Word-of-Mouth (Turtles) Experiment

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An idea disseminates through a community through strong ties (friends) and weak ties (casual acquaintances.
What affects the speed of dissemination?

Using Turtle agents instead of Patches


created with NetLogo

view/download model file: Word-of-Mouth-Replication.nlogo


WHAT IS IT?

Word-of-Mouth-Replication is a replication of a study reported in:

Jacob Goldenberg, Barak Libai and Eitan Muller (2001), "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth</a>," Marketing Letters, (12), pp. 209-221.

--- quote ---
The strength of weak ties
There is scant evidence on the breakdown of the personal communication between closer and stronger communications that are within an individual's own personal group (strong ties), and weaker and less personal communications that an individual makes with a wide, often random, set of other acquaintances and colleagues (weak ties). We model these two phenomena and show that the influence of weak ties is at least as strong as the influence of strong ties. Despite the relative inferiority of the weak tie parameter in the model's assumptions (strong ties reflect greater probability for an individual-level transformation), their effect approximates or exceeds that of strong ties, in all of the process stages.
--- unquote ---


HOW IT WORKS

Five parameters control the diffusion of knowledge through a community:

  • Size of one's personal network - number close friends and family whom we trust
  • Size of one's loose contacts - number of acquaintances met and then perhaps forgotten
  • Salience of contact with the personal network
  • Salience of contact with loose contacts
  • Salience of advertising

In the early stages of new product diffusion then advertising clearly has the greatest influence (as no-one knows about it they can't talk about it), but word-of-mouth quickly takes over.


HOW TO USE IT

Market Size adjusts the number of people in the market. A very large number makes the model VERY slow with a very small improvement in consistency of output. I suggest leaving it at about 1000.

Size of Personal Network is the number of people in constant, highly-regarded, contact. It ranges from 5 to 29 consistent with Goldenberg et al. A larger number implies faster communication within the clique.

Size of Weak Contacts is the number of people that each person speaks with in each period from outside the clique. It ranges from 5 to 29 consistent with Goldenberg et al. A larger number implies faster communication between cliques.

Weak Ties Effect is a parameter affecting the influence of the number of weak contacts on awareness likelihood.

Strong Ties Effect is a parameer affecting the influence of the number of strong contacts on awareness likelihood.

Advertising Effect is a parameter representing the level of information received by all citizens in each time period.


THINGS TO NOTICE

Note how quickly the diffusion process works when more people are added to the Strong and Weak ties.


THINGS TO TRY

 If you have the patience, you'll see that the total number of people in the "world" makes little difference to the number of clicks (or time periods) that are taken in any one experiment - but it does have a huge impact on processing time.


EXTENDING THE MODEL

I have experimented with using a much wider range than is used here.  This follows the Goldenberg paper fairly closely.  However, it is clear that the relationships are logarithmic rather than linear, so I've played with multiples.


NETLOGO FEATURES

This section could point out any especially interesting or unusual features of NetLogo that the model makes use of, particularly in the Procedures tab. It might also point out places where workarounds were needed because of missing features.


RELATED MODELS

This section could give the names of models in the NetLogo Models Library or elsewhere which are of related interest.


CREDITS AND REFERENCES

Jacob Goldenberg, Barak Libai and Eitan Muller (2001), "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth</a>," Marketing Letters, (12), pp. 209-221.