**Links to**: [[Chunk]], [[Parse]], [[World model]], [[Alfred Korzybski]], [[Borges]], [[C. S. Peirce]], [[Irrespondence]], [[Correlation]], [[Correspondence]], [[Gradient descent]], [[Abstraction]], [[Whitehead]], [[William James]], [[Hegel]], [[Abstraction]], [[Concept]], [[Analogy]], [[Reification]], [[Map-territory]], [[Model]], [[Vicious abstraction]], [[Representation]], [[Mathematics]], [[Natural Kind Properties]], [[Structure]], [[Category]], etc. >The lifetime of any given model knows a fairly predictable rhythm. Initially, the new concept releases quantities of new energies, permits hosts of new perceptions and discoveries, causes a whole dimension of new problems to come into view, which result in turn in a volume of new work and research. Throughout this initial stage the model itself remains stable, for the most part serving as a medium through which a new view of the universe may be obtained and catalogued. > >In the declining years of the model’s history, a proportionately greater amount of time has to be spent in readjusting the model itself, in bringing it back into line with its object of study. Now research tends to become theoretical rather than practical, and to turn back upon its own presuppositions (the structure of the model itself), finding itself vexed by the false problems and dilemmas into which the inadequacy of the model seems increasingly to lead it. > >F. Jameson, _The Prison-House of Language_, preface, p. v. The concept of _model_ is treated in entries such as: [[Chunk]], [[Parse]], [[World model]], [[Abstraction]], [[Fallacy of misplaced concreteness]], [[05 Prediction]], [[Score]] and [[Illusion]]. A few general things can be said here, very briefly, to clarify what _model_ means in the context of this research: 1) A model can be understood in the sense Jameson presents above, as an ever-evolving concept or a kind of Kuhnian paradigm of normalized, regulated understandings around a subject/within a discipline; 2) Models can be _neat_ (formalized and closed) or _scruffy_ (underdetermined and open-ended) (Roger Schank), or a mix of both (if they are a combination of model-hierarchies, in the case of _paradigms_); 1) The way in which things can be stacked, then, is something like: **principle** (axiom, rule); **model**; **theory**; **paradigm**; **ideology**/**narrative**. Each level has presuppositional + bidirectional influence on the other(s); 3) A principle (such as the [[Free energy principle]]) _can_ function as a model, but is not a model in the sense that a map is a model of a territory, or in the sense that a theory is a model for a science. A principle, as conceived by this research, functions like an axiom: a constraint which is set on modelling possibilities; 4) [[Model theory]] studies how and where objects such as the ones just mentioned, or (natural) languages, theorems, etc. can function or not as models; 5) There is more to be added here, e.g., Hilbert, Badiou, but it escapes my model now, #todo. Masterman: “What, stated in terms of the model, is the scientist doing?’ The first, and most general, answer to the question obtrudes itself: scientists are individuals who are creating new language points. Scientists, by virtue of their observations and fiddling activities (whether these are done by using mathematical systems or by using other apparatus) force the world to notice new events and situations for which they then coin new key-phrases. Their activities, however, do not stop there; for they are not only observers and reporters; they are also classifiers. Now, of course, once scientists are envisaged as using the model rather than as using language, it follows, by definition, that every time they use the model, they classify; since the model has classification built into it. It is therefore necessary to re-envisage their activity; the activity of classification, as used in science, must become that of reclassification (which, when it is looked at historically, is just what it is). The classificatory activity of scientists therefore becomes that of restructuring the whole or part of the model. This involves re-imagining the model as a self-correcting or a self-organising system. Quite apart from any application to ways of thinking, techniques are currently being developed for doing this, in order to represent such reorientation as it may occur in language. A third and central activity of scientists is always held to be that of producing hitherto unthought-of analogies. It is always assumed, moreover, that there is, and can be, no way of ever computing analogy. The discovery of a new analogy is an ‘intuitive leap’, a ‘lucky guess’; and there can be no philosophy, so it is said, of lucky guesses . . . Carrying this argument further, there can be no real philosophy of science either. For real science consists, as was said at the beginning of this paper, partly in fiddling with apparatus and with systems, partly in retrieving, reselecting and reclassifying known information, and partly, also, in creating new analogies, and in making lucky and unlucky guesses. Here, however, the model really proves itself; for it is possible to develop, by using it, an algorithm for finding analogy. Thus, by using it, not only can ‘concept’ be empirically defined, but, this having been done, analogy itself can now be formally defined. In short, the thesis of this paper is that, except in the sustained, intense stage of actual fiddling, science might well consist in manipulating language points computed from a language. But to do this we need, as philosophers, steadily to envisage not ‘ordinary language’, but ‘language’ language imagined as a theoretic entity: language caused to be manipulatable by being made into a net-like schema. By using this, algorithms can be worked, reclassifications and selections can be made, and answers (which, for scientists, are hypotheses) given to requests. In fact, perhaps scientists do use the model. In this case, perhaps also a basis could be constructed for a philosophy of real science.” Masterman 2005, pp. 79-80. %% Related people: [[Hilbert]], [[Alain Badiou]] https://www.nature.com/articles/s41598-017-17237-w Cogliati Dezza, Irene, et al. "Learning the value of information and reward over time when solving exploration-exploitation problems." _Scientific reports_ 7.1 (2017): 16919. “the world is its own best model” (Brooks [1991](https://link.springer.com/article/10.1007/s11229-016-1239-1#ref-CR5 "Brooks, R. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.")). Bruineberg et al 2019: “Based on Friston ([2013a](https://link.springer.com/article/10.1007/s11229-016-1239-1#ref-CR24 "Friston, K. (2013). Life as we know it. Journal of the Royal Society Interface, 10, 20130475. doi: 10.1098/rsif.2013.0475 .")), one would be required to give an inferential interpretation of the coupling of the two clocks, in which one clock “infers” the state of the other clock hidden behind the veil of the connecting beam. In this example, each clock is a ‘generative model’ of the dynamics of the environment (the other clock), coupled through the Markov blanket of the connecting beam. The synchronizing clocks are therefore excellent examples of _being a model_ (discussed in the previous section). Clock A is a model of clock B if clock A resonates with clock B. Whether synchronization (or “inference”) occurs or not, is dependent on properties that have to do with the whole system: the period of the two clocks should not be too dissimilar; the beam should not be too rigid and not too flexible, and should be flexible in the right direction etc. We think such an inferential interpretation is unnecessary: there is no special inferential system inside of the Markov blanket, but the synchronization _is_ the process of achieving high mutual information. This is emergent from the critically balanced coupled dynamics of the whole system.”