Social Networks & Systems Culture

1. Social Networks & Systems Culture • Out of Control • Social Network Analysis • Class Work: How do you use 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

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

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

17. The 9 Laws of Nature • Distribute being • 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 • 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 • 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