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 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 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 wouldn’t 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.
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
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 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.
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 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.
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 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 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
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 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.
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.