Hosts vs. Instances (was: Re: Teamed Up)

Tim Rhodes (
Wed, 23 Sep 1998 12:24:28 -0700

From: "Tim Rhodes" <>
To: <>
Subject: Hosts vs. Instances (was: Re: Teamed Up)
Date: Wed, 23 Sep 1998 12:24:28 -0700

Aaron Lynch wrote:

>The answer is that we cannot just compute conversion rates on an a priori
>basis, even in cases were it is very tempting to do so. Peer to peer,
>parent to child, and other modes of transmission still have to be measured.

>As Bill points out, the mass media and famous players play an increased
>role in recent decades, too.

Indeed, it would seem that the number of exposures one has to a meme and the
emotional associations linked with such exposure play a large roll
determining whether or not the individual adopts the meme. Which brings me
to a question about some of the terms and formulas in your paper, Aaron.

You define the following terms for non-parental transmission of a meme:
(BTW, if you have a preferred method of translating your terms to ASCII
please let me know)

"Y12(p,a) is the average annual net number of non-parental converts a meme-1
host of age p makes per unit meme-2 host population-age density at age a in
his society. B12(p,a) is the average annual net number of non-parental
converts a meme-1 host of age p makes per percentage-year of meme-2 hosts of
age a in his/her society."

My questions is this: Knowing that the controlling factors governing
conversion are related to number and type of exposures to a meme, rather
than strictly the number of hosts of that meme in the present population, is
the above a useful and descriptive approach? How do the above terms help us
in any way to predict from knowledge of Y and B at t (time), instances of a
meme (in a host or otherwise) that we might find at t+1?

If this seasons hosts use TV to spread their memes, where last season they
used word-of-mouth, your formulas loose any predictive value they may have
had overnight!

Now you do go on to say:

"Although emotional and cognitive receptivity factors are not readily
conspicuous in equation 1 and equation 2, they are in fact represented. The
reason is the K's, B's, and Y's are measures of successful meme transfer
events. As such, they are composites of both the rates at which propagation
is attempted and the rates at which it is cognitively and emotionally

But as such they give us no information. In fact, the formulas seem to
become tautological; well balanced with all the appropriate information
filled in, but with no way of predicting how to fill in those terms at t+1
without going back out and gathering the information anew for t+1.

You also note:

"The K's, B's, and Y's are each modeled as overall effective rates of meme
transmission. The K's, for instance, do not indicate how many times a parent
needs to repeat a message to her children before it is effectively learned.
The B's, and Y's likewise do not reflect how many a message was voiced from
hosts to a non-host before that non-host converted."

I won't query you on the K term, it may or may not be useful to talk about
parent-child conversion rates in these terms. I simply don't know.

But for the non-parental terms it seems here that the need to link adoption
rates to number of _hosts_, rather than _events_ of exposure, clouds the
issue. In fact, the unwillingness to abandon the host-to-conversion-rate
inter-relation would seem to send you down an overly complicated blind
alley, as follows:

"A more detailed model might therefore break down these parameters into the
subfactors of transmissivity, a measure of how often each host attempts to
transmit a meme, and receptivity, a measure of the likelihood each host to
non-host transmission attempt has of actually imparting the meme to a new
person. Much research has been done on how various components of receptivity
affect the diffusion rates of innovations [15]. Receptivity parameters can
also be broken down to reflect different probabilities of meme acceptance on
first, second, third, etc. exposure."

Is all this necessary? At this point, doesn't Occam's Razor demand that we
let loose of the ineffective hosts-to-conversion-rate assumption at this
and approach the problem from an instance-of-meme-to-conversion-rate
viewpoint instead?

-Tim Rhodes

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