INFLUENCE: POLITICAL AFFILIATION & ACTION

1. 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?

2. 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.

5. 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.

6. 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: 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 model’s hypothesized effects (e.g., Friedkin 2004).

7. 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)

8. 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).

9. Alternative Models Contrasted

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

11. 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).

12. 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

13. 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 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)

14. 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.