On Sat, Jan 11, 2025 at 4:53 PM Matt Mahoney <mattmahone...@gmail.com> wrote:
> On Fri, Jan 10, 2025, 5:33 PM James Bowery <jabow...@gmail.com> wrote: > >> >> Stop palavering and start doing algorithmic information criterion >> macrosocial model selection. >> > > That's what I'm doing when I project past trends into the future. You > compress by fitting a line to the data points and encoding the differences. > > But that only finds correlations, not causations. If we think that A > causes B, then we can test P(B|A) > P(B). But you could rewrite that as: > > P(A,B)/P(A) > P(B) > P(A,B) > P(A)P(B) > P(A,B)/P(B) > P(A) > P(A|B) > P(A) > B causes A. > Nope. That's statistics, not dynamics. You need state space aka dynamical models to predict things in time but you also need a dynamical, not statistical, information criterion for selection among those models. Popper and Kuhn both successfully attacked the foundation of data-drive scientific discovery of causality with their psychologically and rhetorically intense popularizations of “the philosophy of science” at the precise moment in history that it became practical to rigorously discriminate mere correlation from causation, even without controlled experimentation, by looking at the data. I only became aware of this after attempting to do first-order epidemiology of the rise of autism that had severely impacted colleagues of mine in Silicon Valley — investigation required since it was apparent that no one more qualified was bothering to do so. However, I did expect to find a correlation with non-western immigrants from India (gut bacteria), and found one. Now, having said that, I’m not here to make the case for that particular causal hypothesis — there are others that I can set forth that I also expected and did find evidence for. What I’m here to point out is that my attempts to bring these hypotheses up were greeted with the usual “social science” rhetoric one expects: “Correlation doesn’t imply causation.” “Ecological correlations are invalid due to the ecological fallacy.” and so forth. This got me interested in precisely how it is that “social science” purports to infer causation from the data — experimental controls being the one widely accepted means of determining causality in the philosophy of science. This interest was amplified when I, on something of a lark, decided to take my data that I’d gathered to investigate the ecology of autism, and see which of the ecological variables was the most powerful predictor of the other variables I had chosen. One variable, in particular, that I had been interested in, not for autism causation, but for social causality in general, was the ratio of Jews to Whites in a human ecology at the State level in the US. Well, out of hundreds of variables, guess which one came out on top? Imagine the kind of rhetorical attacks on this “lark” of mine: Same old, same old… So my investigation of causal inference intensified. Eventually, circa 2004-2005, I intuited that data compression had the answer and suggested something I called “The C-Prize” — a prize that would pay out for incremental improvements in compression of a wide-ranging corpus of data, resulting in computational models of complex *dynamical systems*, including everything from physics to macrosocial models. That’s when you, Matt Mahoney, of all people, alerted me to the information theoretic work that distinguished between Shannon information and what is now called “Algorithmic Information”. The seminal work in Algorithmic Information occurred in the late 1950s and early 1960s — precisely when Moore’s Law was taking off in its relentlessly exponentiating power. Algorithmic Information content of a data set is the number of bits in its smallest executable archive — the smallest algorithm that outputs that data. Shannon information is basically just statistical. Think of the digits of pi. Shannon says the information content is identical with the string of digits. Algorithmic Information says the information content is the size of the program that outputs the digits of pi. That discovery threatened to bring the social sciences to heel with a rigorous and principled information criterion for model selection far superior, and provably so, to all other model selection criteria used by the social sciences. Moreover, the models so-selected would be necessarily causal in nature and be amenable to using the power of silicon to make predictions without any kind of ideological bias. This, I strongly believe, was the precise reason Popper and Kuhn committed their acts of violence against science at the precise moment in history they did. I can’t tell you how depressing it is that I can’t get this across to people even after 18 years of Marcus Hutter sponsoring, out of his own pocket, the C-Prize. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tff34429f975bba30-M87c6ca7e09fb9ebd999412de Delivery options: https://agi.topicbox.com/groups/agi/subscription
