Microsoft Word - GASS_workingpaper_forSSRN_12Nov2019.docx

The Golden Age of

Social Science

Anastasia Buyalskaya

, Marcos Gallo

, Colin F. Camerer

1, 2

California Institute of Technology, Division of Humanities and Social Science

California Institute of Technology, Computational and Neural Systems

12 November 2019. Preliminary. Comments welcome. Corresponding author Anastasia

Buyalskaya (

abuyalsk@caltech.edu

1. Introduction

Social science is entering a golden age. A rise in effective interdisciplinarity, the

explosive growth of available data and computational power to make that data useful, and an

increasing realization that pressing social challenges require cooperation acr

oss disciplines, have

all contributed to this opportune time. Figure 1 presents evidence from cross

citations of how

interdisciplinary research is on the rise in some social sciences, although not uniformly. The

historically most inward

looking social sci

ence disciplines

psychology and economics,

according to the Figure 1 data

have become more open to the idea that researchers in

neighboring fields have important information and methods to contribute to their disciplines.

This opening of disciplinar

y borders is akin to an increasing “trade” of methods, languages, and

knowledge across fields.

The application of economic language about trade begins with the premise that, like

people and countries, each social science discipline has different “endowment

s” (e.g., historical

mastery of tools and accumulated knowledge) and comparative advantage (e.g., anthropologists

carefully study different parts of the world and economists develop formal models). Each

discipline has a specialized view, and none can fully

explain human nature on its own.

Economics is endowed with a set of formal mathematical tools that all graduate students must

master in order to make predictions based on optimization given preferences and beliefs, as well

as an econometrics toolkit for t

esting theories and inferring causation. Psychology explores the

rich web of cognitive and social mechanisms that generate individual beliefs and behaviors.

Anthropology seeks to understand cultural differences using ethnographic observation,

unearthing ph

ysical details of human development, and exploring mathematical models of co

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evolution of culture and genes (among other approaches), hoping one model may effectively

approximate all human behavior at once. Political science studies systems of government,

voting,

juries, law, and a range of other situations in which people make consequential decisions

collectively. Finally, sociology investigates how the social world is created by and influences

how people think, feel, and act.

Figure 1: Changes in rates

of imported citations from neighboring disciplines from

1970 to 2015 for five social science disciplines. Imported citations are generally increasing

over this period, with notable exceptions (e.g., anthropology is not importing more citations

and is not

being imported either). Note the differences in y

axis ranges too: these show that

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political science and sociology import a lot, but psychology and economics do not. The authors

used weighted citation rates to observe changes in citation rates from the fiv

e social sciences to

each of the other four. Plots are 5

year moving averages. Source:

Angrist et al. (2017

One of the challenges of interdisciplinary work is communication among researchers.

Multiple fields may investigate the same behavior but be unable to communicate their methods

in a manner digestible by all potential collaborators. Interdisciplinarity needs a common trade

language across disciplines, a “lingua franca.” In a useful lingua

franca, all disciplines adopt the

‘best’ language from whichever discipline first described an idea or construct with language

clear enough to be imported into a new discipline without much confusion. Working together to

build a common vocabulary will enh

ance the efficiency of trade and collaboration.

Prominent examples of lingua franca include rational choice theory from economics,

human laboratory experimental methods from psychology, culture from anthropology, survey

methods in political science, socia

l networks from sociology, and tools for causal inference from

all over. Another notable example is game theory. Created by mathematicians such as John

Nash, this framework only flourished after the seminal 1944 book by the polymath John von

Neumann and th

e economist Oskar Morgenstern. Game theory is a lingua franca for social

sciences because its basic concepts

players, information, strategies

are defined broadly enough

to potentially apply to genes, viruses, insects, animals, and organizations with shar

ed values,

including nation

states

(Gintis, 2007)

Most importantly, interdisciplinarity is necessary for solving complex multi

dimensional

problems and creating innovations for better health, w

ealth, and well

being

(Watts, 2017)

. Some

of today’s most significant and fastest

growing problems, such as drug addiction, obesity,

changes in political discourse, and climate change, cannot be

understood or solved by one

discipline alone. Instead, solving these issues will require an understanding of the institutional

incentives, cultural norms, cognitive mechanisms, and social network effects that created and

continue to heavily influence thes

e phenomena. Interdisciplinary work has already helped make

progress in fields, including poverty, health epidemics, and mental health.

Drug trafficking is one such example of how an interdisciplinary approach can facilitate

problem

solving.

Magliocca et al. (2019)

combined interdisciplinarity and new data to analyze

international drug trafficking in Central America (Figure 2). The researchers tested an agent

based model against a dat

abase of estimated illicit drug flows from 2000

2014. In their model,

the agents were local suppliers, cartel networks, and interdiction organizations trying to intercept

drugs from the traffickers. The traffickers were motivated by profit, including a pri

ced

interdiction risk premium. Interdiction agents were motivated to intercept drugs (and interdiction

capacity increased based on past success). Agent

based models seek to derive complex, lifelike

behavior that is “emergent” from interactions of simple

agents, typically simulating behavior

which is too complex to solve mathematically. The model is successful at capturing many of the

underlying trends, across time and countries, in trafficking flow and interdiction. It reproduces

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two effects known as the

“balloon” effect (when trafficking spreads into new areas) and the

“cockroach” effect (when trafficking routes become fragmented).

Figure 2: Central America modeling domain (center) with an example simulated narco

trafficking network consisting of inac

tive nodes (gray circles), active nodes (red circles), and

trafficking routes between each active node (dashed lines). The most southern and northern

nodes outside of the model domain represent supply (e.g., Colombia) and demanding nodes

(e.g., Mexico), re

spectively. Around the periphery, comparisons of subnational cocaine

shipment volumes (blue regions in the map) reported at the administrative level of departments

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in the Consolidated Counterdrug Database (CCDB) (red line) and median volumes simulated

by m

odel versions with (blue line) and without (black line) a Network Agent. Shaded regions

represent the bounds of the second and third quartiles of simulated cocaine volumes.

Departments were selected to include at least one location per country and on the b

asis of

having at least 5 y of continuous observations reported in CCDB. Image and legend from

(Magliocca et al., 2019)

This study illustrates practice and promise in the golden age in many w

ays. First, their

team consists of nine co

authors from seven universities, one government organization, and a

coauthor who remained anonymous to protect confidential sources (and perhaps to stay alive).

Their affiliations span geography, politics, biology

, and earth sciences. Second, their model

includes a novel application of ideas from behavioral economics about learning

(Camerer & Ho,

1999)

and salience

(Bordalo, Gennaioli, & Shleifer, 2012)

of specific trafficking events. Third,

their model can be used to analyze how different policies will hypothetically change trafficking,

prices, and drug use. Of course, models like this are nev

er perfect, but they are a starting point

and can always be improved using new evidence and plausible extensions.

In the next section, we present two more general “case studies” of successful

interdisciplinarity

behavioral economics and social network science. In both cases,

interdisciplinary research led to the creation of new cross

disciplinary fields of inquiry b

uilt on

the comparative advantages of contributing fields, inspiring a shared lingua franca, generating

insights about human nature, and improving social outcomes.

2. Case Study: Behavioral Economics

Behavioral economics is the first of our t

wo examples of successful interdisciplinary

enterprises

(Thaler, 2015, 2018)

. Behavioral economics uses evidence and methods from other

social sciences

particularly psychology

to analyze n

atural limits on human computation,

willpower, and selfishness. These analyses make new predictions about natural field data,

including how markets work and can make novel suggestions about policies to improve human

welfare.

Analyzing such limits was of in

terest because conventional rational choice theory

assumes maximization of subjective values (“utilities”) and Bayesian integration of information,

sometimes over a long

time horizon or accounting correctly for risks. Not all people are always

that smart o

r patient.

To be fair, rational choice theory was always intended to be useful, rather than realistic.

The question behavioral economists tackled was whether theories assuming more realistic

psychology could be precise and

useful. Thaler and others

(Camerer, Loewenstein, &

Rabin, 2004)

used an “insider” approach. This insider approach took rational choice theory as a

simple benchmark, identified empirical “anomalies” that could not be sensi

bly explained by that

benchmark, and sought explanations which added extra ingredients sparingly, to explain the

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anomalies and make new predictions. The first step was to begin with highly controlled

laboratory experimental evidence (using hybrid methods f

rom psychology and experimental

economics) to convince skeptics and establish plausible alternative theories. Then we turned to

make new predictions about field data. Alternative theories with a small number of added

parameters were developed so that ratio

nal and behavioral predictions could be compared

(DellaVigna, 2018)

Example #1: Loss aversion.

In their influential prospect theory,

Kahneman

and

Tversky (1979, 1992)

proposed that

outcomes were subjectively valued by their gains and losses relative

to a reference point,

analogous to how a single percept could be subjectively perceived

or “framed”

as one of two

distinct things (such as the face

vase illusion). For example, people may estimate their longevity

to be higher if they judge the chance

that they would live to age 75, compared to when they

judged the chance that they would die before age 75

(Payne, Sagara, Shu, Appelt, & Johnson,

2013)

Tversky and Kahneman also suggested that

losses were disliked much more than equal

sized gains, a phenomenon called “loss

aversion.” Loss

aversion is sometimes conveniently

expressed by a single parameter,

, the ratio of gain utilities to loss utilities (or to their marginal

utilities), which i

s often measured to be around 1.9 (Figure 2b).

Loss

aversion became part of explanations for many different phenomena in social

science, such as (1) which kinds of financial risks people accept or dislike in lab experiments

(Gneezy & Potters, 1997)

, (2) why stocks return so much more than bonds

(Thaler & Benartzi,

2004)

, and (3) why there is a gap between high prices demanded to sell

goods and lower prices

paid to buy the same goods, the “endowment effect”

(Kahneman, Knetsch, & Thaler, 1990)

Psychologists demonstrated how sadness and disgust change the endowment effect

(Lerner,

Small, & Loewenstein, 2004)

and also suggested effects of cognitive sequencing

(Johnson,

Häubl, & Keinan, 2007)

and attention

(Bhatia & Golman, 2019; Yechiam & Hochman, 2013)

Cognitive neuroscientists have found evidence for loss

aversion in neural circuitry

(Tom, Fox,

Trepel, & Poldrack, 2007)

, including dissociations between circuitry valuing gains and losses

(Yacubian et al., 2006)

and an unusual

tolerance

of losses in patients with amygdala dam

age

(De

Martino, Camerer, & Adolphs, 2010)

. Political scientists have posited loss

aversion as one

reason why concessions in bargaining are difficult

(McDermott, 2004)

and have analyzed its

theoretical effects on election outcomes

(Alesina & Passarelli, 2019)

and trade policy

(Tovar,

2009)

. Figure 2 illustrates estimates of loss

aversion along with applications from goal

setting at

round numbers and “narrow bracketing” of local losses and gains that should, rationally, be

added up.

Many behavioral economists have

not been keenly interested in the evolutionary and

cultural origins of phenomena like loss

aversion (an unfortunate omission, in our view). There is

evidence that loss

aversion and endowment effects are present in monkeys

(Lakshminaryanan,

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Chen, & Santos, 2008)

and great apes

(Kanngiesser, Santos, Hood, & Call, 2011)

though only

for food and not for other valued goods (e.g., tools).

Apicella, Azevedo, Christakis

and

Fowler

(2014)

also found an unusual

lack of endowment effects among market

isolated Hadza villagers

in Tanzania. These data indicate that loss

aversion is not as universal as thought, and show why a

wider scope of new data is needed.

Figure 2: Loss

Aversion. (a) The gain

loss utility function over money derived from group

parameters estimated from risky choices

(Baillon, Bleichrodt, & Spinu, 2018)

. (b) The

empirical distribu

tion of the loss

aversion parameter

λ

from a meta

analysis. The blue and red

panels include 286 and 469 effects, respectively (unpublished author data). (c) The

distribution of marathon finishing times, with over nine million data points

(Allen, Dechow,

Pope, & Wu, 2017)

. Note the peaks at round numbers. (d) Actual point values in each period,

plotted against optimal conditional point values from consumption choices, in a 50

period

savings experi

ment

(Brown, Chua, & Camerer, 2009)

. Note how few actual point values are

negative even when optimal point values should be negative. This indicates that most subjects

do not like to make choice

s that generate individual

period losses (“narrow bracketing”), even

though performance is determined by the sum of all 50 periods.

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Loss

aversion contributes to a “status quo bias,” the exaggerated tendency to choose a

suggested default (or a previous st

atus quo) when other choices are readily available

(Samuelson

& Zeckhauser, 1988)

. Countries in which organ donation is the default have much higher rates of

donation than those in which it is n

ot the default, even though the choice is simple to make

(Johnson & Goldstein, 2003)

. The first impactful application of status quo bias is the “Save More

Tomorrow” (SMART) plan

(Thaler & Benartzi, 2004)

. In this plan, companies auto

enroll

workers into tax

advantaged 401(k) plans and invest a fraction of their next pay raise into the

plan (so their paycheck does not go down and create

s a subjective loss). These plans have

increased savings substantially, with no apparent reduction in savings from other sources

(Chetty, Friedman, Leth

Petersen, Nielsen, & Olsen, 2014)

. The SM

ART plan became a poster

child for many types of “nudges,” designed choices that help some people make better decisions

than they are likely to make on their own

(Camerer, Issacharoff, Loewenste

in, O’Donoghue, &

Rabin, 2003; Thaler & Sunstein, 2009)

Example #2: Social preferences

Humans are the most prosocial species

helping unrelated others at a cost to ourselves

and we alsoe create large

scale institutions to facilitate such prosocial behavior. Cutting across

social sciences is the question of how to express this prosociality m

athematically, and what data

to collect. Behavioral economics contributed both theories and a menu of economic choices and

games.

Of course, prosociality has long been contemplated in all social sciences, as well as in

biology, moral philosophy, literatur

e, and beyond. In economics, Adam Smith discussed “moral

sentiments” and “fellow

feeling”

(Smith, 1759)

. In 1881, Edgeworth included a “coefficient of

effective sympathy”

the weight one person

places on the utility of another

to try to make

bargaining theory more precise

(Edgeworth, 1881)

. That simple formulation and other variants

(Loewenstein, Thompson, & Bazerman, 1989)

are still used today. In the 1960s, social

psychologists began to describe social value orientations in the style of psychometrics

(Messick

McClintock, 1968)

and distinguished importantly between equal outcomes and equitable ones,

reflecting differences in need or inputs

(Messick & Cook, 1983)

Game theory offers canonical strategi

c interactions that can be used to dissect elements of

prosociality. A famous example is the “ultimatum game”

(Camerer, 2003; Güth, Schmittberger,

& Schwarze, 1982)

. In this game, a proposer off

ers a share of a known amount of resources, such

as $10, to a responder. The responder can accept the offer, in which case bargaining ends and

they collect their money, or the responder can reject it, and then they both get zero. Games like

this can measu

re whether and why people will reject money and whether the proposer correctly

anticipates rejection.

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Rejecting a low ultimatum offer is now thought to show negative reciprocity

willingness to sacrifice resources to harm another person who has been unf

air (relative to a social

norm, often with cultural content). This tendency is even evident at the collective level. For

example, police effectively solve fewer criminal cases after losing a wage arbitration

(Mas,

2006)

As the ultimatum game caught on across social sciences, other fascinating games quickly

followed, each with a natural interpretation about psychological motives

(Cam

erer & Fehr,

2004)

: (1) dictator allocations, in which the responder must accept the offer (measuring altruism

and norm

sensitivity); (2) trust games, in which a first

mover invests money, with a social risk to

potentially benefit both parties, gambling th

at the second

mover will share the total gain

(Berg,

Dickhaut, & McCabe, 1995; Camerer & Weigelt, 1988)

; and (3) many

person gift

exchange

labor markets in which firms prepay wages and hope that

workers exert costly effort which

benefits the firms and repays their trust

(DellaVigna & Pope, 2018; Ernst Fehr, Kirchsteiger, &

Riedl, 1993)

Anthropologists also adopted these games. An interdisciplinary team, including

anthropologists and behavioral economists, used economic games to study cross

cultural

sociality in small

scale societies

(Henrich et al., 2005, 2010)

. They learned that stronger sharing

norms (which were punished by ultimatum rejections) were associated with societal cooperation,

such as, building houses together, and with the extent of market trading.

As interest in

these games grew, the sociological lingua franca of a “norm” got imported

into other social sciences. Norms are informal social rules that are expected to be followed, and

usually informally self

enforced by social punishment for deviations (even absent le

gal

enforcement). In dictator allocation games, for example, people have different subjective norms

about what is fair to share. Their sharing of actual money is closely tied to their norm perceptions

(Krupka & Weber, 2009)

. Thus, sharing money seems to reflect “manners” consistent with

perceived norms rather than altruism per se

(Camerer & Thaler, 1995)

Cognitive neuroscientists have also used these games to measure social preferences,

identify circuitry implementing prosociality

(Tricomi, Rangel, Camerer, & O’Doherty, 2010)

associate brain le

sions with abnormal social preference

(Krajbich, Adolphs, Tranel, Denburg, &

Camerer, 2009)

, and linking to individual differences in neurotypical populations more generally

(Bruhin, Fehr, & Schunk, 2019)

Knowing more about social preferences has not contributed immediately to solving social

problems at the scale that “nudging” has. However, experiments have suggested social forces

hat could enhance prosociality. For example, allowing people to punish others who have

behaved antisocially seems to increase cooperation

(Fehr & Gächter, 2000; Yamagishi, 1986)

although the re

sults vary culturally

(Herrmann, Thoni, & Gächter, 2008)

. New evidence has also

invigorated understanding of charitable giving

(Dell

aVigna, List, & Malmendier, 2012)

. In the

future, diagnostic tools will likely emerge from a better understanding of sociality, with

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applications ranging from psychiatry, methods to develop empathy, and people analytics

enhancing the matching of workers wi

th jobs.

Summary:

Progress in understanding loss

aversion and social preferences in behavioral economics

(and many intellectually neighboring fields) are two example illustrations of how

interdisciplinarity and better measurement create progress and ev

en help solve problems

in our

golden age. The simple idea of loss

aversion came from perceptual psychology and compelling

early data. There was substantial hostility to the idea of loss

aversion in mainstream economics

for many years. The idea that people

have preferences over what others get, familiar within

social psychology, was less controversial, but there is still debate about the best general theory.

In general, behavioral economists won over skeptics by weaponizing the mantra that ‘the

easiest way

to win an argument is to run another experiment or another statistical regression’

(Thaler, 2018)

. In many areas of behavioral economics and finance, large data sets played an

important role, i

ncluding more recently, multi

site lab and field experiments

(Cohn, Maréchal,

Tannenbaum, & Zünd, 2019; Herrmann et al. 2008)

. A treasure trove of experimental data came

about as nudges and othe

r ideas were implemented by “behavioral insight teams” in firms, and

governments on every continent, currently just over 200, according to the OECD

(“Behavioural

insights

—

OECD,” n.d.)

to create better outcomes for citizens and consumers (Halpern 2015).

As noted in discussing loss

aversion above, there has been limited interest of many

behavioral economists in the deeper biological and cultural origins of preferences, norms, and

ognitive limits. Such data and models are necessary to generalize ideas beyond activity in

developed societies that are “WEIRD”

(Henrich et al., 2005)

and not representative of all human

activit

y. How preferences are formed and changed is also central to essential discussions about

the value of economic institutions, like market development

(Bowles & Polanía

Reyes, 2012)

3. Case Stud

y: Social Networks

Social networks are our second example of successful interdisciplinary enterprises. Network

analysis uses methods from physics, computer science, and applied math to analyze questions

often studied by sociologists, anthropologists, and

psychologists regarding how interpersonal

relationships are formed and how behaviors, beliefs, and emotions are transmitted across

connected individuals

(Watts & Dodds, 2007)

. One striking featu

re of network analysis is the

diversity of scholars contributing to intellectual progress. People from different fields, traditions,

and countries have worked together on related questions

(Free

man, 2004)

. Network analysis has

been significantly enabled by the availability of novel datasets, such as social media connections

as well as increasingly “connected” devices, such as fitness trackers with social aspects

(Aral &

Nicolaides, 2017; Coviello et al., 2014; Phan & Airoldi, 2015)

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The recent history of network analysis owes a lot to

Watts & Strogatz (1998)

, who highlighte

several key network properties, including that real networks are neither totally ordered nor

random. However, it turned out that simple mathematical models could capture features of

complex networks, and those models could be applied to network dynamics

across a variety of

phenomena. It turns out that the seemingly unrelated affiliations between actors, power grid

transmission lines, and the neural network of

C. elegans

could all be captured via a simple

“small

world” network model, a mathematical graph i

n which the nodes (individuals) are not

neighbors with most of the other nodes, and yet all other nodes can be reached in a small number

of steps (limited degrees of freedom connecting individuals)

(Christakis & Fowler, 2011;

Jackson, 2019; Watts, 2003, 2004)

Figure 3: (a) A network of human traffic reveals cities that are important nodes (in

yellow) and effective borders (in red)

(Thiemann, Theis, Grady, Brune, & Brockmann, 2010)

(b) A network of international financial institutions. Edges symbolize mutual share

holdings

(Schweitzer et al., 2009)

. Note the high connectivity among nodes that can create systemic risk

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and network vulnerability. (c) Effects of the distribution of sexual partner concurrency on

network connectivity

(adapted from Morris, Goodreau, & Moody, 2008)

. Note how a slight

increase in average concurrent partners (from the top left to right histograms) dramatically

impacts the number of nodes in the largest component of the network. (

d) A network of brain

regions where edges represent developmental increases in streamline density

(Baker et al.,

2015)

Example #1: Spread of Infectious Disease

Sociologists have been int

egral to guiding the development of these network models, given how

ubiquitously they can help explain the spread of anything from disease to innovation

(Davis &

Greve, 1997)

. Most infectious di

seases spread through human contact, making the study of

infection a natural place to apply network analysis. One of the first and longest

used models of

disease spread, known as the

SIR model

, was introduced by

Kermack & McKendrick (1927)

and

included some highly oversimplified assumptions, including that individuals of different classes

(i.e., infected vs. susceptible) connected exclusively in pairs, an

d that those connections occurred

randomly.

As recounted by Martina Morris, it was feedback from a man in Uganda which illuminated the

severe limitation of a model unable to handle multiple connections (multiple sexual partners) at

once

–

something which is still closer to the norm than the exceptio

n in several societies

(Kretzschmar & Morris, 1996)

. This insight led Morris and collaborators to create better models,

ones which more accurately explained how the AIDS epidemic was spreading

–

specifically,

how small variations in concurrency (simultaneous sexual partners) could have dramatic effects

on a population’s vulnerability to HIV

(Morris & Kretzschmar, 1997)

It is unclear

whether concurrency explains the full story, given that empirically it does not

explain why places with high rates of concurrency do not necessarily have high rates of HIV and

vice versa. One response to this is the potential misreporting regarding sexual

activity, a private

matter that complicates accurate data collection. Morris’s team continues to collaborate across

disciplines (with sociologists and statisticians

–

she is a professor of both), as well as across

geographies (with several collaborators i

n Africa), to refine and improve models of the spread of

infection, and apply them to new and better datasets.

Example #2: Revisiting Influence and Information Transmission

Several social science disciplines, from anthropology to political science, are

particularly

interested in collective decision making. While often studied at a static point in time, implicitly

assuming that all individuals simultaneously make independent decisions, the heterogeneous

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process of information accumulation and integration

prior to decision making suggests that

decisions are actually made sequentially and that beliefs can be similarly “transmitted” from one

individual to the next. The field of cultural evolution has been modeling information

transmission for several decades

, using both epidemiological and social network models in their

approach

(Richerson & Boyd, 1989)

Broadly,

social contagion models allow simulating the speed at which individuals receive

information and how past interactions influence their behavior

(Watts & Dodds, 2007)

. These

models focus on a hand

ful of key parameters, which can be grouped as: (1) degree centrality, (2)

eigenvector centrality, (3) diffusion centrality, and (4) bridging

(Jackson, 2019)

. While one

might

not

wish to be cent

ral in an HIV infection network, centrality is viewed as an advantage in

most social networks and is correlated with financial success

(Burt & Ronchi, 2007)

and well

being

(Morelli, Ong, Makati, Jackson, & Zaki, 2017)

. Degree centrality captures “popularity,”

the sheer number of connections an individual might have, capturing the speed at which these

individuals can easily transmit inf

ormation to a wide group at once. Eigenvector centrality,

which captures how many well

connected others one is connected to, has been used to study

social status and scapegoats

(Weaverdyck & Par

kinson, 2018)

. Diffusion centrality is a measure

of “reach,” capturing how well

positioned an individual is to spread and hear about information.

Finally, bridging captures “social chameleons” who connect otherwise disparate groups.

Interestingly, all of t

hese positions appear to be context general: if an individual is central in one

network, they are likely to be central in another, and so forth

(Jackson, 2019)

Network analysis has therefore a

llowed researchers to apply new tools while revisiting old

questions about social influence. For example, researchers have investigated whom individuals

gravitate to in a network, finding that empathetic people are chosen for situations which require

trust

and support, while positive people are chosen for situations that are fun and exciting

(Morelli et al., 2017). Other work has found that people give less money to those who are more

socially distant (unknown) friends of friends in standard economic games

(Candelo, Eckel, &

Johnson, 2018; Goeree, McConnell, Mitchell, Tromp, & Yariv, 2010)

. Computational modeling

methods have also been used to show that there is quicker consolidation of majority

opinion and

more successful spread of initially unpopular beliefs in populations characterized by their greater

susceptibility to social influence

(Muthukrishna & Schaller, 2019)

Given how ma

ny behaviors

–

from smoking to divorce

–

are “contagious” across individuals, the

dynamics of such contagion are of immense interest to social scientists and non

social scientists

alike.

Summary:

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Network science would have been less successful without sc

ientists from different disciplines

borrowing ideas and communicating in a shared language about constructs and methods. To take

a meta

perspective, innovation in network science has benefited from the wide network of

researchers who share a lingua franca,

can transmit high fidelity information, and bring a

diversity of perspectives to the table (Muthukrishna & Henrich 2016).

Networks and their properties are fundamentally interesting because they underpin such a wide

range of phenomena. Unlike behavioral

economics, there was less conflict among those studying

networks because the concept of a network was so obviously appealing and useful from the start

(i.e., there was no interdisciplinary conflict about whether people “were networked” as occurred

about w

hether people “were rational”). Furthermore, while sociologists studied networks first,

the difficult question of what networks arise when people have scarce social bandwidth and can

choose network links was cracked by economists

(Jackson & Wolinsky, 1996)

. Moreover, the

increasing availability of large, novel datasets that capture connections between individuals, such

as social media and online communication data, has truly turbo

charged network

science.

4. Spotlight on Studies

Table 1 displays twelve studies that epitomize the golden age of social science research

(Salganik, 2018)

. Each of these papers is an excellent example of one or more of these features:

(1) collaborating in an interdisciplinary team; (2) using new types of data; and (3) answering

important and difficult questions. The table also demonstrates a wide variety o

f research topics,

from exercise habits and social inequality to political preferences; and a diversity of datasets,

including genetics, brain imaging, and browsing history. One notable study using CCTV footage

tests the long

lived belief from early social

psychology experiments that bystanders do not

intervene to help strangers if there are other bystanders around (they actually do).

Summary

Subfield(s)*

Main Novelty

Reference

Evolutionary changes

in hominins created a

niche that favored

individuals with the

ability to

communicate and

persuade,

transforming

sociopolitical life.

Anthropology,

Political Science

Interdisciplinarity

(Gintis, van Schaik,

& Boehm, 2015)

Greater exposure to

war increases

Anthropology,

Biology, Economics

Interdisciplinarity

(Henrich, Bauer,

Cassar, Chytilová, &

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3486314

religiosity.

Purzycki, 2019)

Rwandans use the

mobile phone

network to transfer

“mobile money” to

those affected by

unexpected economic

shocks.

Economics

New type of data:

mobile phone usage

(Blumenstock,

Fafchamps, & Eagle,

2011)

Describing the

barriers to

understanding the

impact of AI on the

labor market.

Economics

Interdisciplinarity

(

Frank et al., 2019

)

Railroad expansion

from 1870 to 1890 in

the U.S. increased

agricultural land

value.

Economic History

New type of data:

geographic

information system

network database

(Donaldson &

Hornbeck, 2016)

Brain responses to

emotionally evocative

images predict

political ideology.

Polit

ical Science,

Neuroscience,

Psychiatry

Interdisciplinarity,

new type of data:

fMRI

(Ahn et al., 2014)

Genetic data can

predict economic and

political preferences.

Political Science,

Economics,

Psychology,

Sociology

Interdisciplinarity,

new type of data:

GWAS

(Benjamin et al.,

2012)

Musical preferences

and personality traits

are linked.

Psychology,

Market

ing

New type of data:

Facebook likes

(Nave et al., 2018)

Bystanders will help

in public conflict.

Psychology,

Sociology

Interdisciplinarity,

new type of data:

CCTV footage

(Philpot, Liebst,

Levine, Bernasco, &

Lindegaard, 2019)

Social networks

strongly influence

exercise habits.

Sociology

New type of data:

fitness tracking

(Aral & Nicolaides,

2017)

Fatal shootings of

police officers

increases police

Sociology

New type of data:

NYC stop

and

frisk

reports

(Legewie, 2016)

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3486314

violence towards

black suspects.

Estimating social,

environmental, and

health inequalities

with neural networks

and street images.

Sociology,

Economics

New type of data:

street images

(Suel, Polak, Bennett,

& E

zzati, 2019)

*As much as we admire those who cross disciplinary boundaries, making it difficult to identify

home fields, authors’ departmental affiliations are used here as a heuristic description.

5. Conclusion and Challenges

Interdisciplinary research inevitably presents new challenges. The following obstacles are worth

noting given they disproportionately concern teams working on questions that cut across

disciplines. It is advised that interdisciplinary research teams discus

s and plan for dealing with

these challenges before the research work begins:

The question of

informatics

, or where and how information is accumulated, can be

a special challenge for teams who are used to contributing to traditionally disparate

sciplines. Many journals cater to the readership of a specific discipline or discipline

subfield, with authors citing papers predominantly from like

minded journals. While

cross

citation is on the rise, it is not guaranteed that interdisciplinary work will

make

equal contributions across fields, presenting the possibility of losing valuable insight

with relevance to one of the fields.

Closely tied to informatics is the question of

incentives and authorship

Academics are often encouraged to remain f

ocused on contributing to their respective

subject areas, which means working with other academics in the same subfield and

publishing in a specialized set of journals. While often practical and well

meaning

advice, it constrains people to narrow paths wit

h little upside to taking on

interdisciplinary ventures. Furthermore, differences in authorship norms across

disciplines (such as the strong emphasis on solo

authored papers in economics) make

young researchers reluctant to join projects where a bigger tea

m size is necessary in

order to capture the range of specialized contributions necessary to tackle big research

questions. If interdisciplinary work is to take off truly, research which makes

contributions to other fields cannot be discounted, and papers w

ith multiple co

authors

should not automatically be seen as a smaller contribution than single

authored work.

Interdisciplinarity possesses unique challenges for

“open science”

i.e., the sharing

of procedures, data, and code intended to make res

earch more widely accessible

because different social science disciplines often have different tools and norms. As

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3486314

Stodden et al. (2016) note, “Current reporting methods are often uneven, incomplete, and

still evolving.” However, this challenge is now wi

dely recognized, and efforts are

underway to improve open science in practice.

As mentioned in the introduction, interdisciplinary teams tend to work on big

questions, applying a range of tools and insights to solve important, complex problems.

A ch

aracteristic feature of these problems is their tendency to bring up novel concerns

regarding individual

privacy and ethics

. New types of data

–

from genetics to real

time

biofeedback

–

carry potentially sensitive information. Sensors have great promise fo

changing behavior and mitigating risk as a real

time physical nudge. However, they also

present ethical quandaries, such as what happens if the sensors make a mistake or the

nudge is overridden. Scientists take experimentation and transparency for grante

d, but

the average citizen is more skeptical

(Meyer et al., 2019)

. Interdisciplinary teams across

social science should seek ethicists and legal scholars to join the conversation.

Anoth

er challenge is the creation of

unifying frameworks

to explain behaviors

across disciplines. Better theories will restrain the number of explanations that could be

derived from big data by setting appropriate priors for hypotheses (Muthukrishna &

Henrich,

2019). An expansion of methodological approaches alone will not increase

scientific knowledge unless there is common lingua franca or, even better, genuinely

unifying frameworks. Akin to the construction of modern biology from unifying

principles in chemi

stry and physics, social science would benefit from evolutionarily

plausible theories that provide ultimate (function) and proximate (mechanism)

explanations.

(Gintis, 2007)

makes an argument that game theory is one promising

candidate for unification.

The challenges of informatics, incentives, open science, ethics, and theoretical unification

are serious. However, the same interdisciplinarity, creation of institutions, and

reliance on better

and more extensive data that make for our golden age should help solve these challenges too.

Furthermore, there is reason to be optimistic: our increasingly connected age means that

knowledge from other disciplines is much easier to acce

ss. Cross

disciplinary citations no longer

require walking between libraries to find journals siloed in between the literal four walls of their

own field!

It is foolish, of course, to forecast both a number and a time at which problems like the

worldwide

rise of obesity or “fake news” will be reined in. However, it is safe to say that our

golden age will be marked by faster rates of progress in producing breakthrough social science

and solutions to such human suffering than ever before.

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3486314

Acknowledgments

: We thank Fred Blum, Jonathan Katz, Joseph Henrich, Philip Hoffman, Scott

Page, and Jerry Davis for helpful comments and discussions on related concepts in social

science. We are grateful for the support of the T&C Chen Center (Fellowships for MG and AB,

support for CFC), the Behavioral and Neuroeconomics Discovery Fund (for CFC), and

especially Trilience (for CFC) for a longstanding interest in, and financial support of this topic.

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