Archive for the ‘self-organization’ Category

Revisiting Hayek’s Relevance to Measurement

May 31, 2018

As so often happens, I’m finding new opportunities for restating what seems obvious to me but does not impact others in the way it ought to. The work of the Austrian economist Friedrich Hayek speaks to me in a particular way that has always, to me, self-evidently expressed ideas of fundamental value and interest. Reviewing his work again lately has opened it up to a new level of detail that is worth sharing here.

Hayek (1948, p. 54) is onto a key point about measurement and its role in economics when he says:

…the spontaneous actions of individuals will, under conditions which we can define, bring about a distribution of resources which can be understood as if it were made according to a single plan, although nobody has planned it…?

Decades of measurement research shows that individuals’ spontaneous responses to assessment and survey questions conform to one another in ways that might appear to have been centrally organized according to a single plan. But over and over again the same patterns are produced with no efforts made to guide or coerce responses that conform in that way.

The results of testing and assessment produced in educational measurement can be expressed in economic terms fitting quite well with Hayek’s observation. Student abilities, economically speaking, are human capital resources. Each student has some amount of ability that can be considered a supply of resources available for application to the demands of the challenges posed by the assessment questions. When assessment data fit a Rasch model, the supply of student abilities have spontaneously organized themselves in relation to challenging demands for that supply of abilities posed by the test questions. The invariant consistency of the data and resulting model fit has not been produced by coercing or guiding the students to respond in a particular way. Although questions can be written to vary in difficulty according to a construct theory, and though educational curricula traditionally vary in difficulty across grade levels, the patterns of growth and change that are observed are plainly not taking place as a result of anyone’s intentions or plans.

This kind of complex adaptive, self-organizing process (Fisher, 2017) describes not just the relations of student abilities and task difficulties, but also the relations of customer preferences to product features, patient health and functionality relative to disease and disability, etc. It also, of course, applies to supply and demand relative to a price (Fisher, 2015). For students, the price to be paid follows from the probability of a supply of ability meeting the demand for it posed by the challenges encountered in assessment items.

Getting back to Hayek (1948, p. 54), here we meet the relevance of the

…central question of all social sciences: How can the combination of fragments of knowledge existing in different minds bring about results which, if they were to be brought about deliberately, would require a knowledge on the part of the directing mind which no single person can possess?

Per Hayek’s point, no one student will know the answers to all of the questions posed in a test, and yet all of the students’ fragments of knowledge combine in a way that bring about results seemingly defined by a single intelligence. It is this bottom up and self-organized emergence of knowledge structures that we capture in measurement and bring into our culture, our sciences, and our economies by bringing things into words and the common languages of standardized metrics.

This spontaneous emergence of structure does not lead directly of its own accord to the creation of markets. Rather, it is vitally important to recognize, along with Miller and O’Leary (2007, p. 710) that:

Markets are not spontaneously generated by the exchange activity of buyers and sellers. Rather, skilled actors produce institutional arrangements, the rules, roles and relationships that make market exchange possible. The institutions define the market, rather than the reverse.

The institutional arrangements we need to make to create efficient markets for human, social, and natural capital will be staggeringly difficult to realize. But a point in time will come when the costs of remaining in our current cultural, political, and economic ruts will be greater, and the benefits will be lower, than the costs and benefits of investing in a new future. That time may be sooner than anyone thinks it will be.


Fisher, W. P., Jr. (2015). A probabilistic model of the law of supply and demand. Rasch Measurement Transactions, 29(1), 1508-1511  [].

Fisher, W. P., Jr. (2017). A practical approach to modeling complex adaptive flows in psychology and social science. Procedia Computer Science, 114, 165-174. Retrieved from

Hayek, F. A. (1948). Individualism and economic order. Chicago: University of Chicago Press.

Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-734.

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Excellent articulation of the rationale for living capital metrics 

November 2, 2017

I just found the best analysis of today’s situation I’ve seen yet. And it explicitly articulates and substantiates all my reasons for doing the work I’m doing. Wonderful to have this independent source of validation.

The crux of the problem is spelled out at the end of the article, where the degree of polarizing opposition is so extreme that standards of truth and evidence are completely compromised. My point is that the fact will remain, however, that everyone still uses language, and language still requires certain connections between concepts, words, and things to function. Continuing to use language in everyday functions in ways that assume a common consensus on meaningful reference may eventually come to be unbearably inconsistent with the way language is used politically, creating a social vacuum that will be filled by a new language capable of restoring the balance of meaning in the word-concept-thing triangles.

As is repeatedly argued in this blog, my take is that what we are witnessing is language restructuring itself to incorporate new degrees of complexity at a general institutional, world historic level. The falsehoods of our contemporary institutional definitions of truth and fact are rooted in the insufficiencies of the decision making methods and tools widely used in education, health care, government, business, etc. The numbers called measures are identified using methods that almost universally ignore the gifts of self-organized meaning that offer themselves in the structure of test, assessment, survey, poll, and evaluation response data. Those shortcomings in our information infrastructure and communication systems are causing negative feedback loops of increasingly chaotic noise.

This is why it is so important that precision science is rooted in everyday language and thinking, per Nersessian’s (2002) treatment of Maxwell and Rasch’s (1960, pp. 110-115) adoption of Maxwell’s method of analogy (Fisher, 2010; Fisher & Stenner, 2013). The metric system (System International des Unites, or SI) is a natural language extension of intuitive and historical methods of bringing together words, concepts, and things, renamed instruments, theories, and data. A new SI for human, social, and natural capital built out into science and commerce will be one component of a multilevel and complex adaptive system that resolves today’s epistemic crisis by tapping deeper resources for the creation of meaning than are available in today’s institutions.

Everything is interrelated. The epistemic crisis will be resolved when our institutions base decisions not just on a potentially arbitrary collection of facts but on facts internally consistent enough to support instrument calibration and predictive theory. The facts have to be common sensical to everyday people, to employees, customers, teachers, students, patients, doctors, nurses, managers. People have to be able to see themselves and where they stand relative to their goals, their origins, and everyone else in the pictures drawn by the results of tests, surveys, and evaluations. That’s not possible in today’s systems. And in those systems, some people have systematically unfair advantages. That has to change, not through some kind of Brave New World hobbling of those with advantages but by leveling the playing field to allow everyone the same opportunities for self-improvement and the rewards that follow from it.

That’s it in a nutshell. Really good article:

America is facing an epistemic crisis – Vox


Fisher, W. P., Jr. (2010, June 13-16). Rasch, Maxwell’s method of analogy, and the Chicago tradition. In G. Cooper (Chair), Https:// Probabilistic models for measurement in education, psychology, social science and health: Celebrating 50 years since the publication of Rasch’s Probabilistic Models, University of Copenhagen School of Business, FUHU Conference Centre, Copenhagen, Denmark.

Fisher, W. P., Jr. (2010). The standard model in the history of the natural sciences, econometrics, and the social sciences. Journal of Physics Conference Series, 238(1),

Fisher, W. P., Jr., & Stenner, A. J. (2013). On the potential for improved measurement in the human and social sciences. In Q. Zhang & H. Yang (Eds.), Pacific Rim Objective Measurement Symposium 2012 Conference Proceedings (pp. 1-11). Berlin, Germany: Springer-Verlag.

Nersessian, N. J. (2002). Maxwell and “the method of physical analogy”: Model-based reasoning, generic abstraction, and conceptual change. In D. Malament (Ed.), Reading natural philosophy: Essays in the history and philosophy of science and mathematics (pp. 129-166). Lasalle, Illinois: Open Court.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.

With Reich in spirit, but with a different sense of the problem and its solution

October 4, 2015

In today’s editorial in the San Francisco Chronicle, Robert Reich seeks some way of defining a solution to the pressing problems of how globalization and technological changes have made American workers less competitive. He rightly says that “reversing the scourge of widening inequality requires reversing the upward distributions [of income] within the rules of the market, and giving average people the bargaining power they need to get a larger share of the gains from growth.”

But Reich then says that the answer to this problem lies in politics, not economics. As I’ve pointed out before in this blog, focusing on marshaling political will is part of the problem, not part of the solution. Historically, politicians do not lead, they follow. As is demonstrated across events as diverse as the Arab Spring and the Preemption Act of 1841, mass movements of people have repeatedly demanded ways of cutting through the Gordian knots of injustice. And just as the political “leadership” across the Middle East and in the early U.S. dragged its feet, obstructed, and violently opposed change until it was already well underway, so, too, will that pattern repeat itself again in the current situation of inequitable income distribution.

The crux of the problem is that no one can give average people anything, not freedom (contra Dylan’s line in Blowin’ in the Wind about “allowing” people to be free) and certainly not a larger share of the gains from growth. As the old saying goes, you can lead a horse to water, but you can’t make it drink. People have to take what’s theirs. They have to want it, they have to struggle for it, and they have to pay for it, or they cannot own it and it will never be worth anything to them.

It is well known that a lack of individual property rights doomed communism and socialism because when everything is owned collectively by everyone, no one takes responsibility for it. The profit motive has the capacity to drive people to change things. The problem is not in profit itself. If birds and bees and trees and grasses did not profit from the sun, soil, and rain, there would be no life. The problem is in finding how to get a functional, self-sustaining economic ecology off the ground, not in unrealistically trying to manipulate and micromanage every detail.

The fundamental relevant characteristic of the profits being made today from intellectual property rights is that our individual rights to our own human and social capital are counter-productively restricted and undeveloped. How can it be that no one has any idea how much literacy or health capital they have, or what it is worth?! We have a metric system that tells us how much real estate and manufactured capital we own, and we can price it. But despite the well-established scientific facts of decades of measurement science research and practice, none of us can say, “I own x number of shares of stock in intellectual, literacy, or community capital, that have a value of x dollars in today’s market.” We desperately need an Intangible Assets Metric System, and the market rules, roles, and responsibilities that will make it impossible to make a profit while destroying human, social, and natural capital.

In this vein, what Reich gets absolutely correct is hidden inside his phrase, “within the rules of the market.” As I’ve so often repeated in this blog, capitalism is not inherently evil; it is, rather, unfinished. The real evil is in prolonging the time it takes to complete it. As was so eloquently stated by Miller and O’Leary (2007, p. 710):

“Markets are not spontaneously generated by the exchange activity of buyers and sellers. Rather, skilled actors produce institutional arrangements, the rules, roles and relationships that make market exchange possible. The institutions define the market, rather than the reverse.”

We have failed to set up the institutional arrangements needed to define human, social, and natural capital markets. The problem is that we cannot properly manage three of the four major forms of capital (human, social, and natural, with the fourth being manufactured/property) because we do not measure them in a common language built into scientifically, economically, legally and financially accountable titles, deeds, and other instruments.

And so, to repeat another one of my ad nauseum broken record nostrums, the problem is the problem. As long as we keep defining problems in the way we always have, as matters of marshalling political will, we will inadvertently find ourselves contributing more to prolonging tragic and needless human suffering, social discontent, and environmental degradation.

Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-734.

Measurement, Metrology, and the Birth of Self-Organizing, Complex Adaptive Systems

February 28, 2011

On page 145 of his book, The Mathematics of Measurement: A Critical History, John Roche quotes Charles de La Condamine (1701-1774), who, in 1747, wrote:

‘It is quite evident that the diversity of weights and measures of different countries, and frequently in the same province, are a source of embarrassment in commerce, in the study of physics, in history, and even in politics itself; the unknown names of foreign measures, the laziness or difficulty in relating them to our own give rise to confusion in our ideas and leave us in ignorance of facts which could be useful to us.’

Roche (1998, p. 145) then explains what de La Condamine is driving at, saying:

“For reasons of international communication and of civic justice, for reasons of stability over time and for accuracy and reliability, the creation of exact, reproducible and well maintained international standards, especially of length and mass, became an increasing concern of the natural philosophers of the seventeenth and eighteenth centuries. This movement, cooperating with a corresponding impulse in governing circles for the reform of weights and measures for the benefit of society and trade, culminated in late eighteenth century France in the metric system. It established not only an exact, rational and international system of measuring length, area, volume and mass, but introduced a similar standard for temperature within the scientific community. It stimulated a wider concern within science to establish all scientific units with equal rigour, basing them wherever possible on the newly established metric units (and on the older exact units of time and angular measurement), because of their accuracy, stability and international availability. This process gradually brought about a profound change in the notation and interpretation of the mathematical formalism of physics: it brought about, for the first time in the history of the mathematical sciences, a true union of mathematics and measurement.”

As it was in the seventeenth and eighteenth centuries for physics, so it has also been in the twentieth and twenty-first for the psychosocial sciences. The creation of exact, reproducible and well maintained international standards is a matter of increasing concern today for the roles they will play in education, health care, the work place, business intelligence, and the economy at large.

As the economic crises persist and perhaps worsen, demand for common product definitions and for interpretable, meaningful measures of impacts and outcomes in education, health care, social services, environmental management, etc. will reach a crescendo. We need an exact, rational and international system of measuring literacy, numeracy, health, motivations, quality of life, community cohesion, and environmental quality, and we needed it fifty years ago. We need to reinvigorate and revive a wider concern across the sciences to establish all scientific units with equal rigor, and to have all measures used in research and practice based wherever possible on consensus standard metrics valued for their accuracy, stability and availability. We need to replicate in the psychosocial sciences the profound change in the notation and interpretation of the mathematical formalism of physics that occurred in the eighteenth and nineteenth centuries. We need to extend the true union of mathematics and measurement from physics to the psychosocial sciences.

Previous posts in this blog speak to the persistent invariance and objectivity exhibited by many of the constructs measured using ability tests, attitude surveys, performance assessments, etc. A question previously raised in this blog concerning the reproductive logic of living meaning deserves more attention, and can be productively explored in terms of complex adaptive functionality.

In a hierarchy of reasons why mathematically rigorous measurement is valuable, few are closer to the top of the list than facilitating the spontaneous self-organization of networks of agents and actors (Latour, 1987). The conception, gestation, birthing, and nurturing of complex adaptive systems constitute a reproductive logic for sociocultural traditions. Scientific traditions, in particular, form mature self-identities via a mutually implied subject-object relation absorbed into the flow of a dialectical give and take, just as economic systems do.

Complex adaptive systems establish the reproductive viability of their offspring and the coherence of an ecological web of meaningful relationships by means of this dialectic. Taylor (2003, pp. 166-8) describes the five moments in the formation and operation of complex adaptive systems, which must be able

  • to identify regularities and patterns in the flow of matter, energy, and information (MEI) in the environment (business, social, economic, natural, etc.);
  • to produce condensed schematic representations of these regularities so they can be identified as the same if they are repeated;
  • to form reproductively interchangeable variants of these representations;
  • to succeed reproductively by means of the accuracy and reliability of the representations’ predictions of regularities in the MEI data flow; and
  • adaptively modify and reorganize representations by means of informational feedback from the environment.

All living systems, from bacteria and viruses to plants and animals to languages and cultures, are complex adaptive systems characterized by these five features.

In the history of science, technologically-embodied measurement facilitates complex adaptive systems of various kinds. That history can be used as a basis for a meta-theoretical perspective on what measurement must look like in the social and human sciences. Each of Taylor’s five moments in the formation and operation of complex adaptive systems describes a capacity of measurement systems, in that:

  • data flow regularities are captured in initial, provisional instrument calibrations;
  • condensed local schematic representations are formed when an instrument’s calibrations are anchored at repeatedly observed, invariant values;
  • interchangeable nonlocal versions of these invariances are created by means of instrument equating, item banking, metrological networks, and selective, tailored, adaptive instrument administration;
  • measures read off inaccurate and unreliable instruments will not support successful reproduction of the data flow regularity, but accurate and reliable instruments calibrated in a shared common unit provide a reference standard metric that enhances communication and reproduces the common voice and shared identity of the research community; and
  • consistently inconsistent anomalous observations provide feedback suggesting new possibilities for as yet unrecognized data flow regularities that might be captured in new calibrations.

Measurement in the social sciences is in the process of extending this functionality into practical applications in business, education, health care, government, and elsewhere. Over the course of the last 50 years, measurement research and practice has already iterated many times through these five moments. In the coming years, a new critical mass will be reached in this process, systematically bringing about scale-of-magnitude improvements in the efficiency of intangible assets markets.

How? What does a “data flow regularity” look like? How is it condensed into a a schematic and used to calibrate an instrument? How are local schematics combined together in a pattern used to recognize new instances of themselves? More specifically, how might enterprise resource planning (ERP) software (such as SAP, Oracle, or PeopleSoft) simultaneously provide both the structure needed to support meaningful comparisons and the flexibility needed for good fit with the dynamic complexity of adaptive and generative self-organizing systems?

Prior work in this area proposes a dual-core, loosely coupled organization using ERP software to build social and intellectual capital, instead of using it as an IT solution addressing organizational inefficiencies (Lengnick-Hall, Lengnick-Hall, & Abdinnour-Helm, 2004). The adaptive and generative functionality (Stenner & Stone, 2003) provided by probabilistic measurement models (Rasch, 1960; Andrich, 2002, 2004; Bond & Fox, 2007; Wilson, 2005; Wright, 1977, 1999) makes it possible to model intra- and inter-organizational interoperability (Weichhart, Feiner, & Stary, 2010) at the same time that social and intellectual capital resources are augmented.

Actor/agent network theory has emerged from social and historical studies of the shared and competing moral, economic, political, and mathematical values disseminated by scientists and technicians in a variety of different successful and failed areas of research (Latour, 2005). The resulting sociohistorical descriptions ought be translated into a practical program for reproducing successful research programs. A metasystem for complex adaptive systems of research is implied in what Roche (1998) calls a “true union of mathematics and measurement.”

Complex adaptive systems are effectively constituted of such a union, even if, in nature, the mathematical character of the data flows and calibrations remains virtual. Probabilistic conjoint models for fundamental measurement are poised to extend this functionality into the human sciences. Though few, if any, have framed the situation in these terms, these and other questions are being explored, explicitly and implicitly, by hundreds of researchers in dozens of fields as they employ unidimensional models for measurement in their investigations.

If so, might then we be on the verge of a yet another new reading and writing of Galileo’s “book of nature,” this time restoring the “loss of meaning for life” suffered in Galileo’s “fateful omission” of the means by which nature came to be understood mathematically (Husserl, 1970)? The elements of a comprehensive, mathematical, and experimental design science of living systems appear on the verge of providing a saturated solution—or better, a nonequilbrium thermodynamic solution—to some of the infamous shortcomings of modern, Enlightenment science. The unity of science may yet be a reality, though not via the reductionist program envisioned by the positivists.

Some 50 years ago, Marshall McLuhan popularized the expression, “The medium is the message.” The special value quantitative measurement in the history of science does not stem from the mere use of number. Instruments are media on which nature, human or other, inscribes legible messages. A renewal of the true union of mathematics and measurement in the context of intangible assets will lead to a new cultural, scientific, and economic renaissance. As Thomas Kuhn (1977, p. 221) wrote,

“The full and intimate quantification of any science is a consummation devoutly to be wished. Nevertheless, it is not a consummation that can effectively be sought by measuring. As in individual development, so in the scientific group, maturity comes most surely to those who know how to wait.”

Given that we have strong indications of how full and intimate quantification consummates a true union of mathematics and measurement, the time for waiting is now past, and the time to act has come. See prior blog posts here for suggestions on an Intangible Assets Metric System, for resources on methods and research, for other philosophical ruminations, and more. This post is based on work presented at Rasch meetings several years ago (Fisher, 2006a, 2006b).


Andrich, D. (2002). Understanding resistance to the data-model relationship in Rasch’s paradigm: A reflection for the next generation. Journal of Applied Measurement, 3(3), 325-59.

Andrich, D. (2004, January). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, 42(1), I-7–I-16.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Fisher, W. P., Jr. (2006a, Friday, April 28). Complex adaptive functionality via measurement. Presented at the Midwest Objective Measurement Seminar, M. Lunz (Organizer), University of Illinois at Chicago.

Fisher, W. P., Jr. (2006b, June 27-9). Measurement and complex adaptive functionality. Presented at the Pacific Rim Objective Measurement Symposium, T. Bond & M. Wu (Organizers), The Hong Kong Institute of Education, Hong Kong.

Husserl, E. (1970). The crisis of European sciences and transcendental phenomenology: An introduction to phenomenological philosophy (D. Carr, Trans.). Evanston, Illinois: Northwestern University Press (Original work published 1954).

Kuhn, T. S. (1977). The function of measurement in modern physical science. In T. S. Kuhn, The essential tension: Selected studies in scientific tradition and change (pp. 178-224). Chicago: University of Chicago Press. [(Reprinted from Kuhn, T. S. (1961). Isis, 52(168), 161-193.]

Latour, B. (1987). Science in action: How to follow scientists and engineers through society. New York: Cambridge University Press.

Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. (Clarendon Lectures in Management Studies). Oxford, England: Oxford University Press.

Lengnick-Hall, C. A., Lengnick-Hall, M. L., & Abdinnour-Helm, S. (2004). The role of social and intellectual capital in achieving competitive advantage through enterprise resource planning (ERP) systems. Journal of Engineering Technology Management, 21, 307-330.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.

Roche, J. (1998). The mathematics of measurement: A critical history. London: The Athlone Press.

Stenner, A. J., & Stone, M. (2003). Item specification vs. item banking. Rasch Measurement Transactions, 17(3), 929-30 [].

Taylor, M. C. (2003). The moment of complexity: Emerging network culture. Chicago: University of Chicago Press.

Weichhart, G., Feiner, T., & Stary, C. (2010). Implementing organisational interoperability–The SUddEN approach. Computers in Industry, 61, 152-160.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Wright, B. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14(2), 97-116 [].

Wright, B. D. (1997, Winter). A history of social science measurement. Educational Measurement: Issues and Practice, 16(4), 33-45, 52 [].

Wright, B. D. (1999). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 []). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
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