How Can Social Science Become More Solutions-Oriented?
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Innovation Data Analytics Sep 10, 2018

How Can Social Sci­ence Become More Solutions-Oriented?

A con­ver­sa­tion between researchers at Kel­logg and Microsoft explores how behav­ioral sci­ence can best be applied.

Computational Social Scientists discuss solutions.

Lisa Röper

Based on insights from

Noshir Contractor

Duncan Watts

Researchers com­mon­ly dis­tin­guish between basic” and applied” sci­ence. Basic sci­ence is dri­ven by sheer human curios­i­ty — think knowl­edge for knowledge’s sake — while applied sci­ence draws on this knowl­edge to solve a spe­cif­ic prob­lem. Engi­neers who study robot­ics or nuclear safe­ty, for instance, reg­u­lar­ly bor­row from the work of physi­cists, while med­ical researchers search­ing for the cure to can­cer build from advances in basic biology.

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But what hap­pens to a basic dis­ci­pline with­out an applied coun­ter­part? Social sci­ence aims to inves­ti­gate how peo­ple and soci­eties behave. But who uses its basic prin­ci­ples to under­stand and solve the many prob­lems fac­ing orga­ni­za­tions and soci­eties? And is this fun­da­men­tal­ly a prob­lem for the field? 

Noshir Con­trac­tor, a pro­fes­sor of behav­ioral sci­ences at the McCormick School of Engi­neer­ing, as well as a pro­fes­sor of man­age­ment and orga­ni­za­tions at the Kel­logg School and com­mu­ni­ca­tion stud­ies in the School of Com­mu­ni­ca­tion at North­west­ern, recent­ly sat down with Dun­can Watts, prin­ci­pal researcher at Microsoft and an expert in how social influ­ence spreads on net­works. They dis­cussed whether social sci­en­tists are doing enough to solve prob­lems in the world around us — and what researchers and busi­ness­es can do to push the field forward. 

This inter­view has been edit­ed for length and clar­i­ty.

Noshir CON­TRAC­TOR: I want to start by ask­ing about an arti­cle you wrote last year, which argues that social sci­ence should be more prac­ti­cal and solu­tion-ori­ent­ed. You argued that some of the things that social sci­en­tists have held near and dear — like focus­ing on under­stand­ing and explain­ing phe­nom­e­na in and of itself — is not going to get us far. 

What prompt­ed you to write this arti­cle? Why did this become an impor­tant issue for you? 

Dun­can WATTS: Well, the arti­cle reflects a frus­tra­tion I’ve had for almost 20 years. 

I come from out­side of social sci­ence — physics and engi­neer­ing — so to me the bound­aries between eco­nom­ics and polit­i­cal sci­ence and psy­chol­o­gy and soci­ol­o­gy nev­er real­ly made a whole lot of sense. And so it seemed per­fect­ly nat­ur­al to me to read across all of these dif­fer­ent dis­ci­plines. When I first start­ed try­ing to under­stand social influ­ence and con­ta­gion on net­works, I start­ed look­ing for arti­cles that had those words in the titles. And sure enough, I found arti­cles in eco­nom­ics jour­nals, and I found arti­cles in soci­ol­o­gy jour­nals and in psy­chol­o­gy and polit­i­cal sci­ence journals. 

And one thing that I found real­ly per­plex­ing and frus­trat­ing was that even though they pur­port­ed to be about the same thing, and would often invoke the same exam­ples, the con­tent was unrec­og­niz­ably dif­fer­ent. It was part­ly styl­is­tic, but even the math­e­mat­ics would be impos­si­ble to reconcile! 

As a sci­en­tist you’d like to be able to say, Well, which math­e­mat­i­cal mod­el is bet­ter?” But I couldn’t even get to the point where I could express one mod­el in terms of the oth­er. And one makes an assump­tion that’s fun­da­men­tal­ly incom­men­su­rable with the oth­er one. So they could both be wrong, but they can’t both be right. 

The point I make in the arti­cle is that social sci­ence has this very the­o­ry-ori­ent­ed per­spec­tive on the world. And yet we have this mish­mash of the­o­ries that don’t real­ly add up. Orga­ni­za­tion­al behav­ior” is a per­fect exam­ple of this. We have hun­dreds of the­o­ries of why orga­ni­za­tions do what they do. Yet if you read that lit­er­a­ture with the goal of mak­ing sense of it, it is real­ly just headache-inducing. 

Here’s an exam­ple. Take Microsoft, where I work now. A few years ago, Satya Nadel­la announced a major reorg. This is a mul­ti-hun­dred bil­lion dol­lar com­pa­ny. A hun­dred thou­sand full-time employ­ees. Tens of thou­sands of peo­ple were moved around. Thou­sands of peo­ple lost their jobs. Thou­sands of oth­er peo­ple got jobs. Every­thing about the com­pa­ny changed. 

We have a hun­dred years of orga­ni­za­tion and man­age­ment sci­ence. We have thou­sands and thou­sands of papers. You would think that some­where in that vast vol­ume of things with the word sci­ence” at the end of them, there would be some instruc­tions for Satya Nadel­la. How should he do this? 

I don’t believe there’s an answer to that ques­tion in those thou­sands and thou­sands of papers. And if that’s not a ques­tion that we’re answer­ing in orga­ni­za­tion sci­ence and man­age­ment sci­ence, what are we answering? 

CON­TRAC­TOR: So rather than only being moti­vat­ed by a cer­tain the­o­ry, econ­o­mists and soci­ol­o­gists should try to solve, not just under­stand, the same prob­lem. And if that becomes the focus, then it fol­lows that they will look any­where they can for the rel­e­vant lit­er­a­ture on that prob­lem. That pro­vides the incen­tive to nav­i­gate across disciplines. 

But you make an impor­tant dis­tinc­tion between pure­ly applied research and the kind you’d like to see more of. 

WATTS: Right. What I’m talk­ing about is where you use the applied prob­lem as a way to gen­er­ate new basic science. 

For many years peo­ple would approach me after talks about how infor­ma­tion spreads in social net­works and ask, How do I get my prod­uct to go viral?” And I would say, That’s not the ques­tion we’re ask­ing in soci­ol­o­gy. We just kind of inquire about gen­er­al mech­a­nisms.” But after a while, I thought: maybe we should try to answer that ques­tion. That’s not a dumb ques­tion, that’s the ques­tion that any­one who’s not a social sci­en­tist would ask. 

You’re try­ing to address the out­sider and say, Hey, social sci­ence is use­ful. We can actu­al­ly tell you answers to your ques­tions.” But in order to do that, we’re going to have to gen­er­ate a lot of basic sci­ence our­selves. We don’t have an answer we can pull off the shelf.

Think of what it would cost to build a the­o­ry about how teams inter­act — a pre­dic­tive sci­ence of teams.” But if you want to do social sci­ence the way physi­cists do physics, you need your CERNS and LIGOs and Hub­ble tele­scopes. —Dun­can Watts

CON­TRAC­TOR: This might be a provoca­tive ques­tion, but it’s one I’ve giv­en some thought to. When basic sci­ence gets applied, we call it engi­neer­ing. Is part of the issue that social sci­ence doesn’t have an equivalent? 

Most of us would con­sid­er social engi­neer­ing” a four-let­ter word, but com­pu­ta­tion­al social sci­ence — which uses com­pu­ta­tion­al meth­ods to inves­ti­gate social phe­nom­e­na — might be a plat­form for this. How can we use com­pu­ta­tion­al social sci­ence to address grand soci­etal chal­lenges? How do we accel­er­ate inno­va­tion by assem­bling teams more effec­tive­ly on the fly? How do we scale up glob­al health solu­tions when we know the solu­tions exist, but have not been able to lever­age net­works well enough to prop­a­gate them? 

In each of these cas­es, there’s a very vibrant research agen­da. We don’t know enough about how teams are assem­bled. We don’t know enough about how things spread on the net­work. So there’s a lot of basic-sci­ence ques­tions that are being addressed here. But in the process of address­ing them, we’re also show­ing that we can make a dif­fer­ence. Even if we can’t give the best solu­tion ever, it’s a bet­ter solu­tion than the lim­it­ed solu­tions we have today. 

WATTS: It’s a real­ly inter­est­ing point. One answer to the ques­tion of why social sci­ence isn’t more solu­tion-ori­ent­ed is that we’re miss­ing this trans­la­tion­al bit in the mid­dle, as you say. 

I think a sec­ond answer is more on the demand side. I wrote a whole book about how peo­ple think that social sci­ence is obvi­ous, just com­mon sense. Busi­ness lead­ers and politi­cians think they already know the answers. 

Plus, they’re deal­ing with these incred­i­bly com­plex prob­lems, but they’re also in a rush. They don’t have time to wait for the research. 

And research is expen­sive. Think of what it would cost to build a the­o­ry about how teams inter­act — a pre­dic­tive sci­ence of teams.” But if you want to do social sci­ence the way physi­cists do physics, you need your CERNS and LIGOs and Hub­ble telescopes. 

CON­TRAC­TOR: Yes, it would be very cost­ly. But the fact that oth­er areas are able to demand that kind of mon­ey says that we are not doing some­thing right in sell­ing our ideas. In oth­er words, peo­ple lit­er­al­ly don’t val­ue what social sci­ence could do. That’s true in terms of fund­ing agen­cies but also true in terms of businesses. 

What do you want oth­ers to take away from this conversation? 

WATTS: Well, a lot of the data that’s use­ful to com­pu­ta­tion­al social sci­ence is owned by 

indus­try, and yet a lot of the exper­tise to make sense of it is in acad­e­mia. So we need to do more to facil­i­tate indus­try – aca­d­e­m­ic col­lab­o­ra­tions. Again, this is not a new idea in engi­neer­ing. But it’s quite new to the social sci­ences. If we build col­lab­o­ra­tions around solv­ing big prob­lems, we can help busi­ness­es while also explor­ing fun­da­men­tal sci­en­tif­ic questions. 

CON­TRAC­TOR: The tra­di­tion­al social-sci­en­tist mod­el is to go into a com­pa­ny and beg them to be nice enough to say, Okay, we’ll share some data with you even though you’re not going to come back and help us.” And I think that mod­el is bro­ken. An indus­try – aca­d­e­m­ic part­ner­ship has to be a win – win.

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