Endless Knots

Uh-oh: Don't catch divorce

From the department of everything's related to everything and the seemingly endless findings stemming from the Framingham Heart Study, here's yet another research study proving that networks carry a lot of strange viruses. According to the study, you're much more likely to divorce if a friend divorces (75% more likely) and somewhat more likely (33%) if a friend of a friend divorces.

I'm not making this up. See this article by Rick Morin on the Pew Center site.

Here's the graphic from a new study by Brown University's Rose McDermott and stalwart network-contagion researchers James Fowler and Nicholas Christakis. Its title tells the story: "Breaking Up is Hard to Do, Unless Everyone Else Is Doing It Too: Social Network Effects of Divorce in a Longitudinal Sample."

I draw two conclusions from this: one, get rid of all your friends; and two, don't live in Framingham, which is where many of these studies have been done. You might have a heart attack, smoke, gain weight, and, now get divorced.

(No, no...I greatly admire Christakis et al. Terrific insights into the "stremgth of all ties" -- I just made that up.)

Virtual Learning Company Questionnaire

Writing books has its downsides -- sore neck, publisher disagreements, pressure to always start another one -- but then something like this happens and it's worth writing a few more. This morning I received a really nice email from Archana Chandrasekar, who's working on a master's in Information Systems Management at Warwick Business School. Part of the research includes receiving responses to this survey on learning in virtual organizations. Archana is asking me to post a link to the survey so that my readers hopefully will take it. How can I turn down this request after reading the paragraph below that begins, "On a personal note...?" So, dear readers, please take the survey!

And for the record, as Archana suggests, I am always happy to help students. In the long sweep of human history, the study of virtual organizations is still an emerging field; we need all the research we can muster.

My name is Archana Chandrasekar and I am currently pursuing my Master's in Information Systems Management at Warwick Business School, UK.As a part of my dissertation, I am examining the applicability of Peter Senge's Learning Organizational Model within the context of Virtual Organizations. My research methodology involves a qualitative analysis of the learning processes within an virtually functioning organization. In addition to an in-depth case study analysis I have interviewed a few individuals working in Virtual Organizations and have found the results to be satisfactory and in line with my hypothesis. But as expected the number of employees willing to participate is disappointing as the questionnaire involves questions relating to sensitive topics such as appraisals, managerial approvals etc.

As a regular visitor of your site I have often seen questionnaires posted by other postgraduate students. I would humbly request you to do the same with my questionnaire as it would be of immense help. Kindly note that due to confidentiality concerns individuals are not required to fill in any personal details.

This questionnaire is targeted to individuals working in virtual organizations and how they learn and share knowledge. The questions are based on Pedler et al. (1997) "The Learning Company" questionnaire.

On a personal note I would like to thank you for all your contributions to the academic world. Your works are a prominent feature in my literature review section and have helped me understand virtual worlds better. For someone with no technical IT background, I know jumping head first into a topic like virtual organizations would be a colossal task but Lipnack and Stamps(1997) simplified the topic in the most profound way.

The questionnaire link is attached below and I kindly request you to post it on your site as this would be of great help to my dissertation. Thank you again for playing a vital role in my academic endeavors.


Link to survey   

And in case you don't click, here are the questions:

• 1. Does your company regularly take stock and modify business plans, direction and strategy when appropriate?

  Yes

  No

• 2. Do all members of the company take part in policy and strategy formation?

  Yes

  No

• 3. Is Information Technology (IT) is used to create databases so that everyone can understand what is going on and make sound decisions?

  Yes

  No

• 4. Are Systems of accounting and reporting structured to assist learning?

  Yes

  No

• 5. Do managers facilitate communication and negotiation rather than exerting top-down control?

  Yes

  No

• 6. Do flexible working patterns allow people to make different contributions and draw different rewards?

  Yes

  No

• 7. Are roles and careers flexibly structured to allow experimentation, growth and adaptation?

  Yes

  No

• 8. Does there exist systems and procedures for receiving and sharing information from outside the company?

  Yes

  No

• 9. Does your company participate in joint learning events with its suppliers,customers and other stakeholders?

  Yes

  No

• 10. Is there a general attitude of continuous improvement and learning where employees are encouraged to ask if do not know something?

  Yes

  No

• 11. Do employees have their own self-development budgets where they decide what training and development they want, and what to pay for it?

  Yes

  No

 

The future of computer science -- in one hour

I had the chance to listen to one of the icons of computer science recently. John Hopcroft, IBM Professor of Engineering and Applied Mathematics in Computer Science at Cornell, gave a talk on future directions in theoretical computer science to a standing-room-only crowd at Worcester Polytechnic Institute.

While this is not my field, I was interested to hear how someone who's been in the field -- and received just about every award -- might be thinking about the issues most likely to impact the development of the theory of computer science, especially when addressing a throng of young people who will have to execute against those challenges.

Here are my notes, mainly unedited. I hope they make sense.

The early focus was on making computers useful; now they are useful and as we move forward, we need to develop theory that supports new directions.

While we google quite liberally now, in the future there will be new kinds queries that produce reasonable answers to specific questions, like "which car should I buy?" The "system," knowing all your prior searches (hello, privacy) will be able to cluster results of those searches in "temporal cluster histograms" (which he then showed but which I can't remember and which I'm not now going to google...)

He gave the example of googling Michael Jordan. If you're a sports nut, you'll of course get this Michael Jordan but if you're a computer scientist then you would be looking for this one. The future computer will of course know this -- and know that I wouldn't be looking for either one, to be honest.

Which of course led him to privacy issues, which he parsed nicely in regard to electronic medical records. Your doctor needs your whole record; your insurance company needs only the information pertaining to your last visit; a researcher needs statistical information with no independently identifiable info.

(My next note is one of those that I recorded solely because it sounded like poetry -- "zero knowledge proofs" -- but I have no clue as to what it means.)

His next point was a very good one: in the future (which, I believe is actually right this minute), data sets will be so large that we'll only be able to analyze them by sampling them. As one of the computer scientists at the lecture explained to me, think of Facebook's data on 800 million users. There are no machines large enough to bring that magnitude of data into memory -- thus sampling. Kind of like being in the ice cream store and unable to eat all those gallons as there's not enough room in our tummies -- thus we sample. Well, not exactly, but you get the point.

But before I leave this, a few more thoughts because as we collectively dive more deeply into understanding our vast (and vastly growing social networks, about which more below), we need to better understand the implications of what it takes to fully analyze gigantic amounts of data. Which brings me to the classic large data problem that many may remember: SETI, where many of us gave/have given over our machines to the "search for extraterrestrial intelligence," lending our computers to computationally solve that bigger problem of finding such intelligent life. In that example, the SETI scientists send us a piece of the data along with the algorithm and our computer runs "the package," returning a solution that the SETI folks can then combine with others' solutions.

Graph algorithms, the math by which we analyze network maps (who's connected to you, how many people/degrees are between me and you, what your favorite Facebook group looks like), are different.

Whereas SETI-like data can be partitioned for individual machines to compute pieces of the whole solution, graph algorithms, which is what make up network maps, cannot. Why? Because the graphs only  can be analyzed when the algorithm can see all the links between the nodes, which may not be included in the selected parcel. Thus, we can't parcel out pieces of Facebook's 800 million users' data for analysis on your machine and mine. Notice, for instance, the recent study of how sentiments vary on Twitter -- "Drawing on messages posted by more than two million people in 84 countries..."-- which is to say a mere fraction of the tweets.

Big problem which I'll come back to in later posts but in the meantime, let's return to Dr. Hopcroft's lecture where he...

Then used the word "permute," which I'd never heard before thus recording it. No clue once again. Is this a mathematician-type really meaning permutate? Dunno.

Next example re: privacy had to do with GPS only tracking major roads when minor roads often are the better route. Local drivers always take them but they don't show up when you ask for best routes on your GPS. Getting that info requires sharing back road routes but alas, do you want to be identified everywhere you're driving?

This led to his talking about how to look up weather patterns in your local neighborhood, which he does so that he can figure out whether he can walk home in the rain. Walking is his preferred mode of travel -- and he's figured out how to look at current weather maps and adjust for small time differences so that he knows when to leave his office.

And then he got more fully into some stuff that brushes up against the areas we're all interested in: social networks.

An early measure of the quality of communities was thought to be conductance, a connectivity measure from graph theory that indicates how fast you can go from one part of the graph, er, network, to another. But, it turns out, Dr. Hopcroft says, conductance is not the right model for communities.

Instead, we need to look at overlapping communities, massively overlapping communities with common cores that have very fuzzy boundaries. In fact, we need different algorithms in order to understand different communitites.

We need, he said, a "theory of sparse vectors." (I need a graph theorist for this one.)

He then went on to talk about the need for the new generation to incorporate the "law of large numbers" in their thinking. "When I was grad student," he said, "we analyzed graphs with only 7-8 'edges,'" i.e. the connection/link between two nodes. Now, as he had said earlier, the number of edges is so huge that they don't yet fit into any computer memory known to humans.

"There's something fundamental about giant components," he said, something I took to mean that we still don't understand.

My last note about Dr. Hopcroft's lecture is my favorite line: "We have to upgrade our intuition," i.e. it just plain doesn't work in the new world. What's going on now in understanding patterns of higher dimensions is counter-intuitive. Our intuition, he said, was formed in the world of 2 and 3 dimensions but now we live in a world in which datasets are growing exponentially. Touche.

"The Republic of Letters:" The Electronic Enlightenment Project

Thanks to Tom Stewart, who in turn thanks John Hagel for this network discovery: 18th-century social network of ideas, carried out through letters, and now on YouTube, having been mapped by Stanford's "Mapping the Republic of Letters" study. Assistant French Professor Dan Edelstein, principal investigator, describes how ideas spread via letters. Voltaire, for example, wrote 15,000. Cynthia Haven reports:

Edelstein, principal investigator for "Mapping the Republic of Letters" with history Professor Paula Findlen, has mapped thousands of letters that were exchanged during the period of the Enlightenment to uncover hidden truths about the "Republic of Letters."   The latter is "a shorthand that scholars use to refer to writers and philosophers and clergymen and other early modern intellectuals who corresponded across Europe and even across the world," said Edelstein.

...According to Edelstein, "We tend to think of networks as a modern invention, something that only emerged in the Age of Information. In fact, going all the way back to the Renaissance, scholars have established themselves into networks in order to receive the latest news, find out the latest discoveries and circulate the ideas of others."

"We've known about these correspondences for a long time – some of them have been published – but no one has been able to piece together how these individual networks fit into a complete whole, something we call the Republic of Letters."

Where's the research?

There's a press release circulating along with an article or two derived from the press release, one of which states that "[w]orking remotely causes 243 percent more problems than working together in the same location."

I've requested the actual study that has generated this "data," as per the note on the press release, saying that the "full survey results are available on request," hoping that it will contain detail about who participated in the study ("more than 500 individuals"); how the study was conducted; what the respondents' organizational roles were; what their organizations focused on; what the methodology was for data interpretation; and the like but, until I receive that, all I have available is what everyone else citing this study has, i.e., the press release.

Because the results are being quoted--and passed around--as if this is bona fide research, I feel compelled to ask people not to start referring to this "study" until we know whether the study was conducted according to genuine research protocols and whether the conclusions as stated in the release hold up.

Were you in NETWORKING? Please pass along

In 1982, Doubleday published Networking: The First Report and Directory, Jeff's and my first book.

The story of how that book came to market someday will, or rather, should appear in the annals of publishing -- first publisher (Methuen, a British publisher aiming to break into the US market) shelved its whole list just after we'd submitted manuscript; Doubleday picked it up, offering more than twice the original advance (great agent at time, Ron Bernstein); we got the contract to do the electronic typsetting, making it the first commercial book to be electronically typeset under auspices of the authors, never having been retyped by its publisher; translated into Japanese by that country's Economic Planning Agency, and on and on -- but alas this post is about the treasure hunt that we're launching here today.

Networking was, as its subtitle says, a "report" on 1600 grassroots and nonprofit networks focused on the seven topics listed on the cover, along with a directory of those networks.

Within the book was everything from the theory and practice of networks -- with a chapter-long essay devoted to each topic -- to the directory itself, organized in various ways -- by organization name, by location, by keyword, and by title of the network's publications.

Those listed range from The Sun Magazine to World Future Society to Franz Schumacher's Intermediate Technology Group (now Practical Action) to Tilth to Aspen Institute to CoEvolution Quarterly to Dr. Dobb's Journal to the National Center for Citizen Involvement, now the Open Library (where our two earliest books, including this one are listed) and Boston Women's Health Book Collective, now Our Bodies, Ourselves, to which we devoted a chapter of its own because of its unique networking skills. The fact that it's still going after nearly forty years makes the point.

We did all the research via networking, beginning with one letter (as in type it, sign it, put it in an envelope, attach a stamp, go to the post office - remember that?) that we sent to one person whom we knew to be a richly connected networker, who suggested nine others, whom we wrote to, who in turn sent us other suggestions -- and within 18 months, we'd received the names of 50,000 "networks" around the world. We wrote to 4000 of them and got back an astonishing 40% response rate - 1600 letters, packages, publications, descriptions, many with heartfelt expressions of why their leaders networked and how they did it. Incredible experience, to say the least. No Internet. Limited email (a little because a few of us pioneers were already online). Need I say: No web, no listservs, and, of course, no Google, Wikipedia, Twitter or Facebook. Just the poor postman, who left a trail of whiskey nips along our sidewalk.

So now it's 2009, more than a quarter-century later and we're wondering how many of those networks have survived (I chose the ones above that I could google). Why now? Not just idle curiosity but intense awareness that there are similarities between the early 1980s and today. Principal among them unemployment, as per Paul O. Flaim's, "Unemployment in 1982: the cost to workers and their families:"

The economy entered 1982 in a severe recession and labor market conditions deteriorated throughout the year. The unemployment rate, already high by historical standards at the onset of the recession in mid-1981, reached 10.8 percent at the end of 1982, higher than at any time in post-World War II history.

A few other interesting stats from then: the Dow was, get ready, 776 in August of that year, down 30% from its high of 1011 five years earlier. And, the drop in exports and spending just announced is mere fractions shy of "the 6.4 percent rate drop seen in the first quarter of 1982, when the economy was in a recession that lasted 16 months," according to Lucia Mutukani's "US economy weakens," in the Feb 29, 2009, Boston Globe.

Clearly, we made it through that tough time, not purely because of networking, of course (I'm not that much of a romantic), but in part because people got together and figured out some things they could do themselves. We're interested in how those networks have survived and intend to include them in the new bit of writing we're engaged in, details to come.

So would you please pass this note along to anyone you know who might have been in the book? When we have a spare minute (ha!), we'll scan the whole list and begin publishing that. Thanks in advance! And, Networking networkers, please post comments here.

"Connected: the real matrix" - see it Sunday on Discovery

"Nothing is more practical than a good theory."

So says Duncan Watts in a wonderful documentary on the new science of networks. "Connected: The Real Matrix" airs Sunday night at 9pm on the Discovery Channel in the US. The promo (see below) is enough to get us excited as it features Watts and Albert Laszlo Barabasi, who represent the two great streams of advances in network science in the last decade: small worlds (Watts) and scale-free networks (Barabasi). It was Barabasi's work that inspired us to apply the new network science to organizations, a subject we have been studying for almost three decades (see our Library). From that early investigation came our OrgScope tool that maps and analyzes organizations as networks. We've let Barabasi know how influential his work has been on ours and we're excited that this show will be on TV.

What's the "new" in the "new network science?" Two things: an unexpected discovery and a resolution to an old problem. Barabasi led the team that discovered "scale-free networks," non-random configurations with sparse but many-linked "hubs" that turn out to be key nodes in networks. With Steven Strogatz, Watts finally solved the "six-degrees of separation" conundrum by showing how a few "shortcuts" could dramatically decrease average communication distance.

These final two advances enlarged traditional ("old") network science (known as "graph theory") that underlies social network analysis (SNA) and created a remarkable general-purpose network paradigm that is immediately applicable to both people and organizations. Listen closely, and you will hear news in the program about a "general network theory," a mathematically-rooted, experimentally demonstrated, and highly practical theory that applies across physical, biological, and human domains. Our work shows that this same general network theory applies directly to networked organizations (see, for example, our paper, "Principles for Reorganization").

On Monday, February 16, we'll release "Revolution in Networks: Applying the New Science of Networks to Organizations," the next in our NetAge Working Papers series. There, we tell the story and examine the work of both Barabasi and Watts, elegantly platformed by the upcoming documentary.

Check out what's in store on the documentary:

-- Jeff Stamps

Nodes and links, technically speaking

Like the economy, old mental models of our organizations are bankrupt. We've been developing a new model of organization for many years, one we believe is fit for these times. We're collecting and posting the more technical aspects of this new model, a small book's worth, as NetAge Working Papers, of which we're posting the second today. We'll post several more over the coming weeks.

"Understanding Nodes and Links: Core Concepts of People, Positions, and Relationships," the second working paper, provides the technical background to the idea that all organizations, including hierarchies, are networks. This is a topic we've been beating to death in our writings and on this blog. So for you deep conceptual divers, this one's for you. (Download Understanding Nodes and Links)

A little more formally put, this paper contains the foundation for a management science of networked organizations. Networks are known by their nodes. Key to this approach to nodes is the idea of the position, the central ingredient integrating the ephemeral organization with the flesh-and-blood people who populate it. Connecting the nodes in networks, of course, are links. Here we lay out a taxonomy for these relationships: directed links, both horizontal and vertical, with strong and weak versions. Nodes and links together generate dynamic representations of organizational realities. These ideas contribute the conceptual basis for the design of OrgScope, our tool for mapping, navigating, and analyzing organizations as networks.

Here are the opening paragraphs of "Understanding Nodes and Links:"

What Are We?

A formal organization is an emergent network of positions, a configuration of roles filled by people.

Simple idea, yet significant in consequence. Who or what are we? What is the us that works together as an integrated whole, from families and teams to enterprises and nations. It takes but a small shift in perspective to see individual people working together as an organization that is tangible, knowable, and measurable. An organized us happens when we step into roles and bring a particular configuration of positions to life.

An organization is its people—simple and obvious.

Yes, but…is that all? Is there something more, or different, something that is human in essence but separate from the people who comprise the organization?

Every organization tells a story, is its own play. It has a cast of characters and a plot. To perform their parts, actors step into their roles. Different personalities and capabilities emerge, depending on which actor plays the role. Whether great or poor, an individual’s performance takes place within the same set of formal relationships any actor playing that role must contend with. Thus, the role or position is part of the play’s structure, existing quite independently of the person playing that role. King Lear is William Shakespeare’s play, King Lear is a role in the play, and Sir Lawrence Olivier played the King Lear role to great acclaim. Shakespeare and Olivier are dead. The play—and the King Lear role itself—lives on, and has for four hundred years.

When people step into positions, they take on roles that link into a network of other roles. But that doesn’t prevent them from being the people they are, who come to their jobs with their unique preferences and idiosyncrasies. Organizational life is both:

Formal, with its the network of positions, constituent units, and working groups that stand apart from the people; and

Informal, with its social interactions among particular people, who stand apart from the roles they occupy in and around the organization.

These two kinds of networks--organizational and social--are inextricable (see Figure 1). When we think about all the relationships relevant to organizational life, their entanglement contributes to our sense of chaotic complexity. But we can distinguish these two types of networks. By clearly defining and specifying the organizational network of positions, we can more easily identify relationships that make up social networks of people.

Currently, social scientists and management theorists are trying to understand group life largely by examining the network of relationships among the group’s members. Social relationships are not only highly complex themselves, but they are especially obscured because the formal organizational framework is itself dynamic.

The network of positions moves to its own beat while the entwined people network has its own different rhythms.

The complexity of these two fundamental sorts of human networks is awesome, understandably hindering our ability to develop social and organizational sciences. New insights from network and complexity sciences, however, help us establish a new management science of organizational networks.

(Download Understanding Nodes and Links)

-- Jeff Stamps

Collaborating in complex healthcare systems - yay to these researchers

A few months ago, we had a conversation with a healthcare executive who was lamenting the lack of collaboration among his colleagues. A noted cardiologist, he described the case of one of his patients (he still treats real people despite his admin responsibilities) who has no fewer than ten doctors. Need I report what anyone with a complex condition already knows? The docs don't talk to one another and it's left to the patient to cart around information, even if it's all now supposedly "in the system." That info is stored electronically hardly means it will be retrieved, never mind understood in a comprehensive way.

That's just one obvious problem with collaboration in the healthcare field, about which I claim no specific expertise. I'm always on the lookout, however, for any breakthroughs in understanding collaboration generally and so I was interested to find this pointer to a study by Canadian researchers who've delved into the topic in the medical field, A model and typology of collaboration among professionals in healthcare organizations," by D'Amour et al. Bravo to their opening premise:

Though recent advances have been made in research on collaboration, professionals need conceptual tools to help them develop collaboration amongst themselves in complex systems. If we are to effect a true transition to more integrated forms of organization, we need to better understand collaborative processes.

They studied perinatal services, specifically "short stays or 'early obstetric discharge,'" i.e., new mothers today are out the door in 24 hours "in most Western countries." Whether one wants to leave or not, there can be problems, as the authors note, including "the cessation of breastfeeding, hospitalization of the baby for jaundice [12] and parent-child attachment issues."

Thus, the researchers set out to identify a structural model of collaboration whose elements so accord with what we've observed in other areas (like plain old software development, execution of complex diplomatic missions, and strategic planning in large-scale enterprises) that I've begun this day by reading all 38 pages of this study. Forgive my errors in interpretation, researchers, but I do concur that your "four dimensions and the interaction between them capture the processes inherent in collaboration."

1) Shared Goals and Vision...the existence of common goals and their appropriation by the team, the recognition of divergent motives and multiple allegiances, and the diversity of definitions and expectations regarding collaboration;

2) Internalization...an awareness by professionals of their interdependencies and of the importance of managing them...which translates into a sense of belonging, knowledge of each other’s values and discipline and mutual trust;

3) Formalization..“the extent to which documented procedures that communicate desired outputs and behaviours exist and are being used” (p. 219) [which] clarifies expectations and responsibilities; and

4) Governance, that is, the leadership functions that support collaboration, [i.e. provides] a strategic role in promoting collaboration.

A pretty good list. I'm also impressed with the authors' humility:

The study does have its limitations, though. Firstly, the model cannot capture all the finer points of a concept as complex as collaboration. Nor does it capture all the factors that can influence collaboration; its main objective is to examine processes of collaboration.

Another limitation that should be stressed is that the model focuses on collaboration between health professionals. Do the results apply to collaboration within the same organization or to collaboration between other partners, for example, to intersectorial collaboration?

My guess, based on observation elsewhere as per above, is that yes, it does apply elsewhere. If people line up their visions, develop strong bonds of trust, share work processes, and are governed by leaders who support collaboration, then they can expect collaboration to work. Nice work, researchers.

"Science as collaboration"

Lovely post about the value of collaboration from Star Stryder ("Blogging the sky one sidereal day at a time...), aka Dr. Pamela L. Gay, an astronomer at Southern Illinois University at Edwardsville. In short, it's better together. Begins with this quote and click-worthy:

Science is an act of collaboration. While there are the lone geniuses among us out there making independent breakthroughs in mathematics and thought, these brilliant minds would be nothing if there wasn’t a community to hear their theories, run with their ideas, and evolve in response to their new visualizations of reality.