Lecture 1 - 08/01 - Innovation, Behaviour, Emergence and Markets
IBEM is based on solid transdisciplinary science and it is anchored on Complex Adaptive
Systems (CAS) theory → theory of CAS not only combines, but merges models and views from
different scientific disciplines, such as game theory, psychology, network theory and sociology.
Complex Adaptive System (CAS) = a group of semi-autonomous agents who interact in
interdependent ways to produce system-wide patterns, such that those patterns
then influence the behaviour of the agents (picture right).
→ dynamic and interactive system composed of multiple agents or components that
adapt to their environment based on local interactions → these systems are
characterised by non-linear dynamics, emergence and the capacity for
self-organisation → concept of equilibrium points becomes particularly interesting.
→ informs our understanding of how some of the patterns emerge as dominant
over others and how other patterns may be diminished or eradicated.
→ system in which many interdependent 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 or difficult to predict -> dynamic; moving,
changing -> adaptive; changing to adapt to an environment or condition.
→ elements CAS:
- Consists of several heterogeneous agents, that each make decisions about how to behave
→ the most important dimension is that those decisions will evolve over time.
- Agents interact with each other, which leads to…
- …emergence, in a real way, the whole becomes greater than the sum of the parts → key
issue is that you cannot really understand the whole system simply by looking at
individual parts.
→ example: ant colony, where each ant has a decision role (foraging, midden work), and also
interacts with other ants → a lot of that is local interaction → what
emerges from their behaviour is the ant colony.
→ basic features: heterogeneous agents, interaction and an emergent
global system → are consistent across domains (picture left).
System = collection of interconnected and interdependent elements or
agents that exhibit complex behaviour through adaptive processes → are
characterised by their dynamic nature, non-linear interactions and the
ability to self-organise in response to changes in their environment → types of systems: simple,
complicated, non-linear (chaotic), CAS (non-linear and CAS are dynamic).
Reductionism = understand a system completely if you know the properties of all its things →
CAS is partly unpredictable, shows emergence, and is irrational, even if you know all things.
→ simple systems: 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 → FEX. a bicycle is easy to understand.
→ complicated systems: things are many and can be complex - relations are manyfold and diverse
- difficult to design and repair (need experts) - structures and functions are partly hidden -
engineered → FEX. an airplane is overwhelming for people who are not trained to understand it.
→ non-linear (complex) systems: continuously changing - unpredictable - many things, but no
thinking or adaptation - input-output relations are unclear - butterfly effect = small change may
cause a large effect - difficult to control and change - non-linear (no clear and stable cause and
effect relations) → FEX. the weather continuously changes and birds’ coordination (not a single
, boss, but distributed control, no script prescribing actions of the flock, simple rules; avoid hitting
each other, align flight to match neighbours and fly an average distance from each other).
→ CAS: many things (actors/agents), connected in a network (building blocks) - adaptive
(capacity to change due to feedback or memory) - details are unpredictable, but general laws
exist (open systems that continuously interacts with its environment) - constant change (no fixed
equilibrium) (show emergence, connectivity creates new property) - has multiple equilibria and
changing patterns (constant input of energy to maintain the organisation of the system, which is
essential for emergence).
Characteristics CAS: leaderless - emergent patterns - self-organising (pattern emerges as a result
of the agents following simple rules without external control or a leader) - feedback loops
(circular process in which the systems’ output is fed back to the input) - adaptive (to changes) -
chaotic (small changes can generate large changes in the systems’ outcome) - stochastic
(governed by chance, randomness in movements and interactions).
How do CAS react to change? → sometimes a small change may have a large effect OR the system
is resistant (resilient) against a disturbance → evolution and specialisation of the actors.
CAS examples: ecosystem - healthcare system - city - organisations (like hospitals) - markets
(business ecosystem) - artificial systems - gut (digestive system).
Visible properties CAS: diversity/specialisation of actors -> actors change behaviour (genes and
learning) -> flows (food chains, info, water) -> groups of actors (animals aggregate and cooperate)
-> building blocks (things that are successful can be copied, combined and re-used, a business
model, antibiotic, DNA sequences, vaccine) -> boundaries that are permeable -> adaptation and
behavioural changes (learning) -> tags = visible code to easily identify an actor -> struggle and
survival (competition between actors) -> reward mechanisms (determine actor behaviour) ->
strategies (actors think how they can do better/survive).
→ adaption + rewards + strategies = selection (failure of the weakest, success of the fittest),
inequality (unfair, rich and poor), and continuous change (a CAS is unpredictable).
Example hospital: doctors specialise - different professions (tags) - competition in private
hospitals to provide better services compared to other institutions - reward mechanisms.
Invisible properties CAS: CAS have several equilibrium points -> can switch between these by
passing through a transition point -> perturbations (big/small events) may cause a jump to a new
equilibrium point (revolution → FEX. new organisational structure, collapse of ecosystem,
epidemic disease) -> cause-effect relations are non-linear (cannot calculate the effect of a
change, even if you know everything about the actors, no simple cause-effect relation).
→ non-linearity = 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, leading to a new equilibrium) → FEX. a small nucleotide change in DNA results
in the very rare Kleefstra syndrome.
Hidden order:
- Internal models: an actor’s model of its environment in a form that describes how to
behave → to be adaptive you need something that remembers what you did and how it
worked → internal model is the carrier of adaptivity → can change by coincidence
(mutation), design (programming) and learning from experiences → helps its owner to
survive, because the actor reacts better next time (learning) → vary from very simple to
very complicated → social rules in your head (how to behave and what is normal?).
- FEX. the brain (behaviour), DNA, text (recipe, business plan), software/algorithm
(AI) → example: medical protocol -> if symptom 1, then treatment 1.
