Post by Jon K on Sept 10, 2017 9:16:52 GMT -8
Greg K has written a book called Disrupting Wall Street and there is a free pdf of it from Greg K (available now anyway).
unbouncepages.com/free-ebook-27022049/
He is promoting his non-psi automatic algorithmic investment software (Algolab). This isn't ARV or RV, but I am posting it here for two reasons.
First, for those not familiar with him, Greg K conducted a very extensive solo ARV experiment which netted thousands of dollars, albeit with years of work.
www.remote-viewing.com/indexmain.html
Greg K presented at an APP Conference recounting his successful ARV experiment and the video of that is available to paid APP members.
Greg's work is well worth checking out if you are new to the field. Solo work, very brief sessions, many sessions for each decision and purchase, and the use of filters to get better results. He shows illulminating examples of his sessions (quick drawings) and the target photos in his long-term binary ARV experiment.
Second, because in the book Greg explains Monte Carlo testing of data in the most detail I've ever seen. You may have come across the term too and wondered what it meant. In (very) brief, Monte Carlo testing of data is done to estimate the likelihood that your algorithm/method/hypothesis with regard to a dataset has some merit vs. the likelihood that apparently productive results are due to chance. As he notes, people (investors) fool themselves all the time because they don't check or are not aware of this possibility. As Greg put it, in investing, you have to watch out for "bogus curve-fitted magical systems that are less than useless (they are destructive because they can cost you a fortune)".
In addition, Greg writes about the many ways one might be "form fitting" one's hypothesis to the data, sometimes without being aware of this. That is, you come up with a hypothesis, it seems to produce some results on past data, you tweak it to get better results - you have form fitted your theory to the data set. And when you apply it to new data, it doesn't work.
A form of this happened in the past in ARV (standard binary ARV), IMO, when two people separately claimed they had found golden markers or potent indicators (types of data) within their data that were strong or even sure indicators of a hit. I looked into this, tried to replicate it, and did not find any such markers. Doesn't mean they don't exist; it was just one attempt to see if there was anything there. However, since both people abandoned the claims eventually, AFAIK, my conclusion is that these alleged markers were "artifacts" of the data. They were chance correlations. Having read Greg K's paper, I would say it could also be called (unaware) snapshot form fitting.
unbouncepages.com/free-ebook-27022049/
He is promoting his non-psi automatic algorithmic investment software (Algolab). This isn't ARV or RV, but I am posting it here for two reasons.
First, for those not familiar with him, Greg K conducted a very extensive solo ARV experiment which netted thousands of dollars, albeit with years of work.
www.remote-viewing.com/indexmain.html
Greg K presented at an APP Conference recounting his successful ARV experiment and the video of that is available to paid APP members.
Greg's work is well worth checking out if you are new to the field. Solo work, very brief sessions, many sessions for each decision and purchase, and the use of filters to get better results. He shows illulminating examples of his sessions (quick drawings) and the target photos in his long-term binary ARV experiment.
Second, because in the book Greg explains Monte Carlo testing of data in the most detail I've ever seen. You may have come across the term too and wondered what it meant. In (very) brief, Monte Carlo testing of data is done to estimate the likelihood that your algorithm/method/hypothesis with regard to a dataset has some merit vs. the likelihood that apparently productive results are due to chance. As he notes, people (investors) fool themselves all the time because they don't check or are not aware of this possibility. As Greg put it, in investing, you have to watch out for "bogus curve-fitted magical systems that are less than useless (they are destructive because they can cost you a fortune)".
In addition, Greg writes about the many ways one might be "form fitting" one's hypothesis to the data, sometimes without being aware of this. That is, you come up with a hypothesis, it seems to produce some results on past data, you tweak it to get better results - you have form fitted your theory to the data set. And when you apply it to new data, it doesn't work.
A form of this happened in the past in ARV (standard binary ARV), IMO, when two people separately claimed they had found golden markers or potent indicators (types of data) within their data that were strong or even sure indicators of a hit. I looked into this, tried to replicate it, and did not find any such markers. Doesn't mean they don't exist; it was just one attempt to see if there was anything there. However, since both people abandoned the claims eventually, AFAIK, my conclusion is that these alleged markers were "artifacts" of the data. They were chance correlations. Having read Greg K's paper, I would say it could also be called (unaware) snapshot form fitting.
