1. Social Networks & Systems Culture
Out of Control
Social Network Analysis
Social Network Sites

2. 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

3. 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

4. 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?

5. 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?

6. Living Systems 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

7. Constructing “Vivisystems”
“Human made things are behaving more lifelike
Life is becoming more engineered” p3
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
See “benefits of swarm systems” p 22
And
“Apparent Disadvantages of Swarm systems” p 23

8. 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?

9. 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)

10. 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

11. 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

12. Pros of CADs (Swarms)? 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

13. 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”

14. 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
Both Norbert Weiner and Ampere used feedback and its locus of study as their inspiration

15. 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

16. 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?
Too much change
Territories & habits
Clusters of errors
Reliability over Elegance
Reliable processes over reliable products
That quote is from page 199 in Out of Control (hardback edition)

17. The 9 Laws of Nature Distribute being Control from the bottom up
Intelligence is distributed among many parts
Sum of the parts can be greater
Control from the bottom up
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
Cultivate increasing returns
Positive feedback is powerful, “success breeds success”
Order (of certain types) generates order
Hubs or power (law) basins are formed

18. The 9 Laws of Nature, cont. Grow by chunking Maximize the fringes
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)
Maximize the fringes
Diversity comes from the nooks & borders
Innovation comes from the edges
Honor your errors
Seek true improvement, not tricks
Keep errors in mind & make part of process to learn from them

19. The 9 Laws of Nature, cont. Pursue no optima; have multiple goals
Many strategies can achieve the same success
Insist on diversity of methods & goals
“If it works, it’s beautiful” p 470
Seek persistent disequilibrium
Avoid comfort zones & comfortable decisions
Continual revolution means continual progression
Change changes itself
Allow & coordinate change in the system
Deep evolution - “how the rules for changing entities changes over time”

20. 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

21. 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

22. Insight into Organizations
Patterns of social structure
Commonalities with co-workers, neighbors, professions
The groups we are in vs. the groups we choose
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?)

23. Types of Ties Specialized & Multiplex Strong Weak Density Boundedness
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

24. 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