NHS Cuts by Region

Éoin Clarke’s blog has a post on the uneven geographical distribution of NHS cuts. He writes:

The wealthiest, and dare I say it Toriest, parts of England have actually experienced no job losses. The South East of England has actually grown its NHS workforce since the May General Election, while the North West of England alone has experienced more than 6,500 job losses.

His post includes a chart. Clarke’s chart shows absolute figures – I thought I’d make my own version of it, showing percentage change. This doesn’t make any real difference to the story, but here it is anyway. Note that these figures are Hospital and Community Health Service staff, excluding primary care staff – lots of NHS employment isn’t captured.

Click the chart to enlarge it. Data from here.

Reliabilism

A helpful way to approach Brandom’s inferentialism is to look at some of the positions he takes it to oppose. In this post I will begin to discuss one such position, which Brandom labels ‘reliabilism’.

Recall that the opposition here is between positions that take representation or reference to be explanatorily primitive within semantics, and Brandom’s own position, which argues that representation or reference can be fully explained in terms of inference. For Brandom, inference is a social thing – the language-behaviours with which we attribute and endorse inferences are social behaviours. Reference, by contrast, very much appears to involve something other than social practice – indeed, it is hard to imagine a credible account of reference that does not involve entities and events that would not ordinarily be categorised as social. If I state that the surface temperature of the planet Mercury can reach 700 degrees Kelvin, and that by around 8 billion years from now, by our best estimates, that planet, along perhaps with Earth, will have been destroyed by the expansion of the sun to around 256 times its present radius, I am making claims about entities that do not, in any very narrow sense, participate in our social practices. Neither are the causal mechanisms by which these entities (Mercury; the sun) impact upon our senses and equipment, mechanisms that would ordinarily be called ‘social’

Nevertheless, Brandom believes that representation should be understood in social terms. What does this mean?

We can start to unpack this by contrasting Brandom’s position with a common alternative account of reference, which Brandom (following standard philosophical usage) labels ‘reliabilism’. The issue here, of course, is how we gain empirical knowledge of the world – we are interested for now in language-entry moves (perception), rather than language-exit moves (action).

Recall, then, that Brandom’s account is built up out of reliable differential responsive dispositions (RDRDs). RDRDs are the basic (if you like ‘ontological’) building blocks of Brandom’s account. [Though this is a ‘weak’ rather than a ‘strong’ ontology, in that it does not make any claims about the ‘fundamental’ entities that inhabit our world; merely the kinds of behaviour some entities in our world must be capable of exhibiting if our account is to have any explanatory function.] Given this, it would seem to make sense for Brandom’s system to take reliability of responsiveness to stimuli as the core of its account of representation.

Brandom frames his discussion of reliabilism in relation to the classic definition of knowledge as justified true belief. In his seminal “Is Justified True Belief Knowledge?” Edmund Gettier attributes this position to A.J.Ayer, Roderick M. Chisholm, and, more tentatively, Plato. I need, obviously, to do a more thorough review of the literature here…

A central question for justified true belief accounts of knowledge, is what constitutes justification. Brandom’s overall strategy, in keeping with his normative phenomenalism, is to explain justification in terms of the social practices of taking-as-justified. (Similarly, Brandom will explain truth in terms of taking-true; and he will explain the content of belief – that which makes a belief a belief about something, and thus a belief at all – in terms of the inferential practices of attributing and acknowledging commitments. This is all, for now, a separate set of issues.)

However, the justification of our beliefs does not prima facie have to be accounted for in terms of the activity of justifying them. As Brandom puts it:

It is generally agreed that some sort of entitlement to a claim is required for it to be a candidate for expressing knowledge. But it is not obvious that inferring in the sense of justifying is at all fundamental to that sort of entitlement.

The core point, it appears, is that our beliefs cannot be accidental if they are to be capable of counting as justified. A belief formed by flipping a coin will not (unless, perhaps, we attribute the coin-flip’s outcome to the intervention of a supernatural power) provide justified belief even if (by chance) it provides true belief. An account is needed of the non-fortuitous formation of a belief if it is to be a candidate for knowledge.

‘Reliabilism’ does exactly this, without invoking inferential justification. In Brandom’s words:

[T]he correctness of the belief is not merely fortuitous if it is the outcome of a generally reliable belief-forming mechanism. Epistemological reliabilists claim that this is the sort of entitlement status that must be attributed (besides the status of being a true belief) for attributions of knowledge.

Brandom makes use of Alvin Goldman’s ‘Barn Facade County’ thought experiment (from Goldman’s “Discrimination and Perceptual Knowledge”, Journal of Philosophy 73, no. 20 (1976)) to argue against the reliabilist position. I’ll talk about this next.

Weak, Strong and Hyper- Inferentialism

Brandom’s system is an ‘inferentialist’ one. Brandom frames much of his work by contrasting this ‘inferentialism’ with what he calls ‘representationalism’. These are two different approaches to understanding conceptual content. ‘Representationalism’ is the view that representation should be taken as explanatorily fundamental for semantics. On this picture, certain linguistic units have meanings by virtue of their powers to denote, or refer. These units can then be connected and combined in propositional structures or belief-webs, the subsections of which can be connected by chains of inference. This is the picture of language proposed by Bertrand Russell, and by the early analytic philosophers working within the logistic paradigm Russell popularised within the English-language discipline. There are simple units of reference, which can be combined and manipulated by using fundamental logical tools of inference. Inference is explanatorily basic, also, on this account – inference cannot be explained in terms of reference. But reference likewise cannot be explained in terms of other semantic concepts.

‘Inferentialism’, by contrast, takes inference as explanatorily fundamental. Furthermore – and seemingly implausibly – it suggests that representation can be explained in terms of inference. On this inferentialist picture, inferences do not connect independently-comprehensible representational content-units. Representations can only be understood – and can be fully explained – as products of inferences.

In Making It Explicit (and in other works) Brandom distinguishes between three kinds of inferentialism: weak inferentialism, strong inferentialism, and hyper-inferentialism. Brandom himself endorses ‘strong inferentialism’. Here are his characterisations of the positions (from Articulating Reasons, p. 219-220):

Weak inferentialism is the claim that inferential articulation is a necessary aspect of conceptual content. Strong inferentialism is the claim that broadly inferential articulation is sufficient to determine conceptual content (including its referential dimension). Hyperinferentialism is the claim that narrowly inferential content is sufficient to determine conceptual content.

Obviously the key here is the difference between “broadly” and “narrowly” inferential content. Brandom characterises the difference as follows:

Broadly inferential articulation is sufficient to determine conceptual content. Broadly inferential articulation includes as inferential the relation even between circumstances and consequences of application, even when one or the other is noninferential (as with observable and immediately practical concepts), since in applying any concept one implicitly endorses the propriety of the inference from its circumstances to its consequences of application. Narrowly inferential articulation is restricted to what Sellars calls “language-language” moves, that is, to the relation between propositional contents.

Brandom presents here a ‘web of belief’ picture, in which propositional contents are related inferentially. Proposition A implies proposition B, and both are incompatible with proposition C, etc. If we understand propositional contents in linguistic terms – propositions being things that can be expressed in sentences – then we can think of the inferential relationships between propositions as relationships between specific linguistic contents. An inference is a “language-language” move, in that it connects one linguistic content to another linguistic content.

Hyperinferentialism, as Brandom characterises it, suggests that linguistic content can be fully understood in terms of these language-language moves. This has some similarities to the class of positions discussed by John McDowell (in his Mind and World) under the heading of ‘coherentism’ (McDowell’s particular target in these discussions is Donald Davidson). The objection to this position is that it seems to sever conceptual content from any connection to the outside world (or, more properly, any rational connection – any connection that can rightly be taken as placing a warranted constraint or having a justificatory bearing on the content of our propositions). In McDowell’s words, this picture:

depicts our empirical thinking as engaged in with no rational constraint, but only causal influence, from outside… Coherentist rhetoric suggests images of confinement within the sphere of thinking, as opposed to being in touch with something outside it.

Brandom too regards this as the likely penalty of a hyperinferentialist understanding of conceptual content. Such an understanding, Brandom claims, may be plausible “at most for some abstract mathematical concepts” (AR, p. 220). It is, however, an inadequate explanatory apparatus if we aspire to treat the empirical richness of most conceptual content.

Weak inferentialism, by contrast, suggests that while inferential connections between propositional contents are a necessary component of our explanation of conceptual content (a concept cannot have content if nothing follows from that content), an account of inference cannot be sufficient to fully explain conceptual content: some other category – i.e. reference – must be brought in to account for the (rational) connection between words (or propositional contents) and things.

What is the nature of the ‘strong inferentialism’ Brandom advocates, which aims to chart a course between these two alternatives? Another way of putting this: what is the category of ‘broad inference’ that encompasses more than simply language-language moves under the heading of inference, for Brandom?

The important Chapter 4 of Making It Explicit addresses these issues. There Brandom discusses ‘Perception and Action’ – or, as he also terms then, ‘language-entry’ and ‘language-exit’ moves. Language-entry moves (perceptions) allow things outside of linguistic practice (the regular furniture of our world) to impinge upon, influence, generate and destroy the conceptual contents we manipulate in our thoughts and statements – to have a bearing upon which conceptual contents are warranted, and which are not. Language-exit moves, by contrast, allow our concepts to impact upon the world in more thoroughgoing ways than via the usual articulation of sentences or interaction of brain-behaviours – we act and transform the world in ways that are connected to our beliefs, and the justification or otherwise of these actions is connected to the content of those beliefs.

How can these perceptions and actions be folded within an ‘inferentialist’ account of conceptual content? In what sense should the perception of a moving rock, or the action of kicking one, be understood ‘inferentially’?

The Space of Possibilities

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.

The Statistical Significance of Differences in Statistical Significance

Continuing my new practice of linking to and attempting to summarise statistics papers, here is a short piece by Andrew Gelman and Hal Stern:

Gelman, Andrew and Stern, Hal, ‘The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant’, The American Statistician, November 2006, Vol. 60, No. 4 328-331 [pdf]

If I understand things aright, Gelman and Stern make the following point: that the emphasis on statistical significance in the reporting of results in the social sciences can lead to a misleadingly firm line being drawn between statistically significant and non-statistically significant results – which itself misrepresents the statistical significance of differences between results.

For example: if we are testing the same hypothesis against two different samples, and find a statistically significant result for one but not for another, this may lead us to draw a strong distinction between our two samples. One yields statistical significance and another does not – what difference could be clearer? Nevertheless, this does not itself indicate any statistically significant difference between our samples. If one test yields statistical significance at p = 0.0499, and another test does not yield statistical significance, at p = 0.0501, we have probably not discovered a dramatic difference between them. The actual difference between our samples is presumably tiny – yet because the difference in p value happens to bridge our choice of significance level, this difference can easily be reified, when equally large, or larger, differences between other samples are ignored.

This is intuitive enough – but the same point can apply even when the differences in p value are very substantial. Gelman and Stern write:

Consider two independent studies with effect estimates and standard errors of 25 ± 10 and 10 ± 10. The first study is statistically significant at the 1% level, and the second is not at all statistically significant, being only one standard error away from 0. Thus, it would be tempting to conclude that there is a large difference between the two studies. In fact, however, the difference is not even close to being statistically significant: the estimated difference is 15, with a standard error of … 14.

Additional problems arise when comparing estimates with different levels of information. Suppose in our example that there is a third independent study with much larger sample size that yields an effect estimate of 2.5 with standard error of 1.0. This third study attains the same significance level as the first study, yet the difference between the two is itself also significant. Both find a positive effect but with much different magnitudes. Does the third study replicate the first study? If we restrict attention only to judgments of significance we might say yes, but if we think about the effect being estimated we would say no, as noted by Utts (1991). In fact, the third study finds an effect size much closer to that of the second study, but now because of the sample size it attains significance.

In a blog post that references this paper, Gelman writes:

I’m thinking more and more that we have to get rid of statistical significance, 95% intervals, and all the rest, and just come to a more fundamental acceptance of uncertainty.

I don’t yet know what Gelman means by this latter clause, or what alternative approaches he endorses.

Religiosity and Political Alignment in the UK

There was a piece in the Guardian recently with the headline “Religious people are more likely to be leftwing, says thinktank Demos”

new research suggests… people with faith are far more likely to take left-of-centre positions on a range of issues… The report found that 55% of people with faith placed themselves on the left of politics, compared with 40% who placed themselves on the right.

The figures given here are unhelpful. The relevant comparison is of course not the percentage of people with faith who identify as left, versus the percentage of people with faith who identify as right – but, rather, the political positions of those with faith compared to the political positions of those without.

So – let’s look at the report – specifically figure 7. “The social and political views of people who belong to religious organisations and those who do not, in western European countries and the UK”

The cluster of bars C indicates that 55% of people in the UK who belong to a religious organisation (not “people with faith” as the article says, but so it goes) place themselves on the left in politics. So far so good. What about people in the UK who do not belong to a religious organisation – what percentage of this group places themselves on the left? Well, the chart is a bit hard to read, but we can go to Appendix B and look at table 17a to find that it’s 62%. [Chi-square p = 0.0125]

I.e. those who belong to religious organisations in the UK are on average considerably less likely to identify as left of centre than those who don’t. The headline is precisely wrong – it should read “Religious people are more likely to be rightwing…”

There’s plenty else wrong with the report and its coverage, but that’ll do. Fucking Demos.

[ PDF of the report here: http://www.demos.co.uk/files/Faithful_citizens_-_web.pdf?1333839181 ]

Researcher Degrees of Freedom

Another good piece on common misuses of statistics (full details at the bottom of the post) – this one demonstrating (among other things) that listening to different types of music will change your age:

Using the same method as in Study 1, we asked 20 University of Pennsylvania undergraduates to listen to either “When I’m Sixty-Four” by The Beatles or “Kalimba.” Then, in an ostensibly unrelated task, they indicated their birth date (mm/dd/yyyy) and their father’s age. We used father’s age to control for variation in baseline age across participants. An ANCOVA revealed the predicted effect: According to their birth dates, people were nearly a year-and-a-half younger after listening to “When I’m Sixty-Four” (adjusted M = 20.1 years) rather than to “Kalimba” (adjusted M = 21.5 years), F(1, 17) = 4.92, p = .040

The gag here, of course, is that if you have enough data, and you analyse it in enough different ways, you’ll be able to find a statistically significant result almost anywhere. The authors of the paper reproduce this same passage later, with some additional phrases added to give a fuller account of the data collection and analysis process:

Using the same method as in Study 1, we asked 20 34 University of Pennsylvania undergraduates to listen only to either “When I’m Sixty-Four” by The Beatles or “Kalimba” or “Hot Potato” by the Wiggles. We conducted our analyses after every session of approximately 10 participants; we did not decide in advance when to terminate data collection. Then, in an ostensibly unrelated task, they indicated only their birth date (mm/dd/yyyy) and how old they felt, how much they would enjoy eating at a diner, the square root of 100, their agreement with “computers are complicated machines,” their father’s age, their mother’s age, whether they would take advantage of an early-bird special, their political orientation, which of four Canadian quarterbacks they believed won an award, how often they refer to the past as “the good old days,” and their gender. We used father’s age to control for variation in baseline age across participants. An ANCOVA revealed the predicted effect: According to their birth dates, people were nearly a year-and-a-half younger after listening to “When I’m Sixty-Four” (adjusted M = 20.1 years) rather than to “Kalimba” (adjusted M = 21.5 years), F(1, 17) = 4.92, p = .040. Without controlling for father’s age, the age difference was smaller and did not reach significance (Ms = 20.3 and 21.2, respectively), F(1, 18) = 1.01, p = .33.

The authors dub this sort of problem “researcher degrees of freedom”. It is a form of data mining.

In the course of collecting and analyzing data, researchers have many decisions to make: Should more data be collected? Should some observations be excluded? Which conditions should be combined and which ones compared? Which control variables should be considered? Should specific measures be combined or transformed or both?

It is rare, and sometimes impractical, for researchers to make all these decisions beforehand. Rather, it is common (and accepted practice) for researchers to explore various analytic alternatives, to search for a combination that yields “statistical significance,” and to then report only what “worked.” The problem, of course, is that the likelihood of at least one (of many) analyses producing a falsely positive finding at the 5% level is necessarily greater than 5%.

The authors propose a set of guidelines for researchers to follow that will limit “researcher degrees of freedom” -

1. Authors must decide the rule for terminating data collection before data collection begins and report this rule in the article

2. Authors must collect at least 20 observations per cell or else provide a compelling cost-of-data collection justification.

3. Authors must list all variables collected in a study.

4. Authors must report all experimental conditions, including failed manipulations.

5. If observations are eliminated, authors must also report what the statistical results are if those observations are included.

6. If an analysis includes a covariate, authors must report the statistical results of the analysis without the covariate.

These solutions are oriented towards psychology, and many of them relate to the data collection/creation process and its reporting. I don’t know how one might effectively limit “researcher degrees of freedom” in a discipline like economics, where often the data is already public, and the “researcher degrees of freedom” can lie in analytic choices alone.

Joseph P. Simmons, Leif D. Nelson, and Uri Simonsohn, “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant” Psychological Science, XX(X) 1–8, 2011 [pdf!]

Problems with Regression

Browsing around on Google Scholar I ran across this accessible paper – which seems excellent, to my eyes – on the use and misuse of regression analysis. It’s focused on the use of the technique in criminology, but its claims apply more broadly.

Berk, Richard, “What You Can and Can’t Properly Do with Regression“, Journal of Quantitative Criminology, Volume 26, Number 4, 2010, pp. 481-487

Berk distinguishes between three different levels of regression analysis:

Level I: descriptive – simply identifying patterns in the data. No broader inferential or causal claims made. This can always be justified.

Level II: inferential – estimating parameters of a population, hypothesis testing, use of confidence intervals, etc. This can be justified if the data has been generated by probability sampling. If the data has not been generated by probability sampling, level II analysis is “difficult to justify” (485).

[Berk gives several types of justification that could be offered in this scenario: 1) Treating the data as the population (i.e. falling back to descriptive statistics); 2) Making the case that the data can be treated as if it were a probability sample (“rarely credible in practice”); 3) Treating the data as a random sample from an imaginary ‘superpopulation’ (“even more difficult to justify than inferences from the as-if strategy”); 4) Making use of a model of how the data was generated (risky, because the model might be wrong).]

Level III: causal – estimating causal relationships between variables in the population. “Perhaps too simply put, before the data are analyzed, one must have a causal model that is nearly right” (481) But: “It is very difficult to find empirical research demonstrably based on nearly right models.” (482)

Berk concludes that: “With rare exceptions, regression analyses of observational data are best undertaken at Level I. With proper sampling, a Level II analysis can be helpful.” Level III is very difficult to justify. Unfortunately: “The daunting part is getting the analysis past criminology gatekeepers. Reviewers and journal editors typically equate proper statistical practice with Level III.” (486)

The Next Thing

As readers of the blog (if such there be) will know, my current ongoing project (or, I suppose, sub-project) is a document on the implications of the work of Robert Brandom for social theory. I’m keen to get that document done, but it’s on the back-burner for now, while work and life take well-deserved priority. Still, things churn away in the brain cell, and I’ve been thinking a bit what the next thing to do is, once the Brandom document is complete.

My master-plan for the overall project has (as, again, readers may conceivably recall) six broad stages. The first three of these were, in order:

1) Social-theoretic foundations [the Brandom document]
2) History of capitalism
3) Analysis of value theory

(The remaining stages were, more or less, variations on ‘do political economy’)

I’d planned to move on to my (very brief!) history of capitalism once the Brandom document was done. My idea was that social theory and economic history were the broad areas of study that need to inform an adequate economics, but that are under-represented in current economics education – so I thought I’d get some basic grounding in these areas, before approaching economics itself.

My worry: if I do this, I’ll never get to the economics . So: I’ve had a rethink. I still intend to write a history of capitalism, but I’m now seeing this as something to do in the interstices of my other studies.

This leaves the question: what to move on to once the Brandom document is complete?

The way I currently see it, contemporary economics has two broad areas of technical expertise:

First, statistical analysis of economic data.
Second, modelling of economic structures.
And, of course (third, if you like), drawing connections between them.

Obviously each of these areas have their formal and contentful aspects – the formal being simply how to do statistical analysis or modelling; the contentful being analysis of actual economic data, or discussion of actual economic models.

I want to start getting to grips with these technical areas of economics sooner rather than later. My current background concern is: how. I’m considering taking a higher degree in statistics – or just autodidacting may way through the terrain, as usual. This will have the advantage of having real-world application (i.e. I can use it on the job market, which is important); and it has the further advantage that I have a lot of respect for orthodox statistical theory and practice (whereas I have very little respect for orthodox economic models and modelling) – so I’m less likely to flame out of these studies in the kind of bitter rage that motivates this blog as a space to do heterodox intellectual work. The downside is that statistics still isn’t economics proper – so it’s still a postponement of (what I regard as) the core of my project.

Just putting this up to externalise and help along the thought-process, really. Also to explain a shift in content on the blog – I’m still not blogging properly again, but I aim to starting putting up statistics-related content; this is why.

I hope folks out there are well.

Asking For and Giving Reasons

The next important building block of Brandom’s inferentialism is his analysis of deontic status in terms of deontic attitudes – but I’ve sort of already covered that in broad brush strokes, last year on the blog.

Also worth mentioning the priority of judgements in Brandom’s account of meaning: Brandom does not understand judgements in terms of their composition out of smaller meaning-units, but understands smaller meaning-units in terms of the roles they play in judgements. (And of course a judgement, for Brandom, can only be a judgement as part of a larger social system of judgements.)

So those are two further explanatory approaches that Brandom’s project is committed to. I’m not going to spend much time on them here.

In addition, though, for Brandom [and here we start to move into territory I’ve not covered already on the blog; if you see something you regard as a mistaken interpretation, please feel free to flag it as such] we become concept-mongers by entering the space of reasons; by participating in the social practice of asking for and giving reasons.

This is Brandom mobilising Sellars. I’ve read Empiricism and the Philosophy of Mind – I’m not totally clear what other Sellars I should look at to get an adequate sense of the resources Brandom’s making use of / transforming. Recommendations would be welcome.

What is the explanatory order here?

Brandom thinks that concepts need to be understood in inferential terms. That is, roughly – the meaning of a concept is the things we can infer from it. To be very slightly more precise: concepts can for Brandom only be understood in relation to judgements. A judgement is something that can be expressed in a proposition. So the concept of a dog, for example, needs to be understood in terms of the role the concept of a dog plays in propositions about dogs. Sticking at the propositional level, then – a proposition needs to be understood in terms of the inferences we can make from it. Or, to be very slightly more precise still: “Two claims have the same conceptual content if and only if they have the same inferential role.” (MIE 96)

What is an inferential role? Brandom understands inferential role in terms of deontic statuses – which, as we’ve already seen, he in turn understands in terms of deontic attitudes. There are, I think, two fundamental deontic statuses in Brandom’s account: commitment and entitlement. I’ll need to spend a lot longer with these in future posts. But Brandom’s basic idea is that an assertion of a proposition binds one to certain actions (including the endorsement of other propositions), normatively. Those other actions – including those other propositional contents – follow from the assertion – they can be inferred from it – and this binding or inference is itself the content of the original proposition. The meaning of a judgement is that other judgements or actions are implied by it. Content is explained in terms of implications, rather than implications in terms of content.

Where does this binding or this implication come from? What is it?

Brandom’s claim here, if I understand him aright, is that the brute fact that we are animals who ask for and give reasons is the source of the bindingness of commitments etc. – and thus of normativity. That means we are creatures who ask each other for demonstrations of entitlement. This is a brute fact about us. And the content of an entitlement – indeed, the very nature of entitlement itself – needs to be understood in terms of this social practice of challenge and response.

We are creatures who, discursively, ask each other for reasons – that is, challenge social entitlement – and who feel the force of such challenges. That we feel this force is a brute social fact, on Brandom’s account. That we engage in a practice in which challenges of entitlement are responded to by giving reasons for entitlement – this is a brute fact. Brandom says nothing about what counts as a good reason – this is to be hashed out entirely in the social practice of asking for and giving reasons itself. But the basic challenge-response structure of entitlement to assertions is a brute (biologically evolved) fact about the nature of the human organism – and this fact, more than any other, is generative of our capacity to wield conceptual content.

Note that ‘entitlement’ is a deontic status, and is thus to be explained in terms of deontic attitudes – attributions of entitlement. So we’re not positing a mysterious thing called ‘entitlement’ here. We’re saying that as evolved organisms we attribute social statuses to each other, challenge those attributions, and that, when challenged, we characteristically offer reasons (are capable of doing so – this capacity is determining of sapience; the claim is not that we always do offer reasons; still less that those reasons are always good reasons). Entitlement can be explained in terms of behaviour. But the behaviour itself is distinctive – the ‘attribute; challenge; response’ structure of that behaviour is distinctive.

This is very confused as formulated here. But I want to start to work through this aspect of Brandom’s apparatus. I’ll aim to continue with that in my next set of posts.