Very brief reading notes on a paper by Benoit Godin, ‘National Innovation System: the System Approach in Historical Perspective’. The basic goal of Godin’s paper is to argue that many of the core concepts of the National Innovation Systems literature – as articulated by Freeman, Lundvall, Nelson and others, from the late 1980s onwards – were already present in publications put out by the OECD in the 1970s. In these OECD publications, Godin argues, the ‘research system’ was composed of four sectors – government, university, industry, and nonprofit – and embedded within a broader economic and international environment. Analysis of the research system focused on five relationships: between economic sectors; between basic and applied research; those determined by policy itself; between the research system and the broader economic environment; and those associated with international cooperation.

This research system framework therefore already incorporated many of the elements of the later National Innovation Systems approach. Godin argues that there are two big differences between the research system and the NIS approaches. First, for the research system approach, government was regarded as having “prime responsibility in the performance of the system”. For the later NIS approach, “it would rather be the role of government as facilitator that was emphasised”. Second, the research system approach focused on the research system as a whole, whereas the NIS approach privileges the firm as the key component of the system. Both of these shifts (I would argue) can be seen as representative of the shift towards neoliberal economic governance and theory.

Some quick notes on Mirowski’s ‘Science-Mart: Privatizing American Science’. This is a wide-ranging semi-popular book about neoliberal governance of US science, with different chapters addressing different elements of the topic. These include:

– a very critical survey of the economics of science;
– periodisation of twentieth and twenty-first century US science governance into three regimes: the ‘captains of erudition’ regime in which the modern research laboratory developed; the ‘cold war’ regime in which the state greatly increased both its funding and its control of scientific production; and the ‘neoliberal’ regime characterised by privatisation of the research process and greater ‘enclosure’ of scientific inputs and outputs in intellectual property law;
– a discussion of material transfer agreements and the constraints they place on researchers;
– a critique of biotech – and, more broadly, commercialised science – as a ‘Ponzi scheme’ in which very few companies are, in fact, commercially viable;
– an argument that the quality of scientific research outputs is declining as a result of the neoliberalisation of science;
– a discussion of a range of different ways in which the neoliberal regime produces ignorance, rather than knowledge (such as the ghostwriting of apparently independent research papers by employees of pharmaceutical companies, for example).

All up the book is a concerted attack on ‘neoliberal science’, and connects to Mirowski’s critiques of other dimensions of neoliberal economic governance, in other works.

My take, fwiw: some of the book provides a good entry into important issues in the political economy of science, and Mirowski’s periodisation seems like a useful way to carve up both the political and the intellectual history of US science governance. Mirowski’s discussion of the deliberate creation of systematic bias in the scientific literature is good as far as it goes – though I’d recommend Ben Goldacre’s ‘Bad Pharma’ as a popular work focussed specifically on this issue. However, I think Mirowski’s book as a whole should be approached with some caution.

It’s possible I have the wrong end of the stick, but it seems to me that Mirowski’s critique of biotech as a ‘Ponzi scheme’ is based on a misunderstanding: in a speculative industry many companies can fail because the investment gambles they take do not pay off in the creation of a marketable product. This fact enables fraudsters to make money off the industry, because a straight-up fraud is from a distance indistinguishable from a bad but rational bet – so significant segments of a speculative industry based on product innovation will, typically, be actual scams. Nevertheless, provided the few success stories are profitable enough, the industry as a whole can be fulfilling the capitalist social function of profit generation just fine – and the rent-seeking associated with intellectual property monopolies over medical goods means that successful medical innovations are indeed often extremely profitable.

Mirowski also seems to me to sometimes be unreliable as a summariser of the intellectual figures (mostly economists) that he discusses. Mirowski is critical of economists who advocate for neoliberal policies (privatisation, expanded intellectual property rights, etc.); but he is also critical of economists who oppose these policies, on the grounds that – as economists – they are tacitly supporting the same policies regardless, by virtue of their use of ‘neoclassical’ economic theory. So, for example, Paul David (an advocate of open science, who engages heavily with intellectual resources outside economics, and who has also developed models of scientific research dynamics that do not make use of the ‘rational actor’ approaches Mirowski elsewhere criticises) is nevertheless for Mirowski as much a participant in the neoliberalisation of science as those advocating neoliberal policies, by virtue of him using game theoretic and other ‘neoclassical’ modelling tools. Social scientists can of course be criticised for tacit implications of their approaches which contradict their stated policy goals. But Mirowski’s broad brush dismissal of economics of science as a whole seems excessive, to me.

At some point maybe I’ll write something on Mirowski’s criticisms of neoliberalism more broadly – my thoughts on this issue don’t feel quite nailed down enough, yet – but I wanted to put up these very brief notes on Science-Mart now, before it all goes down the memory hole.

Economics as Science

October 29, 2013

The recent Nobel Prize [1] in economics has prompted a fair bit of commentary/discussion along the lines of ‘is economics a science’? I thought I’d add to that commentary. The extremes of the commonly articulated positions are roughly:

“Of course it is – and a stronger, more manful, more mathematical science than your [puny / relativistic / fraudulent / etc.] [psychology / sociology / history / etc.]”

“Of course it isn’t – it’s a series of barely coherent apologies for the interests of the powerful, detached from any reference to or understanding of the suffering inflicted upon billions by the policies it advocates and sophistically excuses”

With of course a range of other positions too.

The former of the two positions above is articulated principally by economists; the latter principally by left critics of economics. I’m in many respects on the left [2] – but I’m also in training to become an economist. Where does that place me? [Well – not to build up suspense: I think economics is indeed a science (that’s why I think it’s worth doing economics). But the longer version follows.]

Prior question: what does it mean for something to be a science? As a first pass, I take a disciplinary research-space to be a science if:

1) The object it studies is a real phenomenon that can actually be empirically studied.[3] [4] (So astrology doesn’t count – because the relationships between celestial objects and human personality is not a real phenomenon; but astronomy does count, because celestial objects are real things.) (What’s actually real is of course itself a scientific question – but so it goes; there’s no paradox there – just the usual Neurath’s Boat principle of there being no discursive ‘outside’.)

2) There exists a set of established norms and research practices for testing claims about these objects against empirical evidence – for an endeavour to be scientific, claims must be vulnerable to rejection in the light of empirical findings.

3) There’s a discursive space, for researchers, within which those norms for testing claims against evidence can themselves be debated, contested and transformed.

Science is therefore a communal endeavour – it can’t exist outside of a community of research. Science relies on the collection of evidence; the positing of claims on the basis of and for testing by evidence; and the collective ongoing assessment of the evidence, the claims, the methodological connections between the two, and the norms governing the whole endeavour, within a community of researchers.

This definition of science does not require the following things:

– That practitioners of scientific inquiry be particularly rational. All else being equal it’s better for practitioners to be reasonable and informed than not, but the ‘rationality’ of the system resides principally in the possibilities made available by the overall institutions of the system, rather than in the virtues of individual researchers. (It is necessary, though, for a sufficiently large number of members of the community to be committed to reproducing those broad institutional practices enumerated above, that the practices are indeed reproduced.)

– That scientific claims be correct. The whole point of the scientific endeavour is that claims (including both empirical claims and the methodological claims that inform empirical claims) are open to revision.

– That members of a research community be capable of predicting the future behaviour of the phenomena studied. Some phenomena are amenable to this, in the current state of knowledge; others are not. It is not a requirement of science that predictions of future events be created, only that evidence (including evidence generated by future events) be capable of modifying our claims.

– Relatedly, that ‘general laws’ be discovered. Science can study unique specificity just as scientifically as it can study general principles; one is not more sciencey than the other.

– That there be broad consensus on most major topics within the research community. One hopes that warranted consensus can be established, but an important part of the mechanism from which it might emerge is disagreement.

None of those things just listed need apply for a community to count as a group of scientific researchers.

In these terms, is economics a science?

I think the answer is: clearly yes, economics is a science. There is a real object of study (the economy, however that’s understood). There are established principles for collecting evidence and testing claims against evidence. There are ongoing (quite sophisticated) debates about the methodological principles involved in these tasks. So I think economics is pretty unambiguously a science, and I’m happy to be a member of and participate in that research community of scientific practitioners.[5]

What about the second objection, though? The objection that economics is just serving the interests of the powerful, etc.?

Well – just because a community of research meets all the criteria for science, doesn’t mean that it isn’t full of bullshit – nor does it mean that the kinds of bullshit dominant in a field are in any way accidental. Here, as often, it’s important to distinguish between best practice and actual practice. Best practice is not something that exists independently of actual practice – it is generated in and emerges out of actual practice. But it is also something that actual practice can be judged against – and often judged severely. Like any discursive space, the discursive space of economics is variegated – it contains many voices in dispute. In participating in that discursive space, we add our own voices and evaluate those voices already in contention. The norms of evaluation – and, therefore, the conclusions – that we take away from engagement in that space may be minority views relative to the space overall.

So it’s important to distinguish between the claim that economics is a science, and the claim that economics in general has things right, or is even on the right path. It’s important to have an account of the many things wrong with economics too – which I’ll start to talk about in a future post.

[1] The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel

[2] This post articulates my politics I think reasonably well – although I’m losing patience with left positions and figures sufficiently rapidly that, while I don’t think I’m on the classic ‘Trot to neocon’ ideological trajectory here [not least because I was never a Trot, but you know what I mean], it’s hard not to see why some such view would look reasonable, from the outside.

[3] “What about mathematics?” Well, mathematical objects (whatever their status – as it happens, I have a conventionalist line on the status of mathematical objects, but nothing here relies on that) can’t be empirically studied, so mathematics isn’t a science in this sense. What gives mathematics its objective character (on my account at least) is the degree of consensus that can be (and has been) attained around mathematical norms – math is pretty much unique in this respect. This is what distinguishes mathematics from, say, theology, which also has an object of study of ambiguous status (real? fictional? social? supernatural?) but where the degree of consensus is far lower, even within specific religious communities, let alone between religions.

[4] “What about the SCIENCE OF BEING that myself and three other graduate students in this Heidegger course are developing?” Sorry – that’s not a science.

[5] Note, though, that economics is not a more manful or vigorous science than any other social science, even if it involves a lot of math.

Interacting with some medical professionals, recently, has made me think a bit more about evidence-based belief and practice. I am in favour of evidence-based belief and practice; my ‘theoretical’ perspective is a broadly empiricist one, with an admixture of (to my mind) relatively sophisticated pragmatism. But what does evidence-based belief and practice consist in?

This is a more complicated question than it may appear, and these remarks don’t aim to do more than touch on the relevant issues. But, trivially, for our belief and practice to be evidence-based is for our beliefs and practices to be oriented to, and ‘checkable by’, the way things are in the world. In evidence-based belief and practice, we grant authority to empirical evidence to legitimise or de-legitimise our beliefs and practices. And, further, we evaluate this evidence itself by sets of rationally and empirically justifiable criteria, to evaluate which evidence counts as good evidence and warrants such authority, and which does not.

This granting of authority to specific types of event or entity – ’empirical evidence’ – is a social practice. Authority – on the Brandomian pragmatist metatheoretical approach I endorse – is created and granted by sapient entities’ social practices. We grant a specific social status to specific kinds of non-human things (pieces of evidence), such that these non-human things can possess social authority within human discourse. Once authority has been granted in this way, it cannot – again, as a matter of social practice – necessarily be easily revoked; this is one reason why, on a Brandomian account, the authority of evidence can go against all human preferences or authority-decisions, even when authority has its source only in human action.

In the ‘analytic’ philosophical tradition – as, often elsewhere – there is a tendency to assimilate the evidence-based responsiveness of the typical sapient organism reacting to environmental stimuli (the phenomenon of experience, or perception) to the formalised truth-seeking investigatory practices of the modern sciences. Willard van Orman Quine puts the point as follows:

The scientist is indistinguishable from the common man in his sense of evidence, except that the scientist is more careful.

I disagree with this assimilation: I think that the belief-forming practices of scientific investigation are quite socially and historically specific, and should not be seen as the extension, or fuller realisation, of more mundane and broadly-engaged-in practices of everyday empirical observation. Science cannot be defended on those grounds; it must be defended in its social and historical idiosyncrasy.

I believe this defence is a worthy one; I am an advocate for scientific practice. But engaging in this metatheoretical defence of science involves steering between two, opposing, flawed accounts.

On the one hand, if we understand science as through-and-through a social practice like any other, there is a temptation to see this perspective as robbing science of its authority (rather than as explicating the nature of its authority); this approach can therefore often lead theorists into a relativism that regards our choice of the scientific approach as arbitrary or unjustified. In classical social theory, this perspective is perhaps best expressed by Max Weber’s movingly pessimistic reflections in Science as a Vocation, where Weber’s own commitment to the social scientific endeavour is presented as an ultimately irrational obedience to a demon “who holds the fibers of his very life.” In more recent social theory, a similar perspective is conveyed well by the Edinburgh strong programme’s conviction that the social-scientific analysis of scientific practice leads, inevitably and correctly, to relativism.

Relativism – whether it understands itself as anti-science, as a consequence of science, or both – is a common object of critique. The opposing flaw is also a serious one, however: this is the perspective that grounds science’s authority in an appeal to the way things are in the world, without seeing how this appeal must itself be understood as a social practice embedded in a complex system of social practices. For this approach, in Hegel – and Marx’s – phrase, “the process vanishes in the result”: the mechanism by which truth-claims are arrived at is forgotten, and truth-claims are wielded as if they are the source of science’s social authority, rather than the result of that authority (as is in fact the case). These approaches, then, are dogmatic – they understand themselves as (and, in most practical contexts, are) pro-science, but they have an inadequate understanding of what science is, as a historically-specific set of social practices. Advocates of this perspective may be able to do science, but they are not able to adequately justify their findings, without relying on a tacit set of social norms that their dogmatism overtly denies. Many of the pugnacious contemporary advocates of science, like Richard Dawkins and Daniel Dennett, belong in this category.

If, then, we are to be good – or, more to the point, metatheoretically enlightened – proponents of evidence-based belief and practice, we need to steer a course between these twin dangers of relativism and dogmatism. This can comfortably be done – in the posts here on Robert Brandom I’ve gone some way towards explaining the broad metatheoretical approach that, to my mind, best enables such a position (though, again, Brandom’s work is pitched at the level of everyday empirical experience, rather than scientific practice). But I am interested, now, in beginning to actually do evidence-based work. I’ve still got a lot to do in elaborating the metatheoretical perspective I endorse; but I also want to begin to leave that space behind. Enough with philosophy; enough, especially, with ‘Theory’ that regards itself, in a smug but profoundly confused way, as ‘post-empiricist’. I’ve spent enough of my life in that space already. The task of social science is to describe and analyse the social world, through the collection and interpretation of data; that’s the project I’m committed to; one of these days I’d like to get to work.

The mathematics of inferential statistics is based on the logic of random sampling: the inferences we make in inferential statistics work on the assumption that the data we are inferring from is randomly sampled from the population we are inferring to – that every member of the population has an equal chance of ending up in our dataset. Obviously this usually isn’t the case; but that’s the assumption, and the further our actual sampling practice deviates from that ideal situation, the less likely our inferences are to have any validity.

In much inferential statistics, the population we are sampling from is an actual population of cases, which could in principle be observed directly if we only had the money, time, staff, access, etc. etc. Here the ideal situation is to create a sampling frame that lists all the cases in the population, randomly select a subset of cases from the sampling frame, and then collect data from those cases we’ve selected. In practice, of course, most data collection doesn’t work this way – instead researchers pick a convenience sample of some kind (sometimes lazily, sometimes unavoidably) and then try to make the argument that this sampling method is unlikely to be strongly biased in any relevant way.

Sometimes, however, the population from which we draw our sample is not an actual population of cases that happen for contingent practical reasons to be beyond the reach of observation. Sometimes the population from which we draw our sample is a purely theoretical entity – a population of possible circumstances, from which actuality has drawn, or realised, one specific instance. Thus our actual historical present is a ‘sample’ from a ‘population’ of possible realities, and the generalisations we aim to make from our sample is a generalisation to the space of possibilities, rather than simply to some aspect of crass and meagre fact.

When we make claims that are predictive of future events, not merely of future observations of present events, we are, tacitly or overtly, engaged in this endeavour. To predict the future is to select one possible reality out of a space of possibilities, and to attribute a likelihood to this prediction is to engage in the statistical practice of assigning probability figures to a range of estimates of underlying population parameters – or, equivalently, to give probability figures to a range of estimates of future sample statistics ‘drawn from’ that underlying population. I may try to articulate this point with more precision in a future post – I’d like to spend more time on Bayesian vs. frequentist approaches to probability. And there is, of course, a ‘metaphysical’ question as to whether such a ‘population’ ‘really exists’, or whether the ‘samples’ themselves are the only reality, and the ‘population’ a speculative theoretical entity derived from our experience of those samples. Functionally, however, these stances are identical: and by my pragmatist lights, to note such functional equivalence is to collapse the two possibilities together for most theoretical purposes.

When we speak of universal natural laws, then, we are stating that a given fact – the law in question – will be true in the entire range of possible worlds that might, in the future, be actualised in reality. (Whether this ‘possibility’ should be understood in ontological or epistemological terms is beside the point). For some, it is the role of science to make such predictions: on this erroneous stance, science attempts to identify universal features of reality, and any uncertainty that accrues to scientific results is the uncertainty of epistemological weakness, rather than ontological variation. Here, for example, is a video of Richard Feynman making fun of social science for its inability to formulate universal laws of history:

To take this attitude is to misunderstand the nature not just of social science, but of science in general. Science is not characterised by a quest for certainty or for permanence, but is rather characterised by an ongoing collective process of hypothesis formation and assessment, based on specific collectively accepted evidentiary standards. The conclusions of science cannot be certain, because they must always be vulnerable to refutation in the light of empirical evidence and the application of community norms of argument. Similarly, the phenomena examined by science need not be necessary, or even ongoing. A scientific endeavour can be entirely descriptive, of the most local and variable phenomena imaginable, so long as the process of description is subject to the appropriate communal evidentiary norms. It can, similarly, be explanatory without being predictive, for we can analyse the causes of the phenomena we observe without being able reliably to predict those causes’ future impacts and interactions. The set of phenomena regarding which long-term or even short-term reliably predictive hypotheses can be formed is smaller than the set of phenomena that can be studied empirically using the relevant community norms of hypothesis formation and assessment.

The social sciences often approach this limit case of the purely descriptive. Social reality is enormously variegated – and often there is little in the way of testable general claims that can be taken from a study of any given social phenomenon. But prediction is nevertheless sometimes the goal of social science. When the social sciences aim to study social phenomena, the ‘laws’ they aspire to uncover are always local and limited in scope – and when we form a hypothesis, this hypothesis applies within a certain local limit and no further. Where to draw the line – where to locate this limit – is a qualitative question that the community of social scientists must always bear in mind, but the existence of this limit in no way renders the endeavour ‘unscientific’.

When we make a social-scientific prediction, then, we are making a claim about what future reality will drawn from the space of possibility. We do not know the scope of this space – nor do we have any reason to regard the principle of selection as random or unbiased – indeed, we have strong reasons to believe the contrary. Further, the nature of social reality is such that we can and do aspire to intervene in this selection – to attempt to influence what possibilities are realised. As social scientists we sometimes aim to predict what outcomes will be drawn from this space of possibilities – and such a prediction can only be made within the framework of a broader, historically informed judgement of the narrower space, within the space of possibilities, that we aspire to model.

But we should also be aware of other, unrealised but potentially realisable social possibilities, beyond the set of possibilities we are modelling at any given moment. Part of the function of the scrupulous social scientist is to describe this space of possibilities itself – to describe not just regularities, but also the possible variety from within which those local regularities are drawn. We cannot know the limits to the space of possibilities – no sampling frame of possible societies exists. But we can explore what the ‘samples’ themselves – existing and historical societies and behaviours – tell us about the scope of that hypothetical space.

This latter task is where social science intersects with political practice. The understanding of the likely behaviour of social reality is important for political practice – but so too is a sense of the larger space of possibilities from which our own past and present societies have been drawn, and from which alternative futures could be drawn, or made, if we only had the political ability to do so.