November 18, 2010
Algorithms, numbers and quantification
On the plane back from Europe the other day I was reading this month's Atlantic Monthly and happened across a little piece by Alexis Madrigal called "Take the Data Out of Dating" about OkCupid's clever use of algorithms to increase the frequency of "three-ways" (which in dating-website-speak means a person sent a note, received a reply, and fired off a follow-up; not exactly a direct measure of their success at helping people find love, but that's their proxy of choice). It's a thoughtful piece largely because the punch line resonates with much of my recent feelings about the creeping use of scientometrics in the attempts of higher eduction administrators to understand what exactly their faculty have done or not done, and how they compare to their peers. (I could list a dozen other ways numbers are increasingly invading decision-making processes that used to be done based on principles and qualities, but ack there are so many.) More generally, I think it puts in a good perspective what exactly we lose when we focus on using numbers or algorithms to automate decisions about inherently human problems. Here it is:
Algorithms are made to restrict the amount of information the user sees—that’s their raison d'etre. By drawing on data about the world we live in, they end up reinforcing whatever societal values happen to be dominant, without our even noticing. They are normativity made into code—albeit a code that we barely understand, even as it shapes our lives.
We’re not going to stop using algorithms. They’re too useful. But we need to be more aware of the algorithmic perversity that’s creeping into our lives. The short-term fit of a dating match or a Web page doesn’t measure the long-term value it may hold. Statistically likely does not mean correct, or just, or fair. Google-generated kadosh [ed: best choice] is meretricious, offering a desiccated kind of choice. It’s when people deviate from what we predict they’ll do that they prove they are individuals, set apart from all others of the human type.
November 10, 2010
Workshop: Interdisciplinary Workshop on Information and Decision in Social Networks
And, here's another workshop; this one on information and social networks, held at MIT in late Spring 2011. I won't say anything more except to point out that social influence is complicated.
Date & Location: 31 May - 1 June 2010 at MIT
Organizers: Vincent Blondel (UCLouvain and LIDS, MIT), Munther Dahleh (LIDS, MIT), Asu Ozdaglar (LIDS, MIT), John Tsitsiklis (LIDS, MIT)
Description: Recent technological and mathematical developments have opened the possibility to considerably improve our understanding of how information flows and decisions are made in large social networks. In this workshop, we bring together researchers from different communities working on information propagation and decision making in social networks to investigate both rigorous models that highlight capabilities and limitations of such networks as well as empirical and simulations studies of how people exchange information, influence each other, make decisions and develop social interactions.
This workshop is being organized by the Laboratory for Information and Decision Systems.
Submission deadline (extended abstracts): 11 March 2011
November 05, 2010
Nathan Explains Science, welcome to the blogosphere!
Nathan is a former theoretical astrophysicist who holds a PhD in political science. The first time I met him, I thought this meant that he studied awesome things like galactic warfare, blackhole coverups, and the various environmental disasters that come with unregulated and rampant terraforming. Fortunately for us, he instead studies actual politics and social science, which is probably more useful (and sadly more interesting) than astro-politics.
Here's Nathan explaining why he's now also a blogger:
...this gives me an place to tell you about science news that I think is interesting but that isn't necessarily going to get published in Science News or Nature's news section. For a variety of reasons, social science news especially doesn't get discussed as science, and that's unfortunate because there are scientific results coming out of psychology, political science, and economics that are vitally important for understanding the problems we face and the solutions we should pursue. In fact, there are a lot of old results that people should know about but don't because social science news seems less attractive than, say, finding a galaxy farther away than any other.
And, if you want to read more about the science, try out these stories, in which Nathan explains the heck out of narcissism, what makes us vote, political grammar and baby introspection:
Is Narcissism Good For Business?
Narcissists, new experiments show, are great at convincing others that their ideas are creative even though they're just average. Still, groups with a handful of narcissists come up with better ideas than those with none, suggesting that self-love contributes to real-world success.
Sweaty Palms and Puppy Love: The Physiology of Voting
Does your heart race at the sight of puppies? Do pictures of vomit make you sweat? If so, you may be more likely to vote.
Politicians, Watch Your Grammar
As congressional midterm elections approach in the United States, politicians are touting their credentials, likability, and, yes, sometimes even their policy ideas. But they may be forgetting something crucial: grammar. A new study indicates that subtle changes in sentence structure can make the difference between whether voters view a politician as promising or unelectable.
‘Introspection’ Brain Networks Fully Formed at Birth
Could a fetus lying in the womb be planning its future? The question comes from the discovery that brain areas thought to be involved in introspection and other aspects of consciousness are fully formed in newborn babies...
More Evidence for Hidden Particles?
Like Lazarus from the dead, a controversial idea that there may be a new, superhard-to-spot kind of particle floating around the universe has made a comeback. Using a massive particle detector, physicists in Illinois have studied the way elusive particles called antineutrinos transform from type or "flavor" to another, and their data bolster a decade-old claim that the rate of such transformation is so high that it requires the existence of an even weirder, essentially undetectable type of neutrino. Ironically, the same team threw cold water on that idea just 3 years ago, and other researchers remain skeptical.
Update 6 November 2010: added a new story by Nathan, on hidden particles.
November 04, 2010
Peer review and the meat grinder
ArsTechnica's Chris Lee has a nice (and brief) meditation on peer review, and it's suitability for vetting both research papers and grant proposals. The title gives away a lot "A trip through the peer review sausage grinder", but that should be no surprise to anyone who lives with the peer-review process. The punch line Lee comes to is that peer review works okay at vetting the results of scientific research but fails at vetting potential research, that is, grant proposals. This conclusion seems entirely reasonable to me. 
Given some interactions with NSF Program Managers and related folks over the past year, peer review is the one thing that is not up for discussion at NSF.  They're happy to hear broad-based appeals for more funding, for suggestions about different types of funding, etc. But they are adamantly attached to sending grant proposals out for review by other scientists and taking the advise they get back seriously. To be honest, I'm not sure how else they could do it. As Lee points out, there are many more scientists now than there is funding, and the fundamental question is how do we allocate money to the projects and people most likely to produce interesting and useful results? This kind of pre-judgement of ultimate quality is fundamentally hard; peer-review frequently fails at doing this for research that is already finished (peer review at journals), and is even worse for research that has not yet been done (peer review for grant proposals). Track-record-based systems are biased against young scholars; shrinking the size of the applicant pool smacks of elitism; and peer-review effectively produces boring, incremental research. Lee suggests a lottery-based system, which is an interesting idea, but it would never fly.
 A part of me is proud of the fact that one of my proposals at NSF was criticized both for being too ambitious and for being too incremental.
November 02, 2010
Workshop: Networks Across Disciplines in Theory and Applications
While I'm at it, here's another interesting looking workshop. This one is at NIPS 2010 and so will have more of a computer science emphasis. I expect it will be good, despite the fact that I'm speaking.
Date & Location: December 11, 2010, NIPS, Whistler Canada
Organizers: Anna Goldenberg (Toronto), Edo Airoldi (Harvard) and Jure Leskovec (Stanford)
Description:Networks are used across a wide variety of disciplines to describe interactions between entities -- in sociology these are relations between people, such as friendships (Facebook); in biology, physical interactions between genes; the Internet, sensor networks, transport networks, ecological networks just to name a few. Researchers in machine learning, statistics and physics communities search for ways to explain and model the phenomena observed across the multitude of these disciplines. The theoretical findings stemming from different areas are heterogeneous and often complementary yet there are few means for intellectual exchange and collaboration across disciplinary boundaries.
The goal of our workshop is to actively promote a collaborative effort in addressing statistical and computational issues arising when modeling collections of data represented by networks -- static or dynamic; to "cross-pollinate" fields with ideas from different areas; to introduce new questions to the theoretical modeling audience and to broaden the focus by considering new areas.
Presentations will include novel network models, the application of established models to new domains, theoretical and computational issues, limitations of current methods and directions for future research.
Carter Butts (Sociology, UC Irvine)
Jonathan Chang (Facebook)
Aaron Clauset (Computer Science, University of Colorado Boulder)
Lise Getoor (Computer Science, University of Maryland)
Trey Ideker (Bioengineering, UCSD)
Sayan Mukherjee (Statistics, Duke University)
Submission Deadline: passed, oops
Workshop: Complex Networks: Dynamics of Networks
This year is "networks year" in North Carolina, with a number of workshops and events being organized by the Statistics and Applied Mathematics Institute as one of their "long-programs". In January, they're holding an interesting looking workshop on the dynamics of networks.
Date & Location: January 10-12, 2011, Research Triangle Park, NC
Organizers: Raissa D'Souza, Stephen Fienberg, Eric Kolaczyk, Jim Moody, Peter Mucha & Mason Porter
Description: The changing structure of networks over time impact and are indeed inherent in the study of a broad array of network phenomena. The network of contacts for the spread of an infectious disease varies in time, with that variation playing a potentially important role in the course of the disease. Ad hoc communications networks between roaming elements must continously readjust and renavigate between nodes according to the changing landscape of connections. Political networks of association connections or voting similarities vary from one legislative session to the next.
The detailed local social and/or technological processes underlying each of these example applications obviously differ, but many of the basic mathematical and statistical questions regarding such networks and the generalized information they carry are similar. Though the importance of dynamics in networks has of course been long recognized, renewed interest has emerged in part due to the increasing accessibility of dynamic network data, ranging from longitudinal data waves to complete time histories of network evolution. Additionally, most of the theoretical modeling work that has been done on the dynamics of networks has been focused on the statistical equilibria of those models (e.g., growing networks by preferential attachment) or on one-time disruption events (e.g., the effect of knocking out hubs). At the same time, statistical and computational tools for analyzing time-varying networks remain relatively few in number, especially as compared to the wealth of advances in methods for modeling and analyzing static networks.
There thus remains an ongoing need and opportunity for more thorough mathematical and statistical analysis and modeling of dynamic networks. This workshop aims to bring together researchers interested in pushing forward this extremely fertile area of research.
Deadline: 23 December 2010
Online Application Process: here