POLITICAL INFLUENCE MODELS

1. 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.

2. 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)

3. 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).

4. Alternative Models Contrasted

5. 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 “self-control” as the N of orgs not confirming the sender’s information exchange offers (i.e., indicator of independence in the system).

6. 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).

7. 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

8. 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)

9. 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.