cdt403 forskningsmetodik inom naturvetenskap och teknik. complexity 130926, 13.15-15.00, zeta
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CDT403 Forskningsmetodik inom naturvetenskap och teknik. Complexity 130926, 13.15-15.00, Zeta. Tomas Backström , professor Innovation management IDT Eskilstuna, MDH. View from my kitchen window. Sofia skola. Konsum. SL. SL. Critical realism (Roy Bhaskar ). - PowerPoint PPT PresentationTRANSCRIPT
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