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The Standard Model in the history of the Natural Sciences, Econometrics, and the social
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2010 J. Phys.: Conf. Ser. 238 012016
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The Standard Model in the History of the Natural Sciences,
Econometrics, and the Social Sciences
W P Fisher Jr1,2
2
Principal and Founder, LivingCapitalMetrics.com
5252 Annunciation St, New Orleans, Louisiana 70115 USA
E-mail: william@livingcapitalmetrics.com
Abstract. In the late 18th and early 19th centuries, scientists appropriated Newton’s laws of
motion as a model for the conduct of any other field of investigation that would purport to be a
science. This early form of a Standard Model eventually informed the basis of analogies for the
mathematical expression of phenomena previously studied qualitatively, such as cohesion,
affinity, heat, light, electricity, and magnetism. James Clerk Maxwell is known for his repeated
use of a formalized version of this method of analogy in lectures, teaching, and the design of
experiments. Economists transferring skills learned in physics made use of the Standard
Model, especially after Maxwell demonstrated the value of conceiving it in abstract
mathematics instead of as a concrete and literal mechanical analogy. Haavelmo's probability
approach in econometrics and R. Fisher's Statistical Methods for Research Workers brought a
statistical approach to bear on the Standard Model, quietly reversing the perspective of
economics and the social sciences relative to that of physics. Where physicists, and Maxwell in
particular, intuited scientific method as imposing stringent demands on the quality and
interrelations of data, instruments, and theory in the name of inferential and comparative
stability, statistical models and methods disconnected theory from data by removing the
instrument as an essential component. New possibilities for reconnecting economics and the
social sciences to Maxwell's sense of the method of analogy are found in Rasch's probabilistic
models for measurement.
1. Introduction
In the late 18th and early 19th centuries, scientists took Newton’s successful study of gravitation and
the laws of motion as a model for the conduct of any other field of investigation that would purport to
be a science. This early form of a “Standard Model” evolved and eventually informed the quantitative
study of areas of physical nature that had previously been studied only qualitatively, such as cohesion,
affinity, heat, light, electricity, and magnetism [1]. Referred to as the “six imponderables,” scientists in
this period were widely influenced in experimental practice by the idea that satisfactory
understandings of these fundamental forces would be obtained only when they could be treated
mathematically in a manner analogous, for instance, with the relations of force, mass, and acceleration
in Newton’s Second Law of Motion.
James Clerk Maxwell is known for his repeated use of a formalized version of this analogy in
lectures, teaching, and the design of experiments [2, 3]. His electromagnetic theory, in turn, then
became the “prototype for all the great triumphs of twentieth-century physics...and for the unified
1
To whom any correspondence should be addressed.
13th IMEKO TC1-TC7 Joint Symposium IOP Publishing
Journal of Physics: Conference Series 238 (2010) 012016 doi:10.1088/1742-6596/238/1/012016
c 2010 Published under licence by IOP Publishing Ltd 1

theory of fields and particles that is known as the Standard Model of particle physics” [4, 5]. Several
economists, such as Jevons, Walras, and I. Fisher, employed Maxwell's method of analogy from the
Standard Model implicitly [6, 7]. (All subsequent references to the Standard Model here concern the
19th century sense of it described by Heilbron [1], not the 20th century meaning referred to by Dyson
[4].) Jan Tinbergen explicitly adopted Maxwell's method as a result of his studies with Ehrenfest and
Boltzmann [8, 9].
The Standard Model is the context most appropriate for grasping the importance of the work of
Georg Rasch, a student of Tinbergen's colleague and co-Nobelist, Ragnar Frisch, and colleague of
Tinbergen's student, Tjalling Koopmans. Rasch presents psychological measurement in terms of an
analogy from Maxwell's analysis of Newton's Second Law [10]. The existence of mathematical
models, and of results, in the social sciences that are structurally identical with those in the natural
sciences, suggests a basis for a fundamental reorientation and reprioritization of methods in
psychosocial research, and in any area in which ordinal counts, ratings, or rankings constitute the form
of observations that must be evaluated and transformed into linearly invariant measures [11, 12].
2. Theoretical Perspective
The basic concept incorporated in the Standard Model is that each parameter has to be measurable
independently of the other two, and that any combination of two parameters has to cause the third to
take predictable values. These relationships are demonstrably causal, and are not just unexplained but
conveniently reproducible associations. So force has to be the product of mass and acceleration; mass
has to be force divided by acceleration; and acceleration has to be force divided by mass.
The ideal of a mathematical model incorporating the kinds of relations found in Newton's second
law guided much of 19th century science, and historians of economics and econometrics have
documented another line of extensions of the Standard Model. For instance, in Walras’ first effort at
formulating a mathematical expression of economic relations, he “attempted to implement a
Newtonian model of market relations, postulating that ‘the price of things is in inverse ratio to the
quantity offered and in direct ratio to the quantity demanded’” [7].
Jevons similarly studied energetics, in his case, with Michael Faraday, in the 1850s. Pareto also
trained as an engineer; he made “a direct extrapolation of the path-independence of equilibrium energy
states in rational mechanics and thermodynamics” to “the path-independence of the realization of
utility” [7]. The concept of equilibrium models in econometrics stems from this work, and was
elaborated in the analogies Jan Tinbergen drew between economic phenomena and pendulum
behavior, following Maxwell’s method [9].
With the transition in physics to quantum mechanics [13], the introduction of analysis of variance
and covariance, and regression methods by Ronald Fisher [14], and the proposal of probability
approach by Haavelmo [15], the Standard Model quietly became statistical. The departure from the
practice of science was not noticed in economics and the social sciences because the rhetorical and
epistemological values of science conformed better with the new statistical methods than they did with
the experimental practices of working scientists [16, 17]. That is, the mutual implication of subject and
object that characterizes captivation with meaning and the propagation of inscriptions across media
[18-20] was as yet unrecognized as a determining factor in the successful identification and
exploitation of scientific capital. Statistical practice therefore assumed the validity of the Cartesian
dualist understanding of scientific method functioning at the time as the dominant paradigm, resulting
in what has been referred to as an “ontological divide” between ostensibly quantitative methods and
qualitative methods focused on the social construction of authentic meaning [21].
The problem, as much for Fisher as for others, such as Thurstone [22], revolved around criteria for
objectivity and the associated necessity of selective attention in the choice of legitimate observations
(for instance, see Good's [23] brief remark on Fisher's tempestuous outburst in response to a comment
in this regard). The same tension between a purely statistical orientation toward models as describing
intervariable relationships and a measurement orientation toward models as prescribing intravariable
relationships continues in today's debates on methodology [24-26]. But perhaps a criterion distinction
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capable of commanding a new consensus emerges in the overlap of (a) dissatisfaction with
quantitative methods that are mere numerical manipulations and (b) enthusiasm for qualitatively
meaningful, applicable, and critical elaborations on ratios of demonstrably invariant comparisons [10,
11, 27-30]. Instead of treating group-level ordinal scores as measures in models that make stochastic
assumptions impossible to derive from theory and to apply to individual cases [30. 31], ought not
approaches more true to the concept of methodical reproducibility root measurement in requirements
derived from theory and applicable to individuals? Instead of making implausible assumptions
concerning normality, independence, and linearity, should not these be formulated as hypotheses and
subjected to experimental tests? The latter has long in fact been the case in Rasch measurement
practice, so let us now turn our attention to it.
3. Discussion
In his 1934-35 studies with Frisch in Oslo and with Ronald Fisher in London, the Danish
mathematician Georg Rasch [32, 33] made the acquaintance of a number of Tinbergen’s students, such
as Koopmans [34], from whom he may have heard of Tinbergen’s use of Maxwell’s method of
analogy, if he did not learn it directly from Tinbergen himself. In Chapter VII of his book [10], after
developing the theme under the headings of “2. Maxwell's analysis of the concepts of mass and force”
(pp. 110-111), “3. The 'multiplicative law of accelerations'” (pp. 111-113), and “4. Units of mass and
force” (pp. 113-114), Rasch employs such an analogy in the presentation of his measurement model,
saying
Maxwell's very detailed analysis, of which this is only an incomplete summary, has
greatly fascinated me on finding that the same kind of argument should be applicable
elsewhere, in particular in problems of measurement in psychology. (p. 113)
In sections 3 and 4 of the chapter, following Maxwell [35] (sections 45-53 in Chapter Three), Rasch
describes the symmetry between mass and force, providing a table noting variations in the amount of
force applied by various instruments to various objects of varying masses (p. 114). This table can be
ordered in such a way as to display the proportionality of the accelerations obtained in both the rows
(masses) and the columns (forces). The multiplicative law of accelerations is then written as
Avj = Fj / Mv
where the acceleration A is the product of two factors, the force F applied by instrument j divided by
the mass of object v. Rasch concludes section 4 by pointing out
“…the acceleration of a body cannot be determined; the observation of it is admittedly
liable to…‘errors of measurement’, but…this admittance is paramount to defining the
acceleration per se as a parameter in a probability distribution—e.g., the mean value of
a Gaussian distribution—and it is such parameters, not the observed estimates, which
are assumed to follow the multiplicative law [acceleration = force / mass].
Thus, in any case an actual observation can be taken as nothing more than an
accidental response, as it were, of an object—a person, a solid body, etc.—to a
stimulus—a test, an item, a push, etc.—taking place in accordance with a potential
distribution of responses— the qualification ‘potential’ referring to experimental
situations which cannot possibly be [exactly] reproduced.
In the cases considered [earlier in the book] this distribution depended on
one relevant parameter only, which could be chosen such as to follow the
multiplicative law.
Where this law can be applied it provides a principle of measurement on a
ratio scale of both stimulus parameters and object parameters, the conceptual status of
which is comparable to that of measuring mass and force. Thus, by way of an
example, the reading accuracy of a child…can be measured with the same kind of
objectivity as we may tell its weight…” (p. 115).
What Rasch provides in the models that incorporate this structure is a portable way of applying
Maxwell’s method of analogy from the Standard Model. Maxwell separated the structure of scientific
3

law from the specifics of a direct analogy with any particular law. Rasch, then, adapted the invariance
properties associated with statistical sufficiency [36] in a probabilistic context to the demands of the
three-part structure of the Standard Model. Just as Maxwell [37] (pp. 159-160) took the properties of a
frictionless fluid subject to variation in pressure, volume, and temperature as a model for electricity,
so, too, do Burdick, Stone, & Stenner [38] present an example relating a Rasch Reading Law to the
Combined Gas Law. Just as Maxwell arrived at a theory capable of demonstrating predictive control
over the parameters in the model, so, too, is the Rasch Reading Law shown to conform with the
expectations formulated on the basis of a substantive manipulation of the variables affecting text
complexity, reading ability, and comprehension rates.
This perspective, that “the best scheme...is the one that makes comprehension the easiest,” dates to
the late 18th century and can be traced to the works of Condorcet and Kant [1]. The echoes of Kant's
[39] assertion that “reason has insight only into that which it produces after a plan of its own” are
unmistakable [40]. The Standard Model provides a simple, parsimonious and elegant plan for
facilitating reason's insights. Rasch's appropriation of Maxwell's method of analogy makes that plan
available to any interested in employing it.
4. Conclusion
The generalized capacity to design and execute experimental tests of hypothesized lawful regularities
in constructs of all kinds suggests the existence of powerful and untapped potentials hidden within
psychometrics and econometrics [25, 41]. There is a great need for studies that focus less on statistical
hypothesis testing and more on the modeling of constructs, the estimation of values in a shared metric
framework, the calibration of instruments of known and appropriately gauged precision, and the
dissemination of those instruments for end use applications that provide useful, relevant, and
interpretable mass customized quantitative information immediately upon administration. Such an
integration of theory and practice would remove the need for many of the cumbersome and inefficient
centralized data gathering, analysis, reporting methods currently in use, and would open the door to
new efficiencies in human, social, and natural capital markets.
8. References
[1] Heilbron J L 1993 Historical Studies in the Physical and Biological Sciences 24 1-337
[2] Nersessian N J 2002 in Essays in the History and Philosophy of Science and Mathematics ed.
Malament D (Lasalle, Illinois: Open Court) 129-166
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Erlbaum Associates) 65-104
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[13] Mirowski P 1989 Oxford Economic Papers 41 217
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[15] Haavelmo T 1944 Econometrica 12/Supplement 1-118
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[32] Andrich D 1997 Rasch Measurement Transactions 11 542-543
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http://www.rasch.org/memo63.htm
[34] Bjerkholt O 2001 Tracing Haavelmo's Steps from Confluence Analysis to the Probability
Approach (Oslo, Norway: Department of Economics, University of Oslo, in cooperation
with The Frisch Centre for Economic Research) No. 25, 33 pp.,
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[35] Maxwell, J C. 1876/1920 Matter and Motion ed. Larmor J (New York: The Macmillan Co)
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[39] Kant I 1929/1965 Critique of Pure Reason trans. Smith N K (New York: St. Martin's Press)
[40] Fisher W P Jr 2010 in Advances in Rasch Measurement, Vol. 1 ed. Garner, M., et al. (Maple
Grove, MN, JAM Press) 12-44
[41] Fisher W P Jr 2009 Measurement 42 1278-1287
Acknowledgments. Thanks to Michael Everett and A. Jackson Stenner for their support of this work.
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