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The problem with the piranha analogy is you can apply the large number of possible causes and effects which occur in different directions to pretty much any science, social or otherwise. There are at any one time a very large number of pathogens competing for the attention of the immune system all associated with overlapping symptoms and thus many competing hypotheses about the cause of a particular ailment, but this doesn't make the study of medicine futile.

Even if you've got competing parties actively trying to nudge people in a particular direction, it doesn't mean there is no discernible effect; increases in political polarisation and emotional attachment to brands can be observed even as people consciously attempt to nudge other people in broadly opposite directions.

This doesn't mean the social psychology and behavioural economics experiments hypotheses are necessarily good ones or the experiments well constructed. But complex systems and competing hypotheses are present in all science and are what controlled experiments and statistical controls are for...




I guess the point is that if there are a large number of big, variable, competing effects with interactions etc, you can't just look at each of them in isolation and expect them to predict a large fraction of the variance. So you need a more sophisticated (possily impossibly complex) model or study, as you said. In the nudging example, this appears to imply something like "conditioned on a large effect having been measured using a sufficient amount of data (!), you have to be extremely confident in the study design and model to actually believe it"?




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