Possible Impact of Indirect Perception Philosophy on other Disciplines
When we shift from a DP philosophy to IP philosophy, the questions we tend to ask
become more directed at constructing models. For example, instead of posing questions like, “are humans
basically good, or bad?” we are inclined to ask, “How best can we model human
behaviour?” The “are we good or
bad?” question suggest that we should choose one of these very simple options,
while the alternative approach suggests that we can create a model to represent human behaviour. Perhaps a more sophisticated
models like, “humans tend to be “good” when not stressed and tend to become “bad”
when stressed”. This model sends
us looking for examples where economic and social stresses affect behaviour and
to also examine antisocial (bad) behaviour and ask, where are the stresses? If we find that this model works well
most of the time we can use it to help understand human behaviour without
making any commitments about human beings being good or bad.
When using simple models to represent complicated systems it is likely that the model will not adequately account for all behaviours. If the intention is to have a model that exactly represents we will need to reject the simple model and construct another, possibly a more sophisticated model. But in many cases (especially when dealing with social sciences) we are willing to sacrifice accuracy for simplicity and simple models are used even when we know that they are not always accurate. Using the IP methodology we can determine the level of accuracy and precision we need. We do not expect that the model will be perfect as we understand that it
is a very simple model representing a much more complicated world. We can also limit the range of conditions under which we apply a model and possibly use different models for different circumstances. We can, for example, use the circumstances to determine which of several possible models we use to model human behaviour.
Another example: prejudices have bad press; we believe them
to be bad and try to get rid of them.
Doing this suggests that we have asked, “are prejudices good or bad?”
and, having concluded that they are bad, we have taken appropriate action. An alternative approach, encouraged by
an IP approach, would be to create a simple model that seeks to explain this
behaviour, test it and use it if it works. I think that models that have a good chance of success can
be developed around the following approach.
We cannot eliminate prejudices, as human decision-making is
inherently prejudicial. The everyday
situations we face have a complexity that defies rational decision-making and
we have to rely on our prejudices to, for example, select clothes, religious
beliefs, political parties, where we live, what we eat, how to make a living,
our friends, beliefs, etc. All our
likes and dislikes are based on prejudices. The majority of these prejudices are very helpful and so it
is highly unlikely that we would wish to eliminate this mode of decision-making,
even if it were possible. Let us
therefore accept that we are prejudicial and ask why we are sometimes
prejudicial in antisocial ways. It
is the antisocial prejudices that we want to eliminate and so instead of trying
to get rid of prejudicial decision-making we need a model that helps us
understand why we make antisocial decisions. There is an immediate tie in to the model discussed above and
we can associate antisocial prejudices with stresses; threats that we may be
conscious of, but which can affect us even without our being conscious of
them.
This alternative encourages an approach that includes
introspection, understanding and exploring our own prejudices, while the
previous approach encourages us to judge, blame and punish those who exhibit certain
antisocial prejudices. Changes
like this can have a profound impact on our society over time as it seems to
suggest that we try to mitigating the circumstances that result in antisocial behaviour, rather
than take the alternative route suggested by a DP approach.
A wider appreciation of the IP approach will also have an
impact on science. It is still
popular to teach that science discovers natural laws (rather than “science
produces models to represent the environment”).
This idea that science discovers laws has several consequences that can
possibly inhibit a student’s understanding of science. Let me illustrate with an example. Our standard model of the environment
includes a past and a future and something we call "time" that puts these in a
sequence, but the external world this represents only exists in the present; past
and future only “exist” in our mental model and are not a part of the external
environment this model represents.
If we keep this in mind when we try to understand something
like Einstein’s relativity theory, we don’t have to try to figure out how time
and space interact in the external environment, as these only interact in the
model being used to represent the environment. Taking this approach allows us to fundamentally separate
theory from what the theory represents and I think that this will make it
easier to understand scientific theory and its limitations. Einstein’s theory evolved from trying
to take into consideration the limits of our ability to measure things like
time and space. He speculated on
what the observer experiences and included this in his theoretical
considerations. Before Einstein a
direct perception perspective was assumed and the observer's limitations did not play a
role. It was assumed that we were
in direct contact with the environment and this encouraged the popular
interpretation of science as being able to discover ultimate truths, nature’s
laws; the laws were assumed to be in nature, not in our imagination – a fallacy
that is exposed by the model building approach (note that Newton rejected this
fallacy and argued that his models were simply models).
Separating the model from what is being modelled can also
help with understanding the application of statistics. We can think of statistics as a method
that can be used to create an abstracted (simplified) representation of a
complicated environment. The statistical
tools available also show under what conditions this model can be adequately
representative. For example, in
applying statistics to come up with Boyle’s law we assume random molecular
motion. This motion is random in
the model. What happens in the
environment may not be random and may instead follow various causal processes,
but at the level of abstraction of the model we are declaring
that, for our purposes we can consider the molecular motion to be random. By
conceptually separating the model from what the model represents we can ignore
detail that is unimportant and simplify what has to be conceptualised, making
it easier to understand.
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