But then, never before has humanity had the combined benefits of an overwhelming case for one correct probability theory, a systematic understanding of human biases and how they work, free access to most scientific knowledge, and a large community of people dedicated to the daily practice of CogSci-informed rationality exercises and to helping each other improve.
How do you know that these set of circumstances are only present, and only have been present, at your institution?
Where does the text claim that this is present only in this institution? It is just that the combination of the conditions was, strictly speaking, infeasible for purely technical reasons (nonexistence of means of access to such large bodies of information and of some of the sceintific fields).
If there are a few weaknesses in the article, I’m not sure this is one of them.
The field of cognitive biases have only been around 40 years; free access to most scientific knowledge has been greatly enhanced by Google Scholars and since it’s otherwise fettered, that part’s not unreasonable; even the Bayesian interpretation of probability theory has only been accepted for… well, I’m not sure, but I think only since World War II; and perhaps most restricting of all, the Web enables large communities that probably couldn’t otherwise exist and is pretty new.
These facts in conjunction make his statement reasonable.
even the Bayesian interpretation of probability theory has only been accepted for… well, I’m not sure, but I think only since World War II
Try half a century later. Until very recently—about twenty years ago—the Bayesian view of probability was very much a minority view, and it has only really picked up steam in the last 10 years. Several things happened around 20 years ago:
Faster and cheaper computers became available. Bayesian methods tend to be computationally intensive, and this limited their use.
Rule-based expert systems fizzled out and began to be replaced by Bayesian networks after practical algorithms for inference with BNs were developed.
Awareness of Markov chain Monte Carlo methods (which can be used to sample from a Bayesian posterior distribution) spread to the statistics community, and the free BUGS software made it easy for non-experts to create and evaluate new Bayesian models.
These developments made it practical to apply Bayesian methods… and people started finding out how well they could work.
How do you know that these set of circumstances are only present, and only have been present, at your institution?
Where does the text claim that this is present only in this institution? It is just that the combination of the conditions was, strictly speaking, infeasible for purely technical reasons (nonexistence of means of access to such large bodies of information and of some of the sceintific fields).
If there are a few weaknesses in the article, I’m not sure this is one of them.
The field of cognitive biases have only been around 40 years; free access to most scientific knowledge has been greatly enhanced by Google Scholars and since it’s otherwise fettered, that part’s not unreasonable; even the Bayesian interpretation of probability theory has only been accepted for… well, I’m not sure, but I think only since World War II; and perhaps most restricting of all, the Web enables large communities that probably couldn’t otherwise exist and is pretty new.
These facts in conjunction make his statement reasonable.
Try half a century later. Until very recently—about twenty years ago—the Bayesian view of probability was very much a minority view, and it has only really picked up steam in the last 10 years. Several things happened around 20 years ago:
Faster and cheaper computers became available. Bayesian methods tend to be computationally intensive, and this limited their use.
Rule-based expert systems fizzled out and began to be replaced by Bayesian networks after practical algorithms for inference with BNs were developed.
Awareness of Markov chain Monte Carlo methods (which can be used to sample from a Bayesian posterior distribution) spread to the statistics community, and the free BUGS software made it easy for non-experts to create and evaluate new Bayesian models.
These developments made it practical to apply Bayesian methods… and people started finding out how well they could work.