Dear Hector, Personally I agree that algorithmic information theory and the related concepts of randomness and Bennett’s logical depth are the best way to go. I have used them in many of my own works. When I met Chaitin a few years back we talked mostly about how unrewarding and controversial our work on information theory has been. When I did an article on information for the Stanford Encyclopaedia of Philosophy it was rejected in part becausewe of fierce divisions between supporters of Chaitin and supporters of Kolmogorov! The stuff I put in on Spencer Brown was criticized because “he was some sort of Buddhist, wasn’t he?” It sounds like you have run into similar problems.
That is why I suggested a realignment of what this group should be aiming for. I think the end result would justify our thinking, and your work certainly furthers it. But it does need to be worked out. Personally, I don’t have the patience for it. John Collier Emeritus Professor and Senior Research Associate Philosophy, University of KwaZulu-Natal http://web.ncf.ca/collier From: Hector Zenil [mailto:hzen...@gmail.com] Sent: Thursday, 30 March 2017 10:48 AM To: John Collier <colli...@ukzn.ac.za>; fis <fis@listas.unizar.es> Subject: Re: [Fis] Causation is transfer of information Dear John et al. Some comments below: On Thu, Mar 30, 2017 at 9:47 AM, John Collier <colli...@ukzn.ac.za<mailto:colli...@ukzn.ac.za>> wrote: I think we should try to categorize and relate information concepts rather than trying to decide which is the “right one”. I have tried to do this by looking at various uses of information in science, and argue that the main uses show progressive containment: Kinds of Information in Scientific Use<http://www.triple-c.at/index.php/tripleC/article/view/278/269>. 2011. cognition, communication, co-operation. Vol 9, No 2<http://www.triple-c.at/index.php/tripleC/issue/view/22> There are various mathematical formulations of information as well, and I think the same strategy is required here. Sometimes they are equivalent, sometimes close to equivalent, and sometimes quite different in form and motivation. Work on the foundations of information science needs to make these relations clear. A few years back (more than a decade) a mathematician on a list (newsgroup) argued that there were dozens of different mathematical definitions of information. I thought this was a bit excessive, and argued with him about convergences, but he was right that they were mathematically different. We need to look at information theory structures and their models to see where they are equivalent and where (and if) they overlap. Different mathematical forms can have models in common, sometimes all of them. The agreement among professional mathematicians is that the correct definition of randomness as opposed to information is the Martin Loef definition for the infinite asymptotic case, and Kolmogorov-Chaitin for the finite case. Algorithmic probability (Solomonoff, Levin) is the theory of optimal induction and thus provides a formal universal meaning to the value of information. Then the general agreement is also that Bennett's logical depth separates the concept of randomness from information structure. No much controversy in in there on the nature of classical information as algorithmic information. Notice that 'algorithmic information' is not just one more definiton of information, IS the definition of mathematical information (again, by way of defining algorithmic randomness). So adding 'algorithmic' to information is not to talk about a special case that can then be ignored by philosophy of information. All the above builds on (and well beyond) Shannon Entropy, which is not even very properly discussed in philosophy of information beyond its most basic definition (we rarely, if ever, see discussions around mutual information, conditional information, Judea Pearl's interventionist approach and counterfactuals, etc), let alone anything of the more advanced areas mentioned above, or a discussion on the now well established area of quantum information that is also comletely ignored. This is like trying to do philosophy of cosmology discussing Gamow and Hubble but ignoring relativity, or trying to do philosophy of language today discussing Locke and Hume but not Chomsky, or doing philosophy of mind discussing the findings of Ramon y Cajal and claiming that his theories are not enough to explain the brain. It is some sort of strawman fallacy contructing an opponent living in the 40s to claim in 2017 that it fails at explaining everything about information. Shannon Entropy is a counting-symbol function, with interesting applications, Shannon himself knew it. It makes no sense to expect a counting-symbol function to tell anything interesting about information after 60 years. I refer again to my Entropy deceiving paper: https://arxiv.org/abs/1608.05972 I do not blame philosophers on this one, phycisists seem to assign Shannon Entropy some mystical power, this is why I wrote a paper proving how it cannot be used in graph complexity as some phycists have recently suggested (e.g. Bianconi via Barabasi). But this is the kind of discussion that we should have having, telling phycisists not to go back to the 40s when it comes to characterizing new objects. If Shannon Entropy fails at characterizing sequences it will not work for other objects (graphs!). I think the field of philosophy of information cannot get serious until serious discussion on topics above starts to take place. Right now the field is small and carried out by a few mathematicians and phycisists. Philosophers are left behind because they are choosing to ignore all the theory developed in the last 50 to 60 years. I hope this is taken constructively. I think we philosophers need to step up, if we are not be leading the discussion at least we should not be 50 or 60 years behind. I have tried to to close that gap but usually I also get convenently ignored =) I have argued that information originates in symmetry breaking (making a difference, if you like, but I see it as a dynamic process rather than merely as a representation) Information Originates in Symmetry Breaking<http://web.ncf.ca/collier/papers/infsym.pdf> (Symmetry 1996). Very nice paper. I agree on symmetry breaking, I have similar ideas: https://arxiv.org/abs/1210.1572 (published in the journal of Natural Computing) On how symmetric rules can produce assymetric information. Best, Hector Zenil http://www.hectorzenil.net/ I adopt what I call dynamical realism, that anything that is real is either dynamical or interpretable in dynamical terms. Not everyone will agree. John Collier Emeritus Professor and Senior Research Associate Philosophy, University of KwaZulu-Natal http://web.ncf.ca/collier From: Guy A Hoelzer [mailto:hoel...@unr.edu<mailto:hoel...@unr.edu>] Sent: Wednesday, 29 March 2017 1:44 AM To: Sungchul Ji <s...@pharmacy.rutgers.edu<mailto:s...@pharmacy.rutgers.edu>>; Terry Deacon <dea...@berkeley.edu<mailto:dea...@berkeley.edu>>; John Collier <colli...@ukzn.ac.za<mailto:colli...@ukzn.ac.za>>; Foundations of Information Science Information Science <fis@listas.unizar.es<mailto:fis@listas.unizar.es>> Subject: Re: [Fis] Causation is transfer of information Greetings all, It seems that the indigestion from competing definitions of ‘information’ is hard to resolve, and I agree with Terry and others that a broad definition is preferable. I also think it is not a problem to allow multiple definitions that can be operationally adopted in appropriate contexts. In some respects, apparently competing definitions are actually reinforcing. For example, I prefer to use ‘information’ to describe any difference (a distinction or contrast), and it is also true that a subset of all differences are ones that ‘make a difference’ to an observer. When we restrict ‘information’ to differences that make a difference it becomes inherently subjective. That is certainly not a problem if you are interested in subjectivity, but it would eliminate the rationality of studying objective ‘information’, which I think holds great promise for understanding dynamical systems. I don’t see any conflict between ‘information’ as negentropy and ‘information’ as a basis for decision making. On the other hand, semantics and semiotics involve the attachment of meaning to information, which strikes me as a separate and complementary idea. Therefore, I think it is important to sustain this distinction explicitly in what we write. Maybe there is a context in which ‘information’ and ‘meaning’ are so intertwined that they cannot be isolated, but I can’t think of one. I’m sure there are plenty of contexts in which the important thing is ‘meaning’, and where the (more general, IMHO) term ‘information’ is used instead. I think it is fair to say that you can have information without meaning, but you can’t have meaning without information. Can anybody think of a way in which it might be misleading if this distinction was generally accepted? Regards, Guy On Mar 28, 2017, at 3:26 PM, Sungchul Ji <s...@pharmacy.rutgers.edu<mailto:s...@pharmacy.rutgers.edu>> wrote: Hi Fisers, I agree with Terry that "information" has three irreducible aspects --- amount, meaning, and value. These somehow may be related to another triadic relation called the ITR as depicted below, although I don't know the exact rule of mapping between the two triads. Perhaps, 'amount' = f, 'meaning' = g, and 'value' = h ? . f g Object ---------------> Sign --------------> Interpretant | ^ | | | | | | |_________________________________| h Figure 1. The Irreducible Triadic Relation (ITR) of seimosis (also called sign process or communication) first clearly articulated by Peirce to the best of my knowledge. Warning: Peirce often replaces Sign with Representamen and represents the whole triad, i.e., Figure 1 itself (although he did not use such a figure in his writings) as the Sign. Not distinguishing between these two very different uses of the same word "Sign" can lead to semiotic confusions. The three processes are defined as follows: f = sign production, g = sign interpretation, h = information flow (other ways of labeling the arrows are not excluded). Each process or arrow reads "determines", "leads", "is presupposed by", etc., and the three arrows constitute a commutative triangle of category theory, i.e., f x g = h, meaning f followed by g ledes to the same result as h. I started using the so-called ITR template, Figure 1, about 5 years ago, and the main reason I am bringing it up here is to ask your critical opinion on my suggestion published in 2012 (Molecular Theory of the Living Cell: Concepts, Molecular Mechanisms, and Biomedical Applications, Springer New York, p ~100 ?) that there are two kinds of causality -- (i) the energy-dependent causality (identified with Processes f and g in Figure 1) and (ii) the information (and hence code)-dependent causality (identified with Process h). For convenience, I coined the term 'codality' to refer to the latter to contrast it with the traditional term causality. I wonder if we can view John's idea of the relation between 'information' and 'cause' as being an alternative way of expressing the same ideas as the "energy-dependent causality" or the "codality" defined in Figure 1. All the best. Sung ________________________________ From: Fis <fis-boun...@listas.unizar.es<mailto:fis-boun...@listas.unizar.es>> on behalf of Terrence W. DEACON <dea...@berkeley.edu<mailto:dea...@berkeley.edu>> Sent: Tuesday, March 28, 2017 4:23:14 PM To: John Collier Cc: fis Subject: Re: [Fis] Causation is transfer of information Corrected typos (in case the intrinsic redundancy didn't compensate for these minor corruptions of the text): information-beqaring medium = information-bearing medium appliction = application conceptiont = conception On Tue, Mar 28, 2017 at 10:14 PM, Terrence W. DEACON <dea...@berkeley.edu<mailto:dea...@berkeley.edu>> wrote: Dear FIS colleagues, I agree with John Collier that we should not assume to restrict the concept of information to only one subset of its potential applications. But to work with this breadth of usage we need to recognize that 'information' can refer to intrinsic statistical properties of a physical medium, extrinsic referential properties of that medium (i.e. content), and the significance or use value of that content, depending on the context. A problem arises when we demand that only one of these uses should be given legitimacy. As I have repeatedly suggested on this listserve, it will be a source of constant useless argument to make the assertion that someone is wrong in their understanding of information if they use it in one of these non-formal ways. But to fail to mark which conception of information is being considered, or worse, to use equivocal conceptions of the term in the same argument, will ultimately undermine our efforts to understand one another and develop a complete general theory of information. This nominalization of 'inform' has been in use for hundreds of years in legal and literary contexts, in all of these variant forms. But there has been a slowly increasing tendency to use it to refer to the information-beqaring medium itself, in substantial terms. This reached its greatest extreme with the restricted technical usage formalized by Claude Shannon. Remember, however, that this was only introduced a little over a half century ago. When one of his mentors (Hartley) initially introduced a logarithmic measure of signal capacity he called it 'intelligence' — as in the gathering of intelligence by a spy organization. So had Shannon chose to stay with that usage the confusions could have been worse (think about how confusing it would have been to talk about the entropy of intelligence). Even so, Shannon himself was to later caution against assuming that his use of the term 'information' applied beyond its technical domain. So despite the precision and breadth of appliction that was achieved by setting aside the extrinsic relational features that characterize the more colloquial uses of the term, this does not mean that these other uses are in some sense non-scientific. And I am not alone in the belief that these non-intrinsic properties can also (eventually) be strictly formalized and thereby contribute insights to such technical fields as molecular biology and cognitive neuroscience. As a result I think that it is legitimate to argue that information (in the referential sense) is only in use among living forms, that an alert signal sent by the computer in an automobile engine is information (in both senses, depending on whether we include a human interpreter in the loop), or that information (in the intrinsic sense of a medium property) is lost within a black hole or that it can be used to provide a more precise conceptiont of physical cause (as in Collier's sense). These different uses aren't unrelated to each other. They are just asymmetrically dependent on one another, such that medium-intrinsic properties can be investigated without considering referential properties, but not vice versa. It's time we move beyond terminological chauvenism so that we can further our dialogue about the entire domain in which the concept of information is important. To succeed at this, we only need to be clear about which conception of information we are using in any given context. — Terry On Tue, Mar 28, 2017 at 8:32 PM, John Collier <colli...@ukzn.ac.za<mailto:colli...@ukzn.ac.za>> wrote: I wrote a paper some time ago arguing that causal processes are the transfer of information. Therefore I think that physical processes can and do convey information. Cause can be dispensed with. * There is a copy at Causation is the Transfer of Information<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fweb.ncf.ca%2Fcollier%2Fpapers%2Fcausinf.pdf&data=01%7C01%7Choelzer%40unr.edu%7C2cfdcd34699449bb000c08d47629a4c0%7C523b4bfc0ebd4c03b2b96f6a17fd31d8%7C1&sdata=y5LYga7SnUhkgN8ZBtkSTW6%2F0PqRFrwvXXO%2FvMYdl%2Fc%3D&reserved=0> In Howard Sankey (ed) Causation, Natural Laws and Explanation (Dordrecht: Kluwer, 1999) Information is a very powerful concept. It is a shame to restrict oneself to only a part of its possible applications. John Collier Emeritus Professor and Senior Research Associate Philosophy, University of KwaZulu-Natal http://web.ncf.ca/collier<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fweb.ncf.ca%2Fcollier&data=01%7C01%7Choelzer%40unr.edu%7C2cfdcd34699449bb000c08d47629a4c0%7C523b4bfc0ebd4c03b2b96f6a17fd31d8%7C1&sdata=%2Btv6lCO6ofLs245tO0VmMZlu%2Fw2GKrNEzbE8jZ%2F6DyA%3D&reserved=0> _______________________________________________ Fis mailing list Fis@listas.unizar.es<mailto:Fis@listas.unizar.es> http://listas.unizar.es/cgi-bin/mailman/listinfo/fis<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Flistas.unizar.es%2Fcgi-bin%2Fmailman%2Flistinfo%2Ffis&data=01%7C01%7Choelzer%40unr.edu%7C2cfdcd34699449bb000c08d47629a4c0%7C523b4bfc0ebd4c03b2b96f6a17fd31d8%7C1&sdata=%2B1A3FcfGK8mpiH1bKqckx3gsU5n9Yb%2F6UbvPHFjvxMQ%3D&reserved=0> -- Professor Terrence W. Deacon University of California, Berkeley -- Professor Terrence W. 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