Summary

Summary Innovation, Behavior, Emergence and Markets (IBEM) - 2023

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Course
Innovation, Behavior, Emergence and Markets (AM_1052)

Institution
Vrije Universiteit Amsterdam (VU)

This summary contains all information from the lectures and the highlights of the articles. At the end, you can find the small mock exam from the last lecture with answers.

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Uploaded on January 30, 2023
Number of pages 25
Written in 2022/2023
Type Summary

emergence
supply demand
survival
cim
herzlinger 6 forces
exploit
myopia
bushy strategy
fail safe
cas
hubs
gini coefficient
nodes
complexity economics
traditiona
staceys model
prisoners dilemma

Institution
Vrije Universiteit Amsterdam (VU)
Education
Management, Policy-Analysis and Entrepreneurship in Health and Life Science
Course
Innovation, Behavior, Emergence and Markets (AM_1052)

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Innovation,
Behavior,
Emergence and
Markets (IBEM)
Lectures and Articles from
2023

©2023 A. Arp

,Innovation, Behavior, Emergence and Markets
Lecture 1 Complex Systems
Objectives:
- To understand the various types of systems, among them CAS
- To identify the general visible and invisible properties of CAS
- To explain the underlying mechanisms and levels of adaptivity in CAS
- To describe the scientific base and some research fields of CAS

Complex Adaptive system = aa system which consists of independent elements/agents that interact
and move towards unpredictable outcomes. Things that comprehend complex adaptive systems:
- Complex = difficult to understand or difficult to predict
- Dynamic = changing, moving
- Adaptive = changing to adapt to an environment or condition.

System = elements that are interconnected. System can be simple, complicated, non-linear (chaotic),
CAS. Non-linear and CAS means is also dynamic.
Ex: simple system is a bike. Has simple things and is easy to repair. Complicated system is for example
a plane. Can become very complex to solve a problem, can be overwhelming amount of parts to think
about.

Reductionism = you can understand a system completely if you know the properties of all its
elements. CAS are partly unpredictable, show emergence, are partly irrational, even if you know all
things. 1+1+1 = 3, all parts work together to a goal.

Non-linear system = continuously changing and is unpredictable. Many things, but no thinking or
adaptation. Input-output relations unclear. 1+1=3, because separate elements do not resemble the
productivity of the system. There is no simple relationship between a change and the reaction of the
system (no simple cause-effect relation).
- Butterfly-effect = a small change may cause a large effect
- Difficult to control and change
• A small change may cause no effect (stability), unexpected effect (emergence) or a
large effect across a transition point.
• A large change may cause: no effect (resilience, stability, adaptation), a minor local
effect, unexpected effect (emergence) or a large effect across a transition point.
Ex: weather, chaotic and unpredictable.

Characteristics Complex Adaptive System
1. Leaderless (decentralized)
2. Non-linear
3. Emergent patterns. Patterns that form even though the agents were not directed to make a
pattern.
4. Self-organizing. A system in which a pattern emerges as a result of the agents following
simples rules without external control or a leader.
5. Feedback loop. Can be positive or negative changes (phase transition). Small initial change
can change up the whole system. A closed system that contains a circular process in which
the system’s output is returned or “fed back” to the system as input.
6. Adaptive. Degree of autonomy and responsibility which leads to self-organization → bottom-
up change.
7. Chaotic behavior of a system. Small changes in initial conditions can generate large changes
in the system’s outcome.

1

, 8. Stochastic. Governed by chance (luck). There is an element of randomness. The behavior 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 in an intricate
arrangement, which are the building blocks.
- Adaptive as they have the capacity to change under influence of feedback or memory (learn
from experience) and thus evolve, giving it resilience in the face of perturbation.
- Are usually open systems: system interacts with its environment which permits feedback.
- Show emergence: the whole is more than the sum of the components and the very specific
connectivity creates a new property.
- Operate far from equilibrium: there has to be a constant input of energy to maintain the
organization of the system, and this is essential for emergence.

How do CAS react to changes?
1) sometimes a small change may have a large effect (butterfly effect) or
2) the system is resistant (resilient) against a disturbance or
3) evolution, specialization of the actors.
Examples of CAS: ecosystem, health care system, city, organizations (like a hospital), market (business
ecosystem), artificial systems (games), gut, a master’s course.

Visible CAS properties:
1. Diversity/specialization of actors
2. Changing behavior by actors
3. Boundaries, but they are permeable
4. Adaptation and behavioral change (learning)
5. Tags: a visible code to easily identify an actor (useful for other actors)
6. Struggle and survival / competition between actors
7. Reward mechanisms: they determine how actor behave
8. Strategies: actors think how they can do better/survive
Adaptation + rewards + strategies result in
1) Selection: failure of the weak- and success for the fittest
2) Inequality: a CAS is unfair (some are rich, most are poor)
3) Continuous change (a CAS is unpredictable)

Invisible CAS properties:
1. Several equilibrium points
2. Transition points lead to switching between forms
3. Perturbations (big or small critical events) may cause a jump to a new equilibrium point.
Ex: revolution, new organizational structure, collapse of an ecosystem, epidemic disease.
4. Cause-effect relations are non-linear: cannot calculate effect of a change.
5. Resistant to change (resilient)
6. Stays close to equilibrium/stable form; small changes do not disturb the system.

Internal model (schemata) = carrier of adaptivity. Also called a scheme. Is an actor’s model of its
environment in a form that describes how to behave. Can change by coincidence (mutation), by
design (programming) or by learning from experiences.
- A good internal model helps its owners to survive, because the actor reacts better next time
- Internal models vary from very simple to very complicated.
- Can be stored in the brain, DNA, text (recipe, BP, bible, protocol) or software/algorithm (AI).

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