Agent-based model of a professional association
T Wallace
twallace12 at yahoo.com
Tue Aug 21 20:41:24 EDT 2007
I've worked in the IT departments of professional
associations for many years, and when I found out
about agent-based models for simulating complex
systems, it seemed like the perfect tool for trying to
figure out what goes on at associations.
I started with NetLogo and after a few tries, ended up
with a system that showed some of the same up-and-down
cycles that I see at associations.
Here's what I know about associations after working
for them behind-the-scenes for years:
1. If you stick with the basic three departments:
conference, members, and publications, you can set up
an association for just about any interest or
professional group. Take a look at all the
associations in Washington DC, for instance.
2. I learned from the marketing people I worked with
that it takes seven to nine "presentations" of a
product before you can get someone to decide to buy
it. These presentations can be advertisements in your
journals, postcards, mentions in your e-newsletters,
etc.
So, turning to a more familiar tool (to me) for making
computer programs, I used VB.NET in Visual Studio 2005
to make an agent-based model of an association. Here
are some screen shots:
http://www.vindustries.com/insideassociation.html
Right now, each agent has a variable associated with
it that keeps track of the recency or effectiveness of
the last "presentation" of the association. Each agent
loses "interest" at slightly different rates. A few
agents are "true believers" who don't lose interest no
matter how few times they get a "presentation" of the
association. There is also an "impulse buy" function
that happens very infrequently immediately after a
presentation, even if the agent's total charge of
recency is low.
Right now, the boost or fade of the variable that
tracks recency-of-presentation is linear, but I
suspect that there are better representations of
people's response to advertising out there -- maybe
the sigma function we use in neural networks? Any
others?
Thanks for your comments and interest,
Todd Wallace
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