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Opinion Diffusion in Congressional Committees

by Maurice Champagne (Submitted: 01/12/2014)

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This model is an adaptation to the homophily and opinion formation model by Michael Mäs and Andreas Flache. This adaptation only makes people influencable if they seek opinions from others. People only seek opinions from others if they perceive that their knowledge level is lower than the average knowledge level of their alters. If that condition is met, then the agent is influencable.

The model assumes the ideological space on any given issue is polarized, but there is some overlap between parties in the ideological distribution. Democrats can take on a random value between 0 and 1. Republicans can take on a random value between 0.85 and 1.85.

The choosers can be used to generate networks with links between congressmembers and lobbbyists, networks in which congressmembers are only tied to congressmembers and lobbyists are only tied to lobbyists, and networks in which every lobbyist contact is replaced with an additional congressmember contact.

Lobbyists have perfect belief resistance, so they do not change their opinions. Notice how the addition of lobbyists to the model makes political agents settle into their final positions much earlier, creating gridlock. The addition of lobbyists also generally prevents consensus, but can create balkanized groups that are internally homogenous.

The code can be modified so that lobbyists are not polarized and have higher knowledge levels than committee members. In this scenario, the addition of lobbyists makes political agents settle into their final positions later, creating consensus.

The topology parameter can be interesting for modeling political parties, since it moderates the number of inter-connections between two relatively intra-connected groups.

This version also adds error terms for all of the opinion updates (random 1.0/1000).

THIS SECTION IS BY Mäs and Flache regarding the foundational "social influence" model:

Based on the famous bounded-confidence model by Hegselmann and Krause (2002), this program allows you to develop hypotheses about the effects of homophily and network structure on the outcomes of social-influence processes in networks. In particular, you can identify the conditions under which the influence process results in perfect opinion homogeneity (consensus) or opinion diversity (clustering).

Homophily, the first independent variable, models that individuals tend to interact only with others who hold similar opinions. The corresponding "BC-level" slider manipulates how similar two agents need to be to have impact on each others' opinions.

The structure of the network is the second independent variable. The R-slider manipulates the degree to which the network is clustered. That is, if R is set to zero, the network consists of two network clusters where all members of a cluster are connected to each other. There is, however, only a single link between the two clusters. Thus, this network is connected but maximally clustered. When R adopts higher values, the program randomly rewires ties R times, which leads to more links between the network clusters. We implemented the Maslov-Sneppen-rewiring algorithm (2002), which only manipulates network structure and keeps the number of network links in the populations as well as the number of connections of each agent constant.

NOTE: Always Use an Even number of agents. Otherwise, the two parties will have an unequal number of edges.

This model is an adaptation to Maes and Flache's model of opinion formation, which has been implemented in Neglogo:

Th Mas-Flache model is similar to Axelrod's model of cultural dissemination, which has been implemented in NetLogo:




Mäs, Michael and Andreas Flache. (2013). “Social Influence In Networks.” NetLogo User Community Models. Retrieved from: %20networks

Hegselmann, Rainer and Ulrich Krause. 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis, and Simulation." Journal of Artificial Societies and Social Simulation 5 (3).

Mäs, Michael, Andreas Flache, and Dirk Helbing. 2010. "Individualization as Driving Force of Clustering Phenomena in Humans." PLoS Computational Biology 6 (10):e1000959.

Maslov, S. and K. Sneppen. 2002. "Specificity and Stability in Topology of Protein Networks." Science 296 (5569):910-913.

Pineda, M., R. Toral, and E. Hernandez-Garcıa. 2009. "Noisy Continuous-Opinion Dynamics." Journal of Statistical Mechanics P08001.

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