[p2p-research] Drone hacking

[Michel Bauwens]

Let me just add here that I don't have an issue with the predictability of
human behaviour, that is obvious, but with the absolute claim made by J.
Andrews, that all behaviour is predictable all the time, given enough
computing power and knowledge of mathematics.

On Tue, Dec 22, 2009 at 6:56 PM, Andy Robinson <ldxar1 at gmail.com> wrote:

> The question of whether people are fundamentally predictable is key here.
> But there is also the problem of data collection.
>
> I still insist there will never be the amount of data available on *
> marginal* populations to make this possible, certainly not on an
> individualised basis.  The kind of thing that can be mathematically modelled
> based on observations of individual behaviour is the kind of thing that
> leaks a lot of data to be collated and which is relatively regular, such as
> online purchasing practices.  This is simply not going to apply in the
> cases: firstly, of indigenous societies which are resistant to modern
> practices generating this kind of paper/data-trail; secondly, of the global
> poor who are working, buying, sharing and living mainly by means which don't
> leave such a trail - in everyday networks and in the informal economy; and
> thirdly of people who have specifically decided to live 'off the grid' and
> who are thus observed a lot less than other people.
>
> Since the context in which this arose is the context of drone hacking and
> the war in Afghanistan, this is absolutely crucial.  Where are the inputs
> going to be coming from from which extrapolations can be made about the
> future behaviour of villagers in Helmand province or informal economy
> workers in Kandahar?
>
> Another problem.  Since the predictions are based on previous individual
> behaviour and/or on comparison with others' patterns of similar individual
> behaviour, they will not be able to predict how individuals will respond to
> situations which are previously unforeseen, or predict cases where
> individuals undergo large transformations in their social behaviour - *
> except* where they have a similar case from which to extrapolate already
> on record.  When the group of occurrences is very small, they will not be
> able to make valid extrapolations, because they will not know which
> individual differences between the cases are relevant for prediction.  They
> are not for instance going to be able to tell how people in London would
> respond to a tsunami, because they have no data on these people individually
> or collectively in relation to tsunamis.  They would either have to
> extrapolate from how each of the individuals in Birmingham responds to
> personal crises and assume that this adds up to their response to the
> collective crisis, or they would have to make aggregate guesses based on the
> behaviour of similar individuals in somewhere where there was a tsunami.  In
> this case, the accuracy of the predictions depends on the accuracy of the
> analogies drawn by the computer - that Fred Smith in London has the same
> personality-type as Dita in Banda Aceh and hence will act the same way; or
> that Fred Smith will react to the tsunami in the same way that Fred Smith
> reacted many years ago to the 7/7 bombings or the way he related to his wife
> dying.  Neither of which is a very solid basis for predicting how Fred Smith
> will act, no matter what algorithms the computer is using to establish the
> best possible analogy.
>
> And given that they only have data from massified processes, their data is
> always incomplete.  Since the context if *complex* and *chaotic*, this
> could easily be fatal: the missing piece of data, however apparently
> trivial, could make the difference in predicting how someone would actually
> act.
>
> People may well display predictable patterns which could be collated but of
> which they are unaware, but these are expressions either of the unconscious
> or of habituated practices.  These could often be spotted by an astute
> anthropologist or psychoanalyst.  Just supposing these kinds of observation
> could be imitated by a computer (and this is*not* what's being discussed in
> relation to mathematical modelling) - it still would not add up to the
> ability to predict individual behaviour, firstly because these bases of
> predictability are malleable, and secondly because the factors which can
> cause the disruption of predictable patterns are themselves complex and
> chaotic.
>
> Hence, the calculations in question will only be useful for
> predicting/directing massified behaviour, and only for as long as people
> remain massified and their lives reproduce familiar patterns which have
> already been observed a great many times.  Actually I would like to see real
> information rather than assertions here - what proportion of the time do
> these 'primitive' versions of the coming supercomputers actually
> successfully predict even something as simple as online buying behaviour?
> How often do they actually manage to induce people to buy something?  If
> they were as successful as is being made out, people would in effect be
> addicted to these sites, since the sites could induce them to constantly
> come back and constantly spend up to their limits there.  They would be
> gaining market share exponentially at the expense of other sites not using
> this technology.
>
>
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