Re: [OPE] Mistaking Mathematical Beauty for Economic Truth

From: Alejandro Agafonow <>
Date: Tue Sep 15 2009 - 08:40:31 EDT

**The reason is, that you then have to introduce a very large number of additional conditional assumptions, to depict the evolving meanings computationally, to the point where theory becomes so complex, that it loses its purpose (the purpose being to generalise comprehensibly from experience in the most economical way possible, in such a way that it can usefully orient behaviour).** (Jurriaan)   Entrepreneurial uncertainty, which is the basis of disequilibrium prices, is the crux of real market economies, and it is subjected to countless additional conditional assumptions that make its mathematical treatment fruitless. Unless labor value theorists stop to think in terms of labor determined prices driven by natural price equilibrium, they won’t be able to introduce any dynamic proviso to their account of capitalism. Unfortunately for them, this change would be devastating for the integrity of the transformation thesis.   A. Agafonow  ________________________________ De: Jurriaan Bendien <> Para: Outline on Political Economy mailing list <> Enviado: martes, 15 de septiembre, 2009 13:34:34 Asunto: [OPE] Mistaking Mathematical Beauty for Economic Truth You can formalize almost anything logically or mathematically if (i) you know how it works, and can break it down into operational steps, and, if (ii) there is constancy of meaning so that +1 does not become -1 in the same conditions, or becomes 2. If however the meaning (the significance) of something is not constant, but originates out of the mediation of a contradiction, then develops into something else, or even changes into its opposite, this is much more difficult to understand computationally, especially with regard to a complex totality which features interactions between many different variables. The reason is, that you then have to introduce a very large number of additional conditional assumptions, to depict the evolving meanings computationally, to the point where theory becomes so complex, that it loses its purpose (the purpose being to generalise comprehensibly from experience in the most economical way possible, in such a way that it can usefully orient behaviour). A model, which is an isomorphism or an analogy, hopes to pick out certain essential relationships in the subject being studied, in a way that it has a lot of explanatory and predictive power. But obviously it is much more difficult to fully explicate and consistently integrate all the assumptions on which the model itself is based. In fact, we have the model precisely because we cannot yet do this, in an all-inclusive way. As I said, I think dialectical reason aims to portray the subject in a way which "validates itself" because the further development of the argument shows, through transformations of meaning, why its initial conditions are appropriate, so that the subject becomes "self-explanatory". > From my previous experience as research statistician working mainly on the so-called "qualitative" (conceptualizing) side of statistical survey research, I also can insert here a paragraph which I posted on wikipedia: In the science of statistics, the collection of quantifiable data from people involves a phenomenological step. Namely, in order to obtain that data, survey questions must be designed to collect measurable responses which are categorized in a logically sound and practical way, such that the form in which the questions are asked does not bias the results. If this is not done, data distortions due to question-wording effects (response error) occur, and the data obtained may have no validity at all, because observations are counted up which do not have the same meaning (it would be like "adding up apples and pears"). A prerequisite of a good survey is that all respondents are really able to give a definite and unambiguous answer to the questions, and that they understand what is asked of them in the same way. One could for example ask farmers "How much risk do you run on your farm?" with a scale of response options ranging from e.g. "a lot of risk" to "no
 risk". But this yields quantitatively meaningless data which is not objective, since the interpretations of "how much risk" by farmers could focus on e.g. on the number, size, frequency, severity or consequence of risks, and each farmer will have his own idiosyncratic idea about that. All farmers may suffer e.g. from a lack of rainfall, but some will personally consider it a large risk, others a low risk and some not a risk at all. Furthermore, in actually asking the questions of respondents and subsequently coding the responses to numerical values, a technique must be found to ensure that no misinterpretation occurs of a type that would lead to errors. In other words, in designing the survey instrument, the researcher must somehow find a satisfactory "bridge" of meaning between the logical and practical requirements of the survey statistician, a statistical classification scheme, the awareness of respondents and the processors of the raw data. Finding
 this "bridge" involves an abstraction process which necessarily goes beyond logical inference, theory and experiment and involves an element of "art", because it must establish an appropriate connection between the language used, the intersubjective interactions between the surveyor and the respondent, and how respondents and those who process the data construct the meaning of what is being asked of them. For this cognitive process, it is impossible to provide a standard procedure which will always work, only "rules of thumb"; it requires a "practical" human insight (See Stanley Payne, The Art of Asking Questions. Princeton: Pinceton University Press, 1980). This is merely to illustrate that mathematics cannot generate, out of itself, all the conceptualizations, ontologies and categorizations used to describe the world, insofar as these involve qualitative distinctions and synthetic judgements which cannot be represented as quantities, at least not until we have already assumed their validity, imported them, or adopted them in quantifiable form. Simply put, quantitative procedure does not suffice to establish and form qualitative categorizations, and this is really where dialectical thought "begins", since somehow we then need methods to relativise both qualitatively and quantitatively. What I tried to highlight in what I said previously is, that the spontaneous capacity of the human mind to create meaning and combine different trains of reasoning, synthetically and simultaneously, carries the implication that deduction and induction cannot fully describe what happens in the reasoning. Similarly, when people say that the "calculative intellect" becomes dehumanizing and alienating, they mean that by reducing everything to quantities we have also annihilated part or all of the meaning which is essential to understand something. The fetish of abstract thinking becomes apparent when it fails to explain anything specific. Autistic Marxists just want to use dialectics as a lever that catapults them instantaneously to the "fount of wisdom" and the "grand overview of everything", from which they can then "manage" the world as its boss, but in reality, to depict a subjectmatter in a dialectical way, takes a lot of scientific work and an enormous technical knowledge of the subjectmatter, so that everything relevant is included and allocated in a non-arbitrary, logically consistent way. So, the ability to represent a subjectmatter dialectically actually presupposes a lot of learning to get to the point where you can not only understand how ideas are really moving but also explain why they move that way. Jurriaan _______________________________________________ ope mailing list

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