I'm back, baby! Did you miss me?

I'm back, so here's why I think I was gone in case you cared.. and also here are some typically rambling thoughts on matters related to the recent 'Sokal Squared' thing

Just had to update my blog for the sake of all the weird girls who stalk me. As to why I've had (by my standards) a massive break in posting, I can identify the following factors: 'external': getting into cricket again, being a little socially busier, spending a lot of time writing a long and well-researched essay on the future of the planet for a writing competition on which I'll soon get feedback (meaning I can post this essay here), and, a little more recently, obsessively playing the classical guitar, after not playing at all for nearly four years straight (and I mean obsessively); 'internal': being extremely happy and mentally relaxed (connected: being socially busier than I was), reaching a tipping point of general disgust with the amount of dogmatic and dimwitted intellectual pollution on the internet and not wanting to contribute to it with a non-excellent, non-cogent post. I've thought for a while I need to write something at some point on the early history of mathematics (in particular, how long it took 'us' to develop the notation that makes 'simple' maths so much easier) and what it tells one about the Flynn Effect, about the role of cognition-externalisation, the importance of language and notation to thought, and on the secret theory-ladenness of many things we forget are theory-laden. I think I will do this eventually. I also keep wondering if I should write reviews of things, like the books and movies I've enjoyed recently (or even not so recently), because that might be fun. The reason I didn't do that in the past with books is because I had this major pathological aversion to summarising things that I read and liked, because I realised that summary always costs nuance and information that the author included for a reason - usually, I feel like almost nothing is indispensable in books I like, so I prefer to quote chapters out of whole cloth. But maybe I should start reviewing. On the other hand, I have this disposition against which I think is a perverse manifestation of the 'sunk-cost' fallacy: my brain secretes this weird thought like, It would be wrong to start reviewing things now, because that's not your thing; you could have reviewed books and movies that you liked years ago but you didn't so you can't just go back on that now.

Anyway, I did have something non-narcissistic to say, induced by the "Sokal Squared" Hoax that just happened. I'm not really going to comment on the Hoax per se (on the ethics or intellectual 'validity' of it, or on the perpetrators (the truth is I don't actually have a strong opinion on, like, the ethics of the Hoax - I think Boghossian and Lindsay seem like massive wankers but whatever)), but I wanted to say something  on related matters.

Sturgeon's Law tells us that most of academia is total bullshit (what I mean is: that should be our default assumption). Most research, even in the harder sciences, goes absolutely nowhere, retreads old ground in a pointless way, or is unrigorous (I hesitate to write disjunctions like these, because one is wont to miss disjuncts (there are probably more failure modes)). Arguably, fundamental physics - that most reverent of scientific disciplines - has been little different from metaphysics for the last several decades (see Sabine Hossenfender's controversial new book (which I haven't actually read, I should admit, though I have read her blogposts, on her blogpost blog)). And, obviously, macroeconomics has some well-known issues with predicting shit, medical science has some serious issues with publication bias and corporate influence, and social scientists are, for the most part, fucking useless idiots who need to increase their n and stop p-hacking shit. (I could link to relevant articles but I'm happy to assume that the kind of people who read my blog are big intellectual playas who have already read such articles).
But in my humble opinion, if there's one kind of area of academia that's worse than all the others, it's specifically the unrigorous part of academia which is also completely obscurantist - the academia of long sentences and pollysyllabic verbiage (or, alternatively, as one can see, for example, in some parts of economics and probably some other disciplines, the academia of obscurantist mathematics - glyphs and runes included to dazzle rather than to give a rigorous structure to a problem). One commonly used shorthand for obscurantist academia is "Postmodern" academia. Whilst I don't really think it poses much of a problem for this term that Postmodern academics themselves think it's painfully simplistic and bigoted to use one term to encompass several decades of evolving scholarship (because I don't give a shit about obscurantist scholarship), I already implied in the preceding parenthetical interjection that there is one problem with using this term as a shorthand: namely, that obscurantist academia is bigger than the parts of academia where words/phrases like "Marx", "Foucault", "Derrida", "Barthes", "Kristeva", "Lacan", "Bourdieu", "Butler", "theory", "hermeneutics", "problematics", "hegemony", "towards a", "beyond the", "territorialisation", "post-structuralist", "meta-textual", and so on, are highly frequent, and an extraordinary degree of prolixity is commonplace (see Postmodernism Generator). 
To be sure, Postmodern academia is definitely a major bastion of such obscurantism and it's probably deserved some of the specific critiques it has gotten (like these two I have linked before; http://faculty.georgetown.edu/irvinem/theory/Nussbaum-Butler-Critique-NR-2-99.pdf, http://bactra.org/chomsky-on-postmodernism.html). Postmodern academia is the area to which the "X studies" fields (the main target of the Hoax) largely belong, along with English literature and (to a lesser extent) sociology and history (I am sure there are some gender, race-studies and English literature academics who are not so into abstruse verbiage and the French theoreticians who perfected it to an artform but prefer to write clearly (which does not necessarily mean their scholarship has merit, but I'm sure some of it is interesting and incisive and cogent and all those other nice things)). 
However, one issue with the critique of Postmodernism that fails to link Postmodern writing with obscurantism generally is that it gives Postmodern critiques an opening to say either "You just don't understand our technical language and theoretical constructs" or "You just don't like our political conclusions". This is shitty, because, in my opinion, the important thing to point out is that Postmodern obscurantism is not really any different, from an epistemological perspective, from any other kind of obscurantism, e.g. religious obscurantism, the 'deepities' of pop-philosophy and self-help books, bureaucratese, Fascist mysticism, or the obscurantism of so much of the 'canon of philosophy' going back centuries (the part that one of a crude bent of mind might classify as "counter-Enlightenment" (e.g. the whole German Idealism thing)). It's not different, from an epistemological perspective, because it's all equally vague, non-naturalistic BS, or, as I prefer to say, bad poetry. As Chomsky points out, the difference between mathematics being used properly as part of a rigorous scientific theory and polysyllables being used in a prolix sentence is that mathematics, used properly, can allow one to give a rigorous testable structure to theories and to find precise solutions to very difficult problems that one couldn't even approach without the language (think of the analytic power and beauty of a matrix (or just think of trying to do physics problems without maths lmao)). But when you decode a typical sentence of Latinate polysyllables in the works of Derrida, Lacan or Butler, it turns out either to be a thought expressible more simply or just pure gobbledegook. 
Why can't truth be found in long, turgid sentences populated with esoteric Latinate words? Well, for one thing, humans are really bad at even parsing long sentences from a memory point of view. (So, just as a kind of abstract theoretical point, supposing you did have something really deep to say, don't you think you'd want to make more of an effort?) For another thing, fuzzy words almost necessarily mean fuzzy thought and they definitely mean ambiguity; hence why a key development of the Enlightenment was this thing called a technical term (a term you explicitly define in the context of some kind of rigorous, self-contained usually mathematical framework). More to the point, I agree with the meta-philosophy and largely with the metaphysic explicated in Ladyman and Ross' magnum opus, Every Thing Must Go. I think that philosophers and theoreticians who don't practise metaphysics in the Quinean fashion, as simply a process of making rigorous the ontological commitments of our best scientific theories, are "neo-scholastics" talking nonsense for no humanly important end. Basically, the long and short of it is that science is hard, naturalistic metaphysics has to be a very humble enterprise which pays due fealty to science, and all scholarship which talks about matters of reality and existence without a serious connection to relevant scientific inquiry is BS.
Yes, ok, I admit it, I'm a "neo-positivist" (which is very different from "logical postivism", per se, because literally everyone agrees that that is a faulty doctrine for various, largely esoteric reasons I will not get into). Which, in truth, means I can't win with Pomo people, because if the other rebuttals don't stick, "Positivist" is the one they are bound to hurl. I mean, I should clarify that I don't actually like the label "neo-positivist", even though some people with similar views to mine call themselves that. What I prefer to call myself is a "neo-pragmatist structural realist", where "pragmatist" here refers to a pragmatist attitude to metaphysics, which says that we should avoid the idea that we can actually do metaphysics 'properly' and instead just accept that we should call real whatever is a projectible phenomenon or cluster of phenomena in the fully mind-independent world (a phenomenon whose postulation allows us to make systematic predictions that help us achieve goals in our navigation in the world as organisms), most of which phenomena just have to be 'read off' scientific theories, with fundamental physics taking priority simply because of its generality (see the book Every Thing Must Go(2007) for more). Basically, this means that I think a whole lot of analytic philosophy is useless, along with virtually all of Continental philosophy and X studies stuff. (Basically, what it means in practice is that when anyone ever tries to sound profound using fancy words, my reaction is "That's some nice poetry". Writing, to me, falls into one of the following categories: poetry, prose (as in prose fiction), science, failed science, or naturalistic philosophy.)
Now, I've been reading some history books lately, and I want to make clear: this is not to imply that, like, history is a waste of time if you're not going full Peter Turchin or some shit. Archival research and analysis would be necessary even for a hypothetical fully rigorous, scientific discipline of historical inquiry, and, in any case, narrativistic history is definitely at least partly distinct from mere story-telling (I don't know if Hayden White literally thought there was no difference at all, but he's definitely wrong if so; in fact, I would go so far as to say that there's a fact of the matter as to whether Inga Clendinnen's interpretation of the spearing of Arthur Phillip at Manly is more correct or scientific than the interpretation given by the English sources on which she relies (i.e. I don't take a super radical view on the epistemological constraints of conventional narrativistic historical inquiry, though I'm also no Geoffrey Elton)).
Now, I admit that much of what I just said, especially when I started using fancy analytic-philosophy terms, would be obscure, especially to people who don't know shit about philosophy, but in my defence, what I'm really doing is saying go read Every Thing Must Go to see why I think what I do. So go do that if you really care. Otherwise, fuck off.

Philosophy of Science: Mathematical Modelling, Assumptions and Causal Realism

Q: How can mathematical modelling succeed when it inevitably involves false assumptions?

We happen to know from the history of science that models with false assumptions can be used to make systematically correct predictions with well-understood conditions of application. Kepler's laws of planetary motion are a canonical example [Colyvan & Ginzburg [2003]]; a more dramatic illustration is provided by the very considerable improvements in the power of metereological and climate-modelling since the 1950s. ¹ The very general explanation for this very general fact is that the dynamics---the progression of state changes---of even complex systems seem to causally depend to a large degree on changes in only a small number of mathematically isolable variables which we can track with mathematical equations. This convenience given us by nature does not, however, entail scientific convenience; instead, it has the consequence that truly successful model-building in higher-level sciences will invariably require the identification of the critical causal variables---that is, the right theory or understanding. This methodological truth sits very uncomfortably with the fact that, precisely because of the large number of variables in play in the large-system modelling in the higher-level sciences, there seems to be very little hope for decisive empirical disconfirmation or falsification of models (embodying theories) which are at least vaguely appropriate for the target system. ²

My thesis in this essay is that these considerations ought to lead one to the view that assumptions do, in fact, really matter: more specifically, that the laissez-faire instrumentalist attitude towards assumptions within scientific theories (which has historically held most sway within the discipline of economics) is deeply misguided. I suggest that the history of science (including recent science, and recent developments within ecology) gives strong weight to the view that models which do not embody the causal structure of their target system either don't have predictive success at all, or eventually badly misfire, on account of being attuned only to some subset of the states of the system, rather than locking into the causal structure which determines the state changes of the system. I submit that the key source of instrumentalist error is the failure to recognise that there are different kinds of modelling assumptions, with different scientific significance, and that models which are not `realistic' in terms of their modelling of causal structure cannot succeed (at least in the long-run). I argue that ecology is an excellent case study for this thesis, by attempting to show that causal realism and the absence of highly unrealistic domain assumptions [Musgrave [1981]] is a feature of successful and commonly used models in ecology.

In his paper “Models and Fictions", the philosopher of science Peter Godfrey-Smith makes much of the fact that the scientific enterprise depends on mathematical fiction in the course of asking how it is that fictions in general can tell us about the world [Godfrey-Smith [2009]]. Left unexplored by Godfrey-Smith in the short paper is the fact that in the realm of science we call “fundamental physics", the history and current practice of the science gives us reason to think that our fundamental reality may be perfectly describable using mathematics.³ Godfrey-Smith does himself observe that one of the viable explanations for the problem

¹ More dramatic since, although the amount of chemical and atmospheric `variables' explicitly encoded in climate models has increased over the last few decades and climate models are complicated enough to require state-of-the-art computing power to run, even our best climate models are still (perforce) attempts only to capture the `core' causal structure of the dynamics of

the chaotic target systems [Lucarini [2013]].

²There always exist escape clauses in the form of phrases like “exogenous shocks" and “disturbing factors". To clarify, I do not mean to convey a nihilistic, defeatist or relativist attitude towards the possibility of rational rejection of theories within the higher-level sciences (leading to genuine scientific progress); it will become clear that I believe, in fact, that models which have patently causally unreal assumptions (models which clearly embody the wrong theory) should be dismissed out of hand regardless of any apparent early empirical confirmation. Unfortunately, in the social sciences, there really does seem to be very little hope at all that such norms could be acceded to by a majority, because of the role of ideology and other social

complications.

³The theories of “fundamental physics" really do seem to be profoundly non- fictional, not embodying any assumptions known to be false in the way that higher-level sciences must. The General Theory of Relativity does not model spacetime as if it were a four-dimensional `pseudo-Riemannian' manifold; instead, we have no other grasp on what spacetime is. Similarly, Quantum Electrodynamics (the relativistic quantum field theory of electrodynamics, which Feynmann called the “jewel of

of how fictional mathematical models can tell us about the world is that many of the features of “reality" we assume away to design a good mathematical model are just ornaments for the fundamental universal mathematical patterns inherent in nature---such that a good mathematical model of a system, making the right kind of assumptions, simply cuts through the frippery. ⁴ Whatever the merits of Platonism, I claim that we have very good reason to believe that a very humanly useful approximation of the `causal architecture' of even highly complex or chaotic natural systems is isolable by means of mathematical equations encoding the dominant (numerically abstractable) dependencies of the system. I claim this in view of our knowledge that the necessary presence of the ceteris paribus clause and the “idealisation" in the canonical models of science as we move beyond fundamental physics into non-fundamental physics (and beyond non-fundamental physics into the rest) are not handicaps to very impressive predictions. As I noted in the introduction, it is not just the familiar tales of early Enlightenment physics that bear this out; one of the less appreciated major scientific success stories of the second half of the 20th Century is the constant leaps and bounds we have made in computer-modelling of the earth's weather and climate since the process started with the primitive computers of the 1950s. A 2015 paper in Nature entitled “The Quiet Revolution of Numerical Weather Prediction" gives us some of the key facts testifying to the magnitude this achievement: “Forecast skill in the range from 3 to 10 days ahead has been increasing by about one day per decade" whilst (as an example of some of the progress in longer-term forecasting that has been made) “tropical sea surface temperature variability following the El Nino/Southern Oscillation phenomenon can be predicted 3-4 months ahead" [Bauer et al. 2015, 47].

Unlike Godfrey-Smith (assuming I interpret him correctly), I reject the position that there is any kind of philosophically interesting analogy to be had between novels or films or other obvious fictions, and successful mathematical models of complex systems used in progressive science. I think instead that understanding the different kinds of assumptions in play in mathematical modelling within the various `higher' realms of the scientific enterprise can help us demarcate pseudoscientific modelling---that is, mathematical models that are more akin to novels or films---from mathematical models of complex systems that, whilst necessarily fictional in the sense that they only trace some of the core emergent dependencies or patterns inherent in the target system, are realistic in the sense that, by foregrounding some of the right emergent dependencies (i.e. by embodying a good theory of the target system), have locked into the “deep structural forces" of the target system that determine its evolution through time. As we'll see, I think that this can be illustrated by case studies: by drawing a contrast between---on the one hand---weather/climate modelling and successful modelling in ecology (population dynamics) (and various work on the margins of economics (complexity economics) and the new field of cliodynamics), and---on the other hand---DSGE-modelling in macroeconomics, whose predictive failures can be explained by the fact that the entire enterprise instantiates false domain assumptions (fundamentally embodying the mistaken assumption that the macroeconomy is an equilibrium system).

Before we can analyse these case studies, it is necessary to introduce an analytical tool: a taxonomy of assumptions in scientific theories due to the philosopher Alan Musgrave (contained in his brief discussion of Milton Friedman's very confused philosophy of instrumentalism from the in influential essay “Methodology of Positive Economics"). Musgrave claims that there are three kinds of assumptions worth distinguishing: negligibility assumptions, domain assumptions and heuristic assumptions. The third is less relevant for our investigations, but I shall quickly describe the first two. A negligibility assumption is an assumption that a certain phenomenon not known to be negligible in the real world (or known to be not negligible at least some of the time) is negligible. Galileo tested his theory about the acceleration of falling bodies in a vacuum in the real world with the assumption that (in his experimental cases) air-resistance was of no consequence (correct, in his experimental cases, though clearly not in general). In the complex-systems-level sciences,

physics" [Feyman 1985, 4]) has been confirmed by predictions at an astonishing level of precision---levels of precision that give us reason to think that the equations we have come up really do describe the fundamental dynamics about as well as one could imagine describing them.

⁴ Godfrey-Smith connects this explanation to the doctrine of Structuralism in philosophy of mathematics, the essence of which is given away by the title of Michael Resnik's 1997 book, Mathematics as the Science of Patterns.

we might consider calling the equivalent assumption-category the relative insignificance assumption, simply because in modelling population dynamics in ecology, financial crises in economics, or the rise and fall of social cohesion in history, a scientist will be forced to leave out plenty of variables which they know to make a super-negligible difference much or all of the time in the real world. This, of course, does not mean that they cannot do proper science: as I'll argue when I introduce the case studies, the scientist maintains a claim to doing science for a complex system if they can generate system state-changes that mirror the dynamics of the target system from the simplest possible model embodying their given theory, because this is a good indication that they have struck causal realism. The second kind of assumption, the domain assumption, is of a rather more general kind: it can be thought of as any kind of positive assumption that specifies the world embodied by the model (as opposed to the real world). More helpfully, an unrealistic domain assumption is an aspect of the mathematical structure of a model that is most definitely at odds with the target system it is supposed to model. I see the core instrumentalist mistake as the elision of the fact that any successful predictions made by a model which embodies too many unrealistic domain assumptions will be entirely accidental (and the addition of parameters and extra variables, bells and whistles, to bootstrap predictive power will just make the situation worse when there is a significant shift in the state of the system).

We now have the framework necessary for our case studies, beginning with ecology itself, or, more precisely, “population dynamics". In his 2002 treatise on the methodology, theory and models of mathematical ecology, Complex Population Dynamics, the ecologist and `cliodynamicist' Peter Turchin argues that ecology has become a mature, predictive science, and he attempts to demonstrate this via an analysis of a series of models. Relevant to this enterprise, the book also contains (and embodies) an expression of Turchin's modelling philosophy for complex systems science, a philosophy he has applied in his research in both ecology and on human societies in his development of the Structural-Demographic Theory of the internal cycles of empires. I think this modelling philosophy gives us the key to understanding what good complex-systems modelling should look like. Turchin's methodological algorithm goes as follows.

First, if there are two or more theories postulating fundamentally distinct (though not necessarily completely incompatible) causal mechanisms for a certain explanandum, then these theories must be given a maximally simple mathematical expression and their predictions/retrodictions compared with the data (maximally being the key word, of course, since `simple' in this context does not mean that they are easy-to-solve differential equations like the models introduced to beginning ecology students, or that they are as blatantly unrealistic as such models are). ⁵ Two miscellaneous examples of such explananda due to Turchin himself (from history and ecology respectively) are:

a)      The `saw-toothed' cohesion-cycles observed throughout historical societies, for which the two general competing theories are the “Malthusian theory" and the “Structural-Demographic (`elite overproduction') theory" (he has written several books explicating the latter).

b)   The fluctuations in “southern pine beetle" population size, for which the dominant explanation in the 1980s was climactic (exogenous), but which now appears (thanks to analysis of time-series data) to be explicable mostly “by second-order endogenous factors" [Turchin 2003, 164].

Ideally, this procedure should at least allow for a pretty confident falsification of very bad theories---since, if the fit for the simple version is far off the mark, then it seems a very strong bet that the theory is causally unrealistic, and if a relatively simple model generates dynamics that broadly mirror those in the target system (of course this requires a variety of quantitative measures determining the average period, period strength etc), then one has strong evidence that it is a theory worth pursuing. Second, when it comes to deciding between models of varying complexity embodying the same theory (i.e. once one has a strong theory), the procedure is again empirical. As Turchin explains in Complex Population Dynamics, “The question of whether to include population structure or not can be resolved by constructing and contrasting with data

⁵ It should also be noted that whilst the above principle was fairly simple to state, this conceals the sophisticated statis-tical techniques and intelligent quantitative-measure choice that are needed to execute it properly (“We can then attempt to distinguish between the competing models by doing parallel time-series analyses on the data and on model outputs, to obtain quantitative measures of their relative success at matching the patterns in the data."[165]).

two versions of the model: one that explicitly incorporates individual variability [fewer relative-insgificance assumptions], and the other that averages over this variability with judiciously chosen functional forms [more relative-insignificance assumptions]." [Turchin 2003, 65] The long and short of the philosophy is this: (a) that we should add extra parameters and include more specific variables iff these lead to a model which is a better fit to the facts, and (b) that what is of most importance fundamentally is causal realism. Crucially, this methodological algorithm should actively discourage the production of models with clearly false domain assumptions, on account of the strongly counter-instrumentalist first step.

The highly advanced science of metereology may, on the surface, seem to impugn the first principle of this methodological philosophy, inasmuch as there are no serious climate models in contemporary use that could be described as `simple' (or even which are solvable by hand at all). However, this is superficial, since one critical factor that distinguishes metereology from ecology is that, from its very inception in the 1950s, the modellers had the key `theory' in place: fluid dynamics. As the aforementioned Nature article notes, “The Navier-Stokes and mass continuity equations (including the effect of the Earth’s rotation), together with the first law of thermodynamics and the ideal gas law, represent the full set of prognostic equations upon which the change in space and time of wind, pressure, density and temperature is described in the atmosphere" [Bauer et al. 2015, 48], meaning that the first principle described by Turchin was irrelevant. As for Turchin's second principle, I believe it is in play; it just so happens that adding complexity to models and taking advantage of all the computing power available has been the way to generate better predictive power in metereology. ⁶

The contrastive case study is the discipline of macroeconomics. I claim that the predictive uselessness of even the most celebrated macroeconomic models, particularly (but by no means solely) when it comes to major recessions and downturns, and particularly since 2008 [Edge et al. [2010], Keen [2011]] ---a state of a airs which has led to foundational questioning by some elder statesmen of the field [Blanchard [2016], Romer [2016] ]---has evolved from a historically rooted excessive tolerance of unrealistic domain assumptions (i.e. a philosophy of instrumentalism), which is anathema to Turchin's methodology. Economics evolved as a mathematical science before the advent of complex-systems mathematics, much like ecology, but the mainstream of the discipline did not respond to the complex-systems revolution at all. Whilst there was much upheaval within economics in the 1970s and 1980s (with the rise of Friedman and Lucas and the turn to microfounded macroeconomics), this only pushed the discipline in a more unrealistic direction, in terms of the fundamental assumptions about the economy, with the paradigm of the `representative consumer' (nothing like a real person) and `representative firm' (with a cost structure unlike any real firm) optimising towards an infinite horizon [Keen [2011], Lavoie [2015]]. More fundamentally, the central paradigm of mainstream neoclassical macroeconomics is the model of the economy as a system that does return to equilibrium absent “exogenous shocks", which is not path-dependent and which functions essentially like a machine [Keen [2011], Lavoie [2015]]. This is fundamentally opposed to reality, which is why the only predictive successes that DSGE models ever did generate were the result of tailoring models to recent data, so-called “overfitting". This kind of methodological practice which may generate very impressive near-term successes but it is also likely to result in massive scientific crisis when the target system state changes in a dramatic way (as in the GFC), because the overfitted model that was in use up to this state change is now doubly useless: not only has it been shown decisively that it does not embody the causal structure of the target system, there is nothing anyone can do about it, because how are we to know what exactly went wrong in such a highly parametrised, massively complicated, baroque model?

A key difference between ecology and economics can be observed in the way and in which the role of assumptions changes as one progresses in the subject. In ecology, the elementary models taught to students---the three central `laws' (as Turchin calls them [Turchin [2001]]) of the exponential growth model,

⁶ Again, we should add a detail here: metereologists and climate scientists don't generate their predictions just from running one perfect model. Because of the path-dependence of chaotic systems and our extremely imperfect knowledge about all the potentially relevant atmospheric data, it is necessary to use what is called “ensemble-modelling" (comparing similar models with different initial conditions, etc) to generate reliable results.

the logistic growth model and the Lotka-Volterra model---are not used precisely because of their very bad domain assumptions: Turchin calls the most complicated of the three, the Lotka-Volterra equations, “a horribly unrealistic model for real resource-consumer systems [...] so bad that, to my knowledge, there has been no successful application of it to any actual population system, whether in the field or laboratory" [Turchin 2001, 21]. But in economics, unlike ecology, the higher-level models, although of course far more complicated than the elementary models (their mastery requiring years of graduate training), are just as profoundly unrealistic as the elementary ones.

In summary, and in answer to the question, mathematical models in ecology (population dynamics) serve their purpose when they are built in such a way that their relative-insignificance assumptions are orthogonal to the key causal structure of the target system, enabling the models to generate solid predictions even as the state of the target system changes (because they are calibrated to the drivers of the system's state-changes). This fact helps us see how and in what sense assumptions matter in complex-systems science: the chief task has to be to find the right theory, and this task will inevitably result in the mulching of mathematical models that instantiate completely unrealistic domain assumptions. The scourge of `overfitting' can be avoided by focussing on arriving at a model that can generate all the states of the target system, not just generate some neat predictions in a period of equilibrium, and this is equivalent to a causally realistic model.

References

Bauer, Peter, Thorpe, Alan, & Brunet, Gilbert. 2015. “The quiet revolution of numerical weather prediction".

Nature, 525(7567), 47{55.

Blanchard, Olivier. 2016. “Do DSGE Models Have a Future?" Peterson Institute of International Economics, 11{16.

Colyvan, Mark, & Ginzburg, Lev R. 2003. “Laws of nature and laws of ecology". Oikos, 101(3), 649{653.

Edge, Rochelle M., Gurkaynak, Refet S., Reis, Ricardo, & A., Sims Christopher. 2010. “How Useful Are Estimated DSGE Model Forecasts for Central Bankers?" [with Comments and Discussion]. Brookings Papers on Economic Activity, 209{259.

Feyman, Richard P. 1985. QED: The Strange Theory of Light and Matter. Princeton University Press.

Godfrey-Smith, Peter. 2009. “Models and Fictions in Science". Philosophical Studies, 143(1), 101{116.

Keen, Steve. 2011. Debunking economics: the naked emperor dethroned? Rev. and expanded edn. London:

Zed Books Ltd.

Lavoie, Marc. 2015. Post-Keynesian Economics: New Foundations. Edward Elgar.

Lucarini, Valerio. 2013. “Modelling Complexity: The Case of Climate Science". Pages 229{253 of: Ghde, Ulrich, Hartmann, Stephen, & Wolf, Jrn H. (eds), Models, Simulations, and the Reduction of Complexity. De Gruyter.

Musgrave, Alan. 1981. “Unreal Assumptions in Economic Theory: The F-Twist Untwisted". Kyklos, 34(3), 377{387.

Romer, C. D. 2016. The Trouble with Macroeconomics. Commons Memorial Lecture of the Omicron Delta Epsilon Society.

Turchin, Peter. 2001. “Does population ecology have general laws?" Oikos, 94(1), 17{26.

Turchin, Peter. 2003. Complex Population Dynamics: A Theoretical/Empirical Synthesis. Princeton Uni-versity Press.

The Problem of Individuals and Species in Biology, and Ontic Structural Realism

To start off this document, here is a very slightly extended version of my uni essay on problem of biological individuality, wherein I rhapsodise over Peter Godfrey-Smith (first ever sub-80 philosophy essay mark but only because I didn't have enough words to actually make my case properly (fucking stupid Neoliberal bureaucratic system where even in a very poorly subscribed unit you have extremely stringent, oppressive word limits)). (Also I think that just adding the sentences I added in this version would have pushed my mark significantly higher (the original was insufficiently explicit about the shortcomings of Clarke's account)).

3.      What is the problem of biological individuality? Compare and contrast two theories of biological individuality, and explain whether they are successful or not.

The “problem of biological individuality” is the problem of how to give a general account of what defines a ‘biological individual’ across all species to which evolutionary theory applies, given the incredible diversity of life. Ellen Clarke [2010] holds that this is a serious issue because of how crucial the notion of an organism is to our understanding of biological evolution, the very concept of “fitness”, and more. In this essay, I will compare Peter Godfrey Smith’s elaborate, continuous account of biological individuality with Ellen Clarke’s own, much simpler functional (and also continuous) account of biological individuality. I will suggest that they are both very impressive taxonomical efforts, but that Godfrey-Smith’s has the greater balance of virtues, on account of (what I believe to be) its greater comprehensiveness and greater potential to defuse controversies.

In her well-cited 2010 paper “The Problem of Biological Individuality”, Ellen Clarke concisely and effectively explains the titular problem, and why one should care. She begins the paper by explicating the centrality of this idea of ‘the organism’ to the biological sciences. This notion of the biological individual is perhaps most important for its starring role in our understanding of evolution: Darwin formulated his theory of evolution in terms of biological individuals, the “received view of biological evolution takes the organism as “the basic unit of selection””, and, as Dawkins admits, even the ‘gene-centrist’ cannot hope to dispense with the biological individual in evolutionary theorising (even in the mathematics) [Dawkins, 1982: 251]. As Clarke puts it: “It is hard to overemphasize the importance of individuals within the Modern Synthesis. They are central to the inner logic of evolution by natural selection, according to which evolution occurs because of the differential survival and reproduction of individuals” [2010: 313]. The organism also plays a massive role, albeit slightly more hidden, in various other sub-fields of the biological sciences “such as medicine, developmental biology, immunology, ecology, and the reductionist sciences such as molecular or cell biology” [313]. Finally, organisms are what population biologists count!

The trouble, of course, is that, despite the immense scientific utility of this concept, philosophers of biology as of 2010 only had a long list of competing criteria for describing biological individuality, all of which individually seem to admit of counterexamples and define strongly “non-overlapping classes” – and this vagueness allows for scientific conflict as well. Clarke discusses, in particular, 13 different candidates for criteria (all biological “properties” in some very wide sense) which could do the work of “differentiating individuals from parts and groups”. [1] Although some are more promising than others, the case studies Clarke introduces demonstrate (I think) that there is not a clear mix-and-match solution.  In her follow-up 2013 paper “The Multiple Realizability of Biological Individuals”, Clarke also shows that the problem has practical import by pointing to some of the scientific controversies that could have been avoided if biologists had agreed on what counts as an individual. The first controversy she cites is the “long-standing debate amongst plant scientists about whether vegetatively produced plants […] ought to add to the parent plant’s fitness or not” [414]. Another, more general one she describes is the long-running controversy in evolutionary theory over ‘levels of selection’: in particular, over propositions like “selection always acts at the level of the individual”. She suggests – rightly, I think – that these debates would dissipate a lot of heat if the interlocutors acceded to a common account of biological individuality (especially to deal with what Godfrey-Smith calls the “problem cases” of collective entities like “ant and bee colonies, and lichens” [Godfrey-Smith, 2012: 3]).

In the same 2013 paper, however, Clarke goes on to propose a solution to her own problem. Her big idea is to ‘compress’ several of the competing properties she highlighted in “The Problem of Biological Individuality” (sex, bottlenecks, germ-soma separation, policing mechanisms, spatial boundaries, and immune response) into a simpler and more economical ‘functional’ definition. The first component of Ellen Clarke’s functional definition is the “policing mechanism”, which she claims is a robust enough functional property to constitute a necessary condition for biological individuality. She defines a policing mechanism as “any mechanism that inhibits the capacity of an object to undergo within-object selection” [2013: 421]. Clarke thinks that as well as helping to end debates over the priority of this or that specific policing mechanism, this kind of functional definition can put us in a better position “to recognize real-life structures that play the desired role” [422]. She justifies this claim by giving a number examples of such real-life structures, including “Resource exchange, synchronized/vertical transmission (especially “co-dispersal”), spatial contiguity or engulfment, the immune system, maternal control of early development, and worker policing” [423].

Of course, whilst having a “policing mechanism” is a necessary condition for something to be a biological individual, Clarke recognises that it is by no means sufficient, on account of its being a “negative” condition: for example, the non-organism that is a human muscle cell “has ample policing mechanisms to cement common purpose amongst its component organelles and genetic material” [423]. The necessary positive mechanism for Clarke then is the “positive capacity to undergo natural selection at its own level” [423]. After relating this suggestion to the biological function of sex, Clarke extends this insight into a second functional criterion for biological individuality: a “demarcation mechanism”, which she defines as “any mechanism that increases or maintains the capacity of an object to undergo between object selection” [424]. Like policing mechanisms, demarcation mechanisms are highly multiply realisable. For example, spatial boundaries and immunity can often play a ‘demarcating’ role. Demarcation, Clarke claims, is also “essential to an evolutionary transition” [426].  As she explains, “Evolutionary transitions in individuality can be viewed as a failure to meet the demarcation challenge on the part of the lower-level individual. Mitochondria, for example, have lost their biological individuality because they became subsumed within eukaryotic cells” [426].

Clarke’s two mechanisms are, she thinks, sufficient criteria for biological individuality. She holds that “Biological individuals are all and only those objects that possess both kinds of individuating mechanism” [427]. This may seem like a very bold claim, but Clarke is anxious to point out is that the very nature of these mechanisms ensures that individuality is a continuous concept. She makes the important observation that “by incrementally increasing an object’s capacity biological individuals for heritable variance in fitness, compared to the capacity of its parts, individuating mechanisms can gradually push the object through an evolutionary transition in individuality” [430]. Indeed, she argues convincingly that it is necessary to recognise this kind of continuity in individuation in order to understand how evolutionary transitions happen at all.

In his 2012 paper “Darwinian Individuals”, Peter Godfrey-Smith outlines a somewhat more complicated account of biological individuality than Clarke’s. Although it has many features in common with Clarke’s, and whilst it’s not clear that the two accounts are in any kind of strong tension, Godfrey-Smith’s separation of two partly-overlapping sub-genres of biological individuals, “Darwinian individuals” and “Organisms”, and his very detailed accounts of each, lead to a scheme which is extremely good at dealing with ‘borderline’ cases, and yet produces much more definite entailments about specific cases than Clarke’s account. Clarke, I think, effectively argues for the value of a ‘functional’ definition of biological individuality, but I will argue that Godfrey-Smith’s work shows that a broadly functionalist approach can be combined with specific biological properties to produce a more complete overall account of biological individuality.

Both Clarke and Godfrey-Smith are chiefly concerned with coming up with an account of biological individuality fully in tune with the usage of “individual” within evolutionary theory. Clarke’s account is peculiarly devoted to what Godfrey-Smith specifically demarcates as the “Darwinian individual”, since her two individuating mechanisms have the ultimate function of enhancing “heritable variation in fitness”. In Godfrey-Smith’s attempt to come up with an account of the Darwinian individual, reproduction is the key factor (whereas for Clarke it goes along for the ride to some extent). Like Clarke, however, Godfrey-Smith is mainly concerned with “collective” individuals in formulating his account. Unlike Clarke, Godfrey-Smith’s account is explicitly continuous: he sees Darwinian individuality in terms of three dimensions, with the most exemplary cases measuring ‘high up’ in all three and non-Darwinian individuals measuring very ‘low down’ in all three. The first dimension or “parameter” is B, which stands for “bottleneck”. By this, Godfrey-Smith means any kind of “narrowing” that “marks the divide between generations […] often to a single cell” (as in humans) [2012: 6]. The second dimension is G, which stands for “germline”. G measures the degree of reproductive specialization within a collective. This property helps usefully distinguish kinds of ‘eusocial’ species. For example, in honey bee colonies “the queen reproduces (along with the male "drones"), and the female workers do not” (high score for G), whereas “In other insects, including other bees, there is no reproductive division of labor” (low score for G) [7]. The third and final, more functional dimension is I, which stands for “integration”. This does the ‘work’ of parts of both of Clarke’s individuating mechanisms, involving a “general division of labor (aside from that in G), the mutual dependence of parts, and the maintenance of a boundary between a collective and what is outside it” [7]. Godfrey-Smith uses this three-dimensional account to come up with an ingenious 1×1×1 cubic visualisation of where various species ‘sit’ in terms of their level of Darwinian individuality. Humans (and other mammals, marsupials, birds, many amphibians and many fish) are prototypical Darwinian individuals, with perfect (1,1,1) scores for each parameter; the Volvox carteri alga also scores highly with 1, 1, 0.5 (B, G, I); clonal colonies like the Aspen ramet scores 0.5, 0.5, 1; sponges score 0, 0, 0.5; and a buffalo herd is not a Darwinian individual at all, since it scores 0, 0, 0.

It seems to me that Godfrey-Smith’s ability to represent his classifications so elegantly represents a distinct advantage of his account over Clarke’s. In this, I fully endorse Daniel Dennett’s praise of the same diagrams in his review of Godfrey-Smiths’s 2009 book Reflections on Darwinian Populations and Natural Selection.

Godfrey-Smith then moves onto his account of ‘organismality’, where an ‘organism’ is understood as something distinct from a Darwinian individual – a concept that Clarke does not have. The way Godfrey-Smith defines an organism is as follows: “Systems comprised of diverse parts which work together to maintain the system's structure, despite turnover of material, by making use of sources of energy and other resources from their environment” [12]. This he calls the traditional, ‘metabolic’ view of a biological individual. Ultimately, the combination of this continuous organism concept and the Darwinian individual concept allows for Godfrey-Smith to define biological individuality for all of life. Many biological individuals – like humans, or fruit flies – are both Darwinian individuals and organisms. A much smaller number would be classified as relatively prototypical examples of Darwinian individuals but not organisms: “scaffolded reproducers” like viruses, along with chromosomes and genes [16]. Finally, some organisms are not Darwinian individuals. The more significant cases in this category “are certain kinds of symbiotic associations” [16]. Godfrey-Smith cites Dupre and Malley [2009] as showing that “most or all plants and animals live in close associations with symbionts” [16]. One specific example of a very close symbiotic relationship whose significance has only recently been uncovered is that between various tree species and “mycorrhizal fungi” which connect root systems in forests such that trees can ‘communicate’ threats and distribute resources to other trees in stress [Macfarlane, 2016]. (Basically, such tree-fungi fusions seem to fall under the category of organisms that are not Darwinian individuals (the fungi are crucial to the tree's fitness, as in the other example of a symbiont that is an organism but not a Darwinian individual which I'm about to discuss, but the tree and the fungi do not reproduce together, as one, in contrast to the aphid-Buchnera symbiosis.) Godfrey-Smith’s best example of an organism (albeit a non-prototypical organism) that is not a Darwinian individual is the “squid-Vibrio combination”, which has a “horizontally transmitted symbiont” as opposed to the “vertically transmitted symbiont” of the oft-cited aphid-Buchnera symbiosis. Even though the squid has evolved six internal ‘chambers’ designed to take in the bacteria that create a luminescent, moon-light-like patterning on their body and help them avoid avian predation at night, the fact that the squid are not born with these bacteria inside them means the partnership does not count as a Darwinian individual.

I think the big advantage Godfrey-Smith’s complicated account has over Clarke’s much more economical one is that, whereas he can apply his scheme to these exotic cases and produce definite (albeit ‘continuous’) verdicts, such verdicts do not directly fall out of Clarke’s considerably looser scheme (it seems to me that Clarke's account makes it very hard to disentangle the very relevant differences between the type of symbionts I discussed, for example). Clarke, in fact, ends her 2013 paper by insisting on the implausibility of a general system of classification for all of life – and yet it seems to me that that’s effectively what Godfrey-Smith achieves.

Reference List

Clarke, Ellen (2010). “The Problem of Biological Individuality”, Biological Theory, 5 (4): 312-325.

(2013). “The Multiple Realizability of Biological Individuals”, Journal of Philosophy 110 (8): 413-435.

Dawkins, Richard (1982). The Extended Phenotype, Oxford University Press.

Dennett, Daniel (2011). “Homunculi rule: Reflections on Darwinian populations and natural selection by Peter Godfrey Smith”, Biology and Philosophy 26 (4): 475-488.

Godfrey-Smith, Peter (2012). Frédéric Bouchard and Philippe Huneman (eds.) “Darwinian Individuals” in From Groups to Individuals: Perspectives on Biological Associations and Emerging Individuality, MIT Press. Accessed from:

<http://www.petergodfreysmith.com/PGS_Darwinian_Individuals.pdf>

Macfarlane, Robert (2016). “The Secrets of the Wood Wide Web”, The New Yorker, August Issue:

<http://www.newyorker.com/tech/elements/the-secrets-of-the-wood-wide-web>

Uncited:

Wilson, Robert A. and Barker, Matthew, "The Biological Notion of Individual", The Stanford Encyclopedia of Philosophy (Spring 2017 Edition), Edward N. Zalta (ed.): <https://plato.stanford.edu/archives/spr2017/entries/biology-individual/>.

Now that you've read that essay, I'm just start talking about how what you just read relates to the very deepest issues in metaphysics. Here goes.

I think that the better-known ‘species’ problem in the philosophy of biology is highly analogous to this problem of ‘biological individuality’, and I think that the metaphysics of Structural Realism helps us to see these similarities more clearly. It is, however, very complicated to show this, so bear with me.

There’s a great Philip Kitcher quote in a 2012 book I haven’t read called Preludes to Pragmatism: Toward a Reconstruction of Philosophy (I found it in Adam Hochman’s reply to Neven Sesardic on race, discussed in my recent post “Solving Race”) which nicely sums up my stance on the issue: “There is a nondenumerable infinity of possible accurate maps we could draw for our planet; the ones we draw, and the boundaries they introduce, depend on our evolving purposes” [150]. What does this mean? It means that the Platonic idea that the philosopher’s job is to “carve nature at its joints” introduces a false teleology which is seriously misleading. If you want an equally pithy slogan for the alternative metaphysical view, try this dialethic aphorism: nature has infinite joints and no joints. What I mean by this is that, although certain sets of joints will have help us increase our store of information, knowledge and our ability to predict the future far better than others, we can’t say where the joints are really because there is no place where the joints are really because there was no designer and nature itself doesn’t carve (agents carve). To focus specifically on biology, what this means is this: there are no absolutely True Biological Categories, there is no Objective Truth about how we should taxonomise dogs and wolves, whether marsupials count as mammals, whether those skeletons found in Morocco were really homo Sapiens or proto-homo Sapiens, or whatever. There is likewise no Objective Fact about whether honey bee colonies or coral and their algae are really one organism or two, or whether (per the Gaia Hypothesis) the earth is really an organism or just a homeostatic system with feedback cycles and some policing mechanisms, or whether there are really human races or just clinally varying ethnic groups. As Godfrey-Smith apparently likes to say, ESSENTIALISM IS DEAD!

Along similar lines (and with much relevance to this ‘problem of individuality’), we should also note, as David Hume did back in 1737, that our fundamental intuitions about the persistence of macroscopic objects and living things make no empirical sense. What is the sense in which that percept of a tree your brain processed ten minutes ago was a representation of the same tree a percept of which your brain is processing now? Only that the time slice you’re ‘looking at’ now evolved directly from the one ten minutes ago. What cannot be true, no matter how we want to believe it, is that the two time slices are identical. Why can’t this be true? Because the two time slices have loads of different properties, even just according to the macroscopic or standard, anthropic descriptions (there are perceptible differences in position of leaves, in position of ants on trunk, on the specific birds nesting or roosting or resting, you know that there has been some capillary action inside the tree to transmit water, and so on and so forth). So it’s a mistake to say that “the same tree persists through time”! The two trees are not the same tree! They’re different trees! Much closer to home are those famous questions of personal identity over time. We surely want to say that the five-year-old time-slice with ‘my name’ is identical with the set of human time slices typing these words. But any two five-year-old human time-slices are going to have more properties in common than this one does with that one to which we nevertheless ‘want to say’ I am identical… And if so, how does it make any sense at all to say “I am the same person as I was when I was five”? [Hume made these observations several hundred years ago, and I defend his bundle theory here (though I don’t endorse my rejection of ‘perdurantism’ here for reasons we’ll come to): http://writingsoftclaitken.blogspot.com.au/2017/05/persistence-and-personal-identity.html]. Now, later, I’ll explain how Structural Realism helps us make sense of how this can all be true while still allowing us to say that trees and people and chairs are perfectly real and also that they do in fact perdure.  The key point is that recognising all this relativity does not at all mean we slide into some kind of weird kind of Idealism or start babbling nonsense about ‘texts’ like some kind of Pomo ninny. To understand why it doesn’t mean this means understanding Structural Realism – so that’s where we’ll turn to now.

I only very recently ‘got’ Structural Realism. After reading the book Every Thing Must Go at the beginning of 2016, the thing that most confused me – though I felt I learnt a huge amount from the book and was taken with a lot of it – was how Ladyman and Ross and the rest of the crew could simultaneously reject the ‘levels’ metaphor, and maintain that oxygen, nitrogen, trees, animals, markets and “prices” (yes, prices) were real (even if non-fundamental and ‘second-order’). This seemed to me like a contradiction. I also strongly shrinked from their Quinean-type claim that any old entity used in a scientific theory ought to be regarded as real simply if it ‘pays its rent’ in contributing to the scientific success (so to speak). This particularly irked me when it came to their discussions of economics (and I generally just disliked the fact they kept talking about economics because I am a Steve Keen fan and (as far as politics and economics go) only read post-Keynesian economists, Stiglitz, Chomsky and Peter Turchin, and consequently have been led to believe that the entire economics profession should be radically reformed (incidentally, I also constantly got this really right-wing vibe from the book, not only because of the early footnote where they randomly slag off Marx and the repetitive references to mainstream economics, but because of the constant aggression and belligerence (only ameliorated by the fact that they used ‘she’ as the default pronoun)).

It was only a couple of months ago that I suddenly understood how Ladyman and Ross could happily and consistently reject the ‘levels’ metaphor and mereology while maintaining that people, chairs, table, cats, lemurs, ants, bacteria, species, prices, markets, oxygen, nitrogen, sulfuric acid, (and so on and so forth) are all “real patterns”. The trick was being reminded of Dennett's discussions of Conway's famous Game of Life cellular automaton. Here's the takeaway:

In some sense, everything is quantum fields (or whatever). This simply has to be true. Fundamental physics is fundamental in the sense that (we’re pretty sure) it describes phenomena to the same level of accuracy in every region of the observable universe. You don’t need what Ladyman and Ross call a “locator”, or an “address” for fundamental physics; it’s fundamental because the laws of fundamental physics are universal laws, describing universal structures of reality. Hence, in some sense, everything is quantum fields. So there’s one level of reality, and it is that described by fundamental physics.

So what’s with all this other less general shit? Where does it fit in? How can you be allowed to say everything that isn't fundamental physics can nevertheless be real if you insist that there’s only one level of reality? The answer is to think about The Game of Life. Out of simple patterns in The Game of Life you see more complicated patterns ‘emerge’ – patterns which are stable and persistent and which, if you track them, allow you to compress a lot of information about the dynamics of the system. What is the analogy with the real world? Well those stable, information-compressing patterns in the Game of Life have a direct analogue: any entity that earns its keep in institutionally approved and predictively successful science basically has to be one of those stable, information-compressing patterns. So how do we decide what is real of the patterns in reality that aren't the structures directly described by fundamental physics? Well, any kind of ‘projectible’ – stable, trackable – pattern is real. And how do we determine the projectible patterns? Well, our heuristic is that any ‘entity’ that pays rent in contributing indispensably to scientific theories that achieve significant empirical success in making predictions is a real pattern. And how have we avoided multiplying the levels of reality? Because even though these patterns have a life of their own, they are still patterns in fundamental physics. You, me, that bug, jellyfish, amoebas and prices are projectible patterns in the fundamental structures of reality (as Ladyman and Ross say, this view dispenses even the need for distinguishing between types and tokens, between categories and instances; real types and real tokens are both just projectible ‘patterns’ (and so the problem of ‘species’ and the problem of ‘individuality’ really become extremely similar problems)). So there is one level of reality, and we are patterns in it. There it is!

Hopefully, it’s possible to see why this metaphysics allows us to defend a kind of ‘perdurantism’ against the extreme Humean bundle theory that I defended just before (days before) I had these insights. An individual human is a stable, persistent, projectible pattern. You can confidently track them as stable patterns, described in different contexts by different fields (economics, anthropology, psychology) but more or less stable in terms of properties, throughout their worldlines. So they are real patterns and an individual human is a really persistent pattern (a real four-dimensional worm), even if the individual time slices (of the four-dimensional worm) are not really identical.

Something like that seems true anyway. (Incidentally, I’m appreciating Ladyman and Ross’ work even more on the second reading. Every Thing Must Go really is an impressive book.)

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[1] It should be noted that most of them clearly require conjunction with another one of the candidates to constitute any kind of non-circular criterion for biological individuality.