From Big Data Comes Small Worlds: How the Technology Powering Social Networks Impacts Youby David Messineo, Senior Software Architect, CA Technologies

Harnessing Algorithms: A Plan of Action

Imagine you’re employed at a small fictional company, Acme Corp. that sells avariety of widgets to promote good health. You run sales and marketing for thecompany, and your primary task is to generate and grow revenue in a consistent,predictable manner.

In one sense your job is easy to explain. You sell widgets for more than it coststo collectively create, deliver, and support over the lifetime of a product. Youhave a network of suppliers and distributors, and of course a collection ofcustomers (both satisfied and not) who sustain both your job and the company.The challenge is that revenue has been dropping as the costs have risen andyour distribution network has not been keeping pace with its sales projections.The question you ask is simple: What should I do now?

Whatever plan you recommend cannot be entirely different from what you dotoday, as it would likely be both expensive and difficult to socialize among yourpeers and management. On the other hand, last quarter’s results acknowledgethat existing processes are clearly not working. In assessing the industry,however, one striking theme is becoming clear: the role of social networks iscreating a form of viral loop of profitability for your competitors whilesimultaneously locking your firm out. One real change is now evident for firmslike yours who in the past spent a fortune pushing out the message. Now it’syour customers pushing the messages among each other and back to you, yoursuppliers, and your distributors. You don’t create the buzz; the buzz finds you.After considerable research and conversations with key stakeholders, you decideon a three step plan.

Step 1: Capture and Parse Information (from everyone). While the informationyou’ve been getting has previously been useful for making accurate forecastsand containing costs, it did little to account for either reductions in overalldemand or the changes in the cyclical nature of how customers purchased your

About the author

David A. Messineo is an IT ServiceManagement (ITSM) Practitioner withmore than 20 years of experiencedeveloping and deployingenterprise-level software solutionsfocused on IT management.

He is currently a Senior SoftwareArchitect at CA Technologies where heis responsible for designing solutionsfocused on strategic planning,investment management, benefitsrealization, portfolio rationalization,performance management, andrelationship management.

A majority of David’s career has beenfocused around IT ServiceManagement with specific focus onIT-Business alignment, IT FinancialManagement, and IT ChangeManagement.

David holds certifications in both ITIL(Masters) and eSCM. David is amember of the CA Council forTechnical Excellence, an electedorganization of top technical leadersin CA Technologies.

product. You want to collect a lot more information around patterns of usage,competitive products, and uncover possible opportunities for cross-selling oraffinity-based selling based on perceived patterns in purchase behaviors.Ultimately you want to forecast based on market behaviors in addition tohistorical sales figures. To really drive value, whatever technology is adoptedmust be capable of mining knowledge and wisdom from realms of unformatted,unreliable, and incomplete data sources. The result of such data parsing mustbe information that is simultaneously decision ready and actionable.

Step 2: Rationalize Resources. While you often provide advice and take counselfrom your business partners (suppliers, distributors, and customers), it is not soclear who, if anyone, is actively influencing your firm’s interests to the market –and when it’s being done – regarding costs, purchases, distribution, marketing,competitive features, regulatory policies, or customers. You’ve spent time bothoptimizing your supply chain to reduce costs and rationalize investments androlling out new marketing plans to expand your competitive territory. Yet yourmargins indicate that the level of pro-active influence you have appears to besliding. You want to revamp your whole company into a value chain and driveinfluence through the entire lifecycle of your products. In particular you want tobeat the strategies employed by your competitors by creating a powerful viralloop, where customers create products, and customers create customers. Whileyou understand that such influence requires social capital, it’s not clear how tobuild a network capable of exploiting opportunities in a timely manner. To reallydrive value, the technology stack you employ must be able to quickly discover,navigate, and exploit communities of influence as it morphs across a largenetwork of stakeholders.

Step 3: Assemble Solutions. The potential amount of raw data and informationavailable to analyze and interpret presents a significant challenge, given thatyou need to further rationalize how to distribute it intelligently (to targetinformation your partners want to know) and productively (to gain a competitiveadvantage from it) throughout your network of resources. Complicating theachievement of success are multiple forces working simultaneously withoutproper leadership or overarching coordination, thereby countering one another’sefforts. What you need is a way to synchronize the efforts of various bodies todeliver a solution to the market that is competitive, timely, profitable, and viral.To really drive value, the technology adopted across your resource network mustbe pervasive and collaborative, all the while supporting the local autonomy andorganic diversity required to sustain individual profitability.

Creating an actionable strategy for each of these three steps is formable.Enterprise-size solutions like ERP and CRM exist to help address and organizeeach of these steps in efficient ways. In addition, many niche-based applicationsare available to optimize a particular step or orchestrate work across a sequenceof steps in a streamlined process. Often it is these applications with targetedfunctionality, arising from academic research, or entrepreneurial innovationcenters that create new paradigms for running your business more effectively.In almost all cases it involves removing the latency and privacy of a middlemanand exposing the world to new ways to reorganize.

Addressing these three steps independently, however, without regard to theunderlying technology, will likely fail. Why? Because shared across all of thesetechnologies is a key set of underlying capabilities, driven by powerfulalgorithms that collectively build a platform or utility, which will really drive thecompetitiveness of your business.

To really drive value, whatevertechnology is adopted must becapable of mining knowledge andwisdom from realms of unformatted,unreliable, and incomplete datasources.

You want to beat the strategiesemployed by your competitors bycreating a powerful viral loop, wherecustomers create products, andcustomers create customers.

The technology adopted across yourresource network must be pervasiveand collaborative, all the whilesupporting the local autonomy andorganic diversity required to sustainindividual profitability.

The Computational Platform

Capability 1: Storage and Retrieval. The way you store information is muchless important than the way you retrieve it. Whether the information is in acorporate repository, a dashboard, a report, an Excel worksheet, a PowerPointfile, or a Word document is much less relevant given the kind of data miningand text mining tools available today. These tools can process thousands, if notmillions of data items and present them in a manner that is actionable. Withthe advent of mobile technology, SaaS delivery, and Cloud Storage, informationis accessible almost anywhere. Key to supporting this capability are many of thealgorithms behind traditional relational databases, hierarchical databases,object-oriented databases, and recent new algorithms supporting Big Data –including those that support Hadoop’s MapReduce, HBase, Hive, HDFS, Mahout,and Whirr. Of particular interest is MapReduce – which changed the landscapeof how to distribute workloads across a cluster of hardware resources.

Capability 2: Searching and Sorting. Without a doubt, one of the cornerstonesof technology and algorithms to change society is the ability to search massivestreams of data and sort it in a manner that is consumable by a user (or asystem). In fact, a historical survey of computer science clearly indicates theyears of effort that been put into creating searching tree algorithms like Binaryand Hash, along with searching algorithms like Merge, Heap, and Quicksort.More recently, additional searching algorithms that apply to networks have beenintroduced including Minimum Spanning Trees, Breadths-First Graph Traversal,Shortest Path algorithms, and Random Walks. Equally important to networks aresorting algorithms like Preferential Attachment, Cliques, Centrality,Betweenness, Strong/Weak Ties and Structural Holes. Rounding out thesealgorithms are ones that focus on unstructured information and relate toconcepts like Semantic Networks, Similarly Networks and Co-OccurrenceNetworks.

Capability 3: Security. Information is only as valuable as the trust you canplace in its accuracy. Whether it’s the information you receive from corporate,external data from partners, the counsel you receive from a peer, theinformation from an article on the internet, or the advice you get from a socialnetwork, all of it must be trusted. Further, it’s not simply the source you musttrust, but it’s the manner in which the source came upon this information. Notsurprisingly, the algorithms needed to ensure such security are critical for theentire 3-step approach to be viable across a wide spectrum of stakeholders. Yourfirm’s reputation is always on the line. Similar to Search and Sorting, Securityhas developed as a result of innovation in the underlying mathematics ofpattern matching. Algorithms that support Public Key Cryptography and DigitalSignatures play an obvious pivotal role. But so do less obvious algorithms, likevoting, that measure the reputation of people in a social network. Whether it is aweighted network based on the evaluation of the members of a social network,or a score based on evaluation of the content they provide, these algorithmsreflect a powerful means to instill trust in the network.

Capability 4: Selection. Selection is often confused with Searching and Sorting.However anyone who has ever used Google to search on a topic mightappreciate its ability to search and sort through trillions of data points andreduce it to an organized list, nonetheless, is still confronted with plenty tochoose from. What distinguished Google was PageRank, an algorithmicapproach to identifying the most important, most relevant sources ofinformation based on the historical selections of millions of people and thelinkages between content sources. There are other types of selection algorithms

The way you store information ismuch less important than the way youretrieve it.

One of the cornerstones of technologyand algorithms to change society isthe ability to search massive streamsof data and sort it in a manner that isconsumable by a user

Information is only as valuable as thetrust you can place in its accuracy.

including affinity analysis, Facebook’s Edgerank, HITS, SALSA, and even one thattransformed the world of finance permanently: Black-Scholes. Selection,however, doesn’t end there. It also includes two of the most importantalgorithms ever created: Linear Programming (i.e. The Simplex Method) andMonte Carlo Simulation that both deal with finding solutions along a spectrumof possibilities. An area of new research, Percolation Theory – simulates theeffect small changes can have on a massive scale. Modern approaches includingTheory of Constraints and the Delphi Method are being re-introduced to helpmake decisions across a broad spectrum of stakeholders with diverse expertise,opinions, and background. Last, but certainly critical to the success of selection,are those algorithms that detect and remove outliers in data. Among the mostcommon are clustering (k-nearest distancing), standard deviation, and variousforms of defining boundaries (e.g. quartile ranges).

Capability 5: Learning. If algorithms are going to have ongoing relevance tosociety they must be able to learn from what works, and what doesn’t work.There are many methods for achieving this capability, but most of them dependupon a few basics: lots of raw data with accurate and clear historical results, acollection of inputs that properly accounts for the significant variation in results,a method of determining whether a given recommendation is closer or furtherfrom a desired goal, and critically important and often left out, a measure ofuncertainty if the system is subject to being impacted by unanticipated risks.From these elements the use of Bayesian networks, genetic algorithms, neuralnetworks, and fuzzy systems can provide reliable and measurableimprovements over time. Recent advancements in game theory (i.e. theMinimax Method) learn from multiple actors looking to optimize their owninterests simultaneously. With the processing power of modern systems,traditional statistical methods are being improved with algorithms like RandomTrees and Random Forests. Finally advanced methods like Fast FourierTransform are finding new applications by being targeted in areas wheremassive amounts of data have patterns of information that are difficult forpeople to detect.

Capability 6: Messaging and Routing. Societies naturally form so that as acommunity we can learn (and survive) from one another’s unique skill sets. Butsuch learning requires a form of messaging between the various audiencemembers. While human language traditionally played that role, in a world oftechnology, it’s the infrastructure that allows messages to be sent and receivedaccurately. Therefore, algorithms around Text Compression, Encoding, and ErrorCorrecting Codes play a pivotal role to ensure that the message sent is the sameone received. Just as critical are those algorithms responsible for routing and

If algorithms are going to haveongoing relevance to society theymust be able to learn from whatworks, and what doesn’t work.

Societies naturally form so that as acommunity we can learn (and survive)from one another’s unique skill sets.

silently searching for the best path between source and destination. Theseinclude A*, Dijkstra’s algorithm, policy-based, Quality of Service, distance-vectorand link-state algorithms, among many others.

Capability 7: Processing and Scheduling. With the plethora of informationavailable it becomes essential that a technology platform can scale to handlethe workload required to execute against the aforementioned capability. A lot ofthese algorithms are computationally heavy, requiring a mainframe, a (virtual)server farm, or some peer-to-peer processing facility that shares the workload.In all cases the work must be scheduled, and such algorithms can becomeexceedingly complicated. In particular, when scheduling requires a specificsequence of interdependencies between the components, scheduling arguablybecomes the most complicated of all the capabilities discussed here. There aremany such algorithms because of the degree of optimization possible, but theprimary ones include Critical Path Method, FIFO, LIFO, Round-Robin, Lottery,Shortest Job, QoS and Weighted Queuing.

All of the algorithms mentioned above, and many others, support thesefundamental capabilities and are critical to making technology support oursociety. Over time, these are the algorithms that will prove conclusively howtechnology really impacts our lives, and ultimately our society. What’s amazingis that many of these algorithms are inherent in our own thought processes –we are simply unaware of the fact that we use such methods to make decisions.

A Universe of Data within a Mobile Device

In November 1963 the BBC launched what was, and is still, one of the mostsuccessful science fiction programs of all time: Doctor Who. Simply put, DoctorWho is a about a humanoid alien known as “the Doctor” who travels around intime and space to save the universe (mostly humans thankfully!). His spaceship,known famously as the TARDIS, looks like a blue British police box. What makesthe TARDIS famous, however, is that it is much bigger on the inside than theoutside. The same is true with those mobile devices you carry around. They maybe small and compact, but in reality they have access to almost limitlessinformation. Several years ago, you would have needed a few bookcases to storea collection of 3000 compact disks. Today, through mp3 compressionalgorithms, you can carry the entire collection in your pocket. More extraordinaryare streaming movies. Movies take a lot of space, whether in physical form, ordigital. With the proliferation of cloud storage and delivery, however, I can haveaccess to thousands of movies at any time, whenever I want them.

As a business consumer, a mobile device allows me to be tethered to thenetwork at all times (should I choose to). I can receive emails, reviewdashboards, execute orders, etc. Additionally through SaaS and Cloud deliveryplatforms, I can have large applications available to process information andintensive tasks like voice recognition, scenario planning, and various statisticaloptimizations, handle the work off-line, and forward the results to me anywhere.Through GPS I can locate where I am and find the personal and professionalservices I might need 24 hours a day, every day. Most importantly I have accessto the people and resources I need to get my job done, almost on demand. Thefreedom provided by mobility redefines the nature of society by providing ameans to communicate with resources, regardless of type, across broaddistances easily and cost effectively. Mobility redefines what it means toorganize resources to work collaboratively. It is the mobile device thatencourages the proliferation of social networks and with it, an underlyingalgorithmic approach to the world.

Many of these algorithms [we use] areinherent in our own thought processes– we are simply unaware of the factthat we use such methods to makedecisions.

The freedom provided by mobilityredefines the nature of society byproviding a means to communicatewith resources, regardless of type,across broad distances easily and costeffectively.

If I’m going to be successful implementing my 3-step plan by realigning my firm to exploit social networks and takeadvantage of a mobile platform, I need to understand the nature of how data plays a significant role in driving thebehavior of my resources, both inside and outside the company. I need to redefine the economics of my company around“going social.” We’re becoming a “people-driven” economy that readily shares cheap information, much of which isgarbage unfortunately, but nonetheless contains the nuggets I need to drive business. Ironically, it’s the same networkthat provides the information that ultimately makes sense of it, if I can find a way to harness it. If social networks (andsocial media) are going to be really successful, technology must enable me to assemble information from disparatesources in a way meaningful to my organization and to the others in my community.

If you recall, the first step of my master plan was to capture and parse information (from everywhere). Up until recently,there was a practical limit to the amount of information available. Typical back-end databases were limited in how muchdata they could consume and still be responsive. Data was often assembled from multiple sources into data warehousesand then broken down for performance reasons into data marts. Even more challenging was that data existed in differentsystems, different media, and different formats. Some of these formats were structured, but many were not. Such datawas difficult to search and sort into meaningful analysis. Security played a huge role because of the challenges withauthentication and local governance polices around what data could actually be carried over a physical border.

In my futuristic vision of the ultimate work, what I want is to start measuring and tracking everything of any interest thatsurrounds my business. I want to be able to identify relationships, correlations and insights into the resources that makeup my network so I can leverage them to the fullest by taking advantage (in a proper way) of opportunities across thevalue chain and driving results. People want to disseminate information and expose others to changes from within andsocial networks make it easy. I want early access to these trends. If technology is really going to serve me it needs toenable my company to have two-way meaningful conversations with the resources that encompass my network andinfluence these changes. The algorithms supporting the platform must enable a new form of transparency with mycustomers and distributors that earns their trust and support. I want to answer the question “What should I do now?” withconfidence, even under changing conditions.

All images clker.com

But as a small company I also need to keep my investments small. I can’t affordto take big gambles. I need to leverage the successes my company has to keepdialogs alive and meaningful, while recognizing that the shelf life ofconversations is short. I need to identify small, incremental experiments ininnovation that keep the network engaged. What I need is to build products thatpeople want to spread the word about, and provide the mechanism to makesuch communication easy and widespread. I don’t want to be the door-to-doorsalesman on the outside; I want to be the trusted neighbor on the inside.

There is, fortunately, a concept that describes the viral nature of products. It’sknown as the network effect and is often calculated by what is called the “viralcoefficient” – a measure of how many new customers each customer brings in.The premise of the network effect is simple – as you increase the number ofconnections in your network, it grows faster. Such growth is why supporting amobile platform is critical to success. If I want to grow my business, I need toengage the network wherever it is. If one exists already, I need a presence. Ifsuch a network doesn’t exist I need to encourage a community of people tocreate one. Either way, speed, transparency, and trust are critical to getting theinside line to real influence. People are hard-wired to socialize, and I need toleverage that in a manner that allows our products to be the center ofconversation.

It should come as no surprise that technically supporting an ability to captureinformation from all the various resources (i.e. nodes) in a network is expensiveand time consuming. The very notion of going viral is that network scalingcauses capacity requirements to increase exponentially. I’m going to have toconstruct a system, and supporting process methodology, that can handle lotsof unstructured information at various points in time and provide for analysisacross potentially many different systems. That is why Big Data is the newcornerstone of what a technology-enabled society is about. In fact one couldeasily argue that data is as important a factor to profitability as labor, capital,manufacturing and distribution. There is a universe of information inside thatlittle mobile device. Even Doctor Who would be impressed.

A World of Big Data

Big Data changes the game because of its ability to store huge amounts ofunstructured data spread out over a large body of repositories (internal andexternal), while simultaneously retrieving it in a manner that is accessible by amobile device. There are several ways that Big Data will help me achievesuccess in Step 1, where I want to capture and parse information.

I need to leverage the successes mycompany has to keep dialogs aliveand meaningful, while recognizingthat the shelf life of conversations isshort.

“Viral coefficient” is a measure of howmany new customers each customerbrings in.

Speed, transparency, and trust arecritical to getting the inside line toreal influence.

First, it can ensure that the information I collect and analyze is available andusable at a much higher frequency. The very foundation of social networks isthat a vast amount of information can be made available through direct andindirect links. By making the data easier for people to share, it accelerates thevalue of my network by providing the means to determine noise from nuggets.

Second, as I collect more information across the value chain, my understandingof the firm’s performance in all areas across the lifecycle of my productsimproves. My knowledge of market trends also expands. This allows me toproactively share such intelligence with the members of various communitiesthat play a role in impacting the welfare of my organization. Such informationallows the network to better coordinate its actions in an agile manner, reducevariability, improve efficiency, and ultimately boost profits.

Third, the capture of data along the lifecycle of my products allows me to betterdefine small experiments and determine whether they will have the necessarymomentum (i.e. viral growth) to be successful. Failing faster is one of the newtenets of business. By isolating information from various points in the valuechain, I can discern where improvements need to be made, or whether to divestentirely from a specific product line quickly and without much impact to mybottom line.

Fourth, the data captured allows me to better profile the types of suppliers,distributors, and customers that I’m working with to determine whether they fitmy business model. Often, communities separate themselves organically intogroups that have their own networks of influence and areas of expertise. Byproviding data relevant to these social networks, I can demonstrate my firm’scommitment to excellence in driving solutions to meet the unique need of thecommunity. I also, over time, drive two-way conversations that build up thereputation of my firm to provide quality products. Big Data gives me a way tobetter influence communities of interest by exposing me to the way myindustry’s network of resources organize themselves.

Finally, Big Data becomes a method for me to ensure that I manufacture theproducts that people want to buy and, in fact, that they will buy them. Big Dataallows me to effectively turn the process of developing products over tocustomers without placing a lot of unnatural risk to my organization. While Istill have to carefully evaluate the information being provided through analysisalgorithms and other sources, allowing the network to effectively create its ownlaboratory of innovation becomes a huge competitive advantage to mycompany and the ecosystem in which I do business.

The Four Laws of Networks

The previous section about Big Data discusses how I could leverage the datacoming from my ecosystem to direct the efforts of my firm and its network ofresources. But what if there is another person, just like me, working for a firmthat participates in a network looking to achieve similar results? What if, in fact,every resource in my network was trying to simultaneously achieve the samebenefits I am by exploiting the benefits of Big Data? That might significantlyimpact the manner in which I decide how to allocate work and, therefore, to addvalue to the various resources of my network.

Step 2 of my plan is to rationalize resources. If I’m going to participate in thisnew paradigm of social networks, I will need to better track all of the resources Ihave at hand, regardless of whether they belong to my organization nor not.Whether it’s my suppliers who provide various ways to source materials, my

Information allows the network tobetter coordinate its actions in anagile manner, reduce variability,improve efficiency, and ultimatelyboost profits.

Big Data gives me a way to betterinfluence communities of interest byexposing me to the way my industry’snetwork of resources organizethemselves.

If I’m going to participate in this newparadigm of social networks, I willneed to better track all of theresources I have at hand.

distributors who provide access to my products throughout the world, or my customers, who see value of my products indifferent ways, I need to exploit these resources in ways convenient to my firm, and to the success of the entire network.

If Step 1 was about capturing the right information, analyzing it, and deciding “what I should do now,” Step 2 is aboutensuring that I build and support the social practices that push and pull information to and from the appropriate partiesin my network – so that together we can organize our efforts and take the appropriate actions to be successful. Iftechnology is really going to be relevant to my firm, the killer applications I’m seeking must implement the algorithmsthat piece together social, technological, and economic components in a manner that builds the kind of relationships andcommunities that will keep me continually agile and relevant. I’m looking to allow people and organizations to coordinatetheir individual actions to create new forms of power along the value chain. I’m looking to encourage and enable a degreeof interaction and collective follow-through that simply was not possible in the past.

Paramount to organizing the collective activities of my community is my ability to understand the underlying networkfrom two perspectives: the first is about the power of certain laws that guide the development and effectiveness of mynetworks, and the second is the underlying analytical capabilities that come from analyzing the resources and theirrelationships to one another. From a top down perspective, several well-known “laws” guide these networks.

All of these laws impact how I execute in Step #2. But a few lessons should be taken when interpreting these laws. Whilecommunities may help organizations to be more competitive, and individuals to work more cooperatively, there is noguarantee the level of expected influence or value will be achieved.

Growing your network is essential, but so is growing the nature of how the resources in that network are linked and howmuch they are trusted. In fact, if I were to define a heuristic about the proper size of a network, it would ultimately lead toa conversation about the reputation I have in the community. I can sell my ideas and products into a smallerself-organized community where I have influence or where people who trust me have influence. Belonging to a largenetwork where my reputation or visibility is limited is likely a waste of my resources and my time.

As a result, these small experiments of innovation where I’m hoping for customer collaboration are focused on mydistribution to niche groups of like-minded audiences, even if they are far apart geographically. That’s the benefit of lowcost communication.

At one time, Henry Ford talked about selling a car with anything you wanted, as long as it was the one model he wasmanufacturing.5 More recently was born the notion of mass customization that market to a person of one. Now, with theadvent of social networks, it turns out the most profitable products are those marketed to a community of like-mindedpeople, exploiting the reputation that influential members have to sell products.

The ‘Commons’ in a World of Networks

Another important concept when talking of networks from the top down is the concept, first introduced by Garret Hardinin 1968, known as the “Tragedy of the Commons.”6 In short it reflects, as described through Wikipedia “the depletion of ashared resource by individuals, acting independently and rationally according to each one’s self interest, despite theirunderstanding that depleting the common resources is contrary to the group’s long-term best interests.” This conceptplays a huge role in the success of social networks because of the nature of how people account for whether they arecontributors, consumers, or simply free-riders. If a network has a large following but most of the people involved aresimply consuming information and not contributing to it, at some point the community no longer serves a purpose.Effectively what makes the network valuable is the availability of new information. Once that information is no longernew, the value of that network diminishes, often to the point where it dies of content starvation.

When communities of interest were limited by physical space, it was reasonably easy to see who was contributingvaluable new content from those who were not. In a world where communities exist in virtual space, identifying thefree-riders can be more challenging. But ultimately, through algorithms that self-monitor, track and often vote, in somecapacity, on content, it becomes clear who the real contributors are. If I want to have a preferential access to the expertiseof the communities I leverage, I simply can’t exploit the network of resources I have for my own benefits until I contributeto theirs.

So as I define how I’m going to execute on Step 2, I need to keep in mind the nature of why my resources are willing tocontribute to my welfare: because I’m willing to contribute to theirs. Therefore one measure of how valuable a resource(or ultimately a community) is, depends upon how often I must interact with it and the amount of time that passes.Iteration, the natural act of mutual exchanges, consequently plays a big role. If I provide critical information to you, andyou don’t reciprocate in kind in a timely manner, our next interaction may be quite different.

Additionally, groups of resources who work together often pool their interests to the benefit of themselves and not thedefectors. A viable step 2 must therefore be explicit in how it accounts for reciprocity, cooperation, and ultimately

reputation. What’s interesting, upon careful inspection, is how communitieshave implemented ways of tracking these things, albeit discreetly in manycircumstances. What’s behind it all? Algorithms running on top of technology.

A final concept important to my success in Step 2: the notion of wisdom. Inknowledge management there is what is called the DIKW Pyramid. The DIKWPyramid is a collection of models that reflect the relationships between Data,Information, Knowledge, and Wisdom. The notion is simple; as I proceed upeach level in the hierarchy I get better educated and am capable of makingbetter decisions. One of the benefits I get from a groomed social network is theability to inquire about a few critical topics to my business:

What does the future look like? What caused sudden changes? How should I go about implementing a new product or service? What should I do now?

An effective community has a diversity of expertise while simultaneouslysharing cause for meaningful communication of ideas. In almost all casesasking the network for observations around a specific event will statisticallytend to descend upon the right answer.

Identifying the Various Network Types

From a bottom-up perspective the notion of networks is a science unto itself.Once you start creating and documenting what your network of resourcesactually look like, you can start to take advantage of the properties of thatnetwork to communicate messages to specific resources who, for variousreasons, have special privileges granted to them. One place to start is to identifythe kind of network you’re dealing with. The five most common networksinclude the following:

An effective community has adiversity of expertise whilesimultaneously sharing cause formeaningful communication of ideas.

Organizational Networks. These networks are common in professional workingenvironments as they focus on distributing tasks to achieve specific goals. Oftenthese kinds of networks are characterized as being formal (i.e. the OrganizationChart) or informal (i.e. the network of Influences that really describes where thepower is).

Randomly-Distributed Networks. Often communities of interest develop in arather random way. People find something of interest and pass it on to theirfriends, who in turn, do the same. Over time the network gets bigger, but itdoesn’t show a concentration of links to a few set of nodes (as it does inScale-Free Networks). Networks that exhibit the property of being randomlydistributed are often organically grown and consist of relationships that areautonomous in their nature of correspondence.

Scale-Free Networks. Unlike randomly-distributed networks these networksfollow a degree distribution called the power law, a concept where a majority ofthe nodes (i.e. resources) are connected through a small number of nodes (i.e.hubs). Many networks, particularly those of interest to the business community,are scale-free networks because of the nature of people and resources toorganize around people, companies, and industries that wield influence on theireconomic circumstances.

Large-Scale Networks. Generally physical networks, like the telephone systemor the internet, are examples of large-scale networks. Because of the hugenumber of nodes, often the typical measures that are used in smaller networksare not as effective because finding a place of influence, or expecting suchinfluences to be persuasive is nearly impossible. There are, however, someconcepts, like viruses, or videos that have gone “viral” for example and can bestudied at this level. The study of “buzz” and how to take advantage oflarge-scale networks is an important extension to social network analysis.

Complex Networks. Most large social networks with complex connectionsbetween them that are neither regular nor random are examples of complexnetworks. Often complex networks lead to the study of biological networksfound in nature, where there are areas of either great density or little density. Ifyou are dealing with business issues, generally networks of this type would betoo complex to adequately study unless you plan to spend a considerableamount of your time, money, and resources. With the advent of Big Data,however, and the analysis tools that take advantage of that environment, theability to capture and identify meaningful patterns of significance in networks ofthis type is less of an issue.

Networks, Relationships, and Power

The science of studying networks has many names, but the primary one is socialnetwork analysis (SNA). It is fundamentally the mathematics behind socialnetworks and consequently the underlying structure that supports many of itsalgorithms. For me to be successful at Step #2, I don’t have to understand all ofthe various concepts available through SNA, but there are some fundamentalones that have practical advantages if I apply them in my day-to-day thoughtprocess. The most important concepts are discussed below.

Connections

Homophily. Within a network there is a tendency of individual nodes (i.e.resources) to link with other nodes that are similar in nature, whether becauseof geographical, economical, philosophical, or other persuasion. Measuringhomophily allows you to identify where individual resources can be leveraged

The Minotaur andthe Labyrinth

The Minotaur was acreature of Greekmythology that hadthe head of a bull andthe body of a man. Ifyou consider, for amoment, how the

combination of powerful algorithmsand technology has bullied itself(with our implicit permission) intoour society and changed itfundamentally, the physicaldescription of such a beast is not afar stretch from the truth.

According to legend, the Minotaurlived in the center of a largelabyrinth where people would belost forever. To appease the monster,seven boys and girls were sacrificedto him every nine years. In an effortto stop such madness our hero,Prince Theseus, went to theLabyrinth to slay the Minotaur.Unlike others who had failed beforehim, he was smart enough to solvethe real problem: bringing both asword and a ball of string. Afterkilling the beast, it was only becauseof the string he had laid down thathe could find his way back andescape alive. Understanding yourway around the network is, in ametaphorical sense, like laying outstring: it keeps you from getting lost.

Ultimately the Labyrinth is notunlike the networks of today. Linksconnect to nodes, which connect tomore links, and so on. And while weare not trying to kill the Minotauranymore, we are seeking to find asimilar beast: the powerful sourcesof influence that nudge thecommunity and its resources tofocus on specific areas, often to theextinction of others. A sword and aball of string aren’t going to cut itthis time. We need an approachmuch more powerful.

by sharing with them the kinds of information that bond them together.

Heterophily. Within a network there is also a tendency of individual nodes toseparate into diverse groups because of their differences in belief. This doesn’tin itself mean they are not part of the network, rather they are clusteredbecause they have different profiles. Measuring heterophily is a powerful way tounderstand how a product can be marketed and channeled to a set of resourcesthat may co-exist collectively for one reason but choose to organize in separateclusters for other reasons.

Multiplexity. While a lot of network analysis tends to focus on simply whethertwo nodes are linked, the reality is that two nodes may share multiple links toone another because they share multiple attributes or other factors. Measuringmultiplexity gives a sense of how strong the tie is between two nodes becausethey share several characteristics in common. It could be they belong to similarnetworks. Often multiplexity can be used as a means of measuring influence ifthe nature of attributes that link nodes can be discerned.

Mutuality/Reciprocity. In directional graphs (i.e. where links have directionality)reciprocity represents the extent to which links exist in both directions for anytwo nodes. Directionality plays a critical role when, for example, you’re chartingthe course a particular message travels to build momentum. Measuringreciprocity reflects, to an extent, two major influences. First, it reflects the factthere is a shared interest by both parties, and second it shows that theinteractions between them will likely continue into the future. In a sense, it’s away of reflecting an iterative tit-for-tat strategy between nodes.

Preferential Attachment. In many societies, there is a tendency for those whoare rich to accumulate wealth faster than others simply because they have moremoney to invest. This forms a positive feedback loop. It also explainspreferential attachment – that new links are disproportionally bonded withnodes that already have a lot of links. Depending upon what you’re trying toachieve this bias can work for you or against you. In either case measuringwhich nodes the proportion of new links are bonding to can be an indication ofpreferential bias and a strategy to leverage or avoid depending on what sideyou’re pursuing.

Assortativity/Dissortativity. Because nodes with a high number of connectionstend to have a significant influence on the network, it is worth evaluating twospecific kinds of behavior. The first, assortativity, focuses on those high-degreenodes that tend to only bond with other high-degree nodes. These tend to bemore common and indicate that resources with a lot of influence tend to focuson other resources with a lot of influence. If you’re looking to take advantage ofthis network, and you don’t have influence already, it may be difficult to gettheir attention and build traction. It indicates that a partnership strategy wouldbe best.

Unlike assortativity, dissortativity tends to focus on high-degree nodes thatbond with low-degree nodes. That is, members who tend to have a high degreeof influence tend to focus on those members who do not have a high degree ofinfluence. There are several reasons for resources wanting to adopt this strategy,including one that is important to building a vibrant community: diversity. Oftendiverse points of view, particularly if new, do not yield much influence when firstpresented. However, in time, it’s these novel ideas that become the seeds ofgrowth in the future for the community and its membership. Whether it’s a newproduct, a new approach, or something else, it often takes nodes that have a

Within a network there is a tendencyof individual nodes to separate intodiverse groups because of theirdifferences in belief.

New links are disproportionallybonded with nodes that already havea lot of links.

Often diverse points of view,particularly if new, do not yield muchinfluence when first presented.However, in time, it’s these novel ideasthat become the seeds of growth.

degree of influence over the network to adopt the resources you bring to thetable. Finding players who invite new points of view is often critical to thesuccess of innovative firms.

Network Closure. One of the most important concepts in Social NetworkAnalysis is that of a triad – a series of three nodes that are connected. (Themath behind why triads are important gets complicated.) If, in a particular triad,node A is connected to node B, node B is connected to node C, and node A isconnected to node C, then the triad is considered to be transitive. Now imaginethat you are looking at an entire network, and measuring how many triadsexpress a transitive property. Such a measurement is what network closure iscapturing. It gives the individual examining a network a sense of whether thereis a lot of “white space” within a network to work with. Many opportunitiesappear in a network as a gap (see “structural hole”), so having a measurementthat allows you to quickly identify how closed a network is can be quiteinsightful. On one hand if you’re in control where customers are locked-in, itworks to your advantage. On the other, if there are gaps, you’ll get anopportunity to exploit them.

Propinquity. Before the time of digital communities, when communities weretied to geographical distances, propinquity was a measure of the tendency fornodes to be close to one another physically. In a digital world, distances aregenerally recast in the form of attributes, like wealth, industry, size, customers,and so on. Measuring propinquity allows you to effectively measure the abilityof a network to be clustered into sub-networks that have little or no overlap. Itrepresents one way to profile specific sub-sections of your network of resources.Companies often pay research firms great sums of money to find out about whyparticular members of a network are closely associated with one another.Measuring propinquity can, at a minimum, give you an indication where yourresearch efforts should be targeted.

Percolation. One of the key characteristics of a network is the ability and timingrequired to disseminate information to all of its nodes. A measure of“percolation” identifies how dense a network needs to be to accomplish thesegoals. Indirectly, it also is a good gauge of how effective resources are atcollaborating.

Distributions

Bridge. Within networks, particularly those with that lack density, there are gapsbetween nodes that are often filled only by connecting through a third node. Forexample if node A is connected with node B, and node B is connected with node

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One of the key characteristics of anetwork is the ability and timingrequired to disseminate informationto all of its nodes.

C, and node B is the only node that connects A with C, it is considered a bridge.Often bridges are not measured alone but are considered a complementary partof taking advantage of structural holes (defined below).

Centrality. In general centrality is a measure of how important a node is in anetwork. There are different kinds of centrality as defined below. Generallyspeaking centrality is an indication of influence. The nature of that influence is,however, dependent upon the specific way in which it is calculated. Each ofthese calculations is considered its own type.

Degree Centrality. The most instinctive way to measure centrality is simply tolook at the number of links going to and from a particular node, adding them upand by sorting them, proclaiming the node with the largest number the mostimportant. However, in practice it doesn’t work that way. There are manyreasons why nodes (i.e. resources) may have a considerable number of links tothem without indicating any sense of importance. The value of degree centralityall depends upon the definition of why the links were created in the first place.If the links reflect the influence they have on the network itself (which is rarelythe case), then degree centrality would be a good indicator of “center.”

Closeness Centrality. Imagine picking a node on a network and calculating theshortest distance between it and each node on the network. Add all of thedistances. Now take 1 and divide it by that number. The result reflects ameasure of centrality and reflects how fast it takes information to spreadinformation from that node to all other nodes in the network. (Please note theactual mathematical calculation for closeness centrality is a bit morecomplicated than presented here.) As you can see by its very definition,calculating closeness is a huge benefit to a business because it helps prioritizewhich nodes should be the focus of their messaging efforts.

Betweenness Centrality. Previously we discussed the nature of a bridge. It wasdefined as a node that connects two nodes that would otherwise not beconnected. Bridges are critical because they can make or break the ability ofmessages to get through. Betweenness quantifies the number of times a node isa bridge along the shortest path between two other nodes. As instinct shouldtell you, the more often a node is a bridge, the more important it is. In fact,betweenness centrality is a measure of pure power because of its ability tocontrol the dissemination of information across a network. From a businessperspective, having knowledge about where power resides is critical to gettingresources to cooperate.

Density. The easiest way to understand density is simply to look at a networkand look how many links exist between the various nodes. If two networks havethe same number of nodes, the one with more links is denser. If you’re lookingat networks with different nodes, it’s simply the one with the higher proportionof actual links divided by the number of possible links. From a businessperspective the density is an indication of how easy (high) or difficult (low) it isto disseminate information.

Distance. Imagine you’re looking at a map and planning a trip between NewYork and San Francisco and the nodes of the networks are all the major cities inbetween. The distance is the number of links required to get from New York toSan Francisco. Often distance refers to the minimum distance, which wouldreflect the minimum number of miles one travels to get between the two cities.From a business perspective distance is used in several formulas but by itselfgives little information unless it’s used as part of some kind of optimizationexercise.

The notion of centrality is critical tounderstand the impact you will havein a social network.

Structural Holes. One of the most revolutionary research efforts in SNA was that of Ronald Burt around the notion of whathe calls structural holes. Defining a structural hole is simple – it’s simply the the absence of ties between two parts of anetwork. However, from a business perspective the power of identifying these gaps and being able to exploit them isincredible. Most products, if they are successful, achieve such status because they filled a gap somewhere in a value chainthat went unrecognized. When people are successful, it’s often because they filled a gap that others didn’t see or wereunable to fill. Finding gaps in a social network (or any network), particularly when paired with a bridge gives you bothinfluence and power.

Giant Component. If you examined a network’s schematic and found an area where there is a large majority ofcomponents – enough that it effectively forms a sub network – then you’ve found a giant component. To make use of agiant component, however, the links themselves must reflect a form of distance that has some relevance. For example, anetwork of major US cities that are simply connected by highway and indifferent to distance would be different from anetwork of US cities that were connected by distances. The latter would show a Giant Component along the East Coast orthe West Coast (particularly California). Depending upon how links are defined, a Giant Component can be an indicator ofwhere to exert the majority of your influence.

Smart Mobs and Small Worlds

Having worked through Step 1 “Capture and Parse” and Step 2 “Rationalize Resources,” I’m now ready to conquer Step 3“Assemble Solutions.” Traditionally the notion of assembling solutions revolved around lifecycle management: designing,testing, marketing, manufacturing, delivering, supporting, and retiring. While the individual requirements of thoseprocesses won’t change, the methods to execute them will. Using the knowledge and advantages attained from Step 2,rationalizing resources, the manner in which I assemble a solution needs to involve one or more communities workingcollaboratively.

Building such collaborationbetween multiplestakeholders is going torequire the skillfulcombination of acomputational platform, acommunication network, asense of flow (that comesfrom timing) and a viablesystem for trackingreputation. Such acombination is known in theindustry as a smart mob. Asmart mob is a dynamiccommunity of one or morenetworks that supportsautonomous emergentcapabilities of its own, yet isconditioned to also fostercooperation among them ina trusted and “profitable”manner.

We discussed the “computational platform” when we discussed the seven shared capabilities that are central to anytechnology centric company. When I mention the term “communication network” we’re talking not only the physicalmanner of communication, but also the underlying nature in which networks amplify how people share what they know.In a sense, the network has a form of persistent storage and retrieval system to recognize and respond to patterns it seesautonomously. This happens only if there are enough eyes navigating the streams of messages being communicatedacross the community. In a mobile environment, there is always the need to have one foot planted in the now and oneplanted in the future. If you can’t anticipate and plan your next steps, the community simply isn’t providing you the kindof information that will maintain your interest or attention.

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Because of the aforementioned “commons” problem, managing and monitoringreputation is critical to having the kind of influence and power that will makethe community valuable to me. Power can only come from my ability to harvestknowledge from the network, make resource decisions in my company, andencourage appropriate decisions to be made that benefit all across thecommunity at large. Trust is not unlike a bank account. You invest in it as yousee the rewards of cooperation. Ultimately, what will build my firm’s reputationis network capital, the same means by which individuals benefit fromknowledge through social capital. Just as it helps an individual to understandthe power structure of his or her organization, I must understand who controlsthe information sources of my community. Otherwise I simply won’t have thereputation, credibility, or information required to build influence and, in turn,cooperation.

Keeping the notion of a smart mob in mind, and simultaneously looking out forthe self-interests of my firm, there are several working assumptions I anticipatehaving to support if the other members of my community are going to considerjoining my efforts for reasons important to them, and perhaps to them alone.

The amount of centralized control throughout the network will need to be1minimal. Organizations have their own interests and their own perspectiveson what makes a profitable company. To the extent that the resources ofmy network collaborate it will be out of their self-interests.

The various resources that make up my community will be, almost by2definition, autonomous. Such autonomy helps to ensure resources have theflexibility to pursue the required diversity in their business models toremain viable, as well as an accurate source of the kinds of changes I needto add value throughout the system.

The organizations involved in the communities of interest to me will benefit3from a large number of connections (i.e. density) between them. Theseconnections should be linked throughout the lifecycle of my products,involving all members of my organizations and the appropriate resourcesthroughout my network (as identified in Step 2).

The benefits attained by the members of my communities of interest will4not be linear. Some will benefit much more than others, but on the wholethe benefits attained for each will be better than those who have chosennot to participate at all.

When these four assumptions hold true, I anticipate that collectively thecommunity I am nurturing will start behaving in such a way that I can enjoy aform of “collective intelligence.” Such “inside information” will make myproducts more competitive by streamlining the lifecycle process with minimalrisks. For example, I would expect and would judge the success of Step 3 basedon the success of answering the following questions in a manner that increasesyour margins:

Designing – What products will I buy?•Testing – How do I know it will work in my environment?•Marketing – Whom should I recommend this product to?•Manufacturing – What level of quality is required?•Delivering – How much demand is there for the product? •Supporting – How can I ensure the product works as advertised?•Retiring – How can I leverage this product to get customers to “upgrade”?•

Managing and monitoring reputationis critical to having the kind ofinfluence and power that will makethe community valuable.

What will build my firm’s reputation isnetwork capital, the same means bywhich individuals benefit fromknowledge through social capital.

So what I need is some way to reflect this notion of Smart Mobs usingalgorithms. If technology is really going to be of value, I need to have some wayof quantifying just how smart the mob is. In fact there are several ways, notsurprisingly. But given that the focus of my three steps is around communities,an approach that supports the concept of a network would reflect a natural kindof consistency. One such notion is what’s called a “small world.”

First identified as a particular kind of network by Duncan Watts and StevenStrogatz in 1998, a small-world network is one in which most nodes are notneighbors of one another but can still reach one another with a very smallnumber of hops between links.7 There are many examples of small worlds butthe one we’re concerned about is identified as a social influence network.

When I consider the number of resources that will be involved in my company’snetwork, I quickly realize that I simply cannot and should not try to reach themall directly. But what if I don’t have to? What if there was a way to get resourcesthat don’t link with my organization directly to display a form of “cooperative”behavior all by themselves, without any direct influence from me? What if suchbehaviors were, in fact, emerging out of their own self-interest and not mine?

Small worlds help to represent such a network simply because the number oflinks I need to reach almost anyone on the network is small. So right now, byhaving trusted relationships with influential resources of communities I engagewith, I can essentially reach out to almost all of them with much less effortthan if I tried to do it directly. And further, because the route I’m using likelyends with each node linked to a resource of trust, if I’m respectful with mydirect links I can ride off their reputation. As was mentioned previously, I don’twant to be the door-to-door salesman. With small worlds I rarely am.

So how can I leverage smart mobs and small worlds to be successful in Step 3?Well, it turns out I don’t have to think all that much about it because nature hasdone much of the thinking for me. It turns out that Mother Nature has beenrather wise to these kinds of networks for some time, and that the answer tomany of these challenges I’m faced with can be answered through carefulobservation of her children, particularly the smaller ones.

What Mother Knows: It’s All About the Algorithm

Imagine the challenge of being an ant. You’re puny, you don’t live long, you’re afood source for just about everything, and you’re not very smart. Yet you’re the

A small world is one in which mostnodes are not neighbors of oneanother but can still reach oneanother with a very small number ofhops between links.

By having trusted relationships withinfluential resources of communities Iengage with, I can essentially reachout to almost all of them with muchless effort than if I tried to do itdirectly.

It turns out that Mother Nature hasbeen rather wise to these kinds ofnetworks ..., and that the answer tomany ... challenges can be answeredthrough careful observation of herchildren.

most successful organism to show a form of “society” in existence. In fact,you’re so successful that legions of researchers observe everything you do sothat we, as humans, can mimic your success.

Now imagine being a fish. You’re small and have limited energy, and like theant, you’re a food source for anything else that swims. Yet you have to swim in aschool for protection, across thousands of miles and through various obstaclesto reach certain destinations throughout the year.

These two examples are very much like the challenges I will have in executingStep 3, Assembling Solutions. While I like to think of my company as smarterthan an ant or a fish, I need the community to help grow my business while mycompetitors are out to trying to take me out.

What we need is some way to get our community to work as a unit. We need aset of rules that describes how we are to act together for the benefit of all. I canpretend to set my own rules. But how long will that last if the rest of mycommunity’s resources don’t follow along? How might we create anenvironment where the whole is greater than the sum of its parts, where thevarious resources within the networks I participate in act with a kind ofcollective wisdom?

Within science there is a branch of study called “complex adaptive systems”(CAS) which studies the nature of how various agents work together for thebenefit of all while simultaneously focusing on what is good for them. The keyto such systems, as you might expect, given the IQ of the aforementionedorganisms, is a simple set of rules to work from. From these simple rulesemerge complex patterns that are referred to as “self-organization.”Self-organization happens everywhere, in families, cities, and society.

Now looking back at the assumptions I made about resources investing to workwith me on Step 3, recall that I stated that we had to avoid the expectation ofsome central point of control and direction. I also mentioned that eachresource would need to be free to pursue its own direction. I would need theresources to have a large number of connections to work together, and finallysome actions would have disproportionate benefits or impacts to members ofthe team. These are, in fact, just the assumptions that are required for CAS topersist and be successful.

Many such systems has been studied and modeled, but the two I plan toleverage for Step 3 are the ones known as flocking and ant optimization.

Flocking

Craig Reynolds was a pioneer in the study of Complex Adaptive System.8 Indoing his research he devised a very simple set of rules to describe how Boids(birds) can organize their behaviors, independently, to be successful collectively.He laid out just three rules.

Avoid bumping into another bird. That is referred to as separation. 1

Move in the average direction to the closet bird around you. That is2referred to as alignment.

Move towards the average position of those closest to you. This is3referred to as attraction.

From just these three rules, birds that are scattered around can actually begin tofly together and act cooperatively according to what is good for the group.

What we need is some way to get ourcommunity to work as a unit.

Within science there is a branch ofstudy called “complex adaptivesystems” which studies the nature ofhow various agents work together forthe benefit of all while simultaneouslyfocusing on what is good for them.

So how can I use this to be successful in executing Part 3 of my plan? First, I can avoid putting my resources in a positionwhere my firm is competing with them at any point in the lifecycle process. Creating artificial overlaps in the value chainis simple one way of bumping into another bird.

Second, I can focus my energy on observing what is going on around me in my community and nudging my organizationto focus on the direction the community appears to be identifying as the future. I can also nudge resources outside mycommunity to focus in a similar direction. But this alignment cannot simply happen at the top levels of the organization.The alignment has to be recognized as occurring in the lower levels of the organization, where many of the relationshipsinside the communities we participate actually live. They also must exist across the entire lifecycle as identifiedpreviously.

Third, I have to make my firm as attractive as possible by looking at those linkages, those resources that are closest to me,and ensuring that I have the trust and reputation for them to do meaningful business with me and see the advantages ofnudging their organizations closer to the needs of mine.

What this spells out, if anything, is how important is the concept that communities that exist inside my organization(whether formal or not) exhibit the same kinds of conditions that the communities outside my firm exhibit. This is one ofthe key aspects of a social network. It really isn’t a set of communities, per se; it’s one community that shares linkages atdifferent places because of the product’s lifecycle.

Ants

One of the major activities of ants is finding food and bringing it back to the nest. The manner in which they find food isn’taltogether that exciting; they search rather randomly for food. When they find food, however, the ability of ants tooptimize their manner of getting back to the nest is fascinating – a real study in nature.

Ants carry a chemical signaling compound, a pheromone that they drop for other ants to follow. The more pheromone thatis dropped along a certain path, the stronger the signal is for ants to follow it. But here is where the optimization comesin: the ant that follows the leader always tries to take the shortest path to its location. So if ant A leaves the nest andtravels to point A, point B, and point C, and when ant B leaves the nest, ant A is at point C, ant B will look to minimize thedistance between point A and point C, effectively eliminating point B as a place to stop. Once food is discovered the antswill start dropping pheromone on the way back to the nest. Over time the optimized path will have concentratedpheromone, so the ants will be able to find the optimized solution without a central leader, each acting on its own, and ina sense, each with a sense of the future as they know the direction they are headed.

Images Produced in Netlogo. Wilensky, U. (1998). NetLogo Flocking model. http://ccl.northwestern.edu/netlogo/models/Flocking. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

This technique has been proven successful in many of the toughest and longest standing problems in mathematics. Inparticular the traveling salesman problem (what’s the fastest path for a salesman to cover every city on a given map) hasproven how effectively ants go about their business.

What does this mean for my business and success at implementing Step 3? It means I need to define the challenges ofmy business in terms of how my resources can go about finding different solutions to various aspects of my lifecycle andconvey to me the knowledge I need to optimize their portion of the value chain. Often this happens automatically whenfirms talk about streamlining the supply chain – but often this is about optimizing linkages two nodes at a time, instead oflooking at the entire community.

One creative way ant optimization was used by a company to the success of their community was to post a competitionwith its resources to find better ways of executing on the distribution portion of its value chain. When a better solutionwas found by one of its resources, the technique was shared with the entire network, and the process resumed again. Inthis way there was a constant pressure for the network to be more productive, without the typical centralized commandstructure that is pervasive in many businesses today.

If I were to fully embrace both of these lessons from Mother Nature, I would engineer Step 3 to focus on havingcompetitions among the appropriate resources in my network while following a loose set of rules as modeled using theflocking approach. The great news is that while I’m building rapport with the communities of interest that surround myfirm, I can ask them for their advice.

What Should I Do Now

We’ve covered a lot of material here, and through example discussed how technology really makes a difference throughthe lens of social networks. Nonetheless, there is much more than can be learned about social networks.

One course of action of capitalizing on social networks is to learn and understand the mechanics behind networks andexploiting their properties to harvest information about the resources around you. The best way to begin this process is byusing a combination of simulations to see how resources can work together through the right incentive system andanalysis tools that can handle the kinds of computation you need to calculate the various metrics and measurementsdiscussed above. Netlogo is an appropriate tool for such simulations, and the many models it has available can help youunderstand how powerful complex adaptive systems are in organizing efforts across a social network. For handlinganalytical calculations, Gephi, NodeXL (for beginners) and/or Pajek are tools to consider.

Images Produced in Netlogo. Wilensky, U. (1997). NetLogo Ant Lines model. http://ccl.northwestern.edu/netlogo/models/AntLines. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

BibliographyAdams, Paul. Grouped How Small Groups of Friends are the Key to Influence on the Social Web. Berkeley, CA: New Riders, 2012•Barabasi, Albert-Laszlo. Linked The New Science of Networks. Cambridge, MA: Perseus Publishing, 2002•Buchanan, Mark. Nexus Small Worlds and the Groundbreaking Science of Networks. New York: W.N. Norton & Co., 2002•Buchanan, Mark. Ubiquity The Science of History. London: The Orion Publishing Group Ltd, 2000•Dewdney, A.K. The New Turning Omnibus 66 Excursions in Computer Science. New York: Holt Paperbacks, 2003•Fisher, Lee. The Perfect Storm The Science of Complexity in Everyday Life. New York: Basic Books, 2009•Johnson, Steven. Where Good Ideas Come From The Natural History of Innovation. New York: Riverhead Books, 2010•Knoke, David and Yang, Song. Social Network Analysis Second Edition. Thousand Oaks, CA: Sage Publications, Inc., 2008•MacCormick, John. 9 Algorithms that Change the Future The Ingenious Ideas That Drive Todays Computers. Princeton, NY:•Princeton University Press, 2012Mihalcea, Rada and Radev, Dragomir. Graph-Based Natural Language Processing and Information Retrieval. New York: Cambridge•University Press, 2011Penenberg, Adam L. Viral Loop From Facebook to Twitter, How Today’s Smartest Businesses Grow Themselves. New York: Hyperion•Books, 2009Qualman, Erik. Socialnomics How Social Media Transforms the Way We Live and Do Business. Hoboken, NJ: John Wiley & Sons.,•2009Rheingold, Howard. Smart Mobs The Next Social Revolution. Cambridge, MA: Perseus Books Group, 2002•Scott, John. Social Network Analysis a Handbook Second Edition. Thousand Oaks, CA: Sage Publications, Inc. 2000•Steiner, Christopher. Automate This How Algorithms Came to Rule Our World. New York: Penguin Group, 2012•Watts, Duncan J. Six Degrees The Science of a Connected Age. New York: W.N. Norton & Co., 2003•Minotaur. (n.d.). In Wikipedia. Retrieved Feb 16, 2013, from http://en.wikipedia.org/wiki/Minotaur•Social Network. (n.d.). In Wikipedia. Retrieved Feb 5, 2013, from http://en.wikipedia.org/wiki/Social_network•Social Network Analysis. (n.d.). In Wikipedia. Retrieved Feb 5, 2013 from http://en.wikipedia.org/wiki/Social_network_analysis•

Notes1 Moore, Gordon E. "Cramming more components onto integrated circuits" . (1965) Electronics Magazine: p. 4. 2 Shapiro, Carl and Varian Hal. Information Rules. Boston: Harvard Business, 19993 Reed, David. "The Law of the Pack". Harvard Business Review, February 2001: 23–404 Beckstrom, Rod. “The Economics of Networks”. ICANN Presentation, July 29 20095 Ford, Henry and Crowther, Samuel . My Life and Work. Garden City, New York, USA: Garden City Publishing Company Inc., 19226 Hardin, G. "The Tragedy of the Commons". (1968) Science 162 (3859): 1243–1248. 7 Watts, Duncan J.; Strogatz, Steven H. "Collective dynamics of 'small-world' networks". (June 1998) Nature 393 (6684): 440–442. 8 Reynolds, Craig. "Flocks, herds and schools: A distributed behavioral model.", (1987) SIGGRAPH '87: Proceedings of the 14th annualconference on Computer graphics and interactive techniques (Association for Computing Machinery): 25–34

Appendix: Simulation ExhibitsExhibit #1: Diffusion on a Directed Network. http://ccl.northwestern.edu/netlogo/models/DiffusiononaDirectedNetwork

Imagine that you have a set of resources that are directionally linked together and you want to share information with them. They inturn want to share with the members of their network. What would happen with the quantity of information as it is dispersed into thenetwork? When you first run the model you will be asked for grid-size, link-chance, and diffusion-rate. The grid size reflects how largethe network is that you want to model. The link-chance describes how dense you want the model to be when it first created. The moredense, the more connections between the nodes. The diffusion-rate reflects how much value is shared with its neighbor. Whathappens as you run the simulation? One obvious conclusion is that certain nodes end up with a majority of the information (orwhatever is being distributed). What happens as you increase or decrease the parameters?

Exhibit #2: Giant Component http://ccl.northwestern.edu/netlogo/models/GiantComponent

One of the interesting characteristics of certain kinds of networks is how fast nodes become linked with other nodes, even if they are“randomly” connected. When you think about building or contributing to a network, you should assume that very little of it willultimately be private. The whole point of social networks is to bring the “news” to you, regardless of how “distant” it is. When you runthis simulation that is one basic parameter – how many nodes are there initially. As you watch the nodes randomly being added youstart to notice at one point, the tipping point, where a huge number of nodes suddenly are connected. This can be verified by theshape of the Giant Component Growth graph. This simulation clearly shows why adopting a mobile platform, where people areconnected at all times, increases the power and reach of your network, while significantly decreasing the risks and costs associatedwith simply pushing out information broadly hoping it will find an audience.

Exhibit #3: Preferential Attachment http://ccl.northwestern.edu/netlogo/models/PreferentialAttachment

If you take any community, whether online or offline and examine it closely, you would likely see that some members tend to havemore connections than others. In fact, you would likely see that some members are effectively “hubs” for the entire network linkinggroups of people together. Why is this the case? It’s in our very nature, our human nature, to seek out those who are successful andwho have proven themselves to be authoritative in some manner. That’s why these people have considerable influence over largepopulations of users in any given network. The challenge is that even as new nodes (or people) are added they tend to only extend theinfluence of people already at the center of power. There isn’t much setup to this model; you simply have to select the setup buttonand go (or go-once if you want it to work in an incremental fashion). The substance of this model s in the “preference” assumption itmakes – that a new node’s chance of being selected is directly proportional to the number of connections it already has.

Exhibit #4: Team Assembly http://ccl.northwestern.edu/netlogo/models/TeamAssembly.

One of the lost opportunities that companies face is when they constantly switch players from various teams as they interact with thenetwork. Trust and reputation are central to growing a network’s viability. As people work in teams they build rapport with one another.Over time such rapport builds currency in the power of the social network to engage collectively towards what’s best for the entirepopulation. This model is a little complicated. Essentially there four parameters you can control. The team-size is the number ofagents in a newly assembled team. The max-downtime is the amount of time (in steps) that an agent will remain withoutcollaborating before it retires. P is the probability that an incumbent is chosen to become a member of the new team, and Q reflectsthe probability that the team assembled will include a previous collaborator of an incumbent on the team. As you run the model keepan eye out on the graphics – particularly the average component size and the # of agents in the giant component. What shouldbecome clear is that having a large number of newcomers means that network is not taking advantage of experienced people, while alot of repeat collaborations indicate there might be a lack of diversity in the team. Both extremes negatively impact the overall socialnetwork. Having too many people participating in “group-think” means original ideas will not be considered, even if they are better.Having too many new ideas will likely lead to a large amount of errors.

Exhibit #5: Virus on a Network http://ccl.northwestern.edu/netlogo/models/VirusonaNetwork

In this exhibit we simulate one way a virus can spread throughout a network. The same model can be used for almost any networkand for any element, like memes for example, as long as the key assumptions about what causes a node to fail reflect reality. In thissimulation you’re able to vary the value of many parameters. You first pick the number of nodes and the average node degree (averagenumber of links coming out of each node). The Initial-Outbreak-Size indicates how many nodes are initially infected. Thevirus-spread-chance reflects the probability that a neighbor is likely infected. The virus-check-frequently is how often the nodes are infact verified to either have a virus or not. And the recovery chance is the chance the node will be resistant to a future attack. The samemodel could be used if you replaced a virus with a negative review about a product.

Exhibit #6: Fire http://ccl.northwestern.edu/netlogo/models/Fire

One of the algorithms we discussed was called Percolation. The science behind this particular approach is that something smallhappens and is percolated (or spreads) to areas surrounding it based on the density of the connections. At some major point, theremay not be enough density to keep the word going, and in other cases, there is enough density to ensure the message is consumed byeveryone in the network. In this exhibit we simulate a fire spreading through a forest. Its ability to spread is based on the density oftrees. When you open the model try various densities to see how quickly the fire either goes out, or consumes the entire forest.Somewhere between 60% and 65% is generally where you see a significant change in the amount of forest damaged by fire. Thequestion is – how dense are resources connected in your environment to percolate ideas to everyone?

Exhibit #7: Rumor Mill http://ccl.northwestern.edu/netlogo/models/RumorMill

Similar to the Fire and Virus on a Network exhibit, in this exhibit we model the spread of a rumor. And similar to the other models thenotion of spatial proximity is important. However, in this model it is assumed the links reflect geographic presence, as opposed tonetwork ones. So each node gets eight neighbors, one for each direction. After each step, the simulation randomly picks one neighborto “spread” the rumor to. While there are a few adjustments that can be made to this model, the most important is the init-clique,which reflects a percentage of people who already know the rumor. What is enlightening is to look at the three included graphs. Therumor-spread illustrates the percentage of people who know the rumor, the successive differences show the number of new peoplewho know the rumor, and the successive ratio illustrates the percentage of people who are hearing the rumor at each step. Afterplaying with several variations you will start to notice the typical S-curve shape that reflects the tipping point style.

Exhibit #8: Ant Lines http://ccl.northwestern.edu/netlogo/models/AntLines

In many ways, the selection process of nature has found ways to optimize the manner in which organisms, regardless of size andintelligence, find and consume energy sources. One of the best examples of this is the manner in which ants are able to find foodalong what are first random paths but over time become optimized. In this simulation there are three parameters to focus on. The firstis num-ants, the number of ants. The second is the leader-wiggle-angle, which reflects how much of an angle the leader is allowed tomake to deflect from the current path they were on. In a sense, this represents how much randomness is allowed in the system. Thethird parameter is start-delays, which specifies how fast the ants leave their nests. This is an important parameter as it represents howfast potentially an optimized solution can be found. In a sense, it represents the availability of resources to search for an optimizedresponse. As you run the model, what’s important to see his how effective ants are at finding the best path, given a set of constraints,in a short period of time.

Exhibit #9: Flocking http://ccl.northwestern.edu/netlogo/models/Flocking

By far one of the most interesting and far-reaching discoveries in the science of swarming, and systems in general, flockingdemonstrates how three simple variables can organize a large community of organisms. In this case simulation there are sixparameters. The first population – allows you to set the number of birds in the model. Vision allows you to set how far ahead a birdcan see. Vision plays a critical role when applying the model to people, for example, because it’s an indicator of transparency and thelatency in time it takes to get actionable information. Minimum-separation identifies how close birds can get to one another.Obviously setting the minimum-separation distance higher than the vision makes it difficult for birds to cooperate, simply becausethey can see what is going on. Organizationally, this is what happens when there are too many layers or in a network when there aretoo many links between resources that need to coordinate responses. The max-align-turn parameter sets how quickly a bird will focusits direction on the other birds encounters. In the business world, this reflects how fast a resource can adapt what it’s doing to act in away that allows for full collaboration. The max-cohere-turn parameter describes how birds naturally follow one another simplybecause they are close. Often, in terms of social networks, this reflects the creation of communities and sub-communities. It alsoreflects the nature of links between nodes because it reflects some kind of shared characteristic. Even resources that may haveindividual requirements separate from the others often have a common purpose. This purpose acts as a form of cohesion. Themax-separate-turn reflects what happens when two birds get too close to one another. In business terms this reflects the level ofcooperation and competition that can be expected from resources in the network. A high capacity for competition will mean a birdturns in an opposite direction, or a high degree, whereas a tendency for cooperation indicates a bird turns at a much smaller angle.Flocking is such an important concept in social networks and business in general that many models have been built from it. Netlogoitself provides several variations to study that can help you to learn about the behaviors they are modeling.

Exhibit #10: Voting http://ccl.northwestern.edu/netlogo/models/Voting

If you have ever wondered why groups of people in a particular geographical area tend to vote for common policies, and yet canchange over time, this model demonstrates one example of why. It also is an excellent example of how mobile devices are changingwhat it means to be geographically connected. No longer is physical distance a limitation to the kinds of information you have accessto. What becomes critical is who your neighbors are, from the standpoint of who has influence on you. The model itself is reallysimple. When the model is first set up it randomly picks a position for each cell. Then, after each cycle, a cell is evaluated to see if itsvote will change. It looks at its neighbors’ votes and whatever position has the majority view, the cell will change its vote accordingly.Depending upon how you set the two parameters, change-vote-if-tied and award-close-calls-to-loser, the final determination of thatvote may change. Over time you will see how votes assemble into groupings until you get to a point where there is little or no change.In terms of social networks, this means that reaching out to your resources requires that your influence be pervasive if you want toensure the ideas you have will be adopted. Having a select few who adopt your ideas may not be enough, particularly if the networkdoes not show signs of preferential treatment through the organic creation of hubs, etc.

Exhibit #11: Small Worlds http://ccl.northwestern.edu/netlogo/models/SmallWorlds

The ultimate expression of social networks comes down to the concept of small worlds. It reflects the simple idea that a person is onlya couple of connections away from any other person in the world. Extreme as it may sound at first, it really personifies the notion thatthe world is much smaller than we think, and that the actions we take can and will be potentially noticed by a larger audience than weexpect, particularly if there are structurally places where messages can get amplified (i.e. hubs, niches, and so on). In this model thereare two parameters: num-nodes, which reflects the number of nodes the model has to start, and rewiring-probability, which reflectsthe chances a link will be rewired as a result of the encounter. Run the model and watch in particular the clustering-coefficient andaverage-path-length. These indicate how “big” or “small” the network actually is. The “smaller” the network, the more likely yourmessages will get to the resources required to work collaboratively on achieving an end goal.

Exhibit SituationsAll Netlogo Exhibits:

Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/ Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #1: Diffusion on a Directed NetworkStonedahl, F. and Wilensky, U. (2008). NetLogo Diffusion on a Directed Network model.http://ccl.northwestern.edu/netlogo/models/DiffusiononaDirectedNetworkCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #2: Giant Component Duncan J. Watts. Six Degrees: The Science of a Connected Age (W.W. Norton & Company, New York, 2003), pages 43-47.S. Janson, D.E. Knuth, T. Luczak, and B. Pittel. The birth of the giant component. Random Structures & Algorithms 4, 3 (1993),pages 233-358.Wilensky, U. (2005). NetLogo Giant Component model. http://ccl.northwestern.edu/netlogo/models/GiantComponent Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #3: Preferential AttachmentAlbert-László Barabási. Linked: The New Science of Networks, Perseus Publishing, Cambridge, Massachusetts, pages 79-92.

Albert-László Barabási & Reka Albert. Emergence of Scaling in Random Networks, Science, Vol 286, Issue 5439, 15 October 1999,pages 509-512.Wilensky, U. (2005). NetLogo Preferential Attachment model. http://ccl.northwestern.edu/netlogo/models/PreferentialAttachmentCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #4: Team AssemblyR Guimera, B Uzzi, J Spiro, L Amaral; Team Assembly Mechanisms Determine Collaboration Network Structure and TeamPerformance. Science 2005, V308, N5722, p697-702Bakshy, E. and Wilensky, U. (2007). NetLogo Team Assembly model. http://ccl.northwestern.edu/netlogo/models/TeamAssemblyCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #5: Virus on a NetworkStonedahl, F. and Wilensky, U. (2008). NetLogo Virus on a Network model.http://ccl.northwestern.edu/netlogo/models/VirusonaNetwork Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #6: FireWilensky, U. (1997). NetLogo Fire model. http://ccl.northwestern.edu/netlogo/models/FireCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #7: Rumor MillWilensky, U. (1997). NetLogo Rumor Mill model. http://ccl.northwestern.edu/netlogo/models/RumorMillCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #8: Ant LinesWilensky, U. (1997). NetLogo Ant Lines model. http://ccl.northwestern.edu/netlogo/models/AntLinesCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #9: FlockingWilensky, U. (1998). NetLogo Flocking model. http://ccl.northwestern.edu/netlogo/models/FlockingCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #10: VotingWilensky, U. (1998). NetLogo Voting model. http://ccl.northwestern.edu/netlogo/models/VotingCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Exhibit #11: Small WorldsDuncan J. Watts, Six Degrees: The Science of a Connected Age (W.W. Norton & Company, New York, 2003), pages 83-100.DJ Watts and SH Strogatz. Collective dynamics of 'small-world' networks, Nature, 393:440-442 (1998)Wilensky, U. (2005). NetLogo Small Worlds model. http://ccl.northwestern.edu/netlogo/models/SmallWorldsCenter for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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