Archive for the ‘networks’ Category
Yesterday, I described a paper written by Kirby Schroeder and my self on infection networks. Yesterday’s post addresses the professional lessons I learned. Today, I want to talk about the impact of the paper on current work. For a long time, the paper, literally, got zero citations in peer reviewed journals. Then, the citations increased around 2010, with people in economics, health, and biology discussing the paper.
Economics: The main commentary among economists is that this is a model of interaction, which can then be used to assess the impact of policy. For example, a paper in the American Law and Economics Review notes that the paper models risky behavior but does not model the law. Other economists are attracted to our prediction about infection knowledge and epidemic plateaus (once the disease becomes common knowledge, people shift behavior and transmission stalls).
Health: The Archives of Sexual Behavior has an article that discusses our article in the context of trying to expand models of disease transmission. For example, we critique the health belief model for ignoring interaction. We criticize sexual scripting theory for ignoring risk and strategic action.
Biology: Perhaps the most interesting impact of the paper is the impact on mathematical biology. In The Journal of Theoretical Biology, a team of mathematicians use the model to address group formation. In a model derived from our Risky Sex Game model, they show that the population, under certain conditions, will separate into specific groups based on HIV status.
Bottom line: People sure hated the paper when I wrote it, but its children are a joy to behold.
My first ever journal publication was an article called “A Game Theoretic Model of Sexually Transmitted Disease Epidemics.” It appeared in the journal Rationality and Society in 2002. As the title suggests, the goal of the paper is to model network diffusion using agents that play games with each other. Specifically, let’s assume people want to have sex with each other. The catch is that some people have HIV (or another disease) and some don’t. Further, let’s assume that people will estimate the probability that the partner has HIV based on the type of sex they offer and the current disease prevalence. In other words, offering unprotected sex in a world without STD’s is interpreted way differently than the same offer in a world where lots of people have infections. In this post, I want to briefly discuss what I learned by writing this paper. Tomorrow, I will talk about the small, but interesting, literature in biology and health economics that has referenced this paper.
Lesson #1: Interdisciplinary work doesn’t have to be garbage. The paper uses ideas from at least three different scholarly areas – game theory/economics; social networks/sociology; and probability theory/epidemiology. Orgtheory readers will be familiar with game theory and networks. But the paper uses a cool idea from probability theory called “pairs at a party model” – to model diffusion, you draw people from a pool and match them. I added these ideas: people can only be paired with people they know (the network) and then to decide if they have intercourse, they play a signalling game (game theory).
Lesson #2: Working with your buddies is amazing. My co-author on the project was Kirby Schroeder, who now works in the private sector. We developed the idea by thinking about his personal experience. Gay men often encounter the signaling problem – say you meet a partner and he offers unprotected sex. What does that suggest? We then joined forces to write the paper. Great experience.
Lesson #3: People can get angry at your research. During conferences and peer review, we experienced great hostility because we relied on the literature showing that sometimes, people don’t tell partners about STD’s and thus put them at risk. One woman, who claimed to be a researcher from Massachusetts General Hospital, literally yelled at me during an ASA session. The paper got rejected from Journal of Sex Research, after an R&R, because one reviewer got very upset and claimed that were defaming gay people and that “you don’t know what love means.” Do any of us, really?
Lesson #4: Long term matters. The paper was published in Rationality and Society and then quickly disappeared. But it had an interesting after life. It got an ASA grad student paper award from the Math Soc section. During the first couple of years on the job, it was the only journal publication on the CV, which saved me from complete embarrassment. In one review of my work, it was the *only* paper that the committee actually liked. Later, a member of the RWJ selection committee said that the paper was the only reason that they invited me for an interview, because it showed a genuine commitment to health research. Even better, starting around 2010, researchers rediscovered the paper and now it is part of a larger literature on sexual risk spanning biology, economics, and health. So even though it didn’t have an immediate impact, a well written paper can pay off in ways you might not expect.
Tomorrow: What people get from the Risky Sex Game paper.
Next week, we’ll discuss sex and sociology. Here are the topics:
- Why sex is important for sociologists to study
- My experience teaching social science research on sex
- Lessons from Laumann et al. (1994)
- Professional lessons from my first article on networks and STD’s
- The unexpected literature that sprung up from that article
If you want to discuss other topics, mention them in the comments and we’ll work it in.
Everyone wants to know the secrets to academic success. But despite the sizable academic self-help genre, actual evidence on whether scholars who pursue certain strategies are more successful than others is fairly thin on the ground.
Erin Leahey has written about the returns to research specialization, and I know of a couple of papers on the characteristics of highly cited scientists (gated links, sorry). There’s probably more in the voluminous scientometrics literature.
Some of our standard theories in organization theory suggest different answers to this question — and in particular, to the question of what research topic you should pick. (Assuming maximum academic success is your goal and not, say, following your passion.)
A whole line of research following from Ezra Zuckerman’s 1999 article on the penalty to category breaching suggests that not fitting into predefined categories can hurt a product. Audiences, for example, find genre-spanning work less appealing. On the flip side, though, Ron Burt’s work on structural holes would seem to imply that academics who bridge poorly connected networks are in a good position to benefit from their brokerage.
Of course, none of this work (at least the stuff I know) has looked specifically at academic research. But both theories fit plausible narratives of scholarly success.
It makes a lot of sense that people who bridge disconnected research communities would be in a position to bring useful ideas from one into the other, and reap the rewards that result. On the other hand, I can think of several examples of folks who seem to achieve less success than they merit because their work falls outside, or fits awkwardly between, well-defined research communities. A penalty to category-breaching or genre-spanning sounds entirely plausible too.
If I had to guess, I’d suspect that these two patterns may both exist in academia but intersect in fairly complex ways. So the network-broker can benefit from her ability to borrow insights from another discipline, or community. But only if the insights are recognizable enough to her home discipline that others can mentally place those insights in an understandable location within their field — that is, in an existing category.
The question is whether there’s a sweet spot — being just enough of a broker to benefit, without being so radical as to trigger a category-breaching penalty. Or maybe there’s a benefit to brokerage, but only in certain structural holes — ones that don’t cause the category problem. Or maybe there are a couple of mutually exclusive strategies for success.
What do you think? Will academic brokers be hit with an illegitimacy penalty for their category breaching? Or are these in fact orthogonal issues for ambitious academics? Maybe there’s actual research that speaks to this.
(H/T to Tim Bartley for the conversation that spurred these musings.)
Vox has a nice interview with Dartmouth political scientist Brendan Nyhan about vaccine skeptics. What can be done to convince them? Brendan does research on political beliefs and has shown that in experimental settings, people don’t like to change beliefs even when confronted with correct information. His experiments show that this is true not only for political beliefs, but also controversial health beliefs like believing in the vaccine-autism link.
But there was an additional section in the interview that I found extremely interesting. Nyhan notes that it is easier to be a vaccine skeptic when you don’t actually see a lot of disease: “… many of the diseases that vaccines prevent today are essentially invisible in the US. Vaccines are a victim of their own success here.” This reminded me of a 2002 paper I wrote on STD/HIV transmission. In a model worked out by Kirby Schroeder and myself about people proposing to have risky sex with each other, we wrote that the model has an unusual prediction. If people are proposing risky sex based on how often their friends are infected, you may get unexpected outbreaks of disease:
In the models we have presented, there is no replacement; the population is stable. If we allow for replacement, then we arrive at a novel prediction: as uninfected individuals the population (through birth, migration, etc.) and HIV+ individuals leave (through illness), the proportion of infected individuals will decrease. Once this proportion falls, prior beliefs about the proportion of infected individuals will fall, and if this new prior belief is low enough , then HIV- negative individuals will switch from protected to unprotected sex. The long-term effect of replacement in our model, then, is an oscillation of infection rates… There is some evidence that oscillations in infection rates do occur… An intriguing avenue for research would be to link these patterns in infection rates to the behavior depicted in our model.
In other words, if your model of the world assumes that people take risk based on the infection rates of their buddies, then it is entirely possible, even predictable, that you will see sudden spikes or outbreaks because people “let their guard down.” For HIV, as more people use condoms and other measures, people may engage in more risky sex because few of their friends are infected. For measles and other childhood infections, people who live in very safe places may feel free to deviate from the standard practices that create that safety in their first place. I don’t know how to make vaccine skeptics change their minds, but I do know that movements like vaccine skepticism are some what predictable and we can prepare for it.
Sociological Science has a new paper by Sara Cowan discusses when people share information using data on abortion:
Though abortion is a more common event in the United States than miscarriage, this article shows that more Americans hear of women who have had miscarriages than they hear of women who have had abortions. This is a result of both the patterns of secret telling and keeping: more Americans tell miscarriage secrets to more people than abortion secrets, and more Americans keep abortion secrets from more people than miscarriage secrets.In the introduction, I described two scenarios: one in which people tend to hear secrets they previously approved, and this pattern would contribute to a stasis in public opinion and a second scenario in which people hear secrets they previously condemned and this scenario would inspire social influence and facilitate social change. The data analyzed here illustrate the first scenario. They show a strong trend whereby individuals who hold restrictive views toward abortion are less likely than their liberal peers to report knowing someone who has had one. People tend to hear those secrets about which they already approve and are less likely to hear secrets about which they disapprove. Secret keeping and selective disclosure intensify this experience of homophily above and beyond any objective network segregation.
This is not a post about Ello. Because Ello is so last Friday. But the rapid rise of and backlash against upstart social media network Ello (if you haven’t been paying attention, see here, here, here) reminded me of something I was wondering a while back.
Lots of people are dissatisfied with Facebook — ad-heavy, curated in a way the user has little control over, privacy-poor. And it looks like Twitter, which really needs bring in more revenue, is taking steps to move in the same direction: algorithmic display of tweets, with the ultimate goal of making users more valuable to advertisers.
The question is, what’s the alternative? There have been a lot of social network flavors of the month, built on a variety of business models. Some of them, like Google Plus, are owned by already-large companies that would be subject to similar business pressures as Facebook and Twitter. Others, like Diaspora (remember Diaspora?), were startups with an anti-Facebook mission (privacy, decentralization), but collapsed under the weight of their own hype.
I can’t imagine that a public utility model would work for a social network — I just don’t see “government-owned” and “fast-moving technological change” going together successfully. But I keep wondering why a Wikipedia model couldn’t work. Make it a 501(c)3. Attract some foundation funding — it’s a pro-democracy project. Solicit gifts from pro-privacy people in the tech industry — there are lots of those. Then once it’s off the ground, ask users for donations.
Sure, there is the huge, huge hurdle of getting enough of a network base to attract new users. But it seems like the costs should not be insane. If it only takes 200 employees to run Wikipedia, as large as it is, how many would it take to get a big social network off the ground? Facebook employs 7000, but a lot of them have to be in the business of figuring out how to sell Facebook.
Maybe there have been (failed) efforts like this and I just haven’t noticed. Or maybe the getting-the-user-base issue is really insurmountable. But it seems like if a real Facebook alternative is to emerge, it can’t just be from a corporate competitor (e.g. Google), and the startup/VC model (e.g. Ello) is going to be susceptible to all the same problems as it grows. Why not a different model?