POLITICAL INFLUENCE MODELS

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POLITICAL INFLUENCE MODELS
Network models of public policymaking examine how
ties in policy networks shape collective
decisionmaking through information exchanges,
political resource pooling, legislative
vote-trading, and other dynamic interactions
among interested policymakers.
Log rolling (aka 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 issues of vital
interest.
Otto von Bismarck Was in die Wurst kommt und
wie Politik gemacht wird, wollen die Leute
vielleicht gar nicht genau wissen. (How sausage
and laws are made, people truly wouldnt want to
know.)
Across 30 years, policy net analyses evolved into
increasingly complex mathematical models, but
whose core assumptions may over-simplify the
confused chaos of actual law-making.
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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 her control over valued resources
for events (i.e., her votes on bills) in which
the other legislators have high
interest. Power-driven actors maximize their
utilities by exchanging votes, giving up their
control of low-interest events in return for
acquiring 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.
Contra Coleman, whose market model allowed every
legislator to trade votes with all others,
Marsden assumed varying 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.
Actor 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 by mobilizing the agents
information.
The moblization 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 extert mutually
  formative influences, then voting occurs based 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 ouctomes of 10
Amsterdam policy decisions.
A 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. 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.