Home > Uncategorized > The history of econometrics

The history of econometrics

from Lars Syll

9780199679348There have been over four decades of econometric research on business cycles …

But the significance of the formalization becomes more difficult to identify when it is assessed from the applied perspective …

The wide conviction of the superiority of the methods of the science has converted the econometric community largely to a group of fundamentalist guards of mathematical rigour … So much so that the relevance of the research to business cycles is reduced to empirical illustrations. To that extent, probabilistic formalisation has trapped econometric business cycle research in the pursuit of means at the expense of ends.

The limits of econometric forecasting have, as noted by Qin, been critically pointed out many times before. Trygve Haavelmo assessed the role of econometrics — in an article from 1958 — and although mainly positive of the “repair work” and “clearing-up work” done, Haavelmo also found some grounds for despair:

Haavelmo-intro-2-125397_630x210There is the possibility that the more stringent methods we have been striving to develop have actually opened our eyes to recognize a plain fact: viz., that the “laws” of economics are not very accurate in the sense of a close fit, and that we have been living in a dream-world of large but somewhat superficial or spurious correlations.

Maintaining that economics is a science in the ‘true knowledge’ business, yours truly remains a sceptic of the pretences and aspirations of econometrics. The marginal return on its ever higher technical sophistication in no way makes up for the lack of serious under-labouring of its deeper philosophical and methodological foundations that already Keynes complained about. The rather one-sided emphasis of usefulness and its concomitant instrumentalist justification cannot hide that the legions of probabilistic econometricians who give supportive evidence for their considering it ‘fruitful to believe’ in the possibility of treating unique economic data as the observable results of random drawings from an imaginary sampling of an imaginary population, are skating on thin ice.

A rigorous application of econometric methods in economics presupposes that the phenomena of our real world economies are ruled by stable causal relations between variables. The endemic lack of predictive success of the econometric project indicates that this hope of finding fixed parameters is a hope for which there, really, is no other ground than hope itself.

On the use and misuse of theories and models in mainstream economics











  1. ghholtham
    March 5, 2020 at 12:25 pm

    If we cannot suppose that “the phenomena of our real world economies are ruled by stable causal relations between variables.” what can we say about those phenomena? Let us agree “that economics is a science in the ‘true knowledge’ business”. In what does that knowledge consist if no stable relations are to be found?
    Lars, the more I try to follow your argument the more it appears that your beef is not with econometrics at all, which is no better nor worse than the theoretical relationships it tries to test or calibrate. It appears you don’t like ceteris paribus generalisations about economic relationships. How then, does economics differ from economic history?
    The key question is not whether unique economic data are random drawings from an imaginary sampling of an imaginary population (they aren’t, let’s agree) but whether those unique economic data are instances of a general relationship holding across time or space – albeit overlayed by lots of extraneous influences that will make the relationship look stochastic in practice. The specification of the relationship is central and means we are not operating in a world of pure statistics and random drawings. If you don’t believe any such specifications can be useful (i.e conditionally “true”), you may or may not be right but your problem is not with econometrics. It is with anyone who tried to theorise about the economy, including your revered Keynes.

    • March 6, 2020 at 8:54 am

      Indeed, the question is whether economic data are instances of time and space independent laws. Though the answer to this question is not hard to come by. It is simply “no”. Economic laws across which are stable time and space cannot exist because the economy is human-made and humans are creative, have agency and hence constantly build, destroy and re-build the economy and its internal mechanisms. Thus, any law that we think is supported by the data is bound to break down sooner or later.
      That’s not the case in science and that’s exactly what makes economics such an exciting science. By pretending to investigate time and space independent laws quite like (natural) scientists do economists give up economics’ unique selling proposition.
      Having answered the basic question the truly challenging riddle emerges: how do we do economics given the ever shifting nature of the laws of economics?

  2. Norman L. Roth
    March 5, 2020 at 4:41 pm

    Mr. Holtham:

    I’m going to try and take hold of your very practical notion, that we adopt a kind of “Gordion Knot” solution to the “too many {extraneous} impacting variables” problem,that dogs Economics and other disciplines, who seek the holy grail of at least enough “stability” to hold the beast down long enough for “scientific” examination of its entrails. It has been commented upon many times, especially by economic thinkers who were far more astute in the limits of applied math, than any of us here. e.g. J. M. Keynes, who noted that most economic data had about five-years of useful life. And several eminences of the “Austrian” School, who noted the highly inconvenient absence of “constants” in Economics, compared to say, Physics & Chemistry. Indeed, the great Hayek {from a family noted in the biological sciences of his time & place} thought that Economics should humbly accept the limiting principle of “general pattern prediction”.. which accepts the boundaries of local circumstances & limits in applicable time frames. Like Mr. Holtham is getting at. Another way of putting it is George Shackle’s way: “Kalidescopic” best describes how Economic events “unfold”{Veblen’s term} across time space. Let us then try out some concrete examples of ‘useful under certain specified circumstances & specified time frames”:
    {1} In operations Research, programmed Inventory control has been used for decades in circumstances where there are many kinds of commodities in large quantity, & associated services, where costs of holding inventory have to be minimized, but “reorder-points” must be acted upon so as not to lose sales because of being “out of stock”.
    {2}Seasonal Adjustment calculations, Useful across many industries, agriculture & Government {public} sectors.
    {3} The highly politically charged question of Immigration policy. Under what circumstances is Immigration a net benefit : And when is it a threat to social stability ? This is covered in TELOS & TECHNOS in Chapter 4, by applying the ’emergence’ of the Natural Participation Rate. NOT the dubious estimates of “unemployment rates”.
    {4} The ‘up-dated’ version of ‘Gresham’s Law”.i.e. sustainable deficit financing, debt-loads, when it’s O.K.to print lots of ‘M’ & how to manipulate interest rates. or credit qualifications. All of this is most useful in the context of Business corporations & Environmental policy.
    Thank you for your patience. Norman L. Roth

  3. March 5, 2020 at 11:01 pm

    A lot of the criticisms of econometrics – both here and elsewhere (e.g. Tony Lawson) are actually criticisms of economic theory. Econometrics itself has the same strengths and weaknesses as statistics applied to other domains where multiple causation and open-endedness are fundamental features, such as epidemiology (my former discipline). There can be stable causal relations that are not deterministic, i.e. they represent one causal relationship among others, and this is reflected in the results – i.e., the proportion of variance explained is very far from 100%. That is not a problem. There are other problems though: e.g. tendency for researchers to focus on statistical significance, even when the magnitude of the effect is unimportant. And in particular, the replication crisis, which is increasingly recognised in all domains that use statistical methods.

    Critics of mainstream economics often make rather wild statements about the uselessness of econometrics. But what about the literature on the minimum wage? – for the past 30 years, research in this area has established that the harmful effects on employment are *much* less than predicted by traditional theory (some studies find no effect, others find an effect but a rather weak one). Is this all useless? – my interpretation is that it is using the methods of econometric analysis to demonstrate the *weakness* of this aspect of standard theory.

    Or are you just more comfortable slagging off anything that is done by economists who do not self-identify as heterodox?

    • Meta Capitalism
      March 5, 2020 at 11:14 pm

      What is the “replication crisis”?

      • March 5, 2020 at 11:21 pm

        The replication crisis is the realisation that many of the classic findings in the literature cannot be replicated. In other words, they appear to be chance findings that just applied to one dataset in past research, rather than a robust feature of the actual world. I believe that it first emerged in psychology, especially social psychology, which was partly due to the prevalence of studies that were too small to generate robust findings. But it is now increasingly recognised as being a much more widespread problem.

      • Meta Capitalism
        March 5, 2020 at 11:38 pm

        Thanks. Reading Stephen T. Ziliak and Deidre N. McCloskey now — The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives — and they are making the point you do above about when the “magnitude of the effect is unimportant” vs. real scientific importance. Great book.

  4. ghholtham
    March 6, 2020 at 11:19 am

    Christian, we substantially agree. I was careful to use the word relationship, not law because I do not suppose economic relations are unchanging. Many things evolve slowly and relationships can be broadly stable or drifting measurably over decades. A broad relationship can sometimes endure even though there is no stability in the parameters used to quantify it. Partial correlation coefficients retain the same sign even though they move around.

  5. March 6, 2020 at 2:34 pm

    Nuancing Lars’ position in order to advance beyond it, I don’t believe economics as practiced IS a real world science, but I believe it SHOULD be. That doesn’t mean I am against the use of statistics in economics. Lars quotes Haavelmo on their value for “repair work” and clearing up work, which correspond to my placing their use in a different phase of economic discovery, i.e. quality control of real world science. What I object to is taking numbers they come up with as more real than the reasons for them. Repeatedly I give the example of a six-sided dice probably throwing a six one sixth of the time. One can see that. Where the statistics come in is in detecting bias which one cannot see, and except where money-makers are playing games this is not usually an issue. But that is the second point I keep emphasising and Lars fails to take up. In the real world one can see economics is based on biological needs (feeding etc ourselves and the kids), not about the chrematism (based on the probability of making money) that now passes for economics. Although even here there are laws of chrematism: the bookmaker, the banker and the ponzi organisers never lose. But that is due to prior mathematical structuring, not the effect of after the event econometrics.

    Googling the history of econometrics proved quite interesting. I’d been thinking Blaize Pascal, but it seems the arabs were (like Shannon) studying probability in relation to cryptography as long ago as c.750. (Biblically, of course, we had Augustus carrying out a census). Cardano c.1550 was into gaming and Pascal c.1638 into theology’s gamble. Quetelet c.1870 was into insurance, Mendel c.1880 genetics, “bumps on the head” Galton the infamous standard error, Weldon correlating populations, Markoff chained dependencies, Pearson c.1900 the now familiar normal, standard deviation and chi-squared significance test. SInce 1945 we have had Fisher’s correlation of small samples and degrees of freedom (the inverse of control).

    E T Bell ends his “Development of Mathematics” (1945) discussing all this. A couple of his comments sound like mine about the dice. “Pearson himself as he aged seems to have lost some of his youthful faith that mathematics can guide biology. Apparently it cannot, any more than it can render physical laboratories superfluous” [p.592]. We also find Bertrand Russell remarking, in 1929, that “probability is the most important concept in modern science, especially as nobody has the slightest notion what it means” [p.587].

    • Meta Capitalism
      March 7, 2020 at 12:25 am

      In the real world one can see economics is based on biological needs (feeding etc ourselves and the kids), not about the chrematism (based on the probability of making money) that now passes for economics. Although even here there are laws of chrematism: the bookmaker, the banker and the ponzi organisers never lose. But that is due to prior mathematical structuring, not the effect of after the event econometrics. ~ Dave Taylor

      In general over these last few years I have been reading two classes of books on economics. One class critiques economics from various perspectives but doesn’t offer much of a way forward in pragmatic business practices. The other class addresses the pragmatic changes along with the a renewal of ethical thinking (along with the history which includes Aristotle and other thinkers). RWER has published some articles on this very subject. One book sitting next to my bed in the current stack is The Ethical Economy: Rebuilding Value After the Crisis by Adam Arvidsson and Nicolai Peitersen.

      Industrial society—that old textbook example of how economics and social systems are supposed to work—was built around connecting economic value creation to overall social values—an imaginary social contract. Business was supposed to contribute to the well-being of society by furthering economic development and a growing prosperity that could trickle down the social pyramid, or be redistributed by the welfare state. Although this “contract” was not always respected in practice and quite intense conflicts arose as to how it should apply, virtually everybody argued that it should apply, at least in theory, and most people agreed on the basic common values: economic growth and increasing prosperity. From this perspective, even the raw profit seeking of business could be understood to be functional for the overall social good; to make a profit could be said to be an important, or even, as Milton Friedman argued, the only social responsibility of business.
      Today that social contract has been shattered. Globalization and the socialization of production have undermined the national arenas in which the redistribution of wealth used to play out, and have radically diminished the power of the industrial working class, who used to be an important party to that process. Three decades of neoliberal policies have separated the market from larger social concerns and relegated the latter to the private sphere, creating a situation where there is no society, only individuals and their families, as Margaret Thatcher famously put it, and no values, only prices. Most importantly, perhaps, a growing awareness of the planetary consequences of industrial capitalism has led to questioning the very desirability of continuous economic growth, at least in its consumerist, materialist version.
      Yet the new values that are emerging in our society—a growing popular demand for a more sustainable economy and a more just and equal global society—have only weak and unreliable ways of influencing the actual conduct of corporations and other important economic actors. True, a lot of companies now claim to be socially responsible and to behave in a sustainable way, but we have no way of reliably or “objectively” evaluating the social efficacy of their efforts. Quite paradoxically, though we are in the midst of an explosion of ethics, with more and more people routinely evaluating their everyday actions in terms of the planetary consequences of those actions (what foods to buy, where to work, how to live), there are few ways in which such ethical values can really influence the processes through which crucial decisions are made. We need a system that can create and strengthen such a link, allowing for new ways to decide the overall social value of economic value creation, and for those decisions to have tangible effects on economic value creation. (Arvidsson, Adam. The Ethical Economy. Columbia University Press. Kindle Edition. pp. ix-x)

      You might enjoy this book Dave. I am interested in starting new business that implement these kinds of ideas and ideals.

      • March 10, 2020 at 10:02 am

        Thanks for this, Meta. Particularly helpful is its observation that

        “there are few ways in which such ethical values can really influence the processes through which crucial decisions are made. We need a system that can create and strengthen such a link, allowing for new ways to decide the overall social value of economic value creation, and for those decisions to have tangible effects on economic value creation”.

        Definitely a good criteria for assessing our own efforts. The hopeful sign coming out of he corona virus scare is the boost being given to computer conferencing facilities to facilitate work at home and eliminate unnecessary long-distance travel.

      • Meta Capitalism
        March 10, 2020 at 11:35 am

        Glad you found it interesting. If only the traditional Japanese corporate culture would learn that lesson.

  6. Ken Zimmerman
    March 17, 2020 at 1:59 pm

    David Amiel Freedman (5 March 1938 – 17 October 2008) was Professor of Statistics at the University of California, Berkeley. He was a distinguished mathematical statistician whose wide-ranging research included the analysis of martingale inequalities, Markov processes, de Finetti’s theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating models. He published extensively on methods for causal inference and the behavior of standard statistical models under non-standard conditions – for example, how regression models behave when fitted to data from randomized experiments. Freedman also wrote widely on the application—and misapplication—of statistics in the social sciences, including epidemiology, public policy, and law.

    Below is a summary of Freedman’s positions on causal inference from observational data vs. statistical models. I’m in total agreement with these positions.

    Drawing sound causal inferences from observational data is a central goal in social science. How to do so is controversial. Technical approaches based on statistical models—graphical models, non-parametric structural equation models, instrumental variable estimators, hierarchical Bayesian models, etc.—are proliferating. David Freedman argues that these methods are not reliable. He demonstrates repeatedly that it can be better to rely on subject-matter expertise and to exploit natural variation to mitigate confounding and rule out competing explanations.

    When Freedman first articulated this position decades ago, many were skeptical. They found it hard to believe that a probabilistic and mathematical statistician of his stature would favor “low-tech” approaches. But the tide has been turning for over ten years now. An increasing number of social scientists now agree that statistical technique cannot substitute for good research design and subject-matter knowledge. This view is particularly common among those who understand the mathematics and have on-the-ground experience. Historically, “shoe-leather epidemiology” is epitomized by intensive, door-to-door canvassing that wears out investigators’ shoes. In contrast, advocates of statistical modeling sometimes claim that their methods can salvage poor research design or low-quality data. Some suggest that their algorithms are general-purpose inference engines: Put in data, turn the crank, out come quantitative causal relationships, no knowledge of the subject required.

    This is tantamount to pulling a rabbit from a hat. Freedman’s conservation of rabbits principle says “to pull a rabbit from a hat, a rabbit must first be placed in the hat.” In statistical modeling, assumptions put the rabbit in the hat.

    Modeling assumptions are made primarily for mathematical convenience, not for verisimilitude. The assumptions can be true or false—usually false. When the assumptions are true, theorems about the methods hold. When the assumptions are false, the theorems do not apply. How well do the methods behave then? When the assumptions are “just a little wrong,” are the results “just a little wrong”? Can the assumptions be tested empirically? Do they violate common sense?

    Freedman asked and answered these questions, again and again. He showed that scientific problems cannot be solved by “one-size-fits-all” methods. Rather, they require shoe leather: careful empirical work tailored to the subject and the research question, informed both by subject-matter knowledge and statistical principles. Witness his mature perspective:

    “Causal inferences can be drawn from nonexperimental data. However, no mechanical rules can be laid down for the activity. Since Hume, that is almost a truism. Instead, causal inference seems to require an enormous investment of skill, intelligence, and hard work. Many convergent lines of evidence must be developed. Natural variation needs to be identified and exploited. Data must be collected. Confounders need to be considered. Alternative explanations have to be exhaustively tested. Before anything else, the right question needs to be framed.

    In the face of all this, there is sometimes a desire to substitute intellectual capital for labor. That is why investigators try to base causal inference on statistical models. The technology is relatively easy to use and promises to open a wide variety of questions to the research effort. However, the appearance of methodological rigor can be deceptive. The models themselves demand critical scrutiny. Mathematical equations are used to adjust for confounding and other sources of bias. These equations may appear formidably precise, but they typically derive from many somewhat arbitrary choices. Which variables to enter in the regression? What functional form to use? What assumptions to make about parameters and error terms? These choices are seldom dictated either by data or prior scientific knowledge. That is why judgment is so critical, the opportunity for error so large, and the number of successful applications so limited.”

    • March 17, 2020 at 4:29 pm

      Thank you for this post, it is extremely interesting and I will check out Freedman’s work. Perhaps Ken you could point us to some key papers or a book that best express his views.

      I am particularly pleased at your emphasis on *subject-matter* knowledge. In so much methodological writing, whether by economists, philosophers or others, they refer to it as “background” knowledge. This seriously underplays its importance. Science is cumulative: new research always takes the established body of theory as its starting point. And key to establishing a body of theory – in the sense of a knowledge of the causal relationships, how they work and how they interrelate – is the congruence of different *types* of evidence – “Many convergent lines of evidence” in the above quote. For a description of how the natural sciences have generated secure knowledge, and its lessons for how economists could do so, see my “Causal theories, models and evidence in economics—some reflections from the natural sciences” at https://www.tandfonline.com/doi/full/10.1080/23322039.2017.1280983.

      The corollary is, if the new research is built on established theory that is merely widely accepted rather than empirically justified, this new research is built on sand. This situation is unfortunately very common in economics (and not only mainstream economics). The problems with economic theorising are methodological – it is not just that much of the substantive theory is wrong, it is that people are often going about it in the wrong way. Often justified in terms of philosophy of science that is totally hopeless at explaining how science actually works and how it produces secure knowledge.

      The message that I get from Ken’s post (I hope I have interpreted it correctly) is that research needs close involvement with the subject matter. Modelling is useful, but only when based on evidence, and on existing theory ultimately based on evidence. In other words, the most useful models are those that are nested inside empirically-based causal theories. To base models merely on assumptions is hazardous – sometimes it produces something useful, but more often it provides something that has the appearance of substance but is actually misleading.

      • March 17, 2020 at 4:47 pm

        The two books I mention in this post contain much of his path-breaking work:

      • March 17, 2020 at 4:53 pm


      • Ken Zimmerman
        March 21, 2020 at 10:44 am

        Lars, we’re definitely on the same page here.

      • Meta Capitalism
        March 18, 2020 at 11:31 pm

        In so much methodological writing, whether by economists, philosophers or others, they refer to it [subject-matter knowledge] as “background” knowledge. This seriously underplays its importance. ~ Michael Joffe

        The history of science, whether it be the history of the earth sciences or the history biology or the history of economics (Mirowski 1989) tell a similar story:

        Harold Jeffreys, the most competent mathematical geophysicist ever, who insisted throughout his long life that the continents were fixed entities on the face of the globe, pooh-poohed geologists who argued otherwise. It was not his numerical skills that let him down, but his intuitive creed. (Carey, Warren S. (1988, 81) Theories of the Earth and Universe: A History of Dogma in the Earth Sciences. Stanford University Press.)
        (….) Mental models bias our thinking, and “continental drift” hobbled Wegener’s concept in the English-reading world from the outset. Wegener’s word was Verschiebung, which was correctly translated by Skerl as “displacement.” “Drift” was substituted by detractors, and as they were the majority, the term gained currency; the theory, saddled with the name, was successfully slanted toward fantasy. (Carey 1988: 89)
        (….) Wegener is universally acknowledged as the patriarch of continental drift. Although it is clear from the forgoing that many aspects of “the Wegener theory” had been published by one or other of his predecessors it was his masterly and comprehensive synthesis that shocked the geological world, and he merits the Domine status accorded him. Wegener drew together all that was then known in every relevant science (geology, geophysics, geodesy, biology, paleontology, oceanography, meteorology, and astronomy), throughout geological time, for every part of the world. (Carey 1988: 93)

        When any field adopts a creed it quickly becomes pseudo-science.

      • Meta Capitalism
        March 18, 2020 at 11:35 pm

        Alfred Wegener had a wide understanding of subject-matter knowledge. He was not blinded by overspecialization nor siloed knowledge. This broad knowledge of the subject-matter of fields outside his own specialty was one of the reasons he was so mercilessly ridiculed by American geologists who were not familiar with the German tradition of education that was broader and less siloed. American intransigence generally mocked Alfred Wegener, mere meteorologist, with the attitude of “Can any good thing come out of Nazareth?”

      • Ken Zimmerman
        March 21, 2020 at 10:27 am

        To borrow from old suspense movies, inference is a mystery within a puzzle. In many social sciences, including economics more than most of the others this difficulty is overcome by simply ignoring it. The work, data gathering, experience, and judgment of social scientists in trying to figure out if event, action, and/or actor b, or b/c, or c+b, etc. following event a, or a/d, or d+a have any significance for humans in their everyday lives, individually or collectively is “thrown under the bus” in favor of theories assumed to be more manageable in terms of mathematics (e.g., statistics), logic, or some other form of particular rules. Freedman has over the years continued to examine every such effort at “effortless” and “painless” inference. Andrew Pickering points out in “Science as Practice and Culture,” “Data processing is embarrassingly plastic. That has long been familiar to students of statistical inference in the case of data assessment and reduction, (12) and (13). … We create apparatus that generates data that confirm theories; we judge apparatus by its ability to produce data that fit. There is little new in this seeming circularity except taking the material world into account. The most succinct statement of the idea, for purely intellectual operations, is Nelson Goodman’s summary (1983,64) of how we ‘justify’ both deduction and induction: ‘A rule is amended if it yields an inference we are unwilling to accept; an inference is rejected if it violates a rule that we are unwilling to amend.’ There is also more than a whiff of Hanson’s (1965) maxim that all observation is theory loaded, and of the corresponding positivist doctrine that all theory is observation loaded. The truth is that there is a play between theory and observation, but that is a miserly quarter-truth. There is a play between many things: data, theory, experiment, phenomenology, equipment, data processing.”

        In my view the best source for grasping Freedman’s views on inference and truth is “Statistical Models and Causal Inference A Dialogue with the Social Sciences.” But Freedman’s book, “Statistics” is also useful, and very entertaining.

  7. Meta Capitalism
    March 18, 2020 at 12:18 am

    Michael, Ken, Lars, truly good content here. Thanks.

    • March 18, 2020 at 9:10 pm

      Agreed. I found the Michael Joffre paper linked in by evidencebased as helpful as anything I’ve read in a long time. One can have all the bits of the jig-saw and still not see how they fit together. The key word here for me was “observational”, as contrasted with “statistical”. One observes structures in 2 or 3-dimensional space, as against quantities varying in one dimension, on which Shannon’s capacity theorem passes judgement: in lay terms you cannot fit a quart into a pint pot – but you can fit a quart in a quart pot! You can also empty the quart pot, observe what fits in it and nest it in another to catch the overflows.

      Apologies for this being so cryptic. Think of it as a riddle and have fun resolving it.

      • March 19, 2020 at 3:53 am

        Joffe, sorry. At 2.1 what is said of the germ theory of disease may equally well be said of Shannon’s communication theory, in which information is carried by a “vector” like print or a radio carrier signal (or specifically laws, money, advertising etc):

        “This clearly meets the criterion for “a genuine scientific theory” set out by Reiss (2011): “a small number of explanatory hypotheses that can be used over and over again in the explanation of a wide range of phenomena”. … “The theory sets out to describe how (this aspect of) the world works, in terms of the structure and capacities of the component entities, i.e. of causation. It is therefore ontic not epistemic in intention”.

        The physical explanation of causality is always the same – energy – so it can be omitted from ours theories. (It is not information: “a difference which makes a difference”). The causal differences lie in the structure which channels and directs the information-carrying energy, like the roads which lead to work or to the bank, the internet, and the neural systems of their clients and position holders.

  8. Ken Zimmerman
    March 22, 2020 at 2:42 pm

    ”When any field adopts a creed it quickly becomes pseudo-science” is not correct, in my view. Results depend on the creed adopted. Most of what’s called the “learned” disciplines – the sciences, arts, humanities, etc. Began as crafts and in practice even today remain crafts. So, scientists, artists, novelists, poets, painters, sculptors, etc. are crafts persons. The historian Thomas P. Hughes writes this in his contribution to the book, “The Social Shaping of Technology: How the Refrigerator Got its Hum,” Edited by Donald MacKenzie.

    “Edison and electric light”

    “Isaiah Berlin in The Hedgehog and the Fox quoted the Greek poet Archilochus, who wrote, ‘The fox knows many things, but the hedgehog knows one big thing.’ This essay on the ‘Electrification of America’ is about hedgehogs. Sir Isaiah describes them as those ‘who relate everything to a single central vision, one system less or more coherent or articulate. ’Foxes, in contrast, pursue many ends, ‘often unrelated and even contradictory.’ Berlin categorizes Dante, Plato, Lucretius, Pascal, Hegel, Dostoevsky, Nietzsche, Ibsen, and Proust among the hedgehogs. I want to add Thomas Edison, Samuel Insull, and S. Z. Mitchell.

    Edison invented systems, lnsull managed systems, and Mitchell financed their expansion. These systems were electric light and power, now usually called utilities. Edison invented the system that took form as the Pearl Street generating station of the New York Edison Illuminating Company, now Consolidated Edison Company; Insull managed electric light and power companies that consolidated into Chicago’s Commonwealth Edison Company; and Mitchell provided for the growth of large regional power systems. The three men focused upon one level of the process of technological change, such as invention, management, or finance, but in order to relate everything to a single central vision they had to reach out beyond their special competencies: Mitchell managed, Insull financed, and Edison knew management and finance, as well. For this reason, Edison should be called an inventor-entrepreneur, Insull a manager-entrepreneur, and Mitchell a financier-entrepreneur– ‘entrepreneur’ indicating the organizational, system-building drive of the three men. One hesitates to speak of inventor-hedgehog, manager-hedgehog, or financier-hedgehog.

    Edison, lnsull, and Mitchell were strong holistic conceptualizers and determined solvers of the problems frustrating the growth of systems. This essay, therefore, is also a history of ideas and a study of problem solving. Their strong concepts resulted from the need to find organizing principles powerful enough to integrate and give purposeful direction to diverse factors and components. The problems emerged as the system builders strove to fulfill their ultimate visions. Not one of them was satisfied to solve a part of the problem, simply to invent, manage, or finance, for each believed that the invention would not become an innovation, the managerial structure would not evolve, and the financial means would not bring growth unless electric light and power were viewed as a coherent system.”

    Hughes is describing craft persons and craft work.

    Similarly, what is considered by many the greatest community of artists in western civilization, the Renaissance artists are also craft persons. Creating their crafts as a community, these craft persons, like the others in medicine, historical studies, engineering, performing arts, creative writing, etc.

    Leonardo da Vinci is today hailed by some as the supreme example of a Renaissance man who mastered many diverse fields of knowledge and combined achievements in the beaux arts and in science to a degree attained by no individual before or since. Ironically, however, in his own time he was denied the prestige of a fully learned man because he lacked a classical education and was not literate in Latin. His originality, historian of science George Sarton has written, “was partly due to his ignorance and his lack of academic inhibitions.”
    Leonardo blasted “certain presumptuous persons” who “scorn me as an inventor” and go about “alleging that I am not a man of letters.” He responded:

    “If indeed I have no power to quote from authors as they have, it is a far bigger and more worthy thing to read by the light of experience, which is the instructress of their masters. They strut about puffed up and pompous, decked out and adorned not with their own labours but by those of others” (Leonardo da Vinci, The Notebooks of Leonardo da Vinci, pp. 57-58).

    Like scientists, the collective determination of Renaissance artists was to depict nature realistically. This put them in the vanguard of the quest for knowledge of nature, just like scientists. One of the results of this quest by the Renaissance artists was their invention of mathematical perspective. Later adopted and used by scientists.

    The artisan’s resentment at being looked down on as a social inferior is evident in Leonardo’s angry retort to university scholars who categorized painters like himself as manual workers: “You have set painting among the mechanical arts! Truly were painters as ready equipped as you are to praise their own works in writing, I doubt whether it would endure the reproach of so vile a name. If you call it mechanical because it is by manual work that the hands represent what the imagination creates, your writers are setting down with the pen by manual work what originates in the mind. If you call it mechanical because it is done for money, who fall into this error–if indeed it can be called an error-more than you yourselves? If you lecture for the Schools do you not go to whoever pays you the most? (Leonardo da Vinci, The Notebooks of Leonardo da Vinci, p. 853)

    Speaking of architects and other builders in the Middle Ages and ancient Greece and Rome, Ross King in Brunelleschi’s Dome, writes “Today we are so used to celebrating the brilliance of architects like Michaelangelo, Andrea Palladio, and Sir Christopher Wren that it is hard to imagine a time when architects and architecture were not esteemed. But the great architects of the Middle Ages had been virtually anonymous …. Part of the reason for this anonymity was a prejudice against manual labor on the part of both ancient and medieval authors, who assigned architecture a low place in human achievement, regarding it as an occupation unfit for an educated man. Cicero claimed that architecture was a manual art on the same level as farming, tailoring, and metalworking, while in his Moral Letters Seneca mired it in the lowest of the four categories of art, those which he classified as volgares et sordidae, “common and low.” Such arts were mere handiwork, he claimed, and had no pretense to beauty or honor. (pp. 157-158).

    Despite what some historians and social scientists claim, these prejudices against so called “manual or artisan” work are still present today. But the elites that denigrate and dismiss such work and workers have changed. It’s no longer classically trained scholars, old world aristocracy, and dilettantes (gentlemen and gentle ladies). Today it is rather plutocrats, corporate rulers, and the “in-groups” of fashion and style who carry out this attack. It is also the “scientific elites” buried in obtuse and useless theory and abstraction that attack the craft of science and the craft persons who practice this craft. It’s my view the most offending sciences here are physics and economics, with economists running a close race with physicists for the most obtuse and useless abstractions and theories.

    • Meta Capitalism
      March 22, 2020 at 10:02 pm

      The term “creed” was used with the meaning, “a brief authoritative formula of religious belief” (i.e., dogma) Ken. There is a second meaning, as in “set of principles,” that better suits your argument Ken. But then, I suspect you already know the difference ;-)
      In the context of science “creed” doesn’t serve well when it is functioning as a “dogma,” but indeed a “set of principles” can be applied to any field it seems, from artisan, craftsman, and mechanic. Your post reminds me of an interesting few papers I read a long while back on the early history of so-called “self made men” of the “middling sort” in early America.

      • March 23, 2020 at 10:52 am

        Having just tried to explain Kant to Asad after his remarks in RWER91, Meta, I perhaps have a better understanding of what ‘principles’ means: as something like the mode of interpretation of an object or (linguistically) the definition of its structure.

        You “hooked” me with this because when (c.1980) I looked in vain for a definition of it in Flew’s dictionary of philosophy, the author, waffling, declined an invitation to explain why! My main dictionary lists over twenty definitions, of which I found most satisfying “a law or doctrine from which others are derived”. So not a ‘principle’ but your ‘set of principles’?

      • Ken Zimmerman
        March 23, 2020 at 11:49 am

        Meta. apologies for the misunderstanding. My comments are focused on the history of craft or artisan work, and the people involved with that work. Unlike many on this blog and those who “follow” science, and even sometimes scientists, it’s my view that the history of science is the history of a craft. Which makes scientists craft persons or artisans. Science as a craft is not focused on theory or abstract layers on everyday life. Rather, the craft of science is concerned with describing and offering possible solutions for everyday concerns and problems. For example, building democratic and resilient governments, economies, families, etc. Or, perhaps mitigating climate change, ensuring food and water security, ameliorating gun violence. Can any on this blog honestly say that economics and economists today have this focus? I don’t believe so. That makes economics and economists of little use in the “real” world humans face today.

        Obviously, for those whose view of science is as a theoretical and abstract enterprise, my view is anathema.

        As to creed as a “set of principles,” it’s my view that the content of these principles matters more than their being principles. Quoting the historian Thomas Hughes, “…but in order to relate everything to a single central vision they had to reach out beyond their special competencies: Mitchell managed, Insull financed, and Edison knew management and finance, as well.” Building systems, an effective principle.

      • Meta Capitalism
        March 23, 2020 at 12:07 pm

        I tend to agree with your point that science is more craft than precise “method.” One of my favorite books on the history of physics was Holton and Brush 2001 because they realize science is often more then method.

  9. March 22, 2020 at 9:04 pm

    Ken, I thoroughly enjoyed the drift of this, but Hughes has his history all wrong.

    Edison was not an inventor, but a fairly unscrupulous employer of inventors. So successfully did he write out of history the real inventor, Nicolas Tesla, that I hadn’t heard of him until the Tesla electric car came out, despite his work being at the heart of my professional training: he having invented most of the alternating current technology on which our civilisation has for a century depended. A couple of months ago the BBC broadcast an eye-opening documentary abouit Tesla’s inventions and battles with Edison, which he won technically but Edison commercially. The battle goes on: I googled the documentary but found access denied.

    • Ken Zimmerman
      March 23, 2020 at 12:42 pm

      Dave, whatever his flaws and there are many, Edison remains a major figure in the invention and propagation of electric power systems. You know. those things at the center of modern life. No Edison did not “invent” the first light bulb alone. Though we rely on alternating current systems today, and Edison favored direct current, direct current remains useful even today. Particularly for renewable electricity. Edison’s revolutionary invention was not the light bulb, however. That would be the phonograph. It’s correct that Edison drove his employees relentlessly. But it’s also true he drove himself in the same way. By today’s standards of safety and health Edison was not an acceptable employer.

      I saw that BBC documentary. It was off by quite a bit. Tesla and Edison fought what’s called “the current wars” for only a short period. After Tesla sold out his interests to George Westinghouse, Edison’s war was with Westinghouse, not Tesla. And the war was never really a war. Edison imagined a very different electric power system than Tesla. Tesla imagined what we’ve had for the last 100 years. A system of power stations distant from population centers with power carried to the centers thorough long (hundreds of miles) transmission systems. Edison imagined local power plants for cities and regions providing power over short distances (100 miles). This was a cultural conflict as reflected in AC vs. DC current. It was a cultural conflict happening over the entire US at the beginning of the 20th century.

      • March 23, 2020 at 11:30 pm

        Ken, I was not trying to disagree with you, but what you say now does not convince me. Edison does not even warrant a mention in the blow-by-blow history of DC generators and motors at https://commons.princeton.edu/josephhenry/wp-content/uploads/sites/71/2019/08/electric-motor-history.pdf. What you say of scientists being dismissed as craftsmen is true, but the converse is also true: that some scientists (like Maxwell below) are theorists, and others (like Faraday and Heaviside) were theorists who had to invent their own craft.

        1831 Michael Faraday (British) discovers and investigates electromagnetic induction, i.e. the generation of an electric
        current due to a varying magnetic field (the reversal of Oersted’s discovery). Faraday lays the foundation for the
        development of the electric generator.

        James Clerk Maxwell (British) summarizes all the current knowledge of electromagnetism in 20 fundamental equations. Around 1882, Oliver Heaviside (British) uses vector calculus and reduces 12 of the equations further to just 4 equations with 4 variables. These equations are still valid today and fully describe the theory of electrical engineering [whether distributing power or information, hence the PID theory of control] .

        Nikola Tesla (Croatian, naturalized US­American) already thinks about a multi­phase voltage system while studying in Austria in 1882. In 1887 Tesla files his first patents for a two-­phase AC system … In 1888 Westinghouse buys his more than 40 patents. Ultimately, he fails to build a reliable induction motor and leaves Pittsburgh and the Westinghouse Company dissatisfied in 1889. By 1893 Tesla had turned to other things, while Westinghouse produced 3-phase German motors..

        Summarily, Tesla was the first to work intensively on electric power transmission through a multi­phase alternating current system, he was the first to find the basics for such a transfer and was the first to present the principles of a multi­phase induction motor. But that was far from all he did.

        From the initial summary, the DC motor was not created from the early engines, but rather from the development of power generators (dynamometers). The foundations were laid by William Ritchie and Hippolyte Pixii in 1832 with the invention of the commutator and, most importantly, by Werner Siemens in 1856 with the Double-­T-­anchor and by his chief engineer,
        Friedrich Hefner ­Alteneck, in 1872 with the drum armature.

        DC motors still have a dominant market position today in the
        low power (below 1 kW) and low voltage (below 60 V) range.
        High-voltage direct current (HVDC) systems are now used for bulk transmission of energy from distant generating stations or for interconnection of separate alternating-current systems. From https://en.wikipedia.org/wiki/War_of_the_currents, Edison in 1887-93 was selling 1800-battery type DC voltages as safer for house lighting. In 1908 he said to George Stanley, son of AC transformer inventor William Stanley, Jr., “Tell your father I was wrong”.

      • March 23, 2020 at 11:38 pm

        It’s late! Apologies for forgetting those hidden new lines in copied text.

      • Ken Zimmerman
        March 24, 2020 at 11:37 am

        Dave, yes Edison played only a minor role in the history of the invention of electric motors. But he was the prime mover in the invention and installation of the first electric utility systems. First in the US. Then in the UK, Germany, Spain, etc. He chose to use dynamos rather than alternators for these systems. This choice reflected his belief that these systems should be small, safe (low voltage), and close to the people who used their electricity. Just like the distributed renewable (wind, solar) generators expanding exponentially over the last 15 years. Direct current meets these objectives. Alternator generated power (Alternating current) does not. It is high voltage, and thus dangerous, with large power stations that can’t be located near the people who use the electricity. But because of its high voltage it can be sent over long distances via transmission lines. Two very different views of electric utility systems. What I’m saying is we can’t say Tesla’s achievements in AC outshine those of Edison in DC electric utility systems. Tesla/Westinghouse defeated Edison. But now the world seems to be turning back to Edison’s version of the electric utility system. It’s also important to note that the devices we depend on every day, from computers to appliances operate on DC electricity. So, the AC electricity that comes to homes and businesses must be converted to DC electricity before use. Wouldn’t it be simpler and more efficient to generate all electricity as DC as envisioned by Edison? None of what you write, Dave is inaccurate. It’s just that you apparently prefer AC electric utility systems, while I prefer DC.

  10. March 25, 2020 at 8:05 am


    “you apparently prefer AC electric utility systems, while I prefer DC.”?

    Appearances can be deceptive. You are an advocate, appealing not to truth but to the jury’s feelings, but my role as a scientist is more like that of an impartial judge, forming a judgement by weighing the evidence. You have a bit of a nerve to judge the judge by his feelings. As it happens my judgement also is that “Small is Beautiful”, but power technology has moved on from AC and DC to digital semiconductor/battery voltage converters and synchronous motors.

    I’d been concerned by how far this has got from the history of econometrics, but looking back, you started the drift with your very interesting and relevant comments on March 21 and 22, to the effect that econometrics was one-dimensional and genuine science needed a craftsman’s understanding of its material. As Evidencebased put it:

    “The message that I get from Ken’s post (I hope I have interpreted it correctly) is that research needs close involvement with the subject matter”.

    That has been my own experience. We differ perhaps in that as a scientist I have seen that scientists largely work in groups of people with different talents: as I said, some more theoretically, others more practically inclined. A craft apprentice who became my friend 67 years ago has left impractical PhD’s standing, but my own theorising, e.g. about languages and the tools of econometrics, was done alongside mathematicians and systems designers, reflecting on “bread and butter” work programming computers and analysing systems failures. Analysing systems failures in economics is surely more a scientist’s forte than an advocate’s?

    • Ken Zimmerman
      March 25, 2020 at 12:01 pm

      Dave, as has been stated on this blog, and certainly elsewhere science does not deal in truth. All scientists are advocates, or using Edison’s choice of words, entrepreneurs. They advocate for research and research findings they believe work. That is, describe concerns accurately and propose solutions the scientist believes resolve the concern – in whole or part – in real time. The history of science is filled with advocates. Take Copernicus, Einstein, Pasteur, Hawking, etc. Or, in the social sciences, B.F. Skinner, Franz Boas, Anthony Giddens, Theda Skocpol, etc. The heart of science is the clash of advocates. Nothing changes in science without these clashes. You are correct that power technology today is digital. But current remains either AC or DC, with both altered by digital switching and digital loads.

      I agree we’ve strayed somewhat from econometrics. But then again, not so much. Econometrics’ real problem seems to me is its often total or near divorce from subject matter experience and knowledge in the areas supposedly explained by econometric methods. That is, its divorce from science.

      As to working in groups, communally, crafts persons have operated this way since ancient times. With a mixture of talents and advocacy positions. Like craft science today, that’s the way science has always found, tested, and put out new approaches to problem solving and creation of useful results.

      • Meta Capitalism
        March 25, 2020 at 1:48 pm

        … as has been stated on this blog, and certainly elsewhere science does not deal in truth. ~ Ken

        As Ours dearly would say, “What a handy view of science …” In fact and truth many views of truth have been expressed on this blog, by both the authors of the posts and within the comments. In fact the more I read in both science and the history of science, including the history of economics, the more clear it becomes many practicing scientists simply don’t hold Ken’s rather one-dimensional view of truth. Many scientists have a more nuanced and yet still pragmatic view of truth. Many indeed claim truth in science is what science is all about. In fact, “The distinction between ‘scientists’ and ‘pseudo-scientists’ presumes a robust theory that is something like a ‘truth criterion’. The history of doing science is full of instances that show the shared view of the community of those who call themselves scientists is normally taken as the truth criterion to be used (spender7, RWER, 12/30/2016).” Indeed, “Truth ought to be as important a concept in economics as it is in real science (Lars Syll, RWER, 2/18/2020)
        I think when viewing that broad ranger of views expressed by practicing scientists, many whom are at the top of the field, it is rather pompus and arrogant claim one view is THE TRUTH. That is more akin to a religious dogma and blind faith than sound philosoph let alone good science.

      • Ken Zimmerman
        March 26, 2020 at 12:09 am

        Here you go. again. I express no view of truth. As I say, “All scientists are advocates, or using Edison’s choice of words, entrepreneurs. They advocate for research and research findings they believe work. That is, describe concerns accurately and propose solutions the scientist believes resolve the concern – in whole or part – in real time. The history of science is filled with advocates.” If you want to call their advocacy truth, that’s your choice. Problem is that scientists change their convictions about what is true and what is not true based on their interpretations of empirical research findings. With such changes scientists then advocate new truth. Truth changes with time and place — with cultural context. Even for scientists. Scientists have the additional factor in the truth debate of commitment to separating scientifically acceptable from scientifically unacceptable empirical information.


      • March 25, 2020 at 3:52 pm

        Ken, you have stated your opinion that science does not deal in truth, which is not that of a scientist who has done his philosophical homework but is borrowed from an arm-chair philosopher of science, David Hume, who constructed his philosophy – not science – of social science on the basis of atheism, crude econometrics and winner-takes-all politics.

        Read what I shared from Bacon and Kant in response to Asad Zaman’s essay in RWER91, and shame on you until you have done so! ‘Truth’ is a word, but knowledge without truth is what Bacon described as “the greatest error of all the rest … vain speculations and whatsoever is empty and void”. Kant too pointed out that “no cognition can contradict it [a logic of truth], without losing at the same time all content, that is, losing all reference to an object, and therefore all truth”.

        Shame on you and all you Humean political advocates: more interested in winning arguments than finding and building on common ground. What you argued and I conceded on March 22 is to be found in Bacon: “this is that which will indeed dignify and exalt knowledge, if contemplation and action may be more nearly and straitly conjoined than they have been”. When the “contemplative” has produced something worth advocating, it needs the “advocate” to advocate it, not argue the toss.

      • Ken Zimmerman
        March 25, 2020 at 4:18 pm

        Dave, none of the arguments you offer (I did read them) convince me, for one simple reason. If scientists uncover truth or even knew where to look to uncover truth, then Sapiens would not be attempting to annihilate itself in the search for truth. Or, what is even worse in my view, only certain “elite” members of the species are capable of “knowing” truth. Which, of course would make them gods. And then, of course our species destroys itself serving its gods. Either way, the truth isn’t setting us free; it’s destroying us.

      • Meta Capitalism
        March 26, 2020 at 1:31 am

        I express no view of truth. ~ More of Ken’s Made-Up Story

        Ken is a story-teller and nothing more. He is an advocate of his own story chock full of his own unexamined philosophical assumptions often so unhinged from fact that it is not really hard to point out where his story telling departs from reality and becomes a figment of his own imagination. But then, Ken wants us to believe that truth, like reality, is just made-up. All just a figment of our human imagination. Why should anyone take his story-telling seriously then? He claims to express no view of truth, yet when his story-telling is not accepted as “true” he is quick to say one is a thick as stick because his superior “elite” knowledge—that is right, he rails against elites but pontificates like one frequently, albeit ironically so, and takes offense when it is not properly genuflected to.
        Consider the statement,

        If scientists uncover truth or even knew where to look to uncover truth, then Sapiens would not be attempting to annihilate itself in the search for truth. Or, what is even worse in my view, only certain “elite” members of the species are capable of “knowing” truth. ~ Ken’s Petulant Child

        Ken is angry at the fact that the world doesn’t during his brief span of life meet his expectations of what it should be, so throws a pseudo-intellectual tantrum and kicks and screams there is no such thing as “truth” or “reality” because it doesn’t fit his imagination of the way the world should be. Hardly a very sound intellectual argument.
        Ken confuses facts, meanings, and values and then claims his facts, his meanings, his values are right, while others who simply don’t understand what he “has already explained” is the correct story (a backhanded way of making a ‘truth-claim’) are dolts.
        Assuming the science of sedimentology, archaeology, and paleontology are in fact telling us something truthful about the world we live in by the application of their methodologies doesn’t mean we may not later on gain new information that enlarges the context of a given claim thereby changing its meaning. When a paleontologist finds a foot bone and then within the context of the bedding plane interprets its phylogeny based upon paleontology (comparative morphology) he is giving a material artifact meaning. If later on through the use of new scientific techniques, such as comparative genomics it is discovered that the phylogenetic classification is actually wrong and that comparative genomics assigns the physical artifact to another ancestry or perhaps a completely new discovery, that doesn’t mean the physical foot bone is any less a real material fact. Truth indeed is not static, but rather a dynamic synthesis of facts, meanings, and values. But just because it is dynamic doesn’t mean it is not real.
        The fact that the continents move (i.e., plate tectonics) has been a fact of reality despite human beings not having sufficient knowledge (facts and meanings) to know its reality. It is simply not true that practicing scientists are not interested in the truth and such an interest doesn’t require a naive understanding of what they are doing:

        Scientists are interested in truth. They want to know how the world really is, and they want to use that knowledge to do things in the world. In the earth sciences, this has meant developing methods of observation to determine the shape, structure, and history of the earth and designing instruments to measure, record, predict, and interpret the earth’s physical and chemical processes and properties. The resulting knowledge may be used to find mineral deposits, energy resources, or underground water; to delineate areas of earthquake and volcanic hazard; to isolate radioactive and toxic wastes; or to make inferences and predictions about the earth’s past and future climate. The past century has produced a prodigious amount of factual knowledge about the earth, and prodigious demands are now being placed on that knowledge. (Oreskes 1999, 3)
        The history of science demonstrates, however, that the scientific truths of yesterday are often viewed as misconceptions, and, conversely, that ideas rejected in the past may now be considered true. History is littered with the discarded beliefs of yesteryear, and the present is populated by epistemic resurrections. This realization leads to the central problem of the history and philosophy of science: How are we to evaluate contemporary science’s claims to truth given the perishability of past scientific knowledge? This question is of considerable philosophic interest and of practical import as well. If the truths of today are the falsehoods of tomorrow, what does this say about the nature of scientific truth? (Oreskes 1999, 3)
        — Oreskes, Naomi (1999) The Rejection of Continental Drift: Theory and Method in American Earth Science. Oxford University Press

      • Ken Zimmerman
        March 26, 2020 at 11:54 am

        Meta. These are not just my stories. They are shared. As Richard Feynman put it. “Religion is a culture of faith; Science is a culture of doubt.” The central cultural configuration of science is doubt. Not truth. The social sciences are even more difficult about truth. In 2005, political scientist Ian Lustick wrote this about one of the oldest theories in biology and anthropology, evolution. “Of course, social scientists have no objection to applying evolutionary theory in the life sciences—biology, zoology, botany, etc. Nevertheless, the idea of applying evolutionary thinking to social science problems commonly evokes strong negative reactions. In effect, social scientists treat the life sciences as enclosed within a kind of impermeable wall. Inside the wall, evolutionary thinking is deemed capable of producing powerful and astonishing truths. Outside the wall, in the realm of human behavior, applications of evolutionary thinking are typically treated as irrelevant at best; usually as pernicious, wrong, and downright dangerous.” Truth depends on who’s looking and both their own experiences and the cultures of which they are a part. William James states it this way in “The Meaning of Truth,” “’The truth of an idea is not a stagnant property inherent in it. Truth happens to an idea. It becomes true, is made true by events. Its verity is in fact an event, a process, the process namely of its verifying itself, its verification. Its validity is the process of its validation.”

        Nowhere in my remarks did I declare there is no such thing as truth. My statements relate only to science and truth. A point I make with the first paragraph above. There is certainly, I believe religious, philosophical, and moral truth. These need another discussion, however.

        You write, “Assuming the science of sedimentology, archaeology, and paleontology are in fact telling us something truthful about the world we live in by the application of their methodologies doesn’t mean we may not later on gain new information that enlarges the context of a given claim thereby changing its meaning.” The word truthful is unnecessary. Scientists don’t need their findings to be true, whatever that is. They want them to be accurate, repeatable, useful, and of course accepted by other scientists for verification. If this is your understanding of scientific truth, our views are aligned. Anything beyond that I believe is getting into some other version of truth. For example, religion. Many scientists are religious. But their religions must not impact their scientific work. Otherwise, science is lost. That’s my view, at least. And the quote you cite from Oreskes makes these points very clearly, I believe. That’s why I began this discussion my saying, “All scientists are advocates, or using Edison’s choice of words, entrepreneurs. They advocate for research and research findings they believe work. That is, describe concerns accurately and propose solutions the scientist believes resolve the concern – in whole or part – in real time. The history of science is filled with advocates. Take Copernicus, Einstein, Pasteur, Hawking, etc. Or, in the social sciences, B.F. Skinner, Franz Boas, Anthony Giddens, Theda Skocpol, etc. The heart of science is the clash of advocates. Nothing changes in science without these clashes.” For a time such clashes may lead to some ideas being accepted as “true” (e.g., evolution). But as I’ve pointed out the sciences around evolution are not a picture of acceptance and harmony. There are some dramatic clashes. Now, imagine the kinds of clashes around hypotheses not so well respected and shared as evolution.

      • Meta Capitalism
        March 26, 2020 at 1:45 am

        “… he is giving a material fact meaning.” not artifact.

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