INFLUENCE: POLITICAL AFFILIATION

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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
anothers bill in exchange for the seconds vote
for the firsts 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?
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Political Persuasion
Persuasion occurs when one actor transmits
information that changes anothers 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 egos personal network may
induce overwhelming social pressures towards
conformity to the groups 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 egos alters (1) more frequent
political discussions with alters (2) greater
similarity between ego alter attitudes and
behaviors (3) higher egos political interest
and frequent participation in political
activities.
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Biased Political Perceptions
A problem with using egos perceptions of alters
partisanship is bias a tendency to believe that
our associates closely resemble ourselves. Robert
Huckfeldt John Spragues 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.
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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 actors
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
Friedkin Johnson (1990) generalized this model
to include a matrix X of independent variables
and a column vector b of their regression
coefficients
Many research studies yield results that are
consistent with the social network influence
models hypothesized effects (e.g., Friedkin
2004).
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Colemans 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 legislators 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 models simultaneous
power equation solution is P PXC P each
legislators 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)
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Marsdens Network Access Model
Peter Marsden (1983) modified Colemans market
exchange model so that network relations restrict
access to vote transfers.
In contrast to Colemans 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 models key equation is P
PAXC A aij 1 if vote exchanges are possible
aij 0 if no exchange access
Marsdens 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).
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Alternative Models Contrasted
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Dynamic Policy Models
Franz Pappis institutional access models
distinguished actors (interest groups) from
agents (public authorities with voting rights).
Network structures are built into the interest
component.
An actors 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 models 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 self-control as the N of
orgs not confirming the senders information
exchange offers (i.e., indicator of independence
in the system).
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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.
1996181).
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Dynamic Access Models
Frans Stokmans 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
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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 Two-Stage models.
Policy-driven actors accept requests selectively
to bolster their own preferences as much as
possible.
Realizing that more distant powerful opponents
arent 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 (1998598)
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References
Coleman, James S. 1973. The Mathematics of
Collective Action. Chicago Aldine. Friedkin,
Noah E. 2004. Social Cohesion. Annual Review of
Sociology 30409-425. Friedkin, Noah E. 1984.
Structural Cohesion and Equivalence Explanations
of Social Homogeneity. Sociological Methods and
Research 12235-261. Friedkin, Noah E. and Eugene
C. Johnson. 1990. Social Influence and
Opinions. Journal of Mathematical Sociology
15193-205. Huckfeldt, Robert and John Sprague.
1988. Choice, Social Structure, and Political
Information The Informational Coercion of
Minorities. American Journal of Political
Science 32467-482. Knoke, David. 1990. Networks
of Political Action Toward Theory Construction.
Social Forces 681041-1063. 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 10577-601.