The post truth world: Difference between revisions
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===Modes of prediction=== | ===Modes of prediction=== | ||
====Physical science model==== | |||
'''Carefully constrained conditions for observation''': “laboratory conditions”<br /> | |||
*Extraneous noise removed from the system | |||
*Theoretical assumptions and experimental conditions are absolutely rigid and by definition cannot change | |||
*Outcomes are tightly prescribed and you can’t test assumptions themselves the theory: in an experiment with dice you can’t throw a seven, mich less hypothesise what would happen if you did.<br /> | |||
'''Model''' is to observe discrete events whose occurrence neither depends on nor is affected by <br /> | |||
*your observation: | |||
*other events in the sample: | |||
**If you throw one six, that makes it no more or less likely that you'll throw another. ''Even though instinctively it seems like it''. | |||
**Isn’t variable in retrospect. A six, once thrown, can’t change its mind. | |||
Ordinary “Gaussian” probabilities are appropriate, but even here the model is better at explaining observations ''once they’ve happened'' rather than predicting how they’ll happen ''before they do''. | |||
**Try to catch a cricket ball using only scientific modelling. | |||
====Human sociological events==== | |||
Human sociological events are profoundly different, even though the way we test them is not. | |||
*We do have memory of previous occurrences, | |||
*(outside “laboratory conditions”) it is almost impossible: | |||
*to avoid knowing about other relevant events in a sample | |||
*to avoid changing your behaviour as a result - i.e. ''reacting'' to them. | |||
This changes the statistical analysis. A normal distribution has some value in predicting events which fall broadly within the standard deviation (these are events that conform with general expectations, and against which individuals are less likely to react.) <br /> | |||
When events are significantly outside the standard deviation, people will react, pushing their own reaction outside the standard deviation - the result being the “long tail” phenomenon. Because individuals have memory these events themselves can older the shape of the system itself. Once you have seen a plane flying into a building, it now becomes a more conceivable outcome and individuals will behave differently as a result (usually governments will force them to). Same goes for the financial crises through time: policy results have been to react to an unanticipated event and prevent behaviour which individuals will generally avoid (in the short term) in any case. Again, we see we are better explaining in hinsight what happened rather than explaining what will happen next. | |||
===Path dependency=== | |||
Laboratory conditions are far less appropriate to sociological experiments than to physical sciences. | |||
Revision as of 18:38, 11 December 2016
Faith in experts
We haven't done fabulously well, have they.
- Gordon Brown thought he’d abolished boom and bust in 2005.
- Financial crisis
- credit rating agencies
- risk models failed
- Democratic exercises - count the number of experts who got this wrong:
- polling errors
- economic projections
Modes of prediction
Physical science model
Carefully constrained conditions for observation: “laboratory conditions”
- Extraneous noise removed from the system
- Theoretical assumptions and experimental conditions are absolutely rigid and by definition cannot change
- Outcomes are tightly prescribed and you can’t test assumptions themselves the theory: in an experiment with dice you can’t throw a seven, mich less hypothesise what would happen if you did.
Model is to observe discrete events whose occurrence neither depends on nor is affected by
- your observation:
- other events in the sample:
- If you throw one six, that makes it no more or less likely that you'll throw another. Even though instinctively it seems like it.
- Isn’t variable in retrospect. A six, once thrown, can’t change its mind.
Ordinary “Gaussian” probabilities are appropriate, but even here the model is better at explaining observations once they’ve happened rather than predicting how they’ll happen before they do.
- Try to catch a cricket ball using only scientific modelling.
Human sociological events
Human sociological events are profoundly different, even though the way we test them is not.
- We do have memory of previous occurrences,
- (outside “laboratory conditions”) it is almost impossible:
- to avoid knowing about other relevant events in a sample
- to avoid changing your behaviour as a result - i.e. reacting to them.
This changes the statistical analysis. A normal distribution has some value in predicting events which fall broadly within the standard deviation (these are events that conform with general expectations, and against which individuals are less likely to react.)
When events are significantly outside the standard deviation, people will react, pushing their own reaction outside the standard deviation - the result being the “long tail” phenomenon. Because individuals have memory these events themselves can older the shape of the system itself. Once you have seen a plane flying into a building, it now becomes a more conceivable outcome and individuals will behave differently as a result (usually governments will force them to). Same goes for the financial crises through time: policy results have been to react to an unanticipated event and prevent behaviour which individuals will generally avoid (in the short term) in any case. Again, we see we are better explaining in hinsight what happened rather than explaining what will happen next.
Path dependency
Laboratory conditions are far less appropriate to sociological experiments than to physical sciences.