The New Ecology 17
biodiversity in an age of big data, and that correlations are not sufficient
in themselves. Correlation alone was the approach suggested by Chris
Anderson, based on the assumption that big data are rendering scientific
method obsolete: in such an approach – what Anderson refers to as the
“Petabyte age” – the ability to search, read, and analyse data does not
require a visualisation of the data “as a whole” (which is, in any case,
impossible). Given a large enough corpus, we can find correlations without “knowing” what the underlying model is – something that, prior
to the massive power of contemporary computing, would have been
considered just noise.
26
Marquet et al acknowledge the inefficiencies
of many theoretical approaches to ecology, but still hold that efficient
models are important for helping us make predictions dealing with our
current global environmental crises. The metabolic theory of ecology
(MTE) and the neutral theory of biodiversity (NTB), which deals with
the stochastic – or random probability – distribution of data, have
demonstrated themselves capable of making accurate predictions. Large
populations allow themselves to be studied deterministically, ignoring
chance factors such as accidental births or deaths – as with the famous
predator–prey population cycles outlined in the Lotka–Volterra model
of the 1920s, which showed fluctuations in populations according to the
density of prey and predators, respectively.
27
Such deterministic models
are much more difficult with small populations, and are more likely to
be affected by socially constructed patterns as Shaffer demonstrated in
1978 when examining the effects of the United States National Forest
Management Act of 1976, which mandated the maintenance of viable
populations of native and desirable non-native species and his own studies of the grizzly bear population in Yellowstone: for small populations,
a chance fluctuation can result in extinction and so stochastic models
began to emerge to predict the probabilities of such extinctions within
a given time frame.
28
What should be becoming clear from these observations is that an appropriate theory of media ecology that can help to
explain factors affecting journalism is much more intertwined than the
desiccated “digital ecosystem” hype that is bandied around iOS, Amazon’s market place, or Android (important as all these elements are to a
deeper understanding of media ecologies).
Certain elements of the approach to media ecology considered here
mitigate against common notions of media or technological determinism. The strongest forms of technological determinism, for example the
assumptions by Thorstein Veblen that capitalism represents a struggle
between technology and “ceremonial” culture (the so-called Veblenian
dichotomy whereby institutions adjust technologies to make them more
instrumental – and thus wasteful
29
) or Jacques Ellul’s notion that technologies operate along a form of natural selection,
30
tend to be viewed
as simplistic among media theorists today. Among those more heavily invested in the digital economy of the twenty-first century, unsurprisingly,
biodiversity in an age of big data, and that correlations are not sufficient
in themselves. Correlation alone was the approach suggested by Chris
Anderson, based on the assumption that big data are rendering scientific
method obsolete: in such an approach – what Anderson refers to as the
“Petabyte age” – the ability to search, read, and analyse data does not
require a visualisation of the data “as a whole” (which is, in any case,
impossible). Given a large enough corpus, we can find correlations without “knowing” what the underlying model is – something that, prior
to the massive power of contemporary computing, would have been
considered just noise.
26
Marquet et al acknowledge the inefficiencies
of many theoretical approaches to ecology, but still hold that efficient
models are important for helping us make predictions dealing with our
current global environmental crises. The metabolic theory of ecology
(MTE) and the neutral theory of biodiversity (NTB), which deals with
the stochastic – or random probability – distribution of data, have
demonstrated themselves capable of making accurate predictions. Large
populations allow themselves to be studied deterministically, ignoring
chance factors such as accidental births or deaths – as with the famous
predator–prey population cycles outlined in the Lotka–Volterra model
of the 1920s, which showed fluctuations in populations according to the
density of prey and predators, respectively.
27
Such deterministic models
are much more difficult with small populations, and are more likely to
be affected by socially constructed patterns as Shaffer demonstrated in
1978 when examining the effects of the United States National Forest
Management Act of 1976, which mandated the maintenance of viable
populations of native and desirable non-native species and his own studies of the grizzly bear population in Yellowstone: for small populations,
a chance fluctuation can result in extinction and so stochastic models
began to emerge to predict the probabilities of such extinctions within
a given time frame.
28
What should be becoming clear from these observations is that an appropriate theory of media ecology that can help to
explain factors affecting journalism is much more intertwined than the
desiccated “digital ecosystem” hype that is bandied around iOS, Amazon’s market place, or Android (important as all these elements are to a
deeper understanding of media ecologies).
Certain elements of the approach to media ecology considered here
mitigate against common notions of media or technological determinism. The strongest forms of technological determinism, for example the
assumptions by Thorstein Veblen that capitalism represents a struggle
between technology and “ceremonial” culture (the so-called Veblenian
dichotomy whereby institutions adjust technologies to make them more
instrumental – and thus wasteful
29
) or Jacques Ellul’s notion that technologies operate along a form of natural selection,
30
tend to be viewed
as simplistic among media theorists today. Among those more heavily invested in the digital economy of the twenty-first century, unsurprisingly,