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IBEM – Lecture 1Complexity science
Originated in the 1970’s from system theory. Now it’s a small separate ‘interdiscipline’.
Sante Fe Insttttee top level instttte developing CAS theory.
CAS shotld bee
- Mathematcally robtst
- Qtanttatve (allow predictons tse metricsi
- Generally applicable for many system types
- Allow experimentaton (simtlatonsi
Types of Systems
Defnitone “A system is a set of connected thingsn
Types of systemse
- Simple
- Complicated
- Complex adaptve (Dynamici
- Non-linear (chaotci (Dynamici
Redtctonisme yot can tnderstand a system completely if yot know the propertes of all its partcles
A simple systeme Earth (thingi and litle green ball (thingi
Gravitatonal theory (Newtonie masses atract each other
Connecton atractng force
Simple systems Complicated systems
- Well-ordered predictable catse-efect - Things are many and can be complex
- “Thingsnare simple and few - Relatons are manifold and diverse
- Relatons are simple and fxed - Diffictlt to repair (expertsi
- Easily repairable - Experts also needed for inptt-ottptt
- Inptt-ottptt relatons are simple calctlatons
- Strtcttre and ftnctons are clear - Strtcttres and ftnctons are partly
- “Thingsn have no behaviorn don’t hidden; they have their own logic –
change their “mindn btt its logic
- Engineered
- “Thingsn don’t change their mind
Non-linear systems
- Chaotc contntotsly changing
- Inpredictable
- Many things btt no thinking or adaptaton
- Inptt-ottptt relatons tnclear
- Btterfy efecte a small change may catse a large efect
- Diffictlt to control and change
- Examplee atmosphere and moving ftids
,Complex adaptie systems
- Many things (actors agentsi connected into a network
- Adaptve (at “thing leveln and at the system leveli
- Details tnpredictable btt general law exist
- Constant changee no fxed eqtilibritm instead it has mtltple eqtilibria and changing
paterns
- A CAS is non-linear too
- Btterfy efect (a small change can have a large efecti
- Resilience (system is resistant against disttrbances
- Evoltton specialiaaton emergence
- No single “Big bossn btt “distribtted controln
- System has botndaries btt they are permeable
Remember: CAS theory is a very general “Concepttal modeln of a system. It shotld work for alle
kinds of systems from a ctlttre of bacteria to an ecosystem to Dttch or Belgian Health care. Even a
totrnament between comptter programs.
Examples of CAS
- Ecosystem
- Health care system
- City
- Jtngle
- Hospital
- Market “Btsiness ecosystemn
- IBEM commtnity (self-organiaatoni
- Artfcial systems (Game of Liifei
Visible General CAS propertes
Diversity/ Specialiaaton of actors
Actors change behaviore genes and learning
Food chainse fows (alsoe informaton water etci
Grotps of animalse aggregaton and cooperaton
Btilding blockse things that are stccessftl can be copied combined and retsed (LiCD btsiness model
cotrse slidesi
Botndariese A CAS has botndaries btt they are permeable
Adaptaton and behaviotral change (learningi
Tagse a visible code to easily identfy an actor (tseftl for other actorsi
Strtggle and strvivaln
Competton between actors
Reward mechanismse determine ottcomes/behavior
Adaptaton Rewards restlt ine
- Selectone failtre – and stccess for the ftest
- Ineqtalitye a CAS is tnfair (some are rich most poori
Iniisible general CAS propertes
- A CAS can have several eqtilibritm points
- It can switch between these forms by passing throtgh a “transiton pointn
, - “Perttrbatonsn (big/critcal eventsi may catse a jtmp to another eqtilibritm point
- In a CAS catse-efect relatons are non-lineaire yot cannot calctlate the efect of a change
(opposite to redtctonist modelsi
- Resistant to change (resilienti
- Ustally in a stavle form small changes do not disttrb the system system adapts and stays
arotnd eqtilibritm
- At certain level of perttrbaton (critcal pointi system moves to another stable sittaton
- CAS theory tries to tnderstande
o What makes a system stable
o How to predict what is critcal point
o How to change a system in the right way
Non-linearity
Becatse of complexity of CAS and emergent nattre of phenomena there is no simple relatonship
between a change and reacton of the system
A small change may catsee A large change may catsee
- No efect (stabilityi - No efect (resiliencee stability
- Unexpected efect (emergencei adaptatoni
- Liarge efecte transiton point - Unexpected efect (emergencei
- Liarge efecte across a transiton point
Why study a CAS?
1. To analyse how the systems works as it does
2. To tnderstand what happens
3. To fnd similarites and general laws
4. To predict and forecast what will happen in system
5. To improve it (smart interventons – WHOi
Why and How study a CAS?
Similaritese city stbtrbs brains and broccoli al grow in the same way (why?i
Fid the hidden strtcttre in social networks
Diftsion of virtses ideas and innovatons is determined by the strtcttre of a CAS
Critcal pointse instability and system shif
Understand how coalitons form… and explode
Simtlate events (crowd behaviori
Use adaptaton / reward for comptter programming
Critcal pointse when is a CAS abott to collapse?
- An ecosystem?
- A dictatorial regime?
- A densely poptlated city?
Design smart interventons to improve healthcare (WHO adopted CAS approachi
- Can we predict and engineer how a CAS will react to changes
- How can we make tse of CAS propertes
- How can we make a system stable
- How to implement health care change in a smart way?
, -
Understand realistc catse-efect relatons in society
What goes wrong in policy making?
Neglect the adaptve reacton of actors
No probing btt large scale implementaton (WMOi
Things go wronge repair something with policy A it doesn’t work soe more policy A and more
Don’t tnderstand reward mechanisms
Understand “tnexpectedn side efects of interventons (e.g. privataaton of health carei
Instrance companies make proft
Weak actors in system lose their positon
Entreprenetrs try to earn more money strange..
Belgian hospitals increase ntmber of strgeries (strgery based incentvesi
A CAS is contnuously changing and learning
Any “fxedn method is not realistc
- SWOTe nice try btt next month things have changed
- New strategy? Yotr compettor can copy for free
- Actor analysise fne btt why don’t negotate instead of pinning an actor into fxed role
- Programming by self-learning algorithms
Any “fxedn view is not realistc
- What yot see in the system is a mix of old and new stll strviving jtst started
- No one knows what will strvive and how long and what new things will enter
Lecture 2 - IBEM
Hidden order – What we don’t see
- Internal models
- Adaptvity
- Pathway dependency and “resistancen
- Distribtted control
- Ineqtality
- Emergence
Internal models (Schemata)
To be adaptve yot need something that remembers what yot did and how it worked (John
Hollandi
An internal model is the carrier of adaptvitye
- Forms of internal models
- Why internal models are tseftl
- Relaton strvival – internal models
An internal model is an actors model of its environment in a form that describes to behave
- It can change; by coincidence (mttatoni by design (programmingi ptrposeftl (learning
from experiencesi
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