NetLogo User Community Models

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## 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.
In this model, there are two monetary variants in competition within the social network – one variant generated by “money 0” and the other generated by “money 1”. Money users interact with each other based on whom they are connected to in the network. At each iteration, each individual make exchanges by passing an utterance using either money “0” or money “1” to the neighbors in the network. Individuals then imitate their neighbors and change their money based on what money they use.

## 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.
There are three different options to control how money users interact and learn from their neighbors, listed in the UPDATE-ALGORITHM chooser. For two of these options, INDIVIDUAL and THRESHOLD, money users can only access one type of money at a time. Those that can only access money 1 are white in color, and those that can access only money 0 are black. For the third option, REWARD, each money is associated with a weight, which determines the money user’s probability of accessing that money. Because there are only two types of money in competition here, the weights are represented with a single value - the weight of money 1. The color of the nodes reflect this probability; the larger the weight of money 1, the lighter the node.

- INDIVIDUAL: Money users choose one of their neighbors randomly, and adopt that neighbor’s money.
- THRESHOLD: Money users adopt money 1 if some proportion of their neighbors is already using money 1. This proportion is set with the THRESHOLD-VAL slider. For example, if THRESHOLD-VAL is 0.30, then an individual will adopt money 1 if at least 30% of his neighbors have money 1.
- REWARD: Money users update their probability of using one type of money or the other. In this algorithm, if an individual hears an utterance from money 1, the individual’s weight of money 1 is increased, and they will be more likely to use that type of money in the next iteration. Similarly, hearing an utterance from money 0 increases the likelihood of using money 0 in the next iteration.

## 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.
Press SETUP-EVERYTHING to generate a new network based on NUM-NODES and PERCENT-MONEY-1.
Press GO ONCE to allow all money users to “give” and “take” hence exchange only once, according to the algorithm in the UPDATE-ALGORITHM dropdown menu (see the above section for more about these options). Press GO for the simulation to run continuously; pressing GO again will halt the simulation.
Press LAYOUT to move the nodes around so that the structure of the network easier to see.
When the HIGHLIGHT button is pressed, rolling over a node in the network will highlight the nodes to which it is connected. Additionally, the node’s initial and current money state will be displayed in the output area.
Press REDISTRIBUTE-MONEY to reassign money to all money users, under the same initial condition. For example, if 35% of the nodes were initialized with money 1, pressing REDISTRIBUTE-MONEY will assign money 1 to a new sample of 35% of the population.
Press RESET-STATES to reinitialize all money users to their original money. This allows you to run the model multiple times without generating a new network structure.
The SINK-STATE-1? switch applies only for the INDIVIDUAL and THRESHOLD updating algorithms. If on, once an individual adopts money 1, then he/she can never go back to 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
Over time, money users tend to arrive at using just one money all of the time. However, they may not all converge to the same type of money. It is possible for sub-groups to emerge, which may be seen as the formation of different “dialects” or types of money.

## 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?
In this model, only two money are in competition in the network. Try extending the model to allow competition between three or four types of money on a local and regional level.
The updating algorithm currently has agents updating asynchronously. Currently, the money may spread one step or several within one tick, depending on the links. Try implementing synchronous updating.
Regardless of the updating algorithm, money users always start out using one money categorically (that is, with a weight of 0 or 1). Edit the model to allow some money users to be initialized to an intermediate weight (e.g., 0.5).

## NETLOGO FEATURES

Networks are represented using turtles (nodes) and links. In NetLogo, both turtles and links are agents.

## RELATED MODELS

Preferential Attachment

## 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.
http://repository.upenn.edu/pwpl/vol14/iss1/25/

## 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.
Please cite the NetLogo software as:

* 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.


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 uri@northwestern.edu.

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