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NetLogo User Community Models
## WHAT IS IT?
This model explores how the properties of money users (agents) and the structure of their social networks can affect the course of money usage change.
## HOW IT WORKS
The networks in this model are constructed through the process of “preferential attachment” in which individuals enter the network one by one, and prefer to connect to those money users who already have many connections. This leads to the emergence of a few “hubs”, or money users who are very well connected; most of other money users have very few connections.
- INDIVIDUAL: Money users choose one of their neighbors randomly, and adopt that neighbor’s money.
## HOW TO USE IT
The NUM-NODES slider determines the number of nodes (or individuals) to be included in the network population. PERCENT-MONEY-1 determines the proportion of these money adopters who will be initialized to use money 1. The remaining nodes will be initialized to use money 0.
The LOGISTIC? switch applies only for the REWARD updating algorithm. If on, an individual’s probability of using one of the types of money is pushed to the extremes (closer to 0% or 100%), based on the output of the logistic function. For more details, see https://en.wikipedia.org/wiki/Logistic_function.
The ALPHA slider also applies only for the REWARD updating algorithm, and only when LOGISTIC? is turned on. ALPHA represents a bias in favor of money 1. Probabilities are pushed to the extremes, and shifted toward selecting money 1. The larger the value of ALPHA, the more likely a money user will interact in exchanges using money 1.
The plot “Mean state of money users in the network” calculates the average weight of money 1 for all nodes in the network, at each iteration.
## THINGS TO NOTICE
## THINGS TO TRY
Under what conditions is it possible to get one money to spread through the entire network? Try manipulating PERCENT-MONEY-1, the updating algorithm, and the various other parameters. Does the number of nodes matter?
## EXTENDING THE MODEL
Whether or not two money users interact with each other is determined by the network structure. How would the model behave if money users were connected by a small-world network rather than a preferential attachment network? What if a Bank agent started distributed money through loans? How could we calculate the effects of monetary velocity?
## NETLOGO FEATURES
Networks are represented using turtles (nodes) and links. In NetLogo, both turtles and links are agents.
## RELATED MODELS
## CREDITS AND REFERENCES
This model was also described in Troutman, Celina; Clark, Brady; and Goldrick, Matthew (2008) "Social networks and intraspeaker variation during periods of language change," University of Pennsylvania Working Papers in Linguistics: Vol. 14: Issue 1, Article 25.
## HOW TO CITE
If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.
For the model itself:
* Troutman, C. and Wilensky, U. (2007). NetLogo Language Change model. http://ccl.northwestern.edu/netlogo/models/LanguageChange. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
* Tzouvelekas, M. (2017). NetLogo Money adoption model. http://ccl.northwestern.edu/netlogo/models/LanguageChange. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
* Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
## COPYRIGHT AND LICENSE
Copyright 2007 Uri Wilensky.
![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png)
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.
Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at email@example.com.
<!-- 2007 Cite: Troutman, C. -->
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