CDT403 Forskningsmetodik inom naturvetenskap och teknik.

Complexity130926, 13.15-15.00, Zeta

Tomas Backström, professorInnovation management

IDT Eskilstuna, MDH

Sofia skola

SLSL

Konsum

View from my kitchen window

Critical realism (Roy Bhaskar)

Real events Our interpretationsMechanisms underlying events

Theory

Normal simplification strategy in ScienceReductionism:• Reduce complex systems to ordered systems.• Assume independence and linearity.• Analysis-synthesis, cause-effect, prediction…

Do we need complex systems theory?

http://www.ac.lst.se/naturochmiljo/miljoovervakning/fjall/smadaggdjur

Lemmings

County Administrative Board, 091008

Causesbehindtheseregularpopulationgrowths?

Patterns of population growth

Verhulst equation: dX/dt = μX(1-X/A)(X = population size, μ = intrinsic growth rate, A = maximum size,μX = exponential growth, (1-X/A) = rate-limiting)

1. μ = 2.5 => Xn+1 = Xn Stable

2. μ = 3.3 => Xn+2 = Xn

3. μ = 3.5 => Xn+4 = Xn

4. μ = 4.0 => Xn+a ≠ Xn Chaotic

Bifurkation diagramme

Xn+1 = Xn Xn+2 = Xn Xn+4 = Xn Xn+a ≠ Xn

Three phases

• Stable (Xn+1 = Xn).Trivial patterns.

• Complex (Xn+a = Xn).

Non-trivial patterns.• Chaotic (Xn+a ≠ Xn).

No patterns.

Example double pendulum

• Example pendulum: T and l key variables: T = 2п√l/gUse analysis-synthesis for a double pendulum

• http://www.youtube.com/watch?v=U39RMUzCjiU

Three phases

• Stable (Xn+1 = Xn).Trivial patterns, no adaptation. No learning.

• Complex (Xn+a = Xn).

Non-trivial patterns at many different scales.Order for free, emergence of self-organisation. Learning.

• Chaotic (Xn+a ≠ Xn). No patterns, no stability, no memory. No learning

To use complex system theory in research:Simplification strategies

Science: Reductionism• Assume independence

and linearity• Key variables• Analysis-synthesis

• Cause-effect• Prediction• Control

Complex systems theory: Universality

• Assume same types of processes and structures

• Key processes, e.g. emergence• Actors, their interactions

and the emerging structures• Circular causality• Comprehension• Influence

Three phases, organizations as examples

• Order – the industrial work system with centralized control of details.(Change, development and innovation are side-activities).

• Complexity – a well-functioning and experienced soccer team.

• Chaos – ”In the past 18 months 23 employees at France Telecom have committed suicide, which unions blame on the firm's restructuring program.” 091008 http://www.france24.com/en/20090913-labour-minister-calls-ceo-discuss-suicides-telecom-lombard-darcos

Bikupa

• Bee-hive• Two-cover

• Talk about a married couple from the perspective of complexity. Is it possible that all three phases exists in the same marriage over a short time span?– order (regularities, memory, identity, structures, self-organisation,

integration)

– chaos (unpredictable changes at low level, transformations, autonomy)

– complexity (develops, learns, adapts).

To use complex system theory as an ideal for an organisation

Have both: Order, like regularities, memory and identity. Mechanisms are e.g. emergence of self-organization.

and Chaos, like unpredictable changes at low level and transformations.Mechanisms are e.g. autonomy and external influence.

and, are thus, Complex, adapts, learns and develops.

One requirement to be fulfilled:Sub-actor <–> Actor <–> Meta-actor

• An actor is a wholeness and can only be understood as a wholeness (also true for sub-actors and meta-actors).

• An actor consists of sub-actors and is part of a meta-actor.• An actor is independent, has its own identity, rhythm and

pattern of behavior.• An actor is dependent of a bigger wholeness, the meta-

actor, a slave under structures emerging from interactions (exchange of information/matter/energy ) with other actors of the meta-actor.

Example

• Two ways to understand innovative organization.

Bakgrund till de tre texterna

Industrial mechanical organisation

Techno-structure design standardisations including plans,

rules and routines

Degrees of freedom to

act

Post-industrial organic organisation

Structures formulatedby managers

Tacit emergent collective structures

Freedom of action

Acting

Rela

ting Thinking

Standards, systems, control

Vision, goal, values

Lines

, mee

tings

, sha

red

Institutionalisations

CultureRe

laton

ics

Convergence –strengthens structures

Divergence – increase freedom

Different ways to perform work

Example of convergence: The emergence of culture in a work group

• Values of supervisor.• Talk about goals and

plans.• At least once a

month.• Everyone is included.

Divergence

ConvergenceNew

informa-tion

Customer focus

Motives for

change

Arenas for

commu-nication

Reasons to com-

municate

Quality of commu-nication

Dialogue

PositiveImprovisation

Common goals

Shared tasks

Sharedresponibilities

ActivitiesMeetings

Room design

Weak tiesExternal contacts

External intelligence

Customerexperinces

Feed-backof quality Meet customer

Converging divergence

Comparedwith othersSequrity

Rewards

Structures formulatedby managers

Tacit emergent collective structures

Freedom of action

Acting

Rela

ting Thinking

Standards, systems, control

Vision, goal, values

Lines

, mee

tings

, sha

red

Institutionalisations

CultureRe

laton

ics

IntegrationAutonomy

Different ways to perform work

Autonomy

Integration

Freedom of action

Compe-tence Motives

Partici-pation

Sub-mission

Emergent structures

InstitutionsCulture

Relatonics

Identification

Motivatingleadership

Collective

Includingleadership

Planning process

Openess

Permissiveleadership

Expecteddo different

Permissive collective

Knowledge

Experiences

Social support

Integrated autonomy

Rewarding

OK to make mistakes

Need to increase fitness

Implementation of integrated autonomy (the rheo task of leadership)

24

Collective reflection

New concepts

Formulate task

WS1 WS2 – WS8Task

ExperimentExperience

2 Dialogue

Pre-measure

3 Balanced communication

4 Integrated autonomy

5 Attractive work

6 Group creativity

7 External relations

Post-measure

Scales   N Mean t-valueCreativity pre-ass 241 3,62 1,13  post-ass 178 3,71  Satisfaction pre-ass 241 4,17 0,21  post-ass 180 4,19  Physical milieu pre-ass 241 3,31 4,01**

post-ass 179 3,66  Relation to management 

pre-ass 241 3,94 3,33**post-ass 179 4,27  

Appreciation pre-ass 241 1,99 2,18*  post-ass 180 2,19  Physical work pre-ass 241 3,18 0,19  post-ass 178 3,20  Social milieu pre-ass 241 3,78 2,17*  post-ass 179 3,96  Social contacts  pre-ass 241 4,15 0,36

post-ass 180 4,18  Travel to and from work 

pre-ass 241 3,67 0,94post-ass 180 3,76  

Table 3. Pre- and post-assessments of employees’ perception of the work, a comparison between means for all employees in the included workgroups.**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed).

Changes in the attractivity of the work place

Emergence – more general

Structure(order parameter)

Complex processes with a lot of interactingindependent actors

Changes are based on earlier stages, thus identity is preserved.

Continuous creation and re-creation ofthe structure

The structuregovern the processes

Circular causality

Three phases

• Stable (Xn+1 = Xn).Formulas, mathematical equations.

• Complex (Xn+a = Xn).

Chaos theory.Simulations.

• Chaotic (Xn+a ≠ Xn). Undescribable.

Actor based simulation

• NetLogo:Models library:Social science:Cooperation