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NetLogo User Community Models
## WHAT IS IT?
Studies on word-of-mouth identify two facets in the transmission of information: seeking and spreading. People seek out for information when they become aware of an innovation, in order to gather the expert knowledge required to understand it. Only when they are aware and expert, people can decide to adopt or reject. Past models only saw word-of-mouth as one dimensional epidemic dynamics (Susceptible, Infective, Removed); here we provide a minimalist model to study what happens if we consider both awareness and expertise. In this model, a small proportion of the population is initially holding expertise; the question is how many agents will achieve to gather it because of advertisement or peers raising their curiosity.
Information seeking is only possible if an individual can hold at least two levels of knowledge, namely awareness and expert knowledge:
* **awareness**: does the individual knows the existence of the innovation, even if s/he does not understand it? Encoded as 3 levels: U, S, A
The goal of an institution driving a communication campaign is to **achieve to have a population which is not only aware, but also holds the expert knowledge required to assess it**. Using the model, one can explore the following questions:
* is it more efficient to design advertisement so people are curious of it and engage into information seeking (parameter curiousness), or make them speak about it when they understood the innovation (parameters supporters and enthusiastic)?
## HOW IT WORKS
Each agent in the population represents an individual. Individuals have **constant characteristics** constant during the simulation:
* _curious_ individuals start seeking out information when they become aware; else they are just passively aware.
Individuals also have a **state of knowledge** which evolves during the simulation. It is made of both awareness and expertise knowledge, so individuals hold a tuple (awareness, expertise), for instance (Unaware, Ignorant) for most of them, or (Unaware, Knowledgeable) for people holding the generic expertise but unaware of the innovation of interest.
During initialization, a population of agents is created being in state (Unaware, Ignorant), meaning agents first do not know the existence of the innovation (awareness knowledge) and also don't hold the expertise required to understand it (expert knowledge). A proportion "initial-proportion-knowledgeable" is set to (Unaware, Knowledgeable): they are not aware of the existence of the innovation, but they hold the expertise required to understand it (for instance because they know the category of production, a similar innovation, the brand of the product, attended a training, etc.).
At setup time, the network indicated as a parameter is also loaded and used as a social network in the model. This network defines the structure of interactions in the population.
Also during initialization, agents are randomly assigned three personality characteristics: curious, enthusiastic, supporter, according to the parameters "proportion-curious", "proportion-enthusiastic", "proportion-supporter" defined by the user.
Each time step (tick):
1. the advertisement campaign reaches a given proportion of the population and sends them awareness.
The individual state of knowledge is driven by the following rules (which are easier to understand with the figures on the related publication, see "how to cite" below):
* when an (Unaware,Ignorant) agent receives awareness (by advertisement or an interaction): he becomes (Seeking,Ignorant) if he is _curious_, else he becomes (Aware,Ignorant)
## HOW TO USE IT
Set _proportion-curious_, _proportion-enthusiatic_, _proportion-supporters_ to 0.3; set _initial-proportion-knowledgeable_ to 0.03; that's a way to see many things to happen. Click "setup" then "run".
## THINGS TO NOTICE
Look at the "diffusion" plot. The green and orange curves represent the people Seeking and Promoting the innovation; the diffusion of knowledge is first bootstrapped by people who Seek out for information (if there are enough curious) and thus also propagate the existence of awareness. There is first a _diffusion of seeking_: an "hype" is increasing in the population of people who want to know more. Then when people Seeking for information achieve to find expertise held by one of the few initial experts, they hold the expertise and can pass it back to others. Information seeking acts as a bootstrap for expertise retrieval.
Notice that at the end of the simulation, a large part of the population got the expertise, despite the very low initial proportion of expertise in the population (3%).
Look at the network view of the population. Green edges mean someone discovered awareness thanks to this edge; orange than expertise was propagated; purple that both occurred. At the beginning many awareness cascades happen, where each Seeker is "contaminating" other curious entities. When a Seeker meets an expert, then he becomes expert, and can thus answer the questions asked by people around him; so there is the apparition of "information retrieval chains" in purple, in which individuals A made B curious who made C curious, C gets expertise from D, then C becomes knowledgeable, then tells expertise to B, who passes it to A, etc. This type of chain is capital in the model, and raises the question of their actual existence in the field.
Note how on the diffusion plot the S-curve of the proportion of awareness is always way higher than the curve "AK" (Aware and Knowledgeable). Measuring the impact of advertisement would only measure how many people were made aware, but would not quantify how many people hold enough information to understand and adopt the innovation.
## THINGS TO TRY
Set _proportion-curious_ to 0, so there is no information seeking anymore, falling back to a pure epidemic setting. Explore the proportions of enthusiastic and supporters required to reach high levels of awareness and knowledge at the end of the simulation.
Try also a high proportion of curious and few enthusiastic and supporters; observe how we actually need both seeking and promotion to reach efficient information diffusion.
## ADVANCED USAGE
For computing large simulations, it is better disabling the graphical user interface using the "with-gui" switch at the bottom of the interface.
## NETLOGO FEATURES
Network loading, generation, display, usage to represent a social network.
## HOW TO CITE
If you mention this model in a publication, we ask that you include the citations below.
Samuel Thiriot, Word-of-mouth dynamics with information seeking: Information is not (only) epidemics,
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