the network connection yasmin merali [email protected]
TRANSCRIPT
Thoughts about
Context: the interconnected world Socio-economic systems as CAS The network signature Knowledge Management and the
“networkness” of networks
Interconnected world
Internet
connectivity information
Dynamic context
organisation
Emergence ...
Networks and the Inter-connected World
© Yasmin Merali 2013
Network dynamicsNetwork effects
Responsiveness and FitnessAdaptive &Transformational capacity
Uncertainty DiscontinuitySpeed
Network economy...
Non-equilibrium dynamics
Nonlinearity, network effects
Uncertainty
Ecologies
Yasmin Merali 2013
Agility and Intelligence
Intelligence: internal and external Dynamic contextualisation Speed and Resilience
Act fast (and recover fast from mistakes) Keep options open Maintain reconfiguration capacity (structure and components)
Exploit the networkness of networks
Complex Adaptive Systems
Open Network signature – non-linear dynamics Emergent systemic properties
CAS and the “Networkness of Networks” Nonlinearity – potential for
maintaining steady state and transformation
Requisite Variety: microdiversity, heterogeneity of Types of links Properties of the nodes Types of info and knowledge flows Degree of connectivity and density of connections
Patterns of connectivity and combinatorial potential Potential/latent/active reach/connectivity Mutually defining relationship of dynamics and structure
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KNOWLEDGE MANAGEMENT AND THE NETWORKNESS OF NETWORKS
Social networks as Information Networks
social networks viewed as information networks embodying communication and meaning
Complexity of social information networks
–Nodes– Links– Informational Content
}Dynamics HeterogeneityVariable persistenceCo-presence
Risky Business
Connectivity: the speed and spread of diffusion of information (or mis-information)
Information content: the ability of nodes to transform the message, strip or attach context
Signal or Noise?
STRUCTURE AND DYNAMICS
Examples: Network Structure and Non-linear Dynamics Matter
Internet: structure and vulnerability to attack Epidemiological models for spread of diseases Social applications:
—Imitation, flocking and herding behaviours—opinion dynamics, —influencing behaviours, —logistics (e.g. mobility patterns and urban
planning)
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Patterns of Connectivity and Control
CAS as networks of networks
Han et al (Nature, 430, 88-93 , 2004) – Date Hubs: regulators, mediators or adaptors
(selectively connected across modules)– Party Hubs: integral elements within distinct modules (densely
connected nodes within modules)
Networks of Networks
Yasmin Merali 2013
INFORMATIONAL COMPLEXITY
shift from dealing with observation s of the world to dealing with others’ representations of possible worlds…
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Information Quality Interpretation
Representation
Both are selection, abstraction and recombination exercises raising questions about the need for
—Context (-ual filtering)—Cognitive Filtering—Social Filtering
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Cognition Sensemaking and Heuristics:
—the embedded rules we have are derived from the environment
—social media present us with a different environment from that of the past
Social Strategies: Lower cognitive load, search cost etc —Rely on others’ opinions (e.g. recommendations, reviews,
likes)—Decide who we trust (whose recommendations we follow)
vs decide what we think of the “raw data”
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The Problem of Abstraction
Choosing the requisite level of abstraction
Moving between levels: loss of detail vs loss of dimensions
Moving between dimensional views?
Who controls − the level of abstraction− the dimensions to include?
A requisite dimensionality?
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Representation & Interpretation
Technical level: storing, reconciling and choosing between alternative interpretive and representational frames.
Individual level: application of appropriate interpretative frames for received representations and for creating for a diverse audience
Collective level: legitimisation and imposition of representational and interpretive frames to be used by individual agents on behalf of the collective
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... the importance of context, social and cognitive scaffolding
Big Data Looking for correlations and patterns in large volume data to
extract meaning and trends
Some Issues with attributing causality to correlations:
- Loss of context – Loss of meaning – reference problem
- Checking big data- generation of history expensive and many possible paths exist.
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shift from dealing with observation s of the world to dealing with others’ representations of possible worlds, where others may be humans or machines.
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Who controls the network?
Who controls the message?