Colleagues, For the last three decades I’ve been working—bit by bit—on a project to extend Peirce’s “Guess at the Riddle” and apply pragmatic methods to contemporary questions about the origins of physical order in the cosmos, the origins and evolution of life, and the origins and evolution of intelligent thought and action. Much of the time, it has been difficult for me to see the forest for the trees. In the last few years, however, I’ve made a concerted effort to tackle the first set of questions. An editor at Bloomsbury Academic has expressed interest in publishing the first volume as a monograph, so I'll be focused on turning the current sow's ear of a working draft into something more finished. A while back, Terry Moore and I, with the help of others, attempted to develop a framework for collaborative research—both for (a) the transcription of Peirce’s manuscripts and scholarship and (b) for the application of pragmatic methods to questions in metaphysics and the various sciences. Several members of the list wrote letters of support as a few of us wrote applications for grant funding. After some years of trying and a couple of decisions by the NSF that nearly went our way, we found it necessary to put the grant writing to the side. At the time, Doug Anderson provided some advice, which I now want to put to better effect. He suggested that, if the work was worth doing, then we ought to dig into the project and worry about the funding later. In the spirit of the SPIN and APERI projects, I’ve developed the following framework on the first of Peirce’s questions in “A Guess at the Riddle”: how did physical order first grow in the cosmos? With that much said, here is a very short overview of the research project—together with an offer to share working drafts with those who might want to work collaboratively on the questions.
1. Aims: The Origins of Order in the Cosmos project is my attempt to tell a single, continuous story about how the universe became physically ordered—how law, time, space, and stable objects emerged from a potential field of extreme randomness and indeterminacy. The project is not written as an argument for or against any one orthodox cosmology. Rather, it is written as an invitation to inquiry: a structured attempt to make competing explanations comparable, to expose hidden assumptions, and to build models that can be criticized, repaired, and improved. I want colleagues and students—including philosophers, physicists, mathematicians, engineers, and interested lay people—to treat these drafts as working research instruments: something you can push against, test, and use to generate new questions. 2. Methods and strategies: The trilogy is built around Peircean method, especially the cycle of inquiry involving iterative patterns of surprising observation and abductive, deductive, and inductive inference; the pragmatic maxim; and the principle of continuity. The methods are used to clarify and further develop three comparative families of hypotheses. H₁ treats fundamental physical laws as fixed and primordial; the early cosmos is a parameterized stage-play governed by timeless equations. H₃ treats early history as selection and quenching: many possibilities exist, but only certain channels survive, leaving fossils—suppressed remnants, noise floors, and relic constraints. H₂—the Peircean family I am especially keen to explore and develop—treats laws as the result of the growth of ordered habits: regularities strengthen as degrees of freedom reduce, as coarse-graining stabilizes, and as the very meaning of what is “measurable” sharpens. To sharpen the hypotheses in each family, the books insist on explicit interfaces, “glue rules,” and conceivable tests and predicted consequences that can shift comparative weights rather than merely decorate a narrative. 3. Formal toolkit: We question the presupposition that early regimes are naturally point-like as rational values or fully metric. As such, we develop a modeling toolbox designed to respect structural uncertainty and changing “license conditions” for concepts. Phase and parameter space models are scaffolded with hypercomplex (Cayley–Dickson) and other composition algebras as a way to represent evolving degrees of freedom, compositional stability, and stabilization across epochs of cosmological evolution. We use surreal (non-Archimedean) and interval-valued bookkeeping when the regime does not justify rational-number determinacy, and to permit the natural inclusion of values for our variables that are infinitesimals and infinities. And we use multiple logics to match multiple regimes: probabilistic logic for randomness and inference; constructive logic when existence claims must be operationally witnessed; Peirce’s Gamma existential graphs for higher-order/modal structure; and categorical logic to build disciplined bridges between compositional algebras and between these logical systems and the more deterministic language of first-order theory. The ambition is to make our reasoning about physics more faithful to what the different regimes reasonably allow. 4. Volume I: Origins of Order—Evolution of Law, Time and Space lays down the backbone: an “interface-first” cosmology in which topology, projective comparability, and metric structure are treated as rungs on a ladder rather than as givens. The core question is deceptively simple: How could a world that begins as high-dimensional, highly random potentiality ever become a world where stable quantities, stable geometry, and stable processes are possible? Here we introduce a strategy of non-retrojection—don’t talk as if clocks, particles, or equilibrium thermodynamics were primitive where they are not licensed—and we begin to articulate what would count as a “durable carrier”—something that persists under coarse-graining and can transport structure forward. Volume I is where the comparative posture leads the way: every claim is framed against H₁ and H₃, with H₂ defended by continuity and by its ability to reduce errors while still generating testable proxy profiles. In practice, Volume I builds toy models of order-growth: we start with toy models of weighted dice and urns, and work our way to variance collapse and attractor-like regularities; stabilization under repeated coarse-graining; and the emergence of ordered conditions from chaotic regimes that precede full metric time. The hypercomplex and surreal tools enter here as modeling strategies: they let us represent pre-metric regimes without pretending we already have real-number metrical geometry, and they allow us to treat “dimension” as something that can be effective, local, and historically stabilized rather than eternally fixed. The goal is to explain how the laws expressed as Einstein’s field equations (EFEs) might, under H₂ and H₃, have evolved in the first several epochs of cosmological history. The payoff is a framework that can be carried forward: a way of saying exactly what changes at each interface, what invariants are preserved, and what new operations are meaningful. This is the conceptual platform Volume II then uses to explore how the laws of quantum field theory and the Standard Model might have co-evolved with EFEs. 5. Volume II: First Second of the Cosmos—Grand Metamorphosis takes the ladder and runs it through the most conceptually volatile terrain: the early epochs usually narrated as “the first second.” Here the main claim is not that the standard ΛCDM story is wrong—it’s that its presuppositions about the nature of “fixed” fundamental laws often outrun the observational supports. We reframe the origin talk as a Grand Metamorphosis: a sequence of regime interfaces in which degrees of freedom reduce, effective descriptions become legitimate, and particle/field/vacuum language becomes progressively more stable. Renormalization and effective field theory become central topological “glue rules” in H₂: repeated stabilization under coarse-graining is treated as the physical analogue of habit-formation. Through inflation and reheating to confinement and hadronization epochs, we keep asking: what is durable, what is evolving, what remains vague and interval-valued, and what proxy consequences constrain the story? Two landmarks organize the territory explored in the latter half of Volume II. First, matter asymmetry: the universe’s net matter is an important explanandum, so any plausible family of hypotheses must meet the minimal structural conditions. Second, confinement/hadronization is where “durable carriers” (e.g., protons and neutrons) become legitimate as stable letters in the material alphabet, making later composition of durable particles—nuclei and atoms—possible. The philosophical point follows from a demand for rigor: what is often called “emergence” of such particles is not magic if the interface operations and invariants are declared; but it is magic if one simply retrojects late-time ontology backward. 6. Volume III: Cosmological Evolution: Laws as Nested Modalities (currently in the early drafting stage) aims to extend the same method beyond the “first second” into the long arc where physical and chemical order becomes richly layered: nucleosynthesis and the periodic table; recombination and the CMB as a memory ledger; stars as cyclic engines; galaxies as meso-scale stabilizations; black holes as interface stress tests; and vacuum energy and dark matter as an abductive frontier. The goal is to explain the evolution of the physical and chemical laws we take to be fundamental—starting from the work done on EFEs and QFT in Volumes I and II. The third volume is especially well-suited to comparing the strengths and weaknesses of H₃ and H₂: selection, quenching, and fossil constraints become vivid across structure formation, feedback, and the survival of specific channels under coarse-graining. The guiding idea is that “law” evolves from ordered habits as nested systems of modalities—possibility, actuality, necessity—implemented as operational postures for the development of each family of hypotheses that become sharper as carriers stabilize and as inference pipelines become robust. I’m eager for readers to engage these drafts as collaborators: to challenge the interfaces, sharpen the proxy suites, propose better toy models, and help evaluate where H₂ genuinely earns explanatory continuity—and where H₁ or H₃ may, in particular domains, deserve the stronger score. If you have questions about what collaborative inquiry concerning these questions might look like, let me know. I’d be happy to talk on or off list. I'd be happy to have suggestions for improvement from those interested in reading the introduction or a chapter or two. If there is a small group of colleagues who are interested, I'd be willing to do a series of discussions as Zoom meetings, or something similar. Yours, Jeff ________________________________ From: [email protected] <[email protected]> on behalf of Gary Richmond <[email protected]> Sent: Friday, January 9, 2026 8:39 PM To: Peirce List <[email protected]>; Gary Fuhrman <[email protected]>; Jon Alan Schmidt <[email protected]> Subject: Re: [PEIRCE-L] AI safety and semeiotic, was, Surdity, Feeling, and Consciousness, was, Truth and dyadic consciousnessg Jon, Gary F, List, For me, this has been a most valuable discussion. While I had earlier come to the conclusion that Artificial Intelligence is not intelligent, the comments and quotes included in this exchange strongly suggest to me that it will never be, can never be because it misses the necessary features that characterize intelligence. As Jon concisely put it, "If genuine semiosis is truly continuous. . . then a digital computer, no matter how sophisticated, can only ever simulate it--just as the real numbers do not constitute a true continuum, but usefully approximate one for most practical purposes. After all, whenever we humans break up our own reasoning (arguments) into discrete steps--namely, "definitely formulated premisses" and conclusions (argumentations. . .) --we are always doing so artificially and retrospectively, after the real and continuous inferential process has already run its course." Yet, to the extent that AI may prove dangerous, I continue to think that it behooves us -- to the extent to which it is possible -- to move AI systems toward Peircean theoretical rhetoric within the communities of inquiry in which each of us may be engaged. Nevertheless, Gary F's warning shouldn't be ignored: "If present experience is any guide. . . , clearly AI systems are going to align with the values of the billionaire owners of those systems (and to a lesser extent the programmers who work for them), which is certainly no cause for optimism. Best, Gary R On Fri, Jan 9, 2026 at 12:30 PM Jon Alan Schmidt <[email protected]<mailto:[email protected]>> wrote: Gary R., Gary F., List: GF: Having read the fine print at the end of the paper, it’s clear that Manheim’s article was co-written with several LLM chatbots, and I wonder if some of the optimism comes from them (or some of them) rather than from the human side. I noticed that, too, with the result that it is more difficult for me to take the article seriously. In a 1999 paper<https://www.jstor.org/stable/40320779>, "Peirce's Inkstand as an External Embodiment of Mind," Peter Skagestad quotes CP 7.366 (1902) and points out that Peirce "is not only making the point that without ink he would not be able to express his thoughts, but rather the point that thoughts come to him in and through the act of writing, so that having writing implements is a condition for having certain thoughts" (p. 551). I know firsthand that the act of writing facilitates my own thinking, and I cannot help wondering if Manheim's choice to delegate so much of the effort for drafting his article to LLMs precluded him from carefully thinking through everything that it ended up saying. GF: Successful "alignment" is supposed to be between a super "intelligent" system and human values. One problem with this is that human values vary widely between different groups of humans, so which values is future AI supposed to align with? If an artificial system were really intelligent, then it seems to me that it would be capable of choosing its own values instead of having a particular set of human values imposed on it. In a 2013 paper<https://www.academia.edu/9898586/C_S_Peirce_and_Artificial_Intelligence_Historical_Heritage_and_New_Theoretical_Stakes>, "C. S. Peirce and Artificial Intelligence: Historical Heritage and (New) Theoretical Stakes," Pierre Steiner observes that according to Peirce ... PS: [H]uman reasoning is notably special (and, in that sense only, genuine) in virtue of the high degrees of self-control and self-correctiveness it can exercise on conduct: control on control, self-criticism on control, and control on control on the basis of (revisable and self-endorsed) norms and principles and, ultimately, aesthetic and moral ideals. ... The fact that reasoning human agents have purposes is crucial here: it is on the basis of purposes that they are ready to endorse, change or criticize specific methods of reasoning (inductive, formal, empirical, ...), but also to revise and reject previous purposes. Contrary to machines, humans do not only have specified purposes. Their purposes are often vague and general. In other passages, Peirce suggests that this ability for (higher-order and purposive) self-control is closely related to the fact that human agents are living, and especially growing, systems. (p. 272) I suspect that much of the worry about "AI safety/alignment," as reflected by common fictional storylines in popular culture, is a tacit admission of this. What would prevent a sufficiently intelligent artificial system, provided that such a thing is even possible, from rejecting human values and instead adopting norms, principles, ideals, and purposes that we would find objectionable, perhaps even abhorrent? More on the living/growing aspect of intelligent systems below. GF: LLMs have to be artificially supplied with a giant database of thousands or millions of symbolic texts, and it takes them months or years to build up the level of language competence that a human toddler has; and even then is is doubtful whether they understand any of it. As with intelligence, I am unconvinced that it is accurate to ascribe "language competence" to LLMs, especially given the well-founded doubt about "whether they understand any of it." John Searle's famous "Chinese room" thought experiment seems relevant here, e.g., as discussed by John Fetzer in his online Commens Encyclopedia article<http://www.commens.org/encyclopedia/article/fetzer-james-peirce-and-philosophy-artificial-intelligence>, "Peirce and the Philosophy of Artificial Intelligence." Again, in my view, LLMs do not actually use natural languages, they only simulate using natural languages. GF: I can’t help thinking that all this has a bearing on the perennial question of whether semiosis requires life or not. In light of the following passage, Peirce's answer is evidently that genuine semiosis requires life, given that it requires genuine triadic relations; but he also seems to define "life" in this context much more broadly than what we associate with the special science of biology. CSP: For forty years, that is, since the beginning of the year 1867, I have been constantly on the alert to find a genuine triadic relation--that is, one that does not consist in a mere collocation of dyadic relations, or the negative of such, etc. (I prefer not to attempt a perfectly definite definition)--which is not either an intellectual relation or a relation concerned with the less comprehensible phenomena of life. I have not met with one which could not reasonably be supposed to belong to one or other of these two classes. ... In short, the problem of how genuine triadic relationships first arose in the world is a better, because more definite, formulation of the problem of how life first came about; and no explanation has ever been offered except that of pure chance, which we must suspect to be no explanation, owing to the suspicion that pure chance may itself be a vital phenomenon. In that case, life in the physiological sense would be due to life in the metaphysical sense. (CP 6.322, 1907) Elsewhere, Peirce maintains<https://list.iu.edu/sympa/arc/peirce-l/2025-11/msg00044.html> that a continuum is defined by a genuine triadic relation, so his remarks here are consistent with my sense that what fundamentally precludes digital computers from ever being truly intelligent is the discreteness of their operations. As I said before, LLMs are surely quasi-minds whose individual determinations are dynamical interpretants of sign tokens; but those correlates are involved in degenerate triadic relations, which are reducible to their constituent dyadic relations. In my view<https://list.iu.edu/sympa/arc/peirce-l/2025-11/msg00056.html>, the genuine triadic relation involves the final interpretant and the sign itself, which is general<https://list.iu.edu/sympa/arc/peirce-l/2025-11/msg00019.html> and therefore a continuum of potential tokens that is not reducible to the actual tokens that individually embody it. Regards, Jon Alan Schmidt - Olathe, Kansas, USA Structural Engineer, Synechist Philosopher, Lutheran Christian www.LinkedIn.com/in/JonAlanSchmidt<http://www.LinkedIn.com/in/JonAlanSchmidt> / twitter.com/JonAlanSchmidt<http://twitter.com/JonAlanSchmidt> On Thu, Jan 8, 2026 at 11:17 AM <[email protected]<mailto:[email protected]>> wrote: List, I’d like to add a few comments to those already posted by Jon and Gary R about the Manheim paper — difficult as it is to focus on these issues given the awareness of what’s happening in Minnesota, Venezuela, Washington etc. (I may come back to that later.) Except for the odd usage of the term “interpretant” which Jon has already mentioned, I think Manheim’s simplified account of Peircean semiotics is cogent enough. But his paper seems to get increasingly muddled in the latter half of it. For instance, the “optimism” about future AI that Jon sees in it seems quite equivocal to me. Having read the fine print at the end of the paper, it’s clear that Manheim’s article was co-written with several LLM chatbots, and I wonder if some of the optimism comes from them (or some of them) rather than from the human side. Also, the paper makes a distinction between AI safety and the alignment problem, but then seems to gloss over the differences. Succesful “alignment” is supposed to be between a super”intelligent” system and human values. One problem with this is that human values vary widely between different groups of humans, so which values is future AI supposed to align with? If present experience is any guide (and it better be!), clearly AI systems are going to align with the values of the billionaire owners of those systems (and to a lesser extent the programmers who work for them), which is certainly no cause for optimism. I think Stanislas Dehaene’s 2020 book How We Learn deals with the deeper context of these issues better than Manheim and his chatbot co-authors. Its subtitle is Why Brains Learn Better Than Any Machine … for Now. Reducing this to simplest terms, it’s because brains learn from experience — “the total cognitive result of living,” as Peirce said* — and they do so by a scientific method (an algorithm, as Dehaene calls it) which is part of the genetic inheritance supplied by biological evolution. An absolute requirement of this method is what Peirce called abduction (or retroduction). For instance, human babies begin learning the language they are exposed to from birth, or even before — syntax, semantics, pragmatics and all — almost entirely without instruction, by a trial-and-error method. It enables them to pick up and remember the meaning and use of a new word from one or two encounters with it. LLMs have to be artificially supplied with a giant database of thousands or millions of symbolic texts, and it takes them months or years to build up the level of language competence that a human toddler has; and even then is is doubtful whether they understand any of it. LLM learning is entirely bottom-up and therefore works much slower than the holistic learning-from-experience of a living bodymind, even though the processing speed of a computer is much faster than a brain’s. (That’s why it is so much more energy-hungry than brains are.) I can’t help thinking that all this has a bearing on the perennial question of whether semiosis requires life or not. I can’t help thinking that experience requires life, and that is what a “scientific intelligence” has to learn from — including whatever values it learns. It has to be embodied, and providing it with sensors to gather data from the external world is not enough if that embodiment does not have a whole world within it in continuous dialogue with the world without — an internal model, as I (and Dehaene and others) call it. But I’d better stop there, as this is getting too long already. *The context of the Peirce quote above is here: Turning Signs 7: Experience and Experiment<https://gnusystems.ca/TS/xpt.htm#lgcsmtc> Love, gary f Coming from the ancestral lands of the Anishinaabeg From: Gary Richmond <[email protected]<mailto:[email protected]>> Sent: 8-Jan-26 04:03 To: Peirce List <[email protected]<mailto:[email protected]>>; Gary Fuhrman <[email protected]<mailto:[email protected]>>; Jon Alan Schmidt <[email protected]<mailto:[email protected]>> Subject: AI safety and semeiotic, was, Surdity, Feeling, and Consciousness, was, Truth and dyadic consciousnessg Gary F, Jon, List, In the discussion of Manheim's paper I think it's important to remember that his concern is primarily with AI safety. Anything that would contribute to that safely I would wholeheartedly support. In my view, Peircean semeiotic might prove to be of some value in the matter, but perhaps not exactly in the way that Manheim is thinking of it. Manheim remarks that his paper does not try to settle philosophical questions about whether LLMs genuinely reason or only simulate thought, and that resolving those debates isn’t necessary for building safer general AI. I won't take up that claim now, but suffice it to say that I don't fully agree with it, especially as I continue to agree with your argument, Jon, that AI is not 'intelligent'. Can it every be? What Mannheim claims is necessary re: AI safety is to move AI systems toward Peircean semiosis in the sense of their becoming 'participants' in interpretive processes. He holds that this is achievable through engineering and 'capability' advances rather than "philosophical breakthroughs;" though he also says that those advances remain insufficient on their own for safety. Remaining "insufficient on its own for full safety" sounds to me somewhat self-contradictory. But I think that more importantly, he is saying that if there are things -- including Peircean 'things' -- that we can begin to do now in consideration of AI safety, then we ought to consider them, do them! Manheim claims that AI safety depends on deliberately designing systems for what he calls 'grounded meaning', 'persistence across interactions' and 'shared semiotic communities' rather than 'isolated agents'. I would tend to strongly agree. In addition, AI safety requires goals that are explicitly defined but also open to ongoing discussion rather than quasi-emerging implicitly from methods like Reinforcement Learning from Human Feedback (RLHF) . Manheim seems to be saying that companies developing advanced AI should take steps in system design and goal setting -- including those mentioned above -- if safety is taken seriously. The choice, he says, is between ignoring the implications of Peircean semeiotic and continuing merely to refine current systems despite their deficiency vis-a-vis safety; OR to embrace Peircean semiosis (whatever that means) and intentionally build AI as genuine 'semiotic partners'. But,I haven't a clear notion of what he means by 'semeiotic partners', nor a method for implementing whatever he does have in mind. I think Manheim off-handedly and rather summarily unfortunately dismisses RLHF -- which is, falsely he argues, claimed as a way of 'aligning' models with human values. From what I've read it has not yet really been developed much in that direction. As far as I can tell, and this may relate to the reason why Manheim seems to reject RLHF in toto, it appears to be more a 'reward proxy' trained on human rankings of outputs which are then fed back through some kind of loop to strongly influence future responses. Human judgment enters only in the 'training'', not as something that a complex system can engage with and debate with or, possibly, revise understandings over time. In Manheim's view, RLHF is not 'bridging' human goals and machine behavior (as it claims) but merely facilitating machine outputs to fit learned preferences. Still, whatever else RLHF is doing that is geared specifically toward AI safety, it would likely be augmented by an understanding of Peircean cenoscopic science including semeiotic. I would suggest that the semeiotic ideas that it might most benefit from occur in the third branch of Logic as Semeiotic, namely methodology (methodeutic) , perhaps in the present context representing, almost to a T, Peirce's alternative title, speculative rhetoric. It's in this branch of semeiotic that pragmatism (pragmaticism) is analyzed. There is of course much more to be said on methodology and theoretical rhetoric. For now, I would tweak Manheim's idea a bit and would suggest that we might try to move AI systems toward Peircean semeiotic rhetoric within communities of inquiry. Best, Gary R _ _ _ _ _ _ _ _ _ _ ► PEIRCE-L subscribers: Click on "Reply List" or "Reply All" to REPLY ON PEIRCE-L to this message. PEIRCE-L posts should go to [email protected]<mailto:[email protected]> . ► <a href="mailto:[email protected]<mailto:[email protected]>">UNSUBSCRIBE FROM PEIRCE-L</a> . But, if your subscribed email account is not your default email account, then go to https://list.iu.edu/sympa/signoff/peirce-l . ► PEIRCE-L is owned by THE PEIRCE GROUP; moderated by Gary Richmond; and co-managed by him and Ben Udell.
_ _ _ _ _ _ _ _ _ _ ► PEIRCE-L subscribers: Click on "Reply List" or "Reply All" to REPLY ON PEIRCE-L to this message. PEIRCE-L posts should go to [email protected] . ► <a href="mailto:[email protected]";>UNSUBSCRIBE FROM PEIRCE-L</a> . But, if your subscribed email account is not your default email account, then go to https://list.iu.edu/sympa/signoff/peirce-l . ► PEIRCE-L is owned by THE PEIRCE GROUP; moderated by Gary Richmond; and co-managed by him and Ben Udell.
