Rumor Mill - Networks

(By Forrest Sondahl, 2005)

The applet requires Java 1.4.1 or higher. It will not run on Windows 95 or Mac OS 8 or 9. Mac users must have OS X 10.2.6 or higher and use a browser that supports Java 1.4. (Safari works, IE does not. Mac OS X comes with Safari. Open Safari and set it as your default web browser under Safari/Preferences/General.) On other operating systems, you may obtain the latest Java plugin from Sun's Java site.

Created with NetLogo * Download model: Rumor Mill - Networks.nlogo
* Download model (updated for NetLogo v3.1.2): Rumor Mill - Networks-updated.nlogo
* Download model (updated for NetLogo v4.0): Rumor Mill - Networks-4.0.nlogo

WHAT IS IT?

----------- This program models the spread of a rumor. The rumor spreads when a person who knows the rumor tells someone they are connect to in their social network. Each person is represented as a circle in the network, and the lines drawn between the circles represent social ties. If there is a line between circle A and circle B, it means that A and B are good enough friends that they share gossip with each other. At each time step, every person who knows the rumor randomly chooses a friend to tell the rumor to. The simulation keeps track of who knows the rumor, how many people know the rumor, and how many "repeated tellings" of the rumor occur. (Of course, in real life, people probably remember who they've told the rumor to, and don't tell the same person more than twice or so...) HOW TO USE IT ------------- RANDOMIZE-EDGES? is a switch that determines whether the social network is a completely symmetrical circle of people connect to the two nearest people on either side of them, or whether it has been randomized to be something close to a "small world network". (Using the same algorithm used in the Small World Networks model, with rewiring probability 20%) NUM-FRIENDS determines the initial number of friends each person has in the graph. If RANDOMIZE-EDGES? is on, this will be the average number of friends each person has, otherwise this will be the exact number. (Warning: If NUM-FRIENDS = 2, then disconnected networks often result. The simulation will run indefinitely, since there is no way for the whole population to learn the rumor.) SPRING-LAYOUT? is purely a display option. Would you like the network drawn as a perfect circle, or would you like it spaced out some by using the "spring placement" algorithm for drawing graphs? As with any rumor, it has to start somewhere, with one or more individuals. There are three ways to control the start of the rumor: 1) Single source: Press the SETUP-ONE button. This starts the rumor at one random person in the social network. 2) Random sources: Press the SETUP-RANDOM button with the INIT-CLIQUE slider set greater than 0. This "seeds" the rumor randomly by having each person have a percentage chance of knowing thing rumor initially. This percentage is set using the INIT-CLIQUE slider. To run the model, you can either "step" through each time step using the STEP button or allow the model to simply run continuously using the GO button. The model will stop when everyone in the population knows the rumor. There are three plot windows associated with this rumor model. RUMOR SPREAD - plots the percentage of people who know the rumor at each time step. SUCCESSIVE DIFFERENCES - plots the number of new people who are hearing the rumor at each time step. SUCCESSIVE RATIOS - at each time step, plots the ratio of people who just learned the rumor to people who already knew it. The monitor CLIQUE% is the percentage of the people that have heard the rumor. The three coloring buttons to the right of the view give you topographic maps of the screen. The COLOR: WHEN HEARD button colors the network different shades of YELLOW according to the first time that location heard the rumor. The COLOR: TIMES HEARD button colors the network different shades of GREEN according to the number of times that location has heard the rumor. THINGS TO NOTICE ---------------- There is a significant difference in behavior of this model, depending on whether the RANDOMIZE-EDGES box is checked. Which is the more accurate model of how rumors are spread? Which spreads the rumor more quickly? Why? With the RANDOMIZE-EDGES box checked: An interesting thing to notice about the spread of the rumor is that the "speed" with which the rumor spreads slows down as more and more people know the rumor. Why is that? How is that related to the number of "repeated" or "wasted" tellings of the rumor? How do the two "differences" plot windows help you to understand the dynamics of the rumor spread? When you color the graph by the "number of times heard", remember that you are getting two different levels of information. The color of the circles tells you how many times the given person has heard the rumor. The color of the lines tells you how many times the rumor has passed between these two people. Which are generally brighter, the circles or the lines? Why? Look at the colorings of "times heard" and "when heard". There is a correlation between them. How strong is it, and why does it exist? Are the differences between the graphs attributably solely to chance, or are there contributing factors that make one circle more like to be bright green even if it isn't bright yellow? What about when comparing lines, instead of circles? THINGS TO TRY ------------- Try varying the number of friends each person has. How does this affect the speed of rumor dispersal? How does it it affect the plots? Try it with RANODOMIZE-EDGES off, 100 people, 4 friends per person. Then try with 20 friends per person. Which produces graphs that are closer to those created when randomness is enabled? Why? Look at the original Rumor Mill model in the model library. It uses spatial proximity to demonstrate the spread of rumors. How is the behavior of this model the same as the original Rumor Mill model? How is it different? Can you explain the similarities? Which approach (networks or spatial) do you think is a better one for modeling the spread of rumors? EXTENDING THE MODEL ------------------- Here are some suggestions for ways to extend the model. - Try using a new algorithm to create the original graph layout, different from both the symmetrical one, and the "small world" one. How do rumors spread if your graph is a tree (there is only one path of friendship between any two given people in the society)? If it is a complete graph (every people is friends with every other people). - Assign a probability with which the rumor is told. In the current model, each time a person meets his/her neighbor, s/he tells the neighbor the rumor. How would the spread of the rumor change if the telling of the rumor took place only 50% of the time? or 30% of the time? - Add mouse support. Make it so that you can use the mouse to choose precisely which people know the rumor initially. - Try using a directed graph, instead of an undirected one. i.e. Alice might tell rumors to Bobby, but Bobby doesn't pass rumors back to Alice -- he only passes them along to Cynthia. NETLOGO FEATURES ---------------- This model makes use of the network primitives in NetLogo, to create non-spatial relationships between turtles. RELATED MODELS -------------- Virus, AIDS, Small World Networks CREDITS AND REFERENCES ---------------------- This model is an extension of the Rumor Mill model from the model library in NetLogo. Below are the credits and references for that model. The Rumor Mill model is itself an extension of a physical experiment where spatial proximity was not a factor in the spread of the rumor. Contact Helen M. Doerr at hmdoerr@syr.edu regarding papers in preparation. Thanks to Dr. Doerr for inspiration for this model. To refer to this model in academic publications, please use: Wilensky, U. (1998). NetLogo Rumor Mill model. http://ccl.northwestern.edu/netlogo/models/RumorMill. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. In other publications, please use: Copyright 1998 by Uri Wilensky. All rights reserved. See http://ccl.northwestern.edu/netlogo/models/RumorMill for terms of use.

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