NetLogo Models Library:
Sample Models/Social Science/Unverified

Note: This model is unverified. It has not yet been tested and polished as thoroughly as our other models.

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Language Change

Run Language Change in your browser uses NetLogo 4.0.2 requires Java 1.4.1+ (system requirements) Note: If you download the NetLogo application, every model in the Models Library (besides the Community Models) is included. If you have trouble running this model in your browser, you may wish to download the application instead.

WHAT IS IT?

This model explores how the properties of language users and the structure of their social networks can affect the course of language change.

In this model, there are two linguistic variants in competition within the social network - one variant generated by grammar 0 and the other generated by grammar 1. Language users interact with each other based on who they are connected to in the network. At each iteration, everyone speaks by passing an utterance using either grammar 0 or grammar 1 to their neighbors in the network. Individuals then listen to their neighbors by changing their grammars based on what they received as input from the speakers.

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 language users who already have many connections. This leads to the emergence of a few "hubs", or language users who are very well connected; most other language users have very few connections.

There are three different options to control how language users listen and learn from their neighbors, listed in the UPDATE-ALGORITHM chooser. For two of these options, "individual" and "threshold", language users can only access one grammar at a time. Those that can only access grammar 1 are white in color, and those that can access only grammar 0 are black. For the third option, "reward", each grammar is associated with a weight, which determines the language user's probability of accessing that grammar. Because there are only two grammars in competition here, the weights are represented with a single value - the weight of grammar 1. The color of the nodes represent this probability; the larger the weight of grammar 1, the darker the node.

- Individual: Learners choose one of their neighbors randomly, and adopt that neighbor's grammar.

- Threshold: Learners adopt grammar 1 if some proportion of their neighbors are already using grammar 1. This proportion is set with the THRESHOLD-VAL slider. For example, if THRESHOLD-VAL is 0.30, then a learner will adopt grammar 1 if at least 30% of his neighbors have grammar 1.

- Reward: Learners update their probability of using one grammar or the other. In this algorithm, if an individual hears an utterance from grammar 1, the individual's weight of grammar 1 is increased, and they will be more likely to access that grammar in the next iteration. Similarly, hearing an utterance from grammar 0 increases the likelihood of accessing grammar 0 in the next iteration.

HOW TO USE IT

The NUM-NODES slider determines the number of nodes to be included in the network population. PERCENT-GRAMMAR-1 determines the proportion of these nodes which will be initialized to use grammar 1. The remaining nodes will be initialized to use grammar 0.

Pressing the SETUP-EVERYTHING button generates a new network based on NUM-NODES and PERCENT-GRAMMAR-1.

The REDISTRIBUTE-GRAMMARS button keeps the same proportion of nodes with grammar 0 or 1, but reassigns who has these initial grammars. For example, if 20% of nodes are initialized with grammar 1, clicking REDISTRIBUTE-GRAMMARS will assign grammar 1 to a new sample of 20% of the population.

Press RESET-STATES to reinitialize all nodes to their original grammars. This allows you to run the model multiple times without generating a new network structure.

The LAYOUT button attempts to move the nodes around to make the structure of the network easier to see.

When the HIGHLIGHT button is pressed, roll over a node in the network to see who that node is connected to. Additionally, information about that node's initial and current grammar state will be displayed in the output area.

Press GO ONCE to allow all nodes to "speak" and "listen" once, according to the algorithm in the UPDATE-ALGORITHM dropdown menu (see the above section for more about these options). Press GO for this procedure to repeat continually.

The SINK-STATE-1? switch applies only for the "individual" and "threshold" updating algorithms. If on, once someone adopts grammar 1, they can never go back to grammar 0.

The LOGISTIC? switch applies only for the "reward" updating algorithm. If on, a speaker's probability of using one of the grammars while speaking is pushed to the extremes (closer to 0% or 100%), based on the output of the logistic function.
(See http://en.wikipedia.org/wiki/Logistic_function for more on this 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 grammar 1. Probabilities are pushed to the extremes, then shifted toward selecting grammar 1. The larger the value of ALPHA, the more likely a language user is to speak using grammar 1.

The plot "Mean State of Agents in the Network" calculates the average weight of grammar 1 for all nodes in the network, at each iteration.

THINGS TO NOTICE

Over time, language users tend to arrive at using just one grammar all of the time. However, they may not all converge to the same grammar. It is possible for sub-groups to emerge, which may be seen as the formation of different dialects.

THINGS TO TRY

Under what conditions is it possible to get one grammar to spread through the entire network? Try manipulating PERCENT-GRAMMAR-1, the updating algorithm, and the various other parameters. Does the number of nodes matter too?

EXTENDING THE MODEL

Whether or not two language users interact with each other is determined by the network structure. How would the model behave if language users were connected by a small-world network rather than a preferential attachment network?

In this model, only two grammars are in competition in the network. Try extending the model to allow competition between three grammars.

Regardless of the updating algorithm, language users always start out using one grammar categorically (that is, with a weight of 0 or 1). Edit the model to allow some language users to be initialized to an intermediate weight (i.e., 0.5)

RELATED MODELS

See the Preferential Attachment model in the Networks section of the Models Library.

CREDITS AND REFERENCES

This model was also described in Troutman, C., Clark, B., and Goldrick, M. "Social Networks and Intraspeaker Variation During Periods of Language Change". Paper submitted to the Penn Working Papers in Linguistics.
http://ling.northwestern.edu/~cet883/PLC_TroutmanClarkGoldrick.pdf

To refer to this model in academic publications, please use: 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.

In other publications, please use: Copyright 2007 Uri Wilensky. All rights reserved. See http://ccl.northwestern.edu/netlogo/models/LanguageChange for terms of use.

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