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Social Consensus - Network

by Daniel Diermeier (Submitted: 06/01/2015)

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## WHAT IS IT?

This model studies how public speaking or announcements affect social decision via an influential speaker. Social decision is measured by the opinions present in the network of agents. Speakers try to influence listeners to match their opinion. In this situation, some agents are committed and will not change their opinion no matter what opinion the speaker has.

## HOW IT WORKS

Each agent can have one or both of the opinions "A" and "B". For each tick of the simulation, one agent is randomly selected to be the speaker. This speaker then interacts with one of its neighbors, called the listener. One opinion of the speaker is randomly chosen and called the voice, if the listener has that same opinion, whether by itself or with the other opinion, then the listener's opinion is changed to match the voice. If the listener does not have that opinion, then its opinion is changed to include both "A" and "B". However, if the listener was already marked as committed, then it will not change its opinion at all. All committed agents have opinion "A" only. This process repeats, with a speaker randomly selected each tick, until there are no more "B" opinions present.

## HOW TO USE IT

There are two options to setup the simulation. The basic setup version creates a network of agents using the N, m0, and m variables. These agents are randomly assigned an opinion of "A", "B", or ["A" "B"]. Depending on the p-commit variable, some of these agents are committed to "A". The Setup with positions version follows the same setup procedure, but it selects additional agents to be committed to "A". There are three options for this selection. 1) low-degree selects agents that have few links, 2) high-degree selects agents that have many links, and 3) randomly selects a random number of agents to be committed. The number of agents newly selected to be committed depends on the commitment-percentage variable.

After the agents are created, press the Layout button to aid in visualization. Press it again to stop the movement. The Layout selectively option keeps all the "B" agents position intact relative to one another while the "A" and ["A" "B"] agents are free to move as normal. In addition, by selecting On from the manual-layout? switcher, you can use the spring-length, repulsion-constant, and spring-constant sliders to adjust the layout options.

Once you are satisfied with the layout, you may start the simulation. Press the Go button to run the simulation. The colors of the agents will change to match their new opinions. You can track the opinion shares using the plot on the right side of the interface. The monitor boxes give the exact shares of each opinion as well as the share of committed agents. Note that the A opinion monitor box is the share of agents with opinion "A" who are not committed. The six additional visualization options may also help in your analysis of the simulation.

## THINGS TO NOTICE

Notice what happens to the share of "A" opinions over time and how that varies based on the number of committed agents. Use the various Setup with positions options to see how commitment in high- vs low-linked agents affects the share of opinions.

## THINGS TO TRY

Adjust the various parameters that affect the number of committed agents. In particular, see what happens when there are no committed agents and how that compares to simulations with committed agents.

## EXTENDING THE MODEL

Given that this simulation only includes agents who are committed to "A", it may be interesting to study what happens when agents can also be committed to "B". How would this affect the long-run opinion shares of the agent network?

In this model, commitment is either all or nothing. In the face of a different opinion, either an agent never changes its opinion, or it always changes its opinion. It may be interesting to study what happens if a non-committed agent does not always change its opinion when faced with a voice that does not match its opinion. This may also be a more realistic situation than the one currently simulated.

## RELATED MODELS

Axelrod
Confident Voter
Heterogeneous Voter
Ising
Potts
Turnout

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