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Small World Networks Fin

Small World Networks Fin






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    Small World Networks Fin Small World Networks Fin Presentation Transcript

    • Small World Networks Jim Herman Partner Skylight Associates LLP [email_address] +1-617-834-4388
    • Outline
      • The New Science of Networks
      • Small Worlds in Large Networks
      • Scale-Free Networks
      • Information Cascades
      • Implications for Business
        • Design of Infrastructure
        • Organizational Structure
        • Mergers
        • Design of Collaboration Infrastructure
        • Change Management
    • The New Science of Networks
      • Network models are simple, yet effective ways to look at many social, economic, communication and organizational situations
        • Objects (nodes) connected/related to each other in some fashion (links)
      • Modeling real-world networks has moved ahead by leaps and bounds over the past 4 years
        • A revolution led by empirical studies made possible by the Internet and the large databases now accessible through it, and by computer simulations
      • Looks at systems where collective behavior emerges from the structure of the entire system – part of the study of complex systems
        • Knowing the properties of nodes does not tell you about the bewildering complexity of interaction patterns among them
        • Critical point phenomenon – almost instantaneous state change at a predictable threshold
      • Looks at networks dynamically, as systems that grow and change
      • Looks at what happens on or through these kinds of networks
      • These models exhibit very different properties than the “standard” view of random networks dating from the 1950’s
        • It was possible to analyze this random model mathematically
    • Small Worlds in Large Networks
      • A wide variety of networks exhibit the structure of small densely interlinked clusters of nodes with a few links to other clusters
        • Clusters form naturally in social networks: e.g., if two people share a common friend, it’s likely they are friends, too
          • We have “groups of friends” more than individual friends
        • But weaker links to other clusters allows for the interconnection of a very large number of endpoints
          • This is how the Internet grew: LANs attaching to WAN hubs
        • Networks with clusters are very different from random networks
      • Only takes a few “shortcuts” to dramatically reduce the fragmentation and path length of large networks
        • Six Degrees of Separation
        • Allows viruses and other infections to spread rather easily, but also new ideas and fads
          • Short paths may exist, but can you find them?
          • Infections are “broadcast” searches, knowledge management is about directed searches
          • Today’s interconnected world of air travel and the Internet creates shorter paths between distant people than ever before
    • Small World Network Structure A lot of variation in how the local clusters interconnect - A balance between cost, path length and robustness This structure found everywhere: Cell metabolism, neural nets, the Web, the power grid, etc.
    • Social Networks
      • Ideas, fads, biases, innovations, infections, etc. propagate through social networks
        • People make decisions based on the actions and information of others they know or communicate with
        • People seek information when confronted with a decision or a problem to solve
        • People become infected with diseases by people they come in contact with
      • Social networks exhibit small world structure
        • The fact that most people participate in multiple types of affiliations creates a small world social network structure
          • It’s not just geography any longer – especially with the Internet
          • Potentially much easier and faster for ideas to propagate
          • There is no where to hide!
        • Claire Swire’s email forwarded by her boyfriend reached 7 million people in a week!
        • Understanding social networks can be helpful in getting any new idea or change accepted (or thwarted)
    • Scale-Free Networks
      • Many real-world networks also exhibit a “non-normal” distribution of node sizes
        • A power law distribution
        • Averages don’t mean much
      • A simple model creates real-looking networks
        • Dynamic rather than static view of networks
          • With growth, older nodes get more time to make links
        • Preferential Treatment: when new nodes enter a network, they tend to make links with nodes that have many links
          • The rich get richer
      In a Random Network, most nodes have the same number of links Number of Links (k) Number of Nodes with k Links Number of Links (k) Number of Nodes with k Links A few nodes with many links (hubs) Many nodes with only few links
    • Preferential Treatment
      • It’s easy to see how social networks create preferential treatment all the time
        • People make decisions based largely on how they see other people deciding
      • The classic adoption curve segregates people into how willing they are to try something as a function of how many others have tried it
      • Crossing the chasm seen in this way is about moving from adoption by relatively limited clusters to adoption through a small world network of better connected people
        • You must win over people who look to others more
        • Who could your early adopters influence disproportionately?
        • Otherwise, you’re just one more idea fighting for acceptance
    • Scale-Free Networks and Inequality
      • Situations that lead to scale-free networks also lead to inequalities
        • One node is typically twice as connected as any other
        • The more choices, the greater the disparity in which are adopted
        • Newer ideas find it hard to displace older ones
          • But it can happen – look at Google – they have to be really good
        • Winner could take all if compatibility with others is key
      • In these kinds of networks, once an innovation takes hold, it gains a momentum that is hard to stop
        • People succumb just because so many others have
        • Many situations where people make decisions based on their perception of how many people already have made the same decision (decision externalities)
          • Trust as substitute for information
          • Increasing returns of things like technology
          • Issues of commons: you will cooperate if enough others will
        • Affected by how good the innovation is, people’s threshold of acceptance, and the structure of the social network
          • New ideas are hard to introduce in many areas of the IT industry now: acceptance of new ideas is very low
    • Information Cascades
      • A recognizable aspect of real-world networks is their general stability but rare susceptibility to sudden global state changes
        • Fads, epidemics, market bubbles and busts, tipping points
      • Taking models from the physics of phase transitions, scientists have shown how social networks can experience these rapid changes
        • Percolation theory, critical points, thresholds, non-linear math
        • A cascade is sparked by accessing a large, connected, “vulnerable” subnet
          • But this is rare, which is why systems are stable most of the time
          • Many good ideas don’t fall on fertile ground – the right cluster of early adopters
          • It is still impossible to locate the right path a priori
        • Connectivity is all that matters with a disease; local reinforcement is also needed to propagate innovations
          • Too much connectivity can prevent cascades: hubs will not generally change their views because of one neighbor
          • It’s not the early adopters who are hard to find, but the susceptible next tier
      • Viral marketing tries to tap into this
        • Hotmail – low threshold of adoption and a natural way to use the social network
    • Vulnerability from Small World Nets
      • In a small world network, even a weak pathogen can propagate and stay alive for a long time
        • The average life of software viruses is 6 to 14 months
      • The totally interconnected world of software we live in now creates serious vulnerabilities to attacks and global failures
        • Diversity can be a defense; homogeneity can be dangerous
        • Distance can be deceiving – what seems far away could hurt your local environment suddenly
      • The small world structure of today’s social networks increases the possibility of big epidemics of human disease, too
      Fraction of Random Shortcuts Epidemic Threshold of Infectiousness
    • Adaptability
      • What makes a system adaptable to change and failures?
        • Aisin Seiki failure: the only plant of the only producer of a critical component in Toyota’s supply chain burns down
          • The Toyota ecosystem responded within 3 days to recreate the capability; back to full production in a week – how?
          • They were all trained in problem solving and cooperation
      • Interactions among problem solvers define a robust organization
        • People spend most time in tight local teams, but they have connections to distant parts of the organization (shortcuts) that allow them to solve any kind of problem
        • Firms must support distributed communication and ad hoc team formation to remain flexible
          • Balance between being too isolated and too connected
        • Failure recovery requires maintenance of some form of communication
          • A network structure that doesn’t have highly vulnerable nodes
          • Multi-scale networks – hard to fragment
        • Networks (social, communication, etc.) can be both the source of adaptability and recovery
    • Implications for Business
      • Design of Infrastructure
      • Effective Architecture
      • Organizational Structures
      • Mergers
      • Change Management
    • Design of Infrastructure
      • Scale-free networks are extremely robust in the face of random failures but totally vulnerable to directed attack
        • DOS, virus attacks
        • Small problems in highly connected hubs tend to be amplified throughout the system – e.g., the airline hub system
      • Complex networks can suffer from cascading failures
        • Chance of failure increases if something else fails
      • Move away from purely scale-free but keep the small world structure
        • We may need to build more distributed networks with lots of meshing if we want more resilient systems
          • Multi-scale networks not just hierarchical ones
      Multi-Scale More densely interconnected Core and Periphery Coordination mostly distant Coordination mostly Local Teams at All Levels
    • Organizational Structures
      • Connectedness creates uncertainty
        • Globally altering events can occur at any time
      • Dealing with uncertainty requires flexibility
        • Rigid hierarchies are notoriously bad in uncertain and changing times
        • Adaptable organizations are ones in which teams can self organize when needed to solve small and large problems
          • Find short paths to the right experts or resources
          • Really hard problems are solved by the system not individuals
      • There is a balance between coordination and production
        • If everyone is just going to meetings nothing happens
        • If no one talks to anyone outside their own local cluster, the organization is extremely fragile and resistant to change
        • Find ways for heads down experts to be connected via bridges
      • Multi-scale networks create the most flexible organization
        • Shortcuts across the traditional hierarchy
        • When most people have approx the same number of links at all scales, you get the easiest to search network: there are short paths and they can be found
      • Webs of external relationships also confer robustness if done right
    • Mergers
      • Valdis Krebs has studied how the social network in an organization affects communication and acceptance of new ideas
        • Consider ways to map your various networks
          • Inflow from Krebs ( www.orgnet.com )
          • Email: sent mail in pairs is a good method
      • In trying to merge two companies, and begin to create a single organization, it is helpful to identify the hubs of the social networks in each organization
        • To get information across the old boundary
        • To understand how to influence the people in the other organization
    • Merger Map
    • Design of Collaboration Infrastructure
      • Communication and information sharing drive fast problem solving and adoption of new designs, processes, strategies, etc.
        • Better tools for this can help make an organization more robust and adaptable, but there are limits to what any one person can handle
      • Most use of tools will be for small, highly interactive groups
        • So these types of interactions don’t need to scale
        • With something like Groove, define lots of small groups rather than one big group
          • People can participate in multiple groups
      • Encourage/support “weak” cross-group links differently from group interactions
        • Remember, many broad ties cannot also be deep
        • Local ties are more like conversations, distant ties are more like broadcast (but at least people will listen)
      • Is the solution to KM really about harnessing social networks to solve the “Who Knows the Answer to This?” problem?
    • Effective Architecture
      • Trying to get different workgroups to agree on a common architecture is a difficult undertaking
        • Different understanding of problems, different contexts to use common standards
      • Understanding and using an architecture requires work on the part of people
      • The set of interfaces that define an architecture might be modeled as a network of this sort
        • How many are global versus local?
        • How are the decisions made? Who do you influence or seek advice from?
      • Get the balance right between agreements for clusters and agreements that need to propagate globally
        • Global agreements need participation from the right “hubs”
        • There can’t be too many global standards
    • Change Management
      • A key communication hub in most corporations used to be the smokers, who gather together outside frequently
        • What replaces this fast track for spreading information?
      • Causing a rapid information cascade depends on:
        • Making the threshold of adoption low – ease of use, good out of the box experience, etc.
        • Spreading through the right social network – don’t try to spread through people who will get overwhelmed by lots of other conflicting influences on them
    • Two Great Books
      • Linked – The New Science of Networks
        • Albert-Laszlo Barabasi
      • Six Degrees – The Science of a Connected Age
        • Duncan J. Watts
    • Thank You
        • [email_address]
        • +1-617-834-4388