INFLUENCE: POLITICAL
AFFILIATION & ACTION
Network theories of political persuasion examine how ties among
actors shape individuals’ political attitudes & opinions. Models of
public policymaking explain how collective decisionmaking
emerges from information exchanges, political resource pooling,
legislative vote-trading, and other dynamic political interactions.
Log rolling - (a.k.a. pork-barrel politics)
involves one legislator agreeing to vote
for another’s bill in exchange for the
second’s vote for the first’s favored bill.
Or, legislators make concessions on the
contents of a less-important bill in
exchange for support on vital interests.
Across 30 years, network influence models evolved into increasingly
complex mathematical formulations. But, do their core assumptions
over-simplify the confused chaos of individual & collection decisions?
Political Persuasion
Persuasion occurs when one actor transmits information that changes
another’s beliefs or actions. It involves a communication tie and the
perception that the information is credible and its source is trustworthy.
In political persuasion processes, the
partisan composition of an ego’s personal
network may induce overwhelming social
pressures towards conformity to the
group’s norms. Or it may exert conflicting
cross-pressures resulting in indecision,
delay, or withdrawal from politics.
Knoke (1990) analyzed 1987 GSS egocentric network data on three
named alters’ partisanship. Composition ranged from three Republicans
to three Democrats, with neutral or Independent egonets in the middle.
Hs: The more politically homogeneous an ego’s alters: (1) more
frequent political discussions with alters; (2) greater similarity
between ego & alter attitudes and behaviors; (3) higher ego’s
political interest and frequent participation in political activities.
Biased Political Perceptions
A problem with using ego’s perceptions of alters’ partisanship is bias –
a tendency to believe that our associates closely resemble ourselves.
Robert Huckfeldt & John Sprague’s analyses of voting preferences in
South Bend elections collected survey data directly from discussants.
Egos’ perceptions were 90% accurate when their
nonkin political discussion partners shared a
preference for Reagan or Mondale. But, if alters
actually held opposing views or were apolitical
(nonvoters), egos reported agreement with them
only the range between 32% and 53%!
Political choice apparently involves cognitive balance processes, with
rational information searches socially embedded inside ego networks.
People seek out politically compatible alters, but when they encounter
politically dissonant information, they tend to reinterpret it favorably.
Over time, network selection tends to homogenize partisan networks.
However, if social ties are constrained (family & work), conflicts can be
avoided by biasing perceptions towards greater partisan consensus.
Social Network Influence Models
Peer influence effects of proximities are mathematically formalized in
social influence network models involving both actor ties and attributes.
Noah Friedkin (1984) assumed a deterministic,
discrete-time linear process in which an actor’s
attitudes are adjusted to the views of others who
have some influence (e.g., direct tie) on the actor;
thus, attitudes are simultaneously determined.
Where y is a vector of attitudes at time t, and W is a matrix in network ties:
yt 1  Wyt
Friedkin & Johnson (1990) generalized this model to include a matrix X of
independent variables and a column vector b of their regression coefficients:
yt 1  Wyt  Xb
Many research studies yield results that are consistent with the social
network influence model’s hypothesized effects (e.g., Friedkin 2004).
Coleman’s Collective Action Model
In The Mathematics of Collective Action (1973), James Coleman
modeled legislative vote-trading within a market of perfect
information on policy preferences, and resulting prices (power).
A legislator’s power at market equilibrium is proportional
to control over valued resources for events (i.e., her votes
on bills) in which the other legislators have high interest.
Power-driven actors try to maximize their utilities by
exchanging votes, giving up control of low-interest events
in return for control over events of high interest to them.
In matrix notation, the model’s simultaneous power equation solution is:
P = PXC
P: each legislator’s equilibrium power, following all vote exchanges
X: their interests over a set of legislative events (bills) to be decided
C: their control over each event (i.e., one vote per actor on each bill)
Marsden’s Network Access Model
Peter Marsden (1983) modified Coleman’s market exchange
model so that network relations restrict access to vote transfers.
In contrast to Coleman’s market model allowing every
legislator to trade votes with all others, Marsden
assumed varied opportunities for dyadic vote trades.
Compatibility of interests – based on trust, ideology,
or party loyalty – may restrict the subset of actors
with whom a legislator would prefer to log roll votes.
Network exchange model’s key equation is:
P = PAXC
A: aij =1 if vote exchanges are possible; aij = 0 if no exchange access
Marsden’s simulations of restricted access networks found (1) reduced
levels of resource exchanges among actors; (2) power redistributed to
actors in the most advantaged network positions; (3) possible shift to a
more efficient system (i.e., higher aggregate interest satisfaction).
Alternative Models Contrasted
Dynamic Policy Models
Franz Pappi’s institutional access models distinguished “actors”
(interest groups) from “agents” (public authorities with voting
rights). Network structures are built into the interest component.
An actor’s power comes from ability to gain access to
effective agents, who are a subset (agents are actors
with their own interest in event outcomes). Actors can
gain control over policy events either by deploying their
own policy information or mobilizing the agents’ info.
The mobilization model’s key equation is:
PXA = WK*
K*: equilibrium control matrix (L actors control the votes of K agents)
Resource deployment model operationalized actors’ control as
confirmed policy communication network, measuring “selfcontrol” as the N of orgs not confirming the sender’s information
exchange offers (i.e., indicator of independence in the system).
Legislative Outcome Predictions
Predicting pass/fail of labor policy bills, U.S. better exemplified a
resource mobilization process, while German and Japanese data
better fit a resource deployment model (Knoke et al. 1996:181).
Dynamic Access Models
Frans Stokman’s stage models of dynamic access: (1) actors’
form policy preferences, influenced by the preferences of actors
who have access to them; then (2) officials cast votes based on
preferences formed during that prior stages of influence activity.
Networks & policy preferences exert mutually formative
influences; then votes cast on fixed preferences.
•
Power-driven actors seek access to most powerful
players
•
Policy-driven: interaction of power & policy
positions
Dynamic access models’ key equations are:
C = RA
X = XCS
O = XV
C: control over events R: actors’ resources A: access to other actors
X: preferences on events (interests)
S: salience of event decisions
V: voting power of the public officials
O: expected outcomes
Amsterdam Policy Outcomes
Stokman & Berveling (1998) compared dynamic policy network
models to real outcomes of ten Amsterdam policy decisions.
Policy Maximization model performed better
than either Control Maximization or TwoStage models. Policy-driven actors “accept
requests selectively to ‘bolster’ their own
preferences as much as possible.”
Realizing that more distant
powerful opponents aren’t
readily accessible, actors seek to
influence others like themselves.
“Actors therefore select
influence purposively to ‘bolster’
their own positions. This
prevents them from changing
their own preferences while
trying to influence other actors
to do so” (1998:598)
References
Coleman, James S. 1973. The Mathematics of Collective Action. Chicago: Aldine.
Friedkin, Noah E. 2004. “Social Cohesion.” Annual Review of Sociology 30:409-425.
Friedkin, Noah E. 1984. “Structural Cohesion and Equivalence Explanations of Social
Homogeneity.” Sociological Methods and Research 12:235-261.
Friedkin, Noah E. and Eugene C. Johnson. 1990. “Social Influence and Opinions.” Journal of
Mathematical Sociology 15:193-205.
Huckfeldt, Robert and John Sprague. 1988. “Choice, Social Structure, and Political
Information: The Informational Coercion of Minorities.” American Journal of Political Science
32:467-482.
Knoke, David. 1990. “Networks of Political Action: Toward Theory Construction.” Social
Forces 68:1041-1063.
Knoke, David, Franz Urban Pappi, Jeffrey Broadbent and Yutaka Tsujinaka (with Thomas
König). 1996. “Exchange Processes.” Pp. 152-188 in Comparing Policy Networks: Labor
Politics in the U.S., Germany, and Japan. New York: Cambridge University Press.
Marsden, Peter V. 1983. “Restricted Access in Networks and Models of Power.” American
Journal of Sociology 88: 686-717.
Stokman, Frans and Jaco Berveling. 1998. “Dynamic Modeling of Policy Networks in
Amsterdam.” Journal of Theoretical Politics 10:577-601.