PS: I don't think I've met him before! I'll keep an eye out though whenever campus opens up so I can say hello!

Hi Alyssa,

What a great essay! It was very well written and very easy to follow. Well done!

Going into every computation, knowning as many primes as possible is surely the best way to compute something with maximum predictability, but certainly at a cost! There is just to much information. Elon Musk tweet is the prime you care about, the blade of grass moving in the wind is might contain some signal, but is likely noise. You made this idea crystal clear and how it to learning and Turing machines and stochastic processors.

If you increase your state space to include all information, your not really learning everything, you're just memorising stuff. You need to forget and ignore stuff to learn and thus, follow well worn tracks through your massive state space. As you point, out biology figured all this out long ago.

Great work, I completely agree with your thesis and loved the essay! We certainly have a lot of overlap in our ideas. My essay 'noisy machines' covered a lot of the same topics, but focused on the thermodynamics and computation. I'd love to get your feedback if you have the time!

Thanks,

Michael

    Dear Dr.Alyssa Adams,

    Thanks for presenting an excellent essay. You discussed well about the predictability. Your fig 2. shows an algorithm that is something EXACTLY like my essay "A properly deciding, Computing and Predicting new theory's Philosophy".

    In that philosophy I gave some additional practical points like truth fullness, forcing and manipulation ( fixing) of results, some cheating etc., that you did not include. Probably at your age you did not see the world.

    I want to give the best rating to your essay, but at least make a visit to my essay and leave some comment please......

    Best

    =snp

      Dear Dr Alyssa Adams. Nice essay.you have my high rating. I liked your take on modularity.Seems like you struck the same chord with me here https://fqxi.org/community/forum/topic/3525.kindly read/review on bias.Thanks and wish you all the best in the essay contest.

        Dear Dr.Alyssa Adams,

        First, I am really sorry for a late reply. I somehow missed update on my email ( which is linked to this competition).

        Thanks for your detailed answer!

        And, yes indeed I think machine learning can provide us with strong insight in multiple fields!

        I think the good models indeed selected the right modular features; because a good model has to be computable. For instance, in gravitational physics, we narrowed down the state spaces by simple caring about the mass of an apple, not the arrangement of the particles, its color, etc.

        At the end of the day Newton with the scant data at hand, and no computational capacity could not have reasoned otherwise! Simply focusing on the mass led him to shrink the state space vastly.

        And, I think as we are confronting more and more sophisticated problems, we are realizing we cannot shrink many state spaces as drastically, and this is where machine learning and other modern tools of computation comes in handy!

        (PS: As a layman my response can be completely invalid ofc)

        Kind Regards,

        Raiyan Reza

        Hi Michael!

        It is interesting to think about the process of just memorizing stuff vs. understanding an underlying process (compressing the data into an algorithm). I wish we knew more about the physical instantiation of knowledge in space, since it would clear up a lot of misunderstandings on causality. As an example, it is currently difficult to understand what processes caused the human brain to have the physiology that it currently has. Or, more simply, it's difficult to understand why the grains of sand on a beach are exactly arranged in the way that they are currently. It's too difficult to extract this information and move backward, since for each effect there could be several causes (even if many possible causes are the most likely because they are short explanations (Occam's Razor)).

        I hope you find my posted questions on your essay helpful and interesting! I really loved reading it!

        Cheers!

        Alyssa

        Hi Alyssa,

        thank you for laying out the computing state space so clearly! These are some rather interesting tools to play with.

        So I get how the human brain might be said to 'shrink the state space' of the sense data available to it by massively simplifying what we experience in terms of cross modal sensory perception, object perception, short term memory, attention and so on. I'd be really interested to hear your thoughts on how these sorts of observational processes are being or might be modelled in this computational sense, and especially in terms of any developments in evolutionary biology.

        Is anyone looking at morphogenesis in evo-devo from this computing perspective? I've been playing around with various philosophical and biological notions regarding the modern concept of morphogenesis, and trying to think of it as fundamentally an informational process that organizes matter. And not just in terms of the evolution of the individual organism but more in terms of how biology is a terraforming biosphere process (cf. Sara Imari Walker's work)

        Specifically do you think there might be a way to model an observer-dependent perspective as an information feedback loop driving morphogenetic processes?

        Cheers,

        Malcolm

        Je suis, nous sommes Wigner!

          Dear Alyssa,

          thank you for writing this essay - very enjoyable. Regarding the relation between computation and implementation (your figures 1 and 2) I was wondering if you know the framework of "Abstract representation theory" by Dominic Horsman et al. They deal with the question of what physical systems compute, i.e. which of them are implementing a computation. I was wondering how their perspective fits or compares to your framework.

          Thanks again and best regards,

          Gemma

            Dear Alyssa,

            My first thought was, do we see, between the essays, an ad selling a get-rich-quick scheme? Just kidding, but it made me curious, and after reading your essay, I became indeed richer, intellectually. Also had a lot of fun, I like your style. In addition, I could relate with your essay in many ways (you mention CNC, I worked 8 years for a company making cad/cam software for CNC. I didn't trade, but a friend, one of my former colleagues there, left the company to trade Forex, won 0.25m, then lost 1m. He's fine now, but still not allowed to trade.) I loved your explanations and your ideas, about the limits of computation (indeed, even Laplace's daemon has some serious trouble). I liked especially the ideas you presented about how to beat these limitations by constraining the state space and by modularity. I'll tell my friend these tricks, once the law will allow him again to trade, he can use them. I will not try, I'll let him do this, anyway he said at some point that if he wins big time he'll finance my research :-)

            Thank you for the excellent essay, and I wish you success in the contest!

            Cheers,

            Cristi

              Absolutely! It makes me curious to wonder if there is a nice mathematical framework that would help us shrink the state space in an appropriate way for a particular problem. It makes me think about set theory, and also makes me wish I knew way more about discrete mathematics than I do. The thing is that it's difficult because it depends so much on the problem in question, which has the subjective abilities of the observer built right in.

              Hello SNP!

              Yes! I definitely see a lot of overlap between our two essays! I have some questions and comments for you as well, but I will post them on your essay page.

              Cheers!

              Alyssa

              Thank you so much for reading! I look forward to posting my questions about your essay and having a great conversation!

              Cheers!

              Alyssa

              Hi Malcom!

              Actually, this is my exact interest as well! I can't help but think a mathematical model that captures the subjectivity of an observer could be represented with some kind of set theory.

              On one hand, you have an observer who is only able to make particular observations of the world, due to the lack of complete knowledge of the entire world. On the other hand, you have the rest of the world, which also includes the observer itself, which is often the case in biology.

              I think this "cut" between an observer and the world should have a big impact on the dynamics of both the world and an observer, especially if the observer's dynamics are not fixed in time.

              Plus, there's the physical arrangement of these entities in the world. The physical limits of computation put bounds on the actual tasks any entity could possibly take. I think what makes humans so interesting is our ability to extend our computation power beyond the brain, which I personally think why computers and machines are so important to collective human tasks (this is the extended model of cognition in psychology). It makes me think that humans are extremely good at manipulating state spaces to complete computational tasks. There's a lot of fascinating work in psychology about this, so I think, if anything, we should look to the empirical results of human cognition and other biological computation tasks (like chemical networks in metabolism, viral evolution, etc).

              There are so many moving parts here, but it is my hope that some mathematical model could formalize these ideas so we get a better picture of the computational landscape we have to work with, then if we're lucky, we could see if it has any explanatory power over real data.

              Cheers!

              Alysas

              Hi Gemma!

              I haven't! Thank you so much for sharing this, this is absolutely fascinating. Actually, I think these ideas would fit nicely into Constructor Theory, which I think was missing this exact component. I'll learn AR theory in much more detail because I'd be extremely interested to see how the idea of different observers with access to different knowledge could fit in, especially how observers could be seen as computers using other computers. I think that last part sums up what I think is missing between biology and computation: How do computers use computers?

              Cheers!

              Alyssa

              Ha ha ha, best of luck to him! Maybe, we could all engineer some weird machine learning algorithm to process the logical arguments of these essays, then see if it could come up with some reduced logical set! Then we could apply this to trading XD Glad you enjoyed my essay!

              Dear Dr.Alyssa Adams,

              I gave the best rating as i promised. Thank you for your post on my essay, i am replying there. We will communicate further with Email for bio problems, please check mail....

              Best wishes to your essay

              =snp

              Hi Alyssa:

              Besides novelty in your thinking, you are also an excellent writer, and should get a higehr rating.

              It is not quite the same, perhaps, but Descartes' philosophy of reductionism, somehow can lead to your suggestion of "modularize" complex problems into small segments [ https://en.wikipedia.org/wiki/Reductionism ]. That is the only way for humans, with their limited mind and, especially, starting originally from complete ignornce about the laws of the universe.

              Being an experimental physicist, I have dissected the essential steps behind data gathering in all of our experimental apparatuses. In the process, I have found that nature has saddled us with a perpetual bottleneck to obtain the COMPLETE knowledge about anything. Humans have started with complete ignorance about the laws of nature; and we can understand one bit at a time. We have to keep on gathering more and more bits. However, how the newer COMPLETE set of bits will fit together, will always keep changing as we keep gathering more and more bits.

              We have no choice but to iteratively advance to higher levels of knowledge, leveraging one "working theory" after another "working theory", and so on. However, instead of re-structuring the fundamental postulates of the older working theory to complement the newer "working theory", we have been accepting, as religious dogmas, the older theories and build something above it as the n-th story over the old building, instead of rebuilding a newer edifice. It is very hard; but that is the only way we can INCH towards the ontological reality.

              Even though I do not have proper understanding how my Holobiota keeps generating these sentences; my ontological existence is validated by this writing on this computer. My Holobiont is transcribing my thoughts; therefore I exist; at elast as an assembly of trillions of cells! I am a partial reality of the universe because I am trying to unravel the realities of the universe.

              Many of my earlier papers have also articulated this position. They can be downloaded from:

              http://www.natureoflight.org/CP/

              You can also download the paper: "Next Frontier in Physics--Space as a Complex Tension Field"; Journal of Modern Physics, 2012, 3, 1357-1368,

              http://dx.doi.org/10.4236/However, mp.2012.310173

              You can directly contact me at:

              Chandra.Roychoudhuri@uconn.edu

              Stay happy.

              Stay healthy.

              That is the surest way to keep the Covid-19, and their earlier friends, under control. Avoiding exposure to them forever is impossible.

              Sincerely,

              Chandra.

                Dear Alyssa Adams!

                Thanks for the interesting essay. This is essentially a bold original article for an important newspaper!

                I liked your unexpected association of the current pandemic with the collapse of the financial market. At the 2018 World Philosophical Congress in Beijing, I publicly said: "Soon the world will face a financial crisis. It will be a controlled demolition of the stock market. Market makers want to destroy fictitious capital. For`s this purpose the threat of war is organized!"

                Before that, I wrote several articles about the fact that they will try to mask the coming economic crisis with international military conflicts. But I did not imagine that they use a pandemic scarecrow for this. Let's hope that all these experiences will remain in the past.

                Otherwise, our views are similar. You are also looking for algorithms and calculations in the objective world around us. Yes, it's clear that in a flying fly, very specific calculations take place that control the flight. But what calculations take place in the solar system? It is known that Newton believed that the solar system is unstable, and God constantly intervenes, controlling and coordinating the movement of all the planets. Laplace proved that there are certain mechanisms that determine stability. Can these feedback mechanisms be called computation?

                I wish you success in your scientific work!

                Sincerely, Pavel Poluian.

                  I must first point out a minor but not insignificant error. The complexity class P does, indeed, mean Polynomial-time, however, the complexity class NP means Non-deterministic Polynomial-time. It does not mean Non-polynomial time because, if it did, then that would mean that P does not equal NP, but this is an open question.

                  I think a simple joke expresses why the stock will never be predictable, but also not unpredictable either and also why I think the ("scientific") concept of predictability is intrinsically flawed. Two hunters are in the woods when they suddenly encounter a bear. The bear then proceeds to charge them and the hunters turn and frantically run away. While running away one of the hunters says to the other hunter, "Why are we running? There is no way we can outrun the bear!" To which the other hunter responds, "I don't need to outrun the bear. I only need to outrun you."

                  The stock market simply does not have an "intrinsic value." This concept is a myth. Simply look at the Black-Scholes option pricing model or the Discounted Cash Flow (DCF) method of evaluting equity securities. The most pertinent aspect of both of these pricing mechanisms is the assumptions that must be made. With Black-Scholes it is the risk-free rate. With DCF it is the terminal value. Even the slightest adjustment to these number can produce wide fluctuations in the present value. These methods were chosen as simply a representative sampling. The fundamental point is that is that it ultimately comes down the the judgment of the analyst who is trying to be "more right" than any of the other analysts. (Note: these comments do not apply to simple arbitrage, which is (successfully) more formulaic.)

                  A great example of this is the Asian financial crisis and the "collapse" of LTCM (Long Term Capital Managment) in the late 1990's. Once the unpredictable is made predictable then the unexpected happens, which is predictably the "one factor" that was not considered.

                  Time for a riddle. Whenever you lose something, such as your keys or the TV remote, why is it always (infuriatingly) "in the last place you look?" Because once you find it you stop looking.

                  These are not merely antidotes. A Nobel prize was recently won with the ideas being presented here. I am referring to Richard H. Thaler and the invention of Behavioral Finance, which he discusses in his book Misbehaving. (He talks about the broader idea of Behavioral Economics, but I think his real insight is contained in the more limited idea of Behavioral Finance. I think his ideas about Behavioral Economics takes the insight too far.)

                  So, what does all this have to do with your essay? It appears you used the abstract as a metaphor for the subjective factor and the physical as a metaphor for the objective factor. But I am not sure the human factor can be considered abstractly, which thus changes the objective. But maybe I misunderstood your premise? It was an interesting thesis nonetheless, which obviously spurred many thoughts.

                    Dear Alyssa,

                    This is a systemic look at systems. I enjoyed the essay and part of the reason I liked this work was how it was organized. Ideas were categorized like in the life sciences instead of the instruction manual style of writing Physicists (like me) fall into. What is the smallest amount of information needed to find a pattern and make predictions? How could we determine there is no pattern and we can stop wasting our time looking for a pattern? If we find a pattern can we generalize the function to a whole category instead of a single problem? This reflects the issues presented to science everyday.

                    Sincerely,

                    Jeff Schmitz