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Thanks Sinan,

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.

best,
mm


On Wed, 2 Mar 2011, Sinan Aral wrote:

> *****  To join INSNA, visit http://www.insna.org  *****
>
> Martina,
>
> 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"
> http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1770982
>
> 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...!
>
> Best
>
> SA
>
> 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
> Twitter:http://twitter.com/sinanaral
>
>
> 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.
>> 
>> best,
>> Martina
>> 
>> 
>> On Wed, 2 Mar 2011, Sinan Aral wrote:
>> 
>>> *****  To join INSNA, visit http://www.insna.org  *****
>>> 
>>> Arun,
>>> 
>>> 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 
>>> think.
>>> 
>>> Cheers,
>>> 
>>> Sinan
>>> 
>>> 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
>>> Twitter:http://twitter.com/sinanaral
>>> 
>>> 
>>> 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
>>>> viewing.
>>>> 
>>>> 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:
>>>> http://en.wikipedia.org/wiki/Randomized_controlled_trial#Relative_importance_of_RCTs_and_observational_studies 
>>>> 
>>>> 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 
>>>>> many
>>>>> of our observational studies, and we summarize the pluses and minuses of
>>>>> this approach in a new paper here:
>>>>> 
>>>>> http://jhfowler.ucsd.edu/examining_dynamic_social_networks.pdf
>>>>> 
>>>>> I also in principle like the actor-oriented model approach of Siena, but 
>>>>> in
>>>>> the past I could never get the model to converge for networks larger 
>>>>> than
>>>>> 1000 nodes (this might be my own failing, though, as there is always a 
>>>>> bit
>>>>> of art to getting models like that to work).
>>>>> 
>>>>> We also have relied on experiments like this one in PNAS:
>>>>> 
>>>>> http://jhfowler.ucsd.edu/cooperative_behavior_cascades.pdf
>>>>> 
>>>>> and I am a big fan of David Nickerson's voter experiment and Sinan's new
>>>>> RCT.
>>>>> 
>>>>> 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
>>>>> http://www.youtube.com/watch?v=S6CSIFi78Nw
>>>>> 
>>>>> :)
>>>>> 
>>>>> j
>>>>> 
>>>>> James H. Fowler
>>>>> Professor of Medical Genetics and Political Science
>>>>> UC San Diego
>>>>> http://jhfowler.ucsd.edu
>>>>> 
>>>>> CONNECTED
>>>>> http://connectedthebook.com
>>>>> 
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>>>> 
>>> 
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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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>
> _____________________________________________________________________
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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

  Office:	(206) 685-3402
  Dept Office: 	(206) 543-5882, 543-7237
  Fax: 		(206) 685-7419

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