Thank you for your patience with me, Natesh. I see you are at a nanotechnology lab, so I imagine that "I squared R" dissipates a lot of heat that is of concern. How much dissipation in your hypothesis is from I^(2) R ?

From my work experience in software, implementing a FSM by an array with two indexes, one for current state, one for current input signal, storing at those indexes next state and output signal, is much quicker and easier to debug than leaving the FSM in a lot of "if-then" statements-- I.e. using data instead of code increases the speed of execution and reduces debugging significantly. Then if the rest of the code is needed for the full Turing machine, most of the speed loss and complexity in my coding experience would come from the full Turing machine, not the FSM. That's why I ask. And, in your line of work, it seems to me that the vonNeumann architecture would be more relevant for I^(2) R dissipation than more abstract models like co-algebras, streams, or Chu spaces for modeling computing. For example, as in Samson Abramsky's Big Toy Models.

In engineering terms, apoptosis is a quality control in animals (it never occurs in plants) that seems like a way to minimize energy loss and therefore may be relevant to your dissipation hypothesis. In apoptosis, defective cells that would be energy-wise inefficient are destroyed and their components re-used to build other cells, thus holding onto the potential energy in the sub-assemblies and again reducing energy loss. But I don't see how I^(2) R heat loss plays a role in apoptosis.

If you had an equation for dissipation rather than a verbal explanation, It might give me something more general than I^(2) R to think about-- especially regarding apoptosis as an example of minimizing dissipation of energy. What are your thoughts?

Regarding your references-- I will try to find them online. Are they in arxiv? The closest big universities where I could photocopy these papers are hours away from me.

    Hi Lee,

    I am happy to answer all questions. I want to point out again that I use the dissipation bound as a (good) approximation of the actual dissipation in the processes that we are interested in. The dissipation bound expression as entropy \delta S and mutual information terms I. The dissipation bound is fundamental and relates to dissipation associated with irreversible information loss. It is implementation independent. When you talk about I^2*R expression for dissipation, you are thinking about wires and charges moving through those wires. So that expression will not hold for spin based architectures. However irrespective of implementation, if there is information loss, then there will be fundamental dissipation associated with it characterized by entropy and information terms. Hence their presence in the expression. For a long time, these bounds from fundamental law (informally called Landauer bounds) were many many orders of magnitude lower than the I^2*R and leakage dissipation and were not significant. But decades later and thanks to Moores law, we will hit such Landauer limits in a decade or so. At the most fundamental level, we define a broad physical implementation of finite state machine that is architecture or technology (like CMOS) independent, and the bound in the paper is the bound of that nature and only depends upon just definition achieved physically. We can always add the dissipation of the architecture and circuit on top of this. Unfortunately the paper in the references that can make this so much clearer is not on arxiv. If you can access them on a college campus somewhere, here are some other papers that will make a lot clearer and what exactly I am talking about, especially the second one.

    Anderson, Neal G. "On the physical implementation of logical transformations: Generalized L-machines." Theoretical Computer Science 411.48 (2010): 4179-4199.

    Anderson, Neal G., Ilke Ercan, and Natesh Ganesh. "Toward nanoprocessor thermodynamics." IEEE Transactions on Nanotechnology 12.6 (2013): 902-909.

    Thanks.

    Natesh

    Natesh. All I can get on the second paper is the abstract and a bit of the intro. The rest is behind a paywall.

    You wrote, "When you talk about I^2*R expression for dissipation, you are thinking about wires and charges moving through those wires. So that expression will not hold for spin based architectures." It's Joule heating, as in this article:

    http://poplab.stanford.edu/pdfs/Grosse-GrapheneContactSJEM-nnano11.pdf

    And, you wrote about "entropy":

    "I want to point out again that I use the dissipation bound as a (good) approximation of the actual dissipation in the processes that we are interested in. The dissipation bound expression as entropy \delta S and mutual information terms I."

    But the abstract talks about "energy dissipation":

    ****

    Abstract:

    A hierarchical methodology for the determination of fundamental lower bounds on energy dissipation in nanoprocessors is described. The methodology aims to bridge computational description of nanoprocessors at the instruction-set-architecture level to their physical description at the level of dynamical laws and entropic inequalities. The ultimate objective is hierarchical sets of energy dissipation bounds for nanoprocessors that have the character and predictive force of thermodynamic laws and can be used to understand and evaluate the ultimate performance limits and resource requirements of future nanocomputing systems. The methodology is applied to a simple processor to demonstrate instruction- and architecture-level dissipation analyses.

    ...I. Introduction

    Heat dissipation threatens to limit performance gains achievable from post-CMOS nanocomputing technologies, regardless of future success in nanofabrication. Simple analyses suggest that the component of dissipation resulting solely from logical irreversibility, inherent in most computing paradigms, may be sufficient to challenge heat removal capabilities at the circuit densities and computational throughputs that will be required to supersede ultimate CMOS.1

    For 1010devices/cm3 each switching at 1013sтИ'1 and dissipating at the Landauer limit EminтЙИkBT have Pdiss=414 W/cm2 at T=300K Comprehensive lower bounds on this fundamental component of heat dissipation, if obtainable for specified nanocomputer implementations in concrete nanocomputing paradigms, will thus be useful for determination of the ultimate performance capabilities of nanocomputing systems under various assumptions regarding circuit density and heat removal capabilities.

    ****

    Are you saying the heat dissipation in post-CMOS devices is just due to spin, and none of that heat is due to Joule heating?

    Best Regards,

    Lee

    Natesh, thank you for your reply! You wrote:

    "Also with respect to the fish example, the fish is a system that can act on its environment and thus its behavior is a tradeoff between 'exploration vs exploitation' under this idea and would not just be a form of predictive learning that we would see in a system that cannot act on its environment."

    Is what you wrote, above, about probability-learning foraging fish implied by the definitions of terms in your hypothesis? That is, do the definitions of the terms in your hypothesis (like "open," "constraints," etc.) imply what you have written, above?

    Your hypothesis: "Open physical systems with constraints on their finite complexity, that dissipate minimally when driven by external fields, will necessarily exhibit learning and inference dynamics."

    Or, is more than this required to understand your hypothesis-- more than just the above statement of your hypothesis together with definitions of the terms used in your hypothesis?

    Dear Ganesh,

    I wish you all the best with your in depth analysis of how intentions govern reality. I welcome you to read there are no goals as such in which I propose that consciousness is the fundamental basis of existence and that intent is the only true content of reality. Also that we can quantify consciousness using Riemann sphere and achieve artificial consciousness as per the article Representation of qdits on Riemann Sphere. I saw that you are also arriving at study of consciousness in physical systems in the conclusion of your essay. Also please see all the diagrams I have attached in my essay.

    Love,

    I.

      Dear Ganesh,

      Thank you for the good essay on "Intension is Physical"

      You are observations are excellent, like... "fundamental limits on energy efficiency in new computing paradigms using physical information theory as part of my dissertation"

      I have some questions here, you mean to say for every external input, our Brain predicts and takes a correction. It wont be acting on directly by its own self....

      Probably if we make energy efficient computer, it will become super intelligent, Probably we may require some software also....

      Even though my essay (Distances, Locations, Ages and Reproduction of Galaxies in our Dynamic Universe) is not related to Brain functions, It is on COSMOLOGY....

      With axioms like... No Isotropy; No Homogeneity; No Space-time continuum; Non-uniform density of matter(Universe is lumpy); No singularities; No collisions between bodies; No Blackholes; No warm holes; No Bigbang; No repulsion between distant Galaxies; Non-empty Universe; No imaginary or negative time axis; No imaginary X, Y, Z axes; No differential and Integral Equations mathematically; No General Relativity and Model does not reduce to General Relativity on any condition; No Creation of matter like Bigbang or steady-state models; No many mini Bigbangs; No Missing Mass; No Dark matter; No Dark energy; No Bigbang generated CMB detected; No Multi-verses etc.

      Dynamic Universe Model gave many results otherwise difficult to explain

      Have a look at my essay on Dynamic Universe Model and its blog also...

      http://vaksdynamicuniversemodel.blogspot.in/

      Best wishes................

      =snp. gupta

        Your work is highly mathematical. Although my math skills are not good enough to understand the details, I think it is a tremendous work. I would love to see your work on consciousness, qualia and meaning, which you hinted at in the essay, primarily because I had considered those areas well-nigh impervious to mathematics. But I am pretty sure you will talk even of those areas using math. Rather impressive, I must say.

        I had wanted to evaluate my work on whether it satisfied the mathematical theorems of Ashby and Conant. I am speaking here of the Law of Requisite Variety (Ashby) and the Good Regulator Theorem (Conant). But I could not because my math skills are not good enough for the job. My guess is you would want to evaluate your work as well on its alignment with the basics of those works. Ashby's work is considered a classic in understanding the functioning of all systems, but on glancing through his works, it is clear that he did his work primarily with the human brain in mind.

          Hi Lee,

          Apologies for the late reply. Have been away at a conference to talk about some of the ideas here and how to relate it to computing.

          "Or, is more than this required to understand your hypothesis-- more than just the above statement of your hypothesis together with definitions of the terms used in your hypothesis?"

          --> All that is needed to understand my hypothesis is that statement. I have provided as many definitions as I can there in the essay but due to space limitations I have had to reference some of the other definitions in former papers.

          "Is what you wrote, above, about probability-learning foraging fish implied by the definitions of terms in your hypothesis? That is, do the definitions of the terms in your hypothesis (like "open," "constraints," etc.) imply what you have written, above?"

          --> Yes. It does.

          Hi,

          Thanks for your comments. Here are my answers to some of the questions you have brought up.

          "I have some questions here, you mean to say for every external input, our Brain predicts and takes a correction. It wont be acting on directly by its own self...."

          --> If you think of the brain as an hierarchical predictive machine, everything you experience is a result of the continuous prediction-error correction mechanism it is executing. And using terms like "self" is a little loaded and misleading. The sense of self is the result of a physical system that I have described in the essay just evolving under physical law.

          "Probably if we make energy efficient computer, it will become super intelligent, Probably we may require some software also...."

          --> Yes, it is a very new idea in computing called 'thermodynamic computing'. There is very little work right now but it is gaining momentum. And the point is that there is no fixed algorithm or software. The hardware is set to evolve under larger thermodynamic constraints (as stated in the minimal dissipation hypothesis) for a large set of different environments and it will learn.

          I will take a look at your essay but I am an engineer by training and my knowledge in cosmology is very limited, so please forgive me if I cant fully grasp the ideas you express in them. Good luck in the essay contest and please rate if you have enjoyed the work.

          Cheers

          Natesh

          Hi Willy,

          Thank you for the kind comments. Yes, I did not have space to treat questions like qualia and meaning in detail but I intend to in the near future. I am an engineer and my immediate focus is to leverage the ideas presented here into actual computing systems. The math was a necessary evil and I will work on making the explanations are lot clearer going forward. I find the ability to address such topics with math very exciting.

          I like that you brought up both Ashby and Conant, two people whose work has been very influential on me. The minimal dissipation hypothesis is connected to the Good Regulator Theorem in a straight forward manner, and is something I have thought about and worked on. I will have to think a little bit more about rigorously showing the connection to the law of requisite variety but my initial feeling is that it is not impossible.

          Thanks again for your comments. Please rate if you enjoyed the essay. It looks like I could use the help.

          Cheers

          Natesh

          Hi,

          Thanks for reading my essay and your comments. I have read your work and will politely disagree with you on your premise and conclusion though. I do think that starting from consciousness as a fundamental basis of existence is not the right approach. I would argue that consciousness is an emergent property in input mapping that occur in certain systems due to thermodynamic constraints. I think we will just have to agree to disagree. Also you should check out the integrated information theory of consciousness by Tononi and Koch. I think you will enjoy their work. Please rate my work if you enjoyed reading it. Thanks and good luck in the contest.

          Cheers

          Natesh

          Natesh,

          Great essay, well informed and organised analysis of a very interesting hypothesis.

          I also like most and agree with much, certainly with the addage that; 'our aims and goals are shaped by our history', and the importance of; efficiency, neuronal avalanches, branching parameters, critical regions and that a 'hierarchical predictive coding' model is possible.

          I'm not yet convinced that minimal dissipation itself is a precondition of learning and 'inference dynamics' and didn't feel you proved that. Do you not think it might be more a ubiquitous characteristic than a driver - in a similar way to 'sum over paths'? If 3 lifeguards head off to save a drowning girl 100m down the beach, one heads straight for her, one to the shore point opposite her position, and one at the right angle to allow for swimming slower than running; Of course the 3rd gets there with least energy, but I feel that underlying that may be a still greater and more useful truth and meaning.

          Also might the error/feedback mechanism be better described as iterative value judgement comparisons. Perhaps no 'right or wrong' just different outcomes, which we can't value till compared with previous runs.

          Say the first 'run' of consequences is imaginative drawn from input history. Say Do I want a PhD Y/N? gives an 'AIM' (Y1). We run a scenario to imagine it and implications. We then keep running it as more data comes in. Subsequent lower level/consequential Y/N decisions are the same, taking Y1 into the loop, an on hierarchically. If it turns out not to be as envisaged, or we win millions and want to be a playboy instead, we change the aim to N and form new ones.

          May it be that you're a little too enamored by the formula and suggest conclusions from those rather than the deeper meaning they're abstracted from.

          i.e. Might Lifeguard 3 have said 'how do I get there fastest' rather than '..by using least energy'? (or just done that due to it's successful outcome in past re-runs of the scenario).

          Lastly on 'Agency', which I see as a semantic 'shroud'. If all subsequent Y/N decisions in a cascade are simply consequential on the first Y/N decision, and the branches lead to a mechanical motor neuron or chemical response, all repeated in iterative loops, might the concept 'agency' not fade away with most of the mystery?

          All 'leading edge' questions I think and thank you for a brilliant essay leading to them. Top mark coming.

          Best

          Peter

            Dear Peter,

            Thank you for your kind comments. Much appreciated.

            "I also like most and agree with much, certainly with the addage that; 'our aims and goals are shaped by our history', and the importance of; efficiency, neuronal avalanches, branching parameters, critical regions and that a 'hierarchical predictive coding' model is possible."

            --> Agreed.

            "I'm not yet convinced that minimal dissipation itself is a precondition of learning and 'inference dynamics' and didn't feel you proved that. Do you not think it might be more a ubiquitous characteristic than a driver - in a similar way to 'sum over paths'?"

            --> I started by calling it the minimal dissipation hypothesis because of the optimization principles I was using to minimize the dissipation terms. This is an evolving idea, and after the feedback I have received I am wondering if I should instead talked about learning as a manifestation of dissipation-complexity trade offs and not just minimizing dissipation, since it is a little confusing when stated that way. I will have to think about this in more detail and will get back to you with an answer.

            "Also might the error/feedback mechanism be better described as iterative value judgement comparisons. Perhaps no 'right or wrong' just different outcomes, which we can't value till compared with previous runs."

            --> I had not thought about it that way but it sounds very interesting. Let me mull over it.

            "Lastly on 'Agency', which I see as a semantic 'shroud'. If all subsequent Y/N decisions in a cascade are simply consequential on the first Y/N decision, and the branches lead to a mechanical motor neuron or chemical response, all repeated in iterative loops, might the concept 'agency' not fade away with most of the mystery?"

            --> To a certain extent, I think it does but there is also significant role noise will play in affecting decisions, as well symmetry breaking at critical bifurcation points. I do think that 'agency' in a very a traditional sense is an illusion, something I would have talked about more in detail if I had the space. I would recommend reading Alan Kadin's submission "No ghost in the machine", in which he goes into detail about some of these ideas about agency being an 'illusion', that I did not have the chance to. It is a very interesting read.

            Thanks again for your comments. I will get back to you on some of these once I have thought about a detailed response.

            Natesh

            Hi, Ganesh, I respond here to a question that you asked in my page:

            "I will have to ponder over the idea of ascribing goals to any entropy reduction in a system. I am wondering if that is too narrow a definition. After all, a (conscious) observer should be capable of ascribing a system as performing a computation (and hence the goal of performing that computation,) even with no entropy change(?)"

            If the computation is invertible, then the output is equal to the input, except for a change of names. I believe that computations are interesting only when they are non-invertible. But perhaps I am missing something...

            I saw your essay as soon as it came out, I was impressed, but did not follow all the details. Today I gave it a second look, and I am still impressed, above all, because this strikes me as an original contribution, which I found only very rarely in this forum. Moreover, within the neural networks theory, I've had enough gradient-descent learning rules that come out of the blue, your proposal is so much physical. I confess I must still give it more proper thought - or perhaps, find the time to do the calculations myself - because I intend to take these ideas very seriously. I hope you publish this work as a paper soon, this essay contest does not seem to be the best environment. The work is probably a bit too technical given the contest rules, the length is too constrained, and the audience can be better targeted. I hope that you will consider presenting these ideas in the computational neuroscience audience. They may not have your same physical-computational background, but they will be surely interested in the conceptual result.

            Congratulations!

            inés.

              Hi Ines,

              Thanks for your kind comments and encouragement. Yes, I have had issues with a wide variety of gradient descent based learning rules, which is why I wanted something more physically grounded. I am working on a more formal paper as we speak, where I will have the space to discuss the details. This is a continuously evolving idea and after receiving some great feedback, I have realized I need to make clear some things and provide better explanation for others. I am an engineer by training and I intend to leverage the ideas here to build something, but I do intend to present some of these results to the (computational) neuroscience field, especially ones connected to the critical brain hypothesis.

              I will reply to your comments here, since I get notifications when you reply on my page.

              I agree that bijective identity like mappings which lose no information are not interesting. Computation has been defined by the characteristic loss of information from input to output. Let me clarify what I was thinking about. Consider a physical system containing 4 orthogonal distinguishable states A,B,C and D. The system involves to achieve the identity function and we are left with 4 orthogonal states and no entropy change. A (conscious) observer is capable of associating the logical state 0 with the final state A, and the logical state 1 to the final states B, C and D and claim that this physical evolution represents an AND gate if the initial 4 states corresponds to the inputs 00, 01, 10 and 11. I would refer to this as an unfaithful implementation of the abstract logical AND gate, but nonetheless the observer will claim that this physical evolution with zero entropy change has achieved the goal of being an AND gate. Hence while I agree that there is a relationship between goals and entropy reduction processes, I wonder if with the right observer, goals can be ascribed to a non-entropy reducing process. In fact, I have questioned if this ability to imbue goals to zero entropy change or entropy increasing processes, is a defining characteristic of conscious (and definitely intelligent) observers? After all, while we are able to perform input-output computing at will (without it requiring to be entropy reducing), our computer's outputs have computational value only because we as conscious (intelligent) observers interpret them as such. Please let me know if you have any thoughts for/against this.

              This idea of computational faithfulness of a physical implementation of logical mappings is discussed in detail here if you would like to know more.

              Anderson, Neal G. "On the physical implementation of logical transformations: Generalized L-machines." Theoretical Computer Science 411.48 (2010): 4179-4199.

              Neal is my PhD adviser and has been very influential in my thinking. He has been working recently on addressing the importance of observers in determining information as a physical quantity. This paper discusses that in detail and I think you might like it.

              Anderson, Neal G. "Information as a Physical Quantity." (2016)

              His paper does not state what a conscious observer would be but using the ideas presented in the essay, I have some initial thoughts on how to address that and define such observers in a physically grounded manner.

              I am looking forward to hearing your thoughts. Thanks.

              Cheers

              Natesh

              Hi, Ganesh, I am afraid I do not understand. "the observer will claim that this physical evolution with zero entropy change has achieved the goal of being an AND gate." Why do you say that the evolution has no entropy change, if the observer has made the association A -> 0, and B,C,D -> 1? This association is entropy-reducing, isn't it? I wait for your reply before elaborating more.

              Great to know you are on the way to publish! Your essay is new raw material, so the natural evolution is: get it published. As a neuroscientist, I was more surprised by the learning part of your essay, than by the criticality one, but mind you, I am not truly mainstream, so just take it as one opinion out of many. To me, the learning part is thought provoking, I have the impression that new paradigms, and new understanding may come out of that. The criticality claim seems to be everywhere, but I do not gain much from it, apart from classifying the process as critical. Anyway, surely I am missing something...

              best!

              ines.

              Hi Ines,

              Consider the evolution of the system with 4 initial distinguishable states A,B,C and D to 2 orthogonal states 0 and 1, with A evolving to 0, and B,C,D evolving to 1. There is a clearly a reduction in the physical entropy of this system and an observer with access to observe this evolution might decide to associate the AND operation with this evolution. We will call this a faithful physical realization of the AND operation in a system.

              Now consider the evolution of the system with 4 initial distinguishable states A,B,C and D to 4 orthogonal end states 0, 1,2 and 3 with a one-to-one evolution. There is no reduction in the physical entropy of this system and another observer with access might decide to associate the physical state 0 with the logical state '0' and the physical states 1,2 and 3 with the logical state '1'. Such an observer will associate the AND operation with this evolution (this is the principle of reversible computing where there is no minimum dissipation) and will not be wrong. The difference being that this is what we refer to as an unfaithful physical realization of the AND operation.

              I was trying to point out that it is possible to associate interesting computation with a system evolution in which there is no change in the physical entropy of the system. There might be reduction in the entropy of the observer, not sure there has to be though. Perhaps I am missing/misunderstood something? Are you saying that the reduction in the entropy of the observer (and not necessarily the system) is enough to imbue the system with a goal? I was contesting the idea that entropy reduction in the system alone is enough to achieve that.

              Yes, the equations that I have obtained are themselves well established in the Information Bottleneck method (used in clustering and machine learning) and my main contribution is tying it all together in a physical sense. I was pointing out the criticality part, since it seemed the idea though popular is still debated as there seemed to be no clear theoretical foundation for why the brain needs to be a critical system. Most criticality arguments are made from observing neuronal avalanches on EEGs and other experimental data, which can be explained away using critical behavior. And calculations of expected branching parameters is much lower than what is seen in the critical brain. Being able to view different cognition states as phase transition in input signal mapping can allow us to bypass these past hurdles I think. But I have to give it much more thought.

              And please call me Natesh. Ganesh is my father. We have a whole different first name, last name system.

              Cheers

              Natesh

              Hi Natesh,

              sorry about the names! I am a slave of rhymes, and tend to map together all what sounds similar. Actually it's even worse, I also cluster faces together. By all means, I must learn to represent information more injectively...

              Yes, sure, as I see it, it may well happen in the brain of the observer. There are many possible settings, which I discuss below. But in all settings, ascribing agency (as I understand it) requires an entropy-reducing mapping. If the mapping is not injective, it may still be interesting or useful for the observer, and he or she may still make a valuable acquisition in their life by learning the mapping. But I can hardly relate this operation with perceiving a system that seeks to achieve a "goal". For me, a goal is something that tends to be reached irrespective of factors that tend do interfere with its accomplishment. That is why I require the non-injectivity: the putative obstacles are a collection of input conditions, that are supposed to be overcome by the goal-seeking agent.

              Now for the variable settings.

              One option is: I am the observer, and I receive information of the behavior of some external system (say, a replicating DNA molecule outside me), and by defining the system under study in some specific way, I conclude that there is some goal-oriented behavior in the process.

              Another option is: I am given some information which I represent in my head, and I perform a certain computation with that information, for example, the AND gate you mention. The computation happens inside my head. But I also have observers inside my head, that monitor what other parts of my head are doing. For one such observer, what the other end of the brain is doing, acts as "external" (in all computations except for self-awareness, which is the last part of my essay). One such observer can assign agency (if it wants to!) to the computation, and conclude that the AND gate (inside my head!) "tends" to reduce the numbers 1, 2, 3 to the digit 0 (or whichever implementation we choose). A weird goal for an agent, but why not.

              All I am claiming is: goal-directed behavior does not exist without an observer that tends to see things in a very particular way: as non-injective mappings. I am not claiming that this is the only thing that can be learned by a plastic system (one-to-one mappings can also be learned). I am not claiming that the only thing that can be done with a non-injective mapping is to arrogate it with agency and goals. There are many more things happening in the world that may be good to learn, as well as in the brain of the observer. And there are many more computations to do, other arrogating agency. My only point is: if we see a goal (inside or outside us), then we have trained ourselves to interpret the system in the right manner for the goal to emerge. The goal is not intrinsic to nature, it is a way of being seen.

              Not much, just one tiny point. Or perhaps, just a definition. If you look through the essays, there are as many definitions of "goal", "agent" and "intention" as authors...

              > my main contribution is tying it all together in a physical sense

              Yes, and that is precisely why it is so great! And perhaps you are right, within your framework, what so far has been presented as a mere description of the critical brain, now can be seen as the natural consequence when certain physical conditions are assumed. I do appreciate that.

              Anyhow, time to rest! buenas noches!

              inés.

                Sorry, one more point: when an observer learns a one-to-one mapping, arrogating agency has not much sense, because there is no entropy loss in the mapping. The process of learning the mapping, though, can be arrogated with agency: the observer tends to learn. But here there is a meta-observer observing the first observer, right? It is the learning observer that may be arrogated with agency, not the lower-level system under study. And now yes, I go. Sorry for the long speeches!