often not willing to change their traditional boundary) also get involved to
culture/rules solve the problem
- Inherent pattern (path dependency / negative side effects)
o Fe. the fossil fuel system is hard to get rid of as it is ingrained in our society
- Attractor behaviour
o The system often returns to the old pattern
How do they change?
- Systems are embedded and co-evolve
- Interaction leads to emergent behaviour
- Self-organization
- Landscape developments; ‘perturbations’
o Something happens outside of the system that disrupts the system. Because the
CAS is adaptive, it will react to these shocks.
Transdisciplinary research (TDR) in health systems
Bottom-up experimentation
Complex societal issues
4
,Rogers’ diffusion of innovation model
Aspects that influence speed of diffusion of innovation
- Simplicity or the ease with which the innovation can be understood and how many actors
and processes are involved in its diffusion
- Compatibility or congruence with cultural values, behaviour patterns, the existing
organizational structure etc.
The role of science and society; PAR/TDR/action cycle (participatory action research = PAR)
- In settings where multiple factors are interacting in dynamic and unpredictable ways, a
complex systems approach emphasizes the value of naturalistic methods (where
scientists observe and even participate in real-world phenomena, as in anthropological
fieldwork) and rapid-cycle evaluation
o That is, collecting data in a systematic but pragmatic way and feeding it back in a
timely way to inform ongoing improvement
TDR
- Participation
- Emergent design
- Reflection / co-creation
- (multiple) change processes
- Learning
Systems thinking
5
, - Competences involve leadership, the ability to co-create knowledge through
experimentation, deliberation, understanding of the need for standardisation and creative
deconstruction.
Summary
- CAS is defined as a group of semi-autonomous agents who interact in interdependent
ways to produce system-wide patterns (the structure and culture), such that those
patterns then influence the behaviour/agency of the agents
- Understanding CAS will contribute to innovation and implementation of non-congruent
innovations/policies. Transdisciplinary and systems thinking can support this
- Current societal challenges require a CAS perspective disclaimer: we also need research
anticipating less complexity: e.g. RCTs
Lecture a CAS look at innovation - Anne van der
Geest
Innovation models
Innovation → not a single action but a total process of interrelated sub processes. It is not just
the conception of a new idea, nor the invention of a new device, nor the development of a new
market. The process is all these things acting in an integrated fashion
- Innovation model = an innovation model is a structured framework for managing how
ideas are developed and implemented
o It’s an abstract, generalized view of a complex reality
o And also: it’s tool to analyse innovation processes and to find clues for
improvement
Rothwell’s classification of innovation models
The evolution of how businesses interact with technology and society.
1st Linear innovation model (push model)
- First innovation model (1950s – 1960s), but didn’t
give enough attention to the transformative process
of existing products, or the needs of the customers
- Linear phased process. It has only arrows going
one way
6
, - Mainly focused what happens within an organization
- Most resources were put towards R&D in companies. More R&D → more new products
will be out
- It is called a push model because there wasn’t an explicit need from the consumers!
- For example; the development of the microwave was only based on the new ‘radar’
technology. Consumers did not explicitly ask for it and the product was ‘pushed’ into the
market, and only later did consumers discover its benefits (faster cooking)
2nd Linear innovation model (need-pull model)
- 1960s – 1970s
- Market pull innovation involves the creation of a new
product or service that solves a need in a market
o For example, front facing cameras in your mobile
phones.
- Only using this model, you only focus on existing demand → miss out on creating new
markets or breakthrough innovations.
Both linear models are not realistic because real processes are not linear. They both don’t
contain feedback loops. Sometimes during development, you discover that something needs
change, earlier in the process, where development gets adjusted.
3rd generation: coupled model (combination of 1st and 2nd)
- 1970s – 1980s
- Technological capabilities (Push) + market demands
(Pull)
- Feedback loops are included here, facilitating more
balanced and responsive innovation
- It emphasizes close interaction between R&D and
marketing, ensuring that technology development aligns
with customer needs and vice versa.
4th generation: integrated innovation model
- 1980s – 1990s
- Stresses the importance of integration across different
functions and stages of the innovation process
- → Innovation is cross functional, and R&D is just one of the
functions involved in the innovation process
- Previous model mainly focusses on what happens inside the
organization, but in reality, the innovation process also involves
outside organizations.
o If all smart people work with ‘us’, we get the best knowledge produced and
results. It is also important to control intellectual property, because you want to
be the only one to make this. However, a lot of organizations work within
networks, with many different organizations and actors.
7
, Closed innovation model
- Wins if: all smart people work for us; all innovation is inside; create the
best ideas; control IP (intellectual property)
Open innovation model
- The boundaries of the companies are a bit more fluid
- Buy the knowledge of patent function or license. Or a spin off company that
will be established from company X
- They do not necessarily happen in the organisation itself, but outside and use
- Wins if: work with people outside; use of external R&D; have the best business
model; sell and buy IP
5th generation: system-wide models
- 1990s+
- Cyclic Innovation Model (CIM) → a cross-disciplinary view of change processes as they
occur in an open innovation arena.
- CIM consists of four nodes ↑
8
, - Each node is connected to one another through various
cycles and from one node to another, there the process
can be either feedback or feedforward
- The CIM can be illustrated using the case study of W.L.
Gore & Associates (infographic is made by AI)
o Scientific exploration → discovery of PTFE, and
technical capabilities emerge from natural and life
sciences
o Technological research → technical capabilities in
product functions
o Product creation → in the 1990s, growing interest
in outdoor activities increased demand for Gore-
Tex, leading many brands to adopt it.
So why do old linear models not work anymore and we need
circular models? → in our current society we come across
several problems like
- Unpredictability: cause and consequence are often not linked to each other directly
anymore.
- No end station: innovation doesn’t stop at selling. Users give constant feedback, so
adaptations of products continuously happen.
- Interdependence: The nodes in the CIM model are so interlinked that you can’t see them
apart of each other.
Entrepreneurship
Joseph Schumpeter introduced the concept of entrepreneurship
- Entrepreneurs make ‘new combinations’ of existing elements, that’s how
innovation starts
- Creative destruction → how the introduction of new products can disrupt
existing industries and create new economic opportunities
o The rise of personal computers displaced typewriters
o Streaming services like Netflix disrupted traditional cable TV and
DVD rentals
Schumpeter’s model: Neue Kombinationen
Characteristics
- Entrepreneur is central (inventor ≠ entrepreneur)
- ‘discovery’ ≠ implementation
- Innovation is about combining things
- Entrepreneurs make new combinations of existing building blocks
- ‘New’ can be an old product + old idea + old production process, but combined with
something new. It’s about seeing an opportunity
9
Emergence & Markets
VU Amsterdam
AM_1052
2025-2026
Table of Contents
Lecture complex adaptive systems (CAS) – Dirk Essink .................................................... 2
Complex adaptive system (CAS) ........................................................................................... 3
Lecture a CAS look at innovation - Anne van der Geest ..................................................... 6
Innovation models................................................................................................................... 6
Entrepreneurship .................................................................................................................... 9
Innovations in healthcare ..................................................................................................... 10
Lecture 3 Why markets are a CAS – Linda van de Burgwal ............................................. 13
Classical economic theory and the CAS view on economy/markets .................................. 13
Market imperfections in health care ..................................................................................... 14
Forms of "irrational" behaviour and how to make use of that ("nudging") ........................... 15
Influence of market abnormalities and irrational behaviours on the health care sector ...... 18
Lecture 4 Network approach, emergence and change – Eduardo Urias ......................... 20
Network theory and types ..................................................................................................... 20
Network analysis and metrics............................................................................................... 23
Micro-macro link ................................................................................................................... 24
Emergence............................................................................................................................ 25
Leverage points .................................................................................................................... 26
Case study: Ozempic ........................................................................................................... 26
Lecture 5 lessons for policy makers in CAS – Kristiaan Kok ........................................... 27
Two underlying models ......................................................................................................... 28
Part 2 Eduardo Urias ............................................................................................................ 30
,Lecture complex adaptive systems (CAS) – Dirk
Essink
Systems
Have a goal. This goal is outside the system and is not really part of the system. The outcome is
often separate from the system. Only the system itself creates meaning and value to the goal
Some elements that are successful, remain (just like with evolution). Examples of this are
- Hospital based systems → people often correlate healthcare immediately with hospitals
and doctors, while this wasn’t the normal way in the past
- Fossil fuel systems → our need for energy was met by fossil fuels. Even though we know
the bad side effects, it is now hard to break up that system
Objectives
- To understand the various types of systems, among them CAS
- To identify the general properties of CAS
- To explain the underlying mechanisms and levels of adaptivity in CAS
Reductionism = a philosophical position which holds that a ‘complex system’ is
nothing but the sum of its parts. Solving a systemic problem is breaking it up to
the part that is broken. Innovation is a ‘rational’ add-on to this system.
- Everything functions as a machine and we can break it down into parts
and fix them.
Simple systems
(a combination of parts to make a whole)
- Well-ordered, predictable cause-effect
- Relations are simple and stable
- Input-output relations are simple
- ‘things’ are simple and few
- Easy to repair
- Structure and functions are clear
Complicated systems
- Things are many and can be complex
- Relations are manyfold and diverse
- Difficult to design and repair (experts)
- Structures and functions are partly hidden For example, the climate is
- Engineered unpredictable, the weather itself
has no agency and no thinking.
Non-linear systems
However, this is non-linear but not
- Continuously changing & unpredictable
a complex adaptive system.
- Input-output relations unclear ‘non-linear’: no clear/stable
cause → effect relations
- Butterfly effect: a small change may cause a large effect
- Thus, difficult to control
- But... many ‘things’, but no ‘thinking’, no ideas, no agency
2
,Critical theory, systems thinking, CAS
understanding the (non-)linear interactions of actors
enabled and constrained by system hardware and
software
- In health systems there are system hardware’s,
the human resources, medicines, IDs, software
= culture
- We use the ‘culture’ to actually act in the
system, to have agency.
- What happened is that actors see policy
changes happen and therefore they will
organize themselves around these changes in
(non-)linear ways.
Agents produce actions. Interacting with each other and
through that they create patterns. Because those patterns
are there, they in turn influence how the system works
again. The system then becomes more stable. Actors
reproduce the system to gain structure
Complex adaptive system (CAS)
Complex Adaptive System (CAS) = a system in which many semi-autonomous 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 or difficult to predict
- Dynamic = moving, changing
- Adaptive = changing to adapt to an environment or condition
Examples
- Ecosystem
- Health care system
- City
- Organizations
- Market
- Artificial systems
- Gut
- A master course
3
,Features of CAS
What are CAS?
- The agents and the system are adaptive Imagine a hospital (part of the healthcare
- Non-linearity & unpredictability CAS) facing a perturbation such as a
- (Leaderless; but influential agents) sudden government budget cut. Because
- Fuzzy boundaries = the boundaries of a CAS often overlap the system's boundaries are fuzzy, you
with other systems see that the response doesn't just
Why are they rigid? happen within the hospital; local
- Actions based on internalized rules neighbourhood initiatives or technology
(culture/structure/practice) companies (which fell 'just' outside the
o People are often not willing to change their traditional boundary) also get involved to
culture/rules solve the problem
- Inherent pattern (path dependency / negative side effects)
o Fe. the fossil fuel system is hard to get rid of as it is ingrained in our society
- Attractor behaviour
o The system often returns to the old pattern
How do they change?
- Systems are embedded and co-evolve
- Interaction leads to emergent behaviour
- Self-organization
- Landscape developments; ‘perturbations’
o Something happens outside of the system that disrupts the system. Because the
CAS is adaptive, it will react to these shocks.
Transdisciplinary research (TDR) in health systems
Bottom-up experimentation
Complex societal issues
4
,Rogers’ diffusion of innovation model
Aspects that influence speed of diffusion of innovation
- Simplicity or the ease with which the innovation can be understood and how many actors
and processes are involved in its diffusion
- Compatibility or congruence with cultural values, behaviour patterns, the existing
organizational structure etc.
The role of science and society; PAR/TDR/action cycle (participatory action research = PAR)
- In settings where multiple factors are interacting in dynamic and unpredictable ways, a
complex systems approach emphasizes the value of naturalistic methods (where
scientists observe and even participate in real-world phenomena, as in anthropological
fieldwork) and rapid-cycle evaluation
o That is, collecting data in a systematic but pragmatic way and feeding it back in a
timely way to inform ongoing improvement
TDR
- Participation
- Emergent design
- Reflection / co-creation
- (multiple) change processes
- Learning
Systems thinking
5
, - Competences involve leadership, the ability to co-create knowledge through
experimentation, deliberation, understanding of the need for standardisation and creative
deconstruction.
Summary
- CAS is defined as a group of semi-autonomous agents who interact in interdependent
ways to produce system-wide patterns (the structure and culture), such that those
patterns then influence the behaviour/agency of the agents
- Understanding CAS will contribute to innovation and implementation of non-congruent
innovations/policies. Transdisciplinary and systems thinking can support this
- Current societal challenges require a CAS perspective disclaimer: we also need research
anticipating less complexity: e.g. RCTs
Lecture a CAS look at innovation - Anne van der
Geest
Innovation models
Innovation → not a single action but a total process of interrelated sub processes. It is not just
the conception of a new idea, nor the invention of a new device, nor the development of a new
market. The process is all these things acting in an integrated fashion
- Innovation model = an innovation model is a structured framework for managing how
ideas are developed and implemented
o It’s an abstract, generalized view of a complex reality
o And also: it’s tool to analyse innovation processes and to find clues for
improvement
Rothwell’s classification of innovation models
The evolution of how businesses interact with technology and society.
1st Linear innovation model (push model)
- First innovation model (1950s – 1960s), but didn’t
give enough attention to the transformative process
of existing products, or the needs of the customers
- Linear phased process. It has only arrows going
one way
6
, - Mainly focused what happens within an organization
- Most resources were put towards R&D in companies. More R&D → more new products
will be out
- It is called a push model because there wasn’t an explicit need from the consumers!
- For example; the development of the microwave was only based on the new ‘radar’
technology. Consumers did not explicitly ask for it and the product was ‘pushed’ into the
market, and only later did consumers discover its benefits (faster cooking)
2nd Linear innovation model (need-pull model)
- 1960s – 1970s
- Market pull innovation involves the creation of a new
product or service that solves a need in a market
o For example, front facing cameras in your mobile
phones.
- Only using this model, you only focus on existing demand → miss out on creating new
markets or breakthrough innovations.
Both linear models are not realistic because real processes are not linear. They both don’t
contain feedback loops. Sometimes during development, you discover that something needs
change, earlier in the process, where development gets adjusted.
3rd generation: coupled model (combination of 1st and 2nd)
- 1970s – 1980s
- Technological capabilities (Push) + market demands
(Pull)
- Feedback loops are included here, facilitating more
balanced and responsive innovation
- It emphasizes close interaction between R&D and
marketing, ensuring that technology development aligns
with customer needs and vice versa.
4th generation: integrated innovation model
- 1980s – 1990s
- Stresses the importance of integration across different
functions and stages of the innovation process
- → Innovation is cross functional, and R&D is just one of the
functions involved in the innovation process
- Previous model mainly focusses on what happens inside the
organization, but in reality, the innovation process also involves
outside organizations.
o If all smart people work with ‘us’, we get the best knowledge produced and
results. It is also important to control intellectual property, because you want to
be the only one to make this. However, a lot of organizations work within
networks, with many different organizations and actors.
7
, Closed innovation model
- Wins if: all smart people work for us; all innovation is inside; create the
best ideas; control IP (intellectual property)
Open innovation model
- The boundaries of the companies are a bit more fluid
- Buy the knowledge of patent function or license. Or a spin off company that
will be established from company X
- They do not necessarily happen in the organisation itself, but outside and use
- Wins if: work with people outside; use of external R&D; have the best business
model; sell and buy IP
5th generation: system-wide models
- 1990s+
- Cyclic Innovation Model (CIM) → a cross-disciplinary view of change processes as they
occur in an open innovation arena.
- CIM consists of four nodes ↑
8
, - Each node is connected to one another through various
cycles and from one node to another, there the process
can be either feedback or feedforward
- The CIM can be illustrated using the case study of W.L.
Gore & Associates (infographic is made by AI)
o Scientific exploration → discovery of PTFE, and
technical capabilities emerge from natural and life
sciences
o Technological research → technical capabilities in
product functions
o Product creation → in the 1990s, growing interest
in outdoor activities increased demand for Gore-
Tex, leading many brands to adopt it.
So why do old linear models not work anymore and we need
circular models? → in our current society we come across
several problems like
- Unpredictability: cause and consequence are often not linked to each other directly
anymore.
- No end station: innovation doesn’t stop at selling. Users give constant feedback, so
adaptations of products continuously happen.
- Interdependence: The nodes in the CIM model are so interlinked that you can’t see them
apart of each other.
Entrepreneurship
Joseph Schumpeter introduced the concept of entrepreneurship
- Entrepreneurs make ‘new combinations’ of existing elements, that’s how
innovation starts
- Creative destruction → how the introduction of new products can disrupt
existing industries and create new economic opportunities
o The rise of personal computers displaced typewriters
o Streaming services like Netflix disrupted traditional cable TV and
DVD rentals
Schumpeter’s model: Neue Kombinationen
Characteristics
- Entrepreneur is central (inventor ≠ entrepreneur)
- ‘discovery’ ≠ implementation
- Innovation is about combining things
- Entrepreneurs make new combinations of existing building blocks
- ‘New’ can be an old product + old idea + old production process, but combined with
something new. It’s about seeing an opportunity
9
