While many of my colleagues are attending this week's Network Roundtable conference, I am preparing for my "User Needs" presentation that will help kick off Ben Shneiderman's University of Maryland HCIL SNA Workshop Friday. Basically they want me to complain about SNA software for 15 minutes, thereby giving the lab some potential material for future projects. It will be hard to contain my complaining to 15 minutes--partly because there are so many things to complain about, and also because Ben has assembled a great collection of people to talk to. We met each other over dinner tonight--we are a multi-disciplinary group united at a precise common interest in social network visualization. I am lucky to be here and pretty excited about getting such an audience to sit and listen to me complain for 15 minutes. Now all I need is $10,000 from each of them for the privilege and we'll really have something.
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.
Thursday, May 31, 2007
Tuesday, May 22, 2007
Employee behavior analysis
When is measurement a good thing? Louis Menand's short New Yorker essay "The Graduates" inspired me to continue the thread I started with "When not to connect the dots." Although Menand is writing specifically about American higher education, his diagnosis of its strengths and weaknesses seems intended to apply much more broadly--to any system founded on principles of universal access and meritocracy.
Menand explains, "Meritocratic systems are democratic (since, in theory, everyone gets a place at the starting line) and efficient (since resources are not wasted on the unqualified), but they are huge engines of anxiety. The more purely meritocratic the system—the more open, the more efficient, the fairer—the more anxiety it produces, because there is no haven from competition."
Those of us who are regularly called on to measure social networks see this anxiety play out all the time. Potential survey respondents rightfully fear being rewarded or punished based on their position and activity in the network. Normally in my work, I devote significant energy to acknowledging this "measurement anxiety" and helping leaders and their organizations to manage it productively. For a CEO, however, unmitigated meritocracy has an especially important role to play. I experienced that CEO perspective firsthand while reading Menand's essay. At the time, I was in the middle of assigning semester grades to my 60 students. At such a moment, efficient meritocratic action is necessary, and empathy for rank-and-file anxiety is a burden too heavy to be worth bearing.
The dilemma of measurement anxiety is nothing new. A classic New Yorker cartoon shows the Druids celebrating the creation of Stonehenge.
One remarks, "Now that we can tell time, I'd like to suggest that we begin imposing deadlines."
We take deadlines for granted now and very rarely if ever live without a clock handy. Network measurements are not nearly so ubiquitous as wristwatches, and we are still figuring out where those measurements cross the line and do more harm than good.
Some network practitioners are clumsily crossing this line (in my opinion). I would rather not link to them or name them directly. Thankfully, Bill Ives recently panned the very malefactors I had in mind on FASTForward. I am happy to link to FASTForward, where the world of "Enterprise 2.0" (including messy issues like measurement anxiety) looks to get a good airing.
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.
Menand explains, "Meritocratic systems are democratic (since, in theory, everyone gets a place at the starting line) and efficient (since resources are not wasted on the unqualified), but they are huge engines of anxiety. The more purely meritocratic the system—the more open, the more efficient, the fairer—the more anxiety it produces, because there is no haven from competition."
Those of us who are regularly called on to measure social networks see this anxiety play out all the time. Potential survey respondents rightfully fear being rewarded or punished based on their position and activity in the network. Normally in my work, I devote significant energy to acknowledging this "measurement anxiety" and helping leaders and their organizations to manage it productively. For a CEO, however, unmitigated meritocracy has an especially important role to play. I experienced that CEO perspective firsthand while reading Menand's essay. At the time, I was in the middle of assigning semester grades to my 60 students. At such a moment, efficient meritocratic action is necessary, and empathy for rank-and-file anxiety is a burden too heavy to be worth bearing.
The dilemma of measurement anxiety is nothing new. A classic New Yorker cartoon shows the Druids celebrating the creation of Stonehenge.
One remarks, "Now that we can tell time, I'd like to suggest that we begin imposing deadlines."We take deadlines for granted now and very rarely if ever live without a clock handy. Network measurements are not nearly so ubiquitous as wristwatches, and we are still figuring out where those measurements cross the line and do more harm than good.
Some network practitioners are clumsily crossing this line (in my opinion). I would rather not link to them or name them directly. Thankfully, Bill Ives recently panned the very malefactors I had in mind on FASTForward. I am happy to link to FASTForward, where the world of "Enterprise 2.0" (including messy issues like measurement anxiety) looks to get a good airing.
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.
Friday, May 18, 2007
Mathematics for network analysis
Spring semester at Boston University ended Monday. Once again I had an amazing bunch of students. For the first time, I finished a semester happy with my teaching materials. That's probably because I decided to write my own book. Here it is: Introduction to Network Mathematics. (And now, new and improved here.) This is the most direct way I know to take someone with zero math background to a place where he can perform meaningful network calculations (allowing the occasional detour into theoretical vistas where the view is too beautiful to pass up).
I am totally blown from the stress of creating, administering, and grading final exams, etc. More about that soon. Meanwhile, please read Louis Menand's short and timely New Yorker essay on the anxiety engines that are American universities: The Graduates.
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.
I am totally blown from the stress of creating, administering, and grading final exams, etc. More about that soon. Meanwhile, please read Louis Menand's short and timely New Yorker essay on the anxiety engines that are American universities: The Graduates.
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.Tuesday, May 01, 2007
The world's most popular sociology experiment
Many of my colleagues are off to Greece this week for the annual "Sunbelt" conference of the International Network of Social Network Analysis (INSNA). Greetings to you all--wish I were there.
In past years I have vented about the incomprehensible spaghetti networks displayed far too often at Sunbelt. This year I have a new gripe: the habit of presenting "organizational case studies" where the "organizations" are classrooms full of the researcher's own students.
Sunbelt would do itself a favor by clearly labelling these "classroom case studies" with a big asterisk. It's hard to coordinate sociology experiments on a scale that deserve public presentation, far easier-- and less meaningful-- to conduct experiments on a captive audience presided over by the principal investigator.
I recently drafted my 60 students into a classroom case study where we created our own set of Google rankings, based on students' links to each others' websites (programming projects created over the course of the term).
I concluded the exercise by requesting each student to write an essay, "What do these rankings mean?" The strongest essays responded with pointed criticisms of the classroom case study technique: a classroom population is too small and the classroom environment too artificial for meaningful sociological measurement to emerge from such a setting.
Daniel Essrow writes: "The Google ranking algorithm provide[s] a clear and consistent way to organize recommendations of websites.... In the vast world of the Internet, some voices do indeed matter more than others; but this a world with billions of sites made by millions of people over handfuls of years. Our class, with its limited experience, only artificially made some opinions appear more important than others. Moreover, those voices that were artificially amplified were chosen by a system very susceptible to cumulative advantage, and even vandalism. The small network made up of members of the class lacked the time and nuance needed to truly determine which voices deserved greater importance, and instead relied too heavily on an arbitrary system to make those decisions."
Evan Scott writes: "Google ranking utilizes eigenvector centrality.... This strategy works for Google because when someone performs a Google search, they are searching (whether they know it or not) for the most authoritative websites on their topic. Thus, calculating the indegree of websites, while taking into account the authority of the sites recommending them is a logical way of ranking authority. However in the context of our class, there is no central topic that we are attempting to create websites about. This begs the question: what are the most voted for (authoritative) websites authorities on? Because of vague criteria, the results are highly susceptible to the negative traits of “cumulative advantage.”
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.
In past years I have vented about the incomprehensible spaghetti networks displayed far too often at Sunbelt. This year I have a new gripe: the habit of presenting "organizational case studies" where the "organizations" are classrooms full of the researcher's own students.
Sunbelt would do itself a favor by clearly labelling these "classroom case studies" with a big asterisk. It's hard to coordinate sociology experiments on a scale that deserve public presentation, far easier-- and less meaningful-- to conduct experiments on a captive audience presided over by the principal investigator.
I recently drafted my 60 students into a classroom case study where we created our own set of Google rankings, based on students' links to each others' websites (programming projects created over the course of the term).
I concluded the exercise by requesting each student to write an essay, "What do these rankings mean?" The strongest essays responded with pointed criticisms of the classroom case study technique: a classroom population is too small and the classroom environment too artificial for meaningful sociological measurement to emerge from such a setting.
Daniel Essrow writes: "The Google ranking algorithm provide[s] a clear and consistent way to organize recommendations of websites.... In the vast world of the Internet, some voices do indeed matter more than others; but this a world with billions of sites made by millions of people over handfuls of years. Our class, with its limited experience, only artificially made some opinions appear more important than others. Moreover, those voices that were artificially amplified were chosen by a system very susceptible to cumulative advantage, and even vandalism. The small network made up of members of the class lacked the time and nuance needed to truly determine which voices deserved greater importance, and instead relied too heavily on an arbitrary system to make those decisions."
Evan Scott writes: "Google ranking utilizes eigenvector centrality.... This strategy works for Google because when someone performs a Google search, they are searching (whether they know it or not) for the most authoritative websites on their topic. Thus, calculating the indegree of websites, while taking into account the authority of the sites recommending them is a logical way of ranking authority. However in the context of our class, there is no central topic that we are attempting to create websites about. This begs the question: what are the most voted for (authoritative) websites authorities on? Because of vague criteria, the results are highly susceptible to the negative traits of “cumulative advantage.”
This work is licensed under a Creative Commons Attribution-ShareAlike 2.5 License and is copyrighted (c) 2007 by Connective Associates except where otherwise noted.
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