Transcript Slide 1

Social Networks & Systems Culture
• Out of Control
• Social Network Analysis
• Class Work: How do you use Social Network
Sites?
What are the myths of new tech?
• We’ll automatically be better, smarter & faster
• Society will embrace the change
• Organizations will adapt
- Workers will like it
- Shareholders will be pleased (quarterly)
• Everyone gets a voice
• Coordination is easy, we just needed the tools
What are the myths of networks?
• Networks support logical structures
- Technology = systematic, rigorous, logical
• Networks are always efficient
- Signal without noise
- Routing is perfect - results always arrive
• Communication costs are near 0
• All connections are equal
The Made and the Born
• Are good systems made or born?
- Both, but where do you start?
- Top-down strategies are changing to botton-up
• The new metaphor is the organic,
adaptive,decentralized network
• Is the knowledge producing organization “alive”?
• “Clockwork logic - the logic of the machines - will
only build simple contraptions. Truly complex
systems such as a cell, a meadow, an economy, or a
brain (natural or artificial) require a rigorous
nontechnical logic.” p2
• How can we help a organization to, well, be
organized?
Working with People
• Are people outcomes of “nontechnical
logic”?
- We don’t always do what’s best for us
- We don’t always work together & coordinate
ourselves in the most optimal ways
• If you can’t control it, is it worth doing?
- Growing vs. building
• How much growth do you incent?
Living Systems
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Self-replicating
Self-governing
Self-repairing (within limits)
Mildly evolutionary
Partially intelligent
- Changes in simple states (stimulus-response)
- Communicate the bad with the good
• Technology gives us ways to analyze the
seeming madness in organic, subtle methods
Constructing “Vivisystems”
- “Human made things are behaving more lifelike
- Life is becoming more engineered” p3
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Nearly bottomless (components)
Vast in range (interconnected)
Gigantic in nuance (effectiveness)
Little imposed centralized control
Autonomous nature of subunits
High connectivity between subunits
Webby, nonlinear causality of peers
influencing peers
Nature is a great “meme bank” for ideas about making systems
Hive Mind
• Aren’t we smarter than bees?
- Yes, but we aren’t composed of redundant components
- Swarmed, division of labor is a powerful mechanisms for
some functionality
- We should be so coordinated
• Is “the hive chooses” = democracy?
• People can coordinate quickly & effectively is the
rules are kept simple enough
- Mass, contributed work can exceed individual performance
- “A flock is not a big bird”
• How can KM harness this decentralization
advantage?
Creating Knowledge
• Is an emergent behavior
• The group decides what knowledge is
- Information that is useful
- Information that provides context for other information
- Information about the rules of the group
• Tacit & Explicit values emerge
• Notes, music - performers, symphony
• We are more intelligent & autonomous than ants
- But not as coordinated
- But not as decisive when we acquire data
- (Remembe rDecision Making Systems)
• Knowledge has to be used to establish its value
- “expressing” nonlinear equations (New Kind of Science)
Organization
• A system has context, even amongst its overlapping
& ambiguous parts
• Memory is the means to evolutionary growth
- Documents, procedures & culture
- Recall by context
- Tacit knowledge that puts the explicit knowledge in context
• Overlapping, distributed memory is what KM is trying
to build & make use of when needed
- KMS technology helps recover from damage
- KMS technology can have wear patterns to note quality
• KMS (the network) is more of a process than a thing
Extreme Organizational Structures
• A long set of sequential procedures
- Factory, Shipping, Restaurant
• “A patchwork of parallel operations” p21
- Telephone system, Internet,
• Older technology forces us into this
sequential mode
• Newer technology allows us too many options
• Our knowledge & culture help us find the right
balance
• Complex Adaptive Systems
Pros of CADs (Swarms)?
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Adaptable (to stimuli)
Evolvable (different parts, different rates)
Resilient (redundancy, subunits as parts)
Boundless (feedback shapes order fast)
Novelty
- Sensitive to initial conditions
- Hide countless novel possibilities due to
combinations of possible connections
- No preconception about individuals, only their
outputs
Cons of CADs (Swarms)?
• Nonoptimal (no central control)
- Longer to “make” decisions
- Redundant work
• Noncontrollable (no steering)
- “an economy can’t be controlled from the outside”
• Nonpredictable (novelty not always good)
• Nonunderstanable
- 1994 = no, Now = maybe
• Nonimmediate (gradual, subtle change)
- “the more complex, the longer it takes to warm up”
CONTROL
• Straws, clocks, water, thermostats, steam
• Understanding the system is the first element of
control
• Kubernetes - steering a ship on the water
• Cybernetics - feedback & control
- Key part of Systems Culture
• Holistic Systems Culture: If you keep things linked
together, you can control them all.
- “If all variables are tightly coupled, and if you can truly
manipulate one of them in all its freedoms, then you can
indirectly control alll of them.” p 121
• This is Insanity!
- “If something can be both its own cause & effect, then
rationality is up for grabs.” p 123
Cybernetic influence in the org?
• Where can feedback be seen in
organizations?
• How is feedback used in the KMS
technologies we’ve discussed?
• How would you apply this kind of feedback to
enable knowledge work?
- Measurement
- Understanding
- Open systems design
• It is no longer steam or water, it is information
that we want to regular and task as we see fit
Network(ed) Economics
• “Network Logic” pushes technology
• Control (& insight) from a distance
• Cooperation is cheaper
- Work with specialists
• Adaptation is easier
- Change & add new specialists
• Cultural Impacts?
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Too much change
Territories & habits
Clusters of errors
Reliability over Elegance
• Reliable processes over reliable products
The 9 Laws of Nature
1. Distribute being
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Intelligence is distributed among many parts
Sum of the parts can be greater
2. Control from the bottom up
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If everything is connected to everything, information can be
transferred at once
No hierarchies, just networks
Governance is local & becomes global
Simple control & decision making mechanisms
3. Cultivate increasing returns
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Positive feedback is powerful, “success breeds success”
Order (of certain types) generates order
Hubs or power (law) basins are formed
The 9 Laws of Nature, cont.
4. Grow by chunking
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Build a complex system by starting with a simple system
Grow the system, don’t expect one to work from a complex
plan
Use simple modules that work independently (& compete +
cooperate)
5. Maximize the fringes
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Diversity comes from the nooks & borders
Innovation comes from the edges
6. Honor your errors
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Seek true improvement, not tricks
Keep errors in mind & make part of process to learn from
them
The 9 Laws of Nature, cont.
7. Pursue no optima; have multiple goals
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Many strategies can achieve the same success
Insist on diversity of methods & goals
“If it works, it’s beautiful” p 470
8. Seek persistent disequilibrium
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Avoid comfort zones & comfortable decisions
Continual revolution means continual
progression
9. Change changes itself
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Allow & coordinate change in the system
Deep evolution - “how the rules for changing
entities changes over time”
Adapting, Learning & Evolving
• Static systems are more open to failure
• Communication makes the difference
• Think organic, not mechanic
- Gradual, flexible change
- Systems in crisis may not have time for organic, gradual
change
• The scale of organizations now is simply too large for
central control
• “Technological networks will make human culture
even more ecological & evolutionary” p471
• The best technology will organically change with the
organization (& its goals)
• Fight neo-biological systems with NBS
Social Network Analysis
• Applying these methods to networks
• How many networks are you part of?
• What can we analyze?
- Types of links
• Strong, intermediate, weak
- Types of nodes
• People, documents, locations, interaction,
culture?
• A set of methods to discover, extract &
control tacit knowledge?
- Adds context where there may be none
- Content attributes that re-define content
Insight into Organizations
• Patterns of social structure
- Commonalities with co-workers, neighbors,
professions
- The groups we are in vs. the groups we choose
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How do groups change over time?
How do networks affect people?
Measuring participation
SNA may be the primary way to study online
relationships
• Learning how groups form (online only?)
Types of Ties
• Specialized & Multiplex
- Diverse information gives focus
- Ease of network communication promotes more focus
• Strong
- Built on frequency
- Prior connections over time
- Is frequency relative?
• Weak
- Coincidence or Popular?
- Strong, but temporary
- Are negative ties all weak?
• Density
• Boundedness
SNA & KMS
• How such computer supported social
networks vary in their size, heterogeneity,
density and boundedness both reflects the
social systems in which they are embedded
and the interactions of people within these
social networks. (Wellman 1996, p8)
• Organizational boundaries are more
permeable
- Information flows
- End of single organization careers
• Diversity vs. isolation from networks