[lit-ideas] Re: interaction of polls and public opinion
- From: "Andy Amago" <aamago@xxxxxxxxxxxxx>
- To: lit-ideas@xxxxxxxxxxxxx
- Date: Tue, 24 Oct 2006 22:23:33 -0400
I'm having a hard time seeing opposition to the war as a meme. Book after
book, article after article have been written recounting factual bases for
opposition to it. Support for the war is much sooner a meme, i.e., simply
a belief, in this case countervailed by opposition based in reality.
> [Original Message]
> From: John McCreery <john.mccreery@xxxxxxxxx>
> To: <lit-ideas@xxxxxxxxxxxxx>
> Date: 10/24/2006 10:07:42 PM
> Subject: [lit-ideas] Re: interaction of polls and public opinion
>
> On 10/25/06, Eric Yost <eyost1132@xxxxxxxxxxxxx> wrote:
>
> >
> > If people watch televised poll results that indicate 30
> > percent of the public believes X, will that cause an upward
> > shift in the percentage who believe X? Is there a critical
> > polling mass (50 percent? 60 percent?) when a polled opinion
> > about X multiplies itself? And how does the frequency of
> > publicizing polls alter future polled opinion?
> >
> > I bet John McCreery knows something about this.
> >
>
> There may be such research. If so, I'm unfamiliar with it. A couple of
> things I have read recently do suggest, however, that the "critical
> mass" metaphor may not be appropriate in discussing social phenomena.
>
> One critical flaw in the metaphor may be the assumption that there is
> only one tipping point, as there is when a nuclear exposion occurs.
> Anthropologist/marketing guru Grant McCracken suggests in his new book
> _Flocks and Flows_ that cultural phenomena must typically survive five
> to six tipping points en route from the chaos of innovation to
> becoming conventional wisdom. At each of those tipping points the meme
> in question must break through and appeal to a wider audience than the
> narrower group to which it first appealed.
>
> A similar point is made in one of the books on network analysis that I
> am currently reading as background for my current research project (if
> anyone is interested I will try to locate the particular book in
> question; at the moment it isn't to hand). The topic is the
> application of network analysis to explanation of crowd behavior. The
> specific question is why some bar fights fizzle out while others
> result in full-scale riots. Here, again, a critical issue appears to
> be the way in which the crowd is structured.
>
> Assume, for the sake of argument, that people can be ranked in order
> of propensity to become involved in a bar fight, so that 1s tend to
> start fights, 2s tend to join in immediately, 3s stay out until a
> certain proportion of the crowd is already fighting, 4s stay out
> longer, etc. A single 1 can start a riot if there are enough 2s who
> will leap in to create a fight big enough for the 3s and then the 4s
> to get involved as well. But in a crowd in which there aren't enough
> 2s the fight fizzles out.
>
> The thrust of both of these analyses is that there isn't a single
> "critical mass" threshold. There is, instead, a range of thresholds,
> each a function of the structure of the population in question and
> (the other side of that coin) the propensities of the individuals who
> comprise it.
>
> Cheers,
>
> John
>
> --
> John McCreery
> The Word Works, Ltd., Yokohama, JAPAN
>
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