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Two examples from our team are:
Goodreau, S. M., S. Cassels, et al. (2010). "Concurrent partnerships,
Acute Infection and Epidemic Dynamics in Zimbabwe." AIDS and Behavior
Online First, Dec 29, 2010, DOI: 10.1007/s10461-010-9858-x.
Morris, M., A. Kurth, et al. (2009). "Concurrent partnerships and HIV
prevalence disparities by race: Linking science and public health
practice." Amer J Pub Health 99(6): 1023 - 1031.
On Wed, 2 Mar 2011, Sinan Aral wrote:
> ***** To join INSNA, visit http://www.insna.org *****
> I couldn't agree more. We make some of these same points and try to take this
> combined empirical estimation / simulation approach in this paper (although
> we don't use ERGM):
> Aral, Muchnik and Sundararajan. (2011) "Engineering Social Contagions:
> Optimal Network Seeding and Incentive Strategies"
> We argue here that simulations based on real data are more realistic and
> useful and so pursue this strategy using the 30M node Yahoo IM network from
> our PNAS paper (that I pointed to before) along with data on the day by day
> adoption of a new mobile service product by the same users - which we use to
> estimate the probability of diffusion in the system as you suggest below. We
> then simulate various firm marketing interventions including
> targeting/seeding and incentives designed to promote diffusion.
> We should have a slightly updated draft of this paper available very soon.
> I would love it if people could point us to other papers like this too...!
> Sinan Aral
> Assistant Professor, NYU Stern School of Business.
> Research Affiliate, MIT Sloan School of Management.
> Personal Webpage:http://pages.stern.nyu.edu/~saral
> SSRN Page:http://ssrn.com/author=110270
> WIN Workshop:http://www.winworkshop.net
> On 3/2/2011 1:52 PM, Martina Morris wrote:
>> Entering this conversation late, so I may have missed some things, but I
>> wanted to put a plug in for using a combined empirical + simulation based
>> approach to the problem, in case this hasn't been mentioned before.
>> If diffusion is thought of as a combination of network
>> connectivity/structure, transmission probability, and duration of
>> "infectiousness" (this is the classic formulation in epidemiology), there
>> are quite a few parameters that can determine the dynamics of the
>> diffusion. Unless you're interested in a black-box type of projection,
>> understanding how each of these components influences the process is an
>> important goal.
>> Simulation of the process (before getting into the empirical trenches)
>> allows for quite a bit of exploratory analysis and theoretical development.
>> The factors that influence the formation/dissolution of ties in the network
>> may or may not be different than the factors that influence transmission
>> across the ties. But the impact of these two types of components on
>> diffusion dynamics is quite different. Presence/absence of ties influence
>> the connectivity of the network, which has the usual non-linearities and
>> threshold properties of a complex system (think emergence of the giant
>> component), and this can become a key limiting factor in sparse networks
>> (sexual networks are the paradigmatic example).
>> For those of us interested in the role of network structure, it is
>> important that the model used to simulate the network is one that can also
>> be used to estimate the model parameters from data. That's one of the
>> things that makes ERGMs attractive -- they do this double duty for
>> estimation and simulation, allowing you to simulate networks that match the
>> observable summary network statistics from your data.
>> You would also need empirical estimates of the probability of diffusion and
>> the "duration of infectiousness" to simulate the whole system.
>> In contrast to a single realization of the stochastic diffusion process --
>> which is what most empirical studies would give you (RCT or observational)
>> -- simulating the network (with empirically estimated parameters for
>> network dynamics and diffusion parameters) allows you to map out the
>> distribution of outcomes from the system, and to see how sensitive it is to
>> small changes in the underlying parameters. Arguably, a lot more insight
>> into the phenomenon of interest.
>> On Wed, 2 Mar 2011, Sinan Aral wrote:
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>>> I agree. Important and useful research begins with the right questions -
>>> that goes without saying.
>>> But the problem of looking under the lamp post is not solely a problem of
>>> RCTs. The same problem arises when we consider what observational data we
>>> have access to. In fact, it seems likely that this problem would be more
>>> pronounced for observational data that we happen to get access to than for
>>> experimental studies that we have to design explicitly and in advance in
>>> order to examine a particular question or set of questions.
>>> That we should use theory and scholarly intuition to seek out interesting
>>> questions and phenomena to study is clear. However, in this case, the
>>> value of the "question" of estimating social influence (broadly defined)
>>> is already quite clear and its relevance is relatively well accepted. The
>>> reason why causal estimation is important in this already well defined
>>> research area (and people have been writing about this for decades) is
>>> that separating correlation from causation in this specific case can
>>> inform us about what the effects of various policy alternatives might be
>>> for programs aimed at peer to peer HIV prevention, smoking cessation,
>>> obesity prevention, product marketing, and so on.
>>> I could not agree more that the study that finds a valid instrument and
>>> then searches for the question that the instrument helps address is
>>> misguided. But, it doesn't seem to me that finding novel solutions to
>>> causal estimation in networks leads us to the question of estimating the
>>> magnitude of peer effects. In most contexts, that question itself is
>>> already well motivated. To the contrary, it seems to me that our methods
>>> lag behind the theoretical development of the questions (and the theories
>>> that explain social influence) in this case.
>>> Another danger, besides looking under the lamp post, is remaining content
>>> with showing correlation and assuming causation. Based on your own
>>> previous work and our previous work together, I know you believe this as
>>> well. But, its worth repeating in order to extend this discussion a bit I
>>> Sinan Aral
>>> Assistant Professor, NYU Stern School of Business.
>>> Research Affiliate, MIT Sloan School of Management.
>>> Personal Webpage:http://pages.stern.nyu.edu/~saral
>>> SSRN Page:http://ssrn.com/author=110270
>>> WIN Workshop:http://www.winworkshop.net
>>> On 2/28/2011 11:36 PM, Arun Sundararajan wrote:
>>>> ***** To join INSNA, visithttp://www.insna.org *****
>>>> The Onion clip is wonderful. Very much in snyc with the fake news
>>>> often being more informative than the real...should be required
>>>> It seems to say a lot more than just "give observational data a
>>>> chance" to me in the context of this larger discussion. it isn't
>>>> merely that a large fraction of the phenomena we want to study occur
>>>> before we can design trials to measure them (and economists have been
>>>> dealing with this reality for decades). or that observational data are
>>>> more likely to lead to interesting discoveries of new things. Or that
>>>> whatever the methods, there are always alternative explanations,
>>>> especially when dealing with people in social settings. It's also that
>>>> if we start to believe in experiments and RCT's as the holy grail,
>>>> there's a danger of focusing too much on the kinds of questions that
>>>> lend themselves to that specific methodology, rather than going after
>>>> the ones that matter. (Even in the context of social influence in
>>>> networks.) analogously, there's a lot of time spent by research in
>>>> economics and marketing looking for "natural experiments" for
>>>> identification, and this gets to the point sometimes where it seems
>>>> like the research question was designed merely to exploit the cool
>>>> natural experiment...
>>>> i think that many aspects of this RCT vs. observational data (or for
>>>> observational data, matched-sample versus "structural" methods for
>>>> claiming causation) debates aren't unique to the context of social
>>>> influence in networks. For example:
>>>> cheers, Arun.
>>>> On Mon, Feb 28, 2011 at 12:40 AM, James Fowler<[log in to unmask]> wrote:
>>>>> ***** To join INSNA, visithttp://www.insna.org *****
>>>>> We have also relied on methods like the ones Tom Valente mentioned in
>>>>> of our observational studies, and we summarize the pluses and minuses of
>>>>> this approach in a new paper here:
>>>>> I also in principle like the actor-oriented model approach of Siena, but
>>>>> the past I could never get the model to converge for networks larger
>>>>> 1000 nodes (this might be my own failing, though, as there is always a
>>>>> of art to getting models like that to work).
>>>>> We also have relied on experiments like this one in PNAS:
>>>>> and I am a big fan of David Nickerson's voter experiment and Sinan's new
>>>>> But I would resist abandoning evidence from observational studies.
>>>>> The resistance to observational studies reminds me of this Onion story:
>>>>> Multiple Stab Wounds May Be Harmful To Monkeys
>>>>> James H. Fowler
>>>>> Professor of Medical Genetics and Political Science
>>>>> UC San Diego
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>> Professor of Sociology and Statistics
>> Director, UWCFAR Sociobehavioral and Prevention Research Core
>> Box 354322
>> University of Washington
>> Seattle, WA 98195-4322
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Professor of Sociology and Statistics
Director, UWCFAR Sociobehavioral and Prevention Research Core
University of Washington
Seattle, WA 98195-4322
Office: (206) 685-3402
Dept Office: (206) 543-5882, 543-7237
Fax: (206) 685-7419
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SOCNET is a service of INSNA, the professional association for social
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UNSUBSCRIBE SOCNET in the body of the message.