FORMAL MODELS & NETWORK MECHANISMS

Emerging ideas

Formal network mechanisms

Agent-based models Experiments

Diffusion mechanisms (Pipes) Network size Location of first movers

Prism” mechanisms Power, rationality Centrality may also shape situations Not much explored

Conditional Choice (from Rolfe 2009)

Conditional Decision Rules

Conditional Decision-making:Impact of Network Size on Diffusion

Simulated turnout with 15% unconditional cooperation

What to Measure?

Networks as Pipes* Location of positions,

resources, mobilization, innovation, etc.

* Rate/form of spread Average degree Clustering/density Centralization Redundant Ties Social Cleavages

Network as Prisms Relational style

Underinvestment Overinvestment

Local strategy structural holes brokerage

Status

MEASURING NETWORK SIZE: SOCIAL CIRCLES AND SCALE UP

Emerging ideas

Which Network to Measure?

Specific Transaction (i.e., discuss politics)

Social Circles (Dunbar) 3-5 (core/family) 12-20 (extended family/band) 100-250 (lineage/Christmas Card List) 1000s (Tribe)

Extended Friendship Network Circle

Please think for a moment about your extended network of friends and family. These are people whom you see on a fairly regular basis, or did see regularly in the past.  These may be people on your list for holiday or birthday cards, or people you would be likely to invite to a large party or wedding. Neighbours, co-workers, former schoolmates and people you met through social, political or religious activities may fall into this social circle.

...about how many people do you have in this social circle? Even if you are not sure, please take your best guess.

1 to 25 or open-ended26 to 5051 to 75 (and so on)

Extended friendship Circle, cont.

...about how many have the following first or given names. Even if you are not at all sure, please take your best guess

Patrick Gregory Shaun Rachael Julia Heather

Katharina Anne Eva Martin Paul Marc/Mark Robert

Network Structure Measure: Network Size Self-estimated size

How many people do you know?

Scale-up estimates Killworth, et al. 1988

McCormick, Salganik and Zheng (2010)

Carefully pick names (.1% of population, equal across generations)

Ger

man

y: N

etw

ork

Siz

e

Network Structure Measure: Personal Network Density Is good friends with someone else on this list (Political

Discussion) Burt 1984

What proportion are family members?

Sum of unique categories named from the “How many X do you know?” (may also proxy size)

Network Structure Measure: Overdispersion and cleavages How many people do you know…[in prison]

Zheng, Salganik, Gelman (2006)examples from CCAP

Cleavages and network size

Rolfe, 2012

Network Structure Measure: Social Capital Access & Social Cleavages

...about how many fall into the following categories. Even if you are not at all sure, please take your best guess. None Politicians Leaders in neighborhood Business owners or Self

Employed Corporate Executives Managers Professionals Barkeeps Schoolteachers

Professional writer, artist Unemployed Retired Drives a company car

12-34-56-1011 or more

Ove

r-di

sper

sion

of

Res

ourc

es/P

ositi

ons

Pro

porti

on o

f Rs

Kno

win

g N

o Xs

, by

Soc

ial C

ircle

(UK

)A gender gap in ACCESS to power?

CLEAVAGES: Network size isn’t an individual level mechanism…

Network size varies across the social cleavage (1985 GSS)

HS Degree Some Col-lege

College Degree

Grad. Degree0

0.51

1.52

2.53

3.54

4.5 Rs name low educ friends only

Rs name high & low educ friends

Individual Education

# of

Frie

nds

Voter turnout by social context(1985 GSS)

HS Degree Some Col-lege

College Degree

Grad. Degree

0%10%20%30%40%50%60%70%80%90%

100%

Low educationMulti-education

MEASURING NETWORKS AS PRISMS

Emerging ideas

What to Measure? Networks as Pipes

* Location of positions, resources, mobilization, innovation, etc.

* Rate/form of spread Average degree Clustering/density Centralization Redundant Ties Social Cleavages

Network as Prisms Relational style

Underinvestment Overinvestment

Relational strategy Structural holes Brokerage

Status

Relational Style/Strategy 1

Imagine your were hosting a party, and planned to invite friends from different parts of your life (e.g., work, school, social groups, neighborhood, etc.)

Would you find it appealing Would they already know each other Would they mix and mingle

Relational Style/Strategy II

Are you more comfortable interacting one-on-one or in a group?

When there is a conflict between your friends, are you more likely to..

pick sides mediate stay out of it

Relational Style & Partisanship

Under-investors are more conservative; over-investors are more liberal (preliminary findings)

See Jackson on networks and beliefs about whether friendships are complements or substitutes

Under/Over Investment• Relational Style 1 & 2• Number of

activities/groups• Personal network size

Political Outcomes• Left-right• party choice• issue attitudes

Self-Monitoring: 5 Qs (Berinsky)

ELICITING NETWORK ATTITUDES: ISSUE OR “IDENTITY” SPACE PLACEMENTS

Emerging ideas

Issue Space: Gender Equality

Ger

man

y &

UK

Ave

rage

Issu

e P

lace

men

ts

Ger

man

y (P

ost E

lect

ion)

Per

cept

ions

of D

isag

reem

ent

Sel

f vs.

Fam

ily, F

riend

s,C

owor

kers

& N

eigh

bors

NETWORKS AND OPINION INSTABILITY

Emerging ideas

Predictions: Attitude Instability

Nearly converged Contested

Stable• Low instability• Initial opinion predicts resistance to influence

• Random instability• “Opinion leaders” (value driven?)

Active

• Low random change, moderate/high instability• Initial opinion predicts resistance to influence•“Opinion leaders” are resistant to influence

• High instability•“Opinion leaders” are resistant to influence

Issu

es a

nd O

pini

on S

tabi

lity

Predictions: Attitude Instability

Nearly converged Contested

Stable• Low instability• Initial opinion predicts resistance to influence

• Random instability• “Opinion leaders” (value driven?)

Active

• Low random change, moderate/high instability• Initial opinion predicts resistance to influence•“Opinion leaders” are resistant to influence

• High instability•“Opinion leaders” are resistant to influence

Abs

olut

e va

lue

of c

hang

e in

opi

nion

, by

initi

al o

pini

on

Predicting Attitude Instability

Nearly converged:

Gender

Stable Contested: Immigration

Active Contested:

Environment

ActiveConverging?: Executive Pay

Initial opinion (0-10) 0.17*(0.07)

-0.05(0.09)

-0.07(0.05)

0.26**(0.05)

Extreme initial opinion (0-5)

-0.11(0.11)

0.11*(0.06)

0.70**(0.10)

0.23*(0.11)

Network disagreement 0.22(0.18)

0.03(0.08)

0.21(0.14)

0.37(.26)

Extreme * network disagreement

-0.04(0.04)

-0.03(0.02)

-0.14**(0.04)

-0.13*(0.06)

Political knowledge -1.22(1.65)

-1.67(1.15)

0.76(1.94)

2.91(2.14)

Political knowledge2 -0.03(1.74)

1.46(1.15)

-1.10(2.11)

-2.42(2.23)

(Intercept) 2.10**(0.60)

2.19**(0.67)

1.19*(0.52)

0.08(0.63)

r2 =n=

0.14259

0.02475

0.21222

0.14235

Predictions: Attitude change

Nearly converged Contested

Stable • Change towards converged endpoint

• Change towards midpoint

Active • Network led change towards endpoint

• Network led change towards midpoint

Dire

cted

cha

nge

in o

pini

on,

by in

itial

opi

nion

Predicting Attitude Change

Nearly converged:

Gender

Stable Contested: Immigration

Active Contested:

Environment

Active Converging?: Executive Pay

(Intercept) 0.44(0.67)

2.51**

(0.41)3.85**

(0.68)1.95**

(0.71)Initial opinion -0.35**

(0.084)-0.25**

(0.075)-0.17(0.11)

-0.24*

(0.11)Opinion Extremity 0.10

(0.12)-0.016(0.060)

0.20^

(0.10)-0.034(0.10)

Network disagreement -0.093(0.11)

-0.064(0.13)

-0.71**

(0.15)-0.21(0.15)

Network opinion 0.012(0.13)

-0.15(0.092)

-0.82**

(0.27)-0.36**

(0.14)Political knowledge -0.31

(1.57)-0.41(1.30)

0.85(2.40)

-2.49(2.39)

Political knowledge2 -0.26(1.65)

-0.55(1.34)

-0.61(2.63)

2.95(2.50)

Network opinion * Network disagreement

0.017(0.021)

0.015(0.020)

0.14**

(0.044)0.074*

(0.035)N=r2=

25190.23

4770.22

2230.37

2360.29

POLITICAL BELIEF SYSTEMS:RELATIONAL CLASS ANALYSIS

Emerging ideas

Measuring Political Belief SystemsGoldberg and Baldassarri“a configuration of ideas and attitudes in which the elements are bound together by some form of constraint or functional interdependence" (Converse 1964, 207)

Empirical analyses based on individual attitudes, summary indices, or dyadic interdependence.

Studies assume the existence of a singular system of interconnected beliefs (i.e., liberal-conser vative

polarity). Results: A large majority of citizens exhibit limited

levels of constraint and coherence in the overall organization of their political beliefs (Converse 1964; Campell et al. 1960; Luskin 1987; Delli Carpini, Keeter 1991; Popkin, Dimoch 1999).

RCA – Relational Class Analysis

RCA Identifies political belief networks that are most common across the population.

RCA classifies individuals into groups according to their organization of beliefs.

RCA avoids a priori assumptions about how political beliefs are organized:

belief networks emerge from pattern of responses it allows to identify multiple political belief systems

along which sociodemographic and cognitive characteristics the population should be partitioned.

Measuring Belief Similarity

Spectral Partitioning into Belief “Blocks”

Ideologues

Alternatives