Archive for December, 2009

A Summary End-of-Year Philosophical Overview

December 25, 2009

So the end of the year and the start of a new one makes a good time to reflect a bit on just what the situation in the world looks like, philosophically speaking.

As is so often the case, we hold the keys to our own liberation, but don’t know it, can’t see them, or refuse out of pure contrariness to fit them in the locks. Here, then, is a list of locks and keys for those who might want to match them up and see new ways of doing things.

  • The way we define a problem sets up a class of solutions as a restricted range of ways that things can be done. Historians and philosophers of science have shown that, contrary to the way we usually think of things, solutions come first. As the old expression goes, “When the only tool you have is a hammer, everything looks like a nail.” Science is so dependent on the available technology for the way it defines problems that this point has led to the emergence of the term “technoscience” as an explicit marker of the difference between this new point of view and the old one (among many works in this area, see Ihde, 1983; Latour, 1987).
  • One of the most ancient human technologies is language itself; the word “text” has the same root in the Sanskrit TEK and Greek techne as technique and textile. Just as is the case with technoscience, before we have the  slightest chance to do anything about it, language prethinks the world for us. In the same way that the Grateful Dead sings about the music playing the band, the words and grammar we use are using us much more than vice versa. We have rightly become more sensitive to the way words restrict our expectations, so that “man” is no longer taken to refer to all people. But the problem is far more complex than this example might lead us to believe. The very way in which words represent things is itself the paradigmatic model for science, as becomes apparent as we think this through.
  • One very important way that language sets us up to think in a particular way stems from the subject-verb-object structure of Western European languages. We habitually define problems in terms of what is sometimes called the Cartesian duality or subject-object split. Our language has led to the perception that thinking subjects are completely separate from and independent of the objects they encounter and act on. The limited framework in which this split can be reasonably entertained has been enormously productive, but has led to equally enormous undesired consequences in terms of human, social, and environmental waste.
  • Descartes himself recognized the limits of separating the thinking subject from the world of objects, but took a pragmatic attitude toward simplifying things. If Descartes hadn’t existed, we would have had to invent him, and to some extent, we probably already have. Descartes (1971, pp. 183-4) understood the situation very well, saying: “I have often observed that philosophers make the mistake of trying to explain by logical definitions those things which are most simple and self-evident; they thus only make them more obscure. When I said that the proposition I experience (cogito) therefore I am is the first and most certain of those we come across when we philosophize in an orderly way, I was not denying that we must first know what is meant by experience, existence, certainty; again, we must know such things as that it is impossible for that which is experiencing to be non-existent; but I thought it needless to enumerate these notions, for they are of the greatest simplicity, and by themselves they can give us no knowledge that anything exists.”
  • Descartes then did not use the phrase ‘cogito, ergo sum’ in the rigid and over-simplified way which is often attributed to him.  Heidegger (1967, p. 104) explains that
    “The formula which the proposition sometimes has, ‘cogito, ergo sum,’ suggests the misunderstanding that it is here a question of inference.  That is not the case and cannot be so, because this conclusion would have to have as its major premise: Id quod cogitat, est; and the minor premise: cogito; conclusion: ergo sum.  However, the major premise would only be a formal generalization of what lies in the proposition: ‘cogito-sum.’ Descartes himself emphasizes that no inference is present.  The sum is not a consequence of the thinking, but vice versa; it is the ground of thinking, the fundamentum.”
  • Today, though, the matters that were too simple for Descartes to concern himself with have become problems of huge proportion.  In a note to Heidegger’s discussion of this passage from Descartes, the editor suggests that the greatest part of Heidegger’s philosophical work has been devoted to enumerating and putting on record what Descartes left out as too simple to be concerned with (Krell in Heidegger 1982b, p. 125).
  • No doubt a great many thinkers and scholars have an intellectual grasp of these issues. Putting those thoughts in action is proving difficult, to say the least. Institutionalized habits of mind seem nearly impossible to overcome. In one of those great ironies of history, we now have a situation in which we are trying to solve a new class of problems (nonCartesian ones) using the approaches that are the cause of the class of problems (Cartesian ones). Of course, as long we insist on operating this way, all we can do is make things worse. (For more on this, see a previous blog describing how the problem is the problem.)
  • We can see our way out of this, and moreover find the motivation to act, by considering how we got into it. Descartes (1961, p. 8) held that “…in seeking the correct path to truth we should be concerned with nothing about which we cannot have a certainty equal to that of the demonstrations of arithmetic and geometry.” In saying this, Descartes identifies himself as a student of Plato, as someone experienced enough in mathematics to have met the requirements for admission to the Academy. Plato wanted students familiar with arithmetic and geometry because they know that numeric and geometric figures plainly are not the mathematical objects they stand for. Geometrical analyses of squares, circles, and triangles always come out the same, no matter which particular figure of a type is involved. Understanding this distinction was fundamental to taking up the study of philosophy, which actually involves nothing but the independence of figure from meaning, of word from concept. The Cartesian duality is a natural extension of Plato into the distinction between mind and body, subject and object.
  • So we look right through the particular words, numbers, and geometrical figures representing things and see the things themselves in terms of abstract ideals that are basically mathematical. But even in naming abstract ideals as such we do not come any closer to grasping or apprehending the complete truth of being. All we have are words, but this does not mean that we are trapped forever in a linguistic cage. The situation is quite the contrary, in fact. Science is poetry in motion. Science is a systematic way of simultaneously inventing and discovering things brought into words via dialogues with life. Science is the way we let the metaphoric process do its thing (among many works in this area, see especially Gerhart & Russell, 1984, and Kuhn, 1993; for an example of recent work, see Colburn & Shute, 2008).
  • Far from controlling and dominating the world, what science enables us to do via metaphor is to subject ourselves systematically to very specific aspects of the world.  Our problem today is not one of overcoming the way we have subdued nature, each other, and ourselves so much as it is one of subjecting ourselves to a more comprehensive range of things about which we can “have a certainty equal to that of the demonstrations of arithmetic and geometry,” as Descartes put it. In other words, how do we extend the power of nonCartesian scientific metaphor-making into the human, social, and environmental sciences? This project has been the focus of my work from the beginning of my professional career to the present, and is elaborated in detail in a number of works (Fisher, 1988, 1992, 2004, 2010b).
  • Though explanations and logic can be compelling to some readers, the real power of ideas is exhibited in practice. Living the change we want to see happen has, for me, involved acting on yet another aspect of the way science poetically extends language’s prethinking of the world. The identity and coherence of a culture or an historical epoch is largely a matter of the way particular metaphors inform a worldview and the paradigmatic objects of the conversations of the time. Individual thoughts and behaviors are coordinated and harmonized via conversations that take place in terms, of course, of the words and concepts in circulation. And so we see that language is the original network that makes collective cognition and action possible. Language is the model for the not-always-so-wise wisdom of crowds effect that synchronizes everything from markets to laboratories to rush hours.
  • Seen from this angle, then, the problem is one of seeing how mathematical clarity can be embodied in the instruments of a technoscience distributed across the nodes of networks. How can we think and act together on the problems of the human, social, and environmental sciences with the same kind of coordination we experience in time via clocks or in the sequencing of the SARS virus via laboratories sharing metrological standards (to cite an example given by Surowiecki (2004), with (Latour, 1987, 2005) in the background)? The answer to this question lies in the calibration of instruments that are linked together and are so traceable to reference standards in a kind of metric system for each major construct of interest, such as the abilities, health, attitudes, trust, and environmental qualities essential to human, social, and natural capital (Fisher, 1996, 2000a, 2000b, 2002, 2005, 2009a, 2009b, 2010a).
  • Instruments are being calibrated on a broad scale across a great many applied and research contexts in business and academic contexts (among thousands of publications, see Bezruczko, 2005; Drehmer, Belohlav, & Coye, 2000; Masters, 2007; Salzberger & Sinkovics, 2006). Though local or proprietary implementations work to coordinate thought and behavior within restricted communities, systematic approaches to creating universally uniform metric systems for human, social, and natural capital are as yet nonexistent (Fisher, 2009a, 2009b).
  • Finally, in accord with our acceptance of the way we are always already caught up in the play and flow of language, what does a nonCartesian approach to facilitating networked harmonizations look like? There are four main features to be aware of. First off, we want to be acutely aware of and vigilantly sensitive to the role of metaphor. In abstracting from individuals to universals, we generalize from particulars in ways that must be justified (Ballard, 1978, pp. 186-190; Ricoeur, 1974; Gadamer, 1991, pp. 7-8).  All generalization involves telling a story that is largely true of everyone and everything that has a part in it, but which simultaneously is not perfectly or exactly true of any of them. As Rasch (1960, p. 115) points out, if force, mass, and acceleration are measured with enough precision we see that the actual measures do not accord exactly with Newton’s laws; rather, their parameters in probability distributions do. Respect and attention to the potential for what Ricoeur (1974) called the violence of the premature conclusion must be brought to bear in systematic ways to aid in “recalling the uniqueness of the person measured” (Ballard, 1978, p. 189). It will be essential to incorporate the ontological method’s (Fisher, 2010b; Heidegger, 1982a, pp. 21-23, 32-330) deconstructive moment as a judicial element in a balance of powers with the legislative moment’s experimentally justified reductions and the executive moment’s constructive applications.
  • Second, attuned to those instances in which the philosophical thesis of the independence of figure and meaning, or the separation of signifier and signified, is difficult to satisfy (Derrida, 1982, p. 229; Wood & Bernasconi, 1988, 88-89), a nonCartesian approach to facilitating network harmonizations requires that we focus on identifying where, when, and what signifier-signified separations can be obtained. Because the universality and objectivity of mathematical objects make them “the absolute model for any object whatsoever” (Derrida, 1989, p. 66, also see p. 27), and because it is number and not word that is the real paradigm of the domain of things that can be understood in language (Gadamer, 1989, p. 412), we now strive to test the limits of the mathematical as “the fundamental presupposition of all ‘academic’ work” and “of the knowledge of things” (Heidegger, 1967, pp. 75-76).  This is the same thing as attending to the calibration of the instruments that are ultimately to be linked to reference standards. This is the domain of Rasch measurement (Andrich, 1988, 2004; Bond & Fox, 2007; Rasch, 1960; Wilson, 2005; Wright, 1997), which takes the assessment of data consistency, unidimensionality, reliability, and construct validity as essential.
  • Third, with calibrated instruments in hand, attention turns to linking and equating them systematically in networks tracing connections to and from metrological reference standards, adapting the methods for maintaining the existing metric system (Fisher, 1996, 2000a, 2000b, 2005, 2009a, 2009b, 2010a). The goal here will be one of coordinating and synchronizing the self-organizing structures of each distinct construct, much as was done for the measurement of literacy (Stenner, et al., 2006).
  • Fourth, though we have to this point completely respected our inescapable immersion in the play of language, there still remains the question of how such a massive transformation from the modern Cartesian dualist point of view to a postmodern nonCartesian one will be brought about. Like any paradigm shift, the new way of doing things emerges as a function of the returns–economic, political, social, and psychological–that can be expected from the investments made. And in accord with the broad qualitative sense of the mathematical as learning through what we already know (Heidegger, 1967; Kisiel, 1973), the new will emerge as an amplification of something old. A great deal of attention and investment is currently being focused on creating whole new sources of sustainable, socially responsible, and long-term profits from closer management of human, social, and natural capital. In the same way that the metric system is an essential component of global trade, and in the same way that origins of the metric system coincide with the scientific, industrial, and political revolutions of the late 18th and early 19th centuries, so, too, will a new metric system for human, social, and natural capital provide a foundation for new efficiencies and degrees of effectiveness across multiple domains. The profit motive is an engine of great energy and resources. We need to learn how to harness it as a driver of growth in realized human potential, social cohesion, and environmental quality. What other way of giving ourselves over to the nonCartesian and playful creation of meaning is there, in fact, except to extend the rule of law and the invisible hand’s matching of supply and demand into all of the areas essential to human being?

Philosophically speaking, then, it would seem that all of the elements are in place for a positive answer to Zimmerman’s (1990, p. 274) question, “can we develop the non-absolutist, non-foundational categories necessary to assess, to confront, and to transform the technological and economic mobilization of humanity and the earth at the beginning of the twenty-first century?” Zimmerman might not agree with my sense that we can, since, reflecting on Heidegger’s efforts to put his political philosophy in action, he (1990, p. 257) remarks that “Heidegger’s political engagement in 1933-34 led him to conclude that all merely human ‘revolutions’ and ‘decisions’ would simply reinforce the system already in play. The question for us is: Is that conclusion tenable?” Zimmerman (pp. 245-246) apparently hopes it is not, and looks to love, compassion, and respect as alternatives to Heidegger’s hope for divine intervention.

But let’s consider what is “merely human.” The nonhuman is not necessarily divine, even if that is what Heidegger might have meant. And has not Heidegger (1962) himself already identified care as the defining characteristic of human being, with Habermas (1995) underscoring “considerateness” for our shared vulnerability, Ricoeur (1974) focusing on the desire for meaning and the choice in favor of discourse over violence, and Gadamer (1991, p. 61) also holding that “the first concern of all dialogical and dialectical inquiry is a care for the unity and sameness of the thing under discussion”? Beyond these are shifts of focus away from death as our common end, and toward our common birth from women as our shared beginning (Fielding, 2003; Schues, 1997; Schutz, 1962, 1966; Tymieniecka 1998, 2000; Zaner, 2002). And even in this, we must inevitably draw from Plato, now in Socrates’ stress on his role as a midwife of ideas, and from Aristotle, who provides the model for how to take possession of the value of living meaning in theory (Gadamer, 1980, p. 200).

Further, the conception, gestation, midwifery, and nurturing of ideas that takes place via considerateness and the desire for meaning were never the product of “merely human” intentions or designs, any more than biological reproduction was. Rather, we submit to the demands of the ways meaning is created to the same extent that we submit to the ways that life is recreated; in both cases, there is such Hegelian joy in the ways we find ourselves in each other that we can hardly complain (though whole cultures have figured out ways of doing so).

And we can indeed fault Heidegger, as Zimmerman (1990, p. 244, 258) does, for having “refused to take seriously the organic dimension of human existence,” and for somehow managing “to ignore the concrete history of actual existence and actual inquiry.”  We arrive at an entirely different, democratic, sphere of political implications (Ihde, 1990; Latour, 2004; Latour & Weibel, 2005), when we extend the deconstruction of metaphysics into examinations of the actual material practices of science, as Latour (1987, 2005) and others have done (Ihde, 1991, 1998; Ihde & Selinger, 2003). The dialogue with nonhuman others (Latour, 1994) is conceived as explicitly nonCartesian and nondualist, such that it is literally impossible to conceive of anything that does not incorporate social relations, or of any social relations that do not incorporate nonhuman others.

The self-organized unfolding of such dialogues play out the self-representative activity of the things themselves, with method defined as their movement in thought (Gadamer, 1989; Fisher, 2004). Reinforcing some aspect or aspects of the system already in play is indeed inevitable, as Heidegger concluded. But no important “revolutions” or “decisions” have ever been based in “merely human” inputs (Latour, 1993), as becomes apparent if we pay close attention to the concrete behaviors and communications through which meaning is created and shared. The “non-absolutist, non-foundational categories necessary to assess, to confront, and to transform the technological and economic mobilization of humanity and the earth at the beginning of the twenty-first century” referred to by Zimmerman are indeed in hand. Though many unfamiliar with the evidence, theory, and instruments may doubt this is true, a contemporary Galileo might be heard to mutter, “E pur si muove!”

References

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Ballard, E. G. (1978). Man and technology: Toward the measurement of a culture. Pittsburgh, Pennsylvania: Duquesne University Press.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

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Colburn, T. R., & Shute, G. M. (2008, December). Metaphor in computer science. Journal of Applied Logic, 6(4), 526-533.

Derrida, J. (1982). Margins of philosophy. Chicago, Illinois: University of Chicago Press.

Derrida, J. (1989). Edmund Husserl’s Origin of Geometry: An introduction. Lincoln: University of Nebraska Press.

Descartes, R. (1961). Rules for the direction of the mind. Indianapolis: Bobbs-Merrill.

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Drehmer, D. E., Belohlav, J. A., & Coye, R. W. (2000, Dec). A exploration of employee participation using a scaling approach. Group & Organization Management, 25(4), 397-418.

Fielding, H. (2003, March). Questioning nature: Irigaray, Heidegger and the potentiality of matter. Continental Philosophy Review, 36(1), 1-26.

Fisher, W. P., Jr. (1988). Truth, method, and measurement: The hermeneutic of instrumentation and the Rasch model [Diss]. Dissertation Abstracts International (University of Chicago, Dept. of Education, Division of the Social Sciences), 49, 0778A.

Fisher, W. P., Jr. (1992). Objectivity in measurement: A philosophical history of Rasch’s separability theorem. In M. Wilson (Ed.), Objective measurement: Theory into practice. Vol. I (pp. 29-58). Norwood, New Jersey: Ablex Publishing Corporation.

Fisher, W. P., Jr. (1996, Winter). The Rasch alternative. Rasch Measurement Transactions, 9(4), 466-467 [http://www.rasch.org/rmt/rmt94.htm].

Fisher, W. P., Jr. (2000a). Objectivity in psychosocial measurement: What, why, how. Journal of Outcome Measurement, 4(2), 527-563 [http://www.livingcapitalmetrics.com/images/WP_Fisher_Jr_2000.pdf].

Fisher, W. P., Jr. (2000b). Rasch measurement as the definition of scientific agency. Rasch Measurement Transactions, 14(3), 761 [http://www.rasch.org/rmt/rmt143f.htm].

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2004, October). Meaning and method in the social sciences. Human Studies: A Journal for Philosophy and the Social Sciences, 27(4), 429-54.

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-9 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

Fisher, W. P., Jr. (2009a, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

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Fisher, W. P., Jr. (2010a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 11, in press [http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf].

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Heidegger, M. (1982a). The basic problems of phenomenology (J. M. Edie, Ed.) (A. Hofstadter, Trans.). Studies in Phenomenology and Existential Philosophy. Bloomington, Indiana: Indiana University Press (Original work published 1975).

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s becomes apparent

Protocols for Living Capital

December 23, 2009

David Brooks’ December 22, 2009 column, “The Protocol Society,” hits some really great notes. There are several things worth commenting on. The first point concerns the protection of intellectual property and the encouragement of a free flow of ideas within the overarching operating system of laws, regulations, and property rights. What Brooks is getting at here is the concept of living capital.

A diverse group of writers (Hayek, De Soto, Latour, many others) contrast what they variously term socialist, centralized, and prescientific efforts to control capital’s concrete forms, on the one hand, with capitalist, decentralized, and scientific methods that focus on liberating the flow of capital defined abstractly in terms of the rule of law and transferable representations (titles, deeds, calibrated instruments, etc.). These two senses of capital also apply in the context of intangibles like human, social, and natural capital (Fisher, 2002, 2005, 2009a, 2010).

Second, the movement in economics away from mathematical modeling echoes the broadening appreciation for qualitative methods across the social sciences that has been underway since the 1960s. The issue is one of learning how to integrate substantive concerns for meaningfulness and understanding in the ways we think about economics. The idealized rational consumer typically assumed in traditional mathematical models demands the imposition of a logic not actually often observed in practice.

But just because people may not behave in accord with one sense of rationality does not mean there is not a systematic logic employed in the ways they make decisions that are meaningful to them. Further, though few are yet much aware of this, mathematical models are not inherently irreconcilable with qualitative methods (Fisher, 2003a, 2003b; Heelan, 1998; Kisiel, 1973). Scientifically efficacious mathematical thinking has always had deep roots in qualitative, substantive meaning (Heilbron, 1993; Kuhn, 1961; Roche, 1998). Analogous integrations of qualitative and quantitative methods have been used in psychology, sociology, and education for decades (Bond & Fox, 2007; Fisher, 2004; Wilson, 2005; Wright, 1997, 2000).

Third, yes, those societies and subcultures that have the capacities for increasing the velocity of new recipes have measurably greater amounts of social capital than others. The identification of invariant patterns in social capital will eventually lead to the calibration of precision measures and the deployment of universally uniform metrics as common currencies for the exchange of social value (Fisher, 2002, 2005, 2009a, 2009b).

Fourth, though I haven’t read “Smart World,” the book by Richard Ogle that Brooks refers to, the theory of the extended mind embodied in social networks sounds highly indebted to the work of Bruno Latour (1987, 1995, 2005) and others working in the social studies of science (O’Connell, 1993) and in social psychology (Hutchins, 1995; Magnus, 2007). Brooks and Ogle are exactly right in their assertions about the kinds of collective cognition that are needed for real innovation. The devilish details are embedded in the infrastructure of metrological standards and uniform metrics that coordinate and harmonize thought and behavior. We won’t realize our potential for creativity in the domains of the intangible forms of capital and intellectual property until we get our act together and create a new metric system for them (Fisher, 2009a, 2009b, 2010). Every time someone iterates through the protocol exemplified in Brooks’ column, we get a step closer to this goal.

References

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. (2000). Objectivity in psychosocial measurement: What, why, how. Journal of Outcome Measurement, 4(2), 527-563 [http://www.livingcapitalmetrics.com/images/WP_Fisher_Jr_2000.pdf].

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2003a, December). Mathematics, measurement, metaphor, metaphysics: Part I. Implications for method in postmodern science. Theory & Psychology, 13(6), 753-90.

Fisher, W. P., Jr. (2003b, December). Mathematics, measurement, metaphor, metaphysics: Part II. Accounting for Galileo’s “fateful omission.” Theory & Psychology, 13(6), 791-828.

Fisher, W. P., Jr. (2004, October). Meaning and method in the social sciences. Human Studies: A Journal for Philosophy and the Social Sciences, 27(4), 429-54.

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-9 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

Fisher, W. P., Jr. (2009a, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Fisher, W. P. J. (2009b). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (Tech. Rep., http://www.livingcapitalmetrics.com/images/FisherNISTWhitePaper2.pdf). New Orleans: LivingCapitalMetrics.com.

Fisher, W. P., Jr. (2010). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, p. in press [http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf].

Heelan, P. A. (1998, June). The scope of hermeneutics in natural science. Studies in History and Philosophy of Science Part A, 29(2), 273-98.

Heilbron, J. L. (1993). Weighing imponderables and other quantitative science around 1800 (Vol. 24 (Supplement), Part I, pp. 1-337). Historical studies in the physical and biological sciences). Berkeley, California: University of California Press.

Hutchins, E. (1995). Cognition in the wild. Cambridge, Massachusetts: MIT Press.

Kisiel, T. (1973). The mathematical and the hermeneutical: On Heidegger’s notion of the apriori. In E. G. Ballard & C. E. Scott (Eds.), Martin Heidegger: In Europe and America (pp. 109-20). The Hague: Martinus Nijhoff.

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

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

Latour, B. (1995). Cogito ergo sumus! Or psychology swept inside out by the fresh air of the upper deck: Review of Hutchins’ Cognition in the Wild, MIT Press, 1995. Mind, Culture, and Activity: An International Journal, 3(192), 54-63.

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

Magnus, P. D. (2007). Distributed cognition and the task of science. Social Studies of Science, 37(2), 297-310.

O’Connell, J. (1993). Metrology: The creation of universality by the circulation of particulars. Social Studies of Science, 23, 129-173.

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

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

Wright, B. D. (1997, Winter). A history of social science measurement. Educational Measurement: Issues and Practice, 16(4), 33-45, 52 [http://www.rasch.org/memo62.htm].

Wright, B. D., Stone, M., & Enos, M. (2000). The evolution of meaning in practice. Rasch Measurement Transactions, 14(1), 736 [http://www.rasch.org/rmt/rmt141g.htm].

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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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On Leaping to Conclusions: Learning Through Prejudices and Evaluating Them

December 22, 2009

Back at Marianjoy Rehab Hospital in the late 1980s and early 1990s, Richard F. Harvey, MD, the Medical Director, had a sign in his office that’s always stuck in my mind. It had an image of a kangaroo on it, with the words “Some people get their exercise by leaping to conclusions.”

Yes, I am as guilty as anyone of that. And I’m particularly sensitive to the issue because my work involves a lot of thinking and research into how we all inevitably learn through what we already know. We develop physically, cognitively, and morally by filtering incoming data through the screen of what we already are, what we’ve already experienced and learned. As we integrate physical sensations and learn to coordinate our limbs and hands with our eyes, we move from babyhood to childhood. As we learn how to pronounce words and construct sentences, we learn to speak. With the basics of communication in hand, we pick up the alphabet, spelling, grammar, and composition in the course of learning to read and write. Then we use what we’ve read and experienced to think through what is and what ought to be as we try to build a better world.

But we often leap to conclusions when we hear, see, or read something that doesn’t quite make sense to us. I’m becoming increasingly attuned and sensitive to the ways in which I, and others, do this. It happens subtly sometimes, when perhaps we’ve encountered something we don’t really know much about, but which seems obviously wrong for some reason. It is basically an issue of prejudice, but not in the big sense of the word. I’m thinking of the little ways in which we filter experience, in which attention is directed to what we find especially meaningful, and in which matters presumed to be of peripheral concern are pushed to the margins. We must inevitably do these kinds of things; if we didn’t, we’d be overwhelmed with uninterpretable data.

The philosophical issues involved have been an explicit focus of interpretation theory (hermeneutics) for over a century, with roots dating to ancient Greece. Changes in the perception of prejudice as the necessary door through which all new experience and knowledge is processed have led to thorough reconsiderations of what it is and what its place in clear thinking might be. In his landmark work on the creation of meaning in interpretation, Gadamer (1989, p. 490), for instance, remarks that “there is undoubtedly no understanding that is free of all prejudices, however much the will of our knowledge must be directed toward escaping their thrall.”

It happens that some fields of research make investigators more aware of the need to pay attention to prejudices and presuppositions than others. In the preface (pp. xi-xiii) to his classic 1977 book, The Essential Tension, Thomas Kuhn recounts an experience from the summer of 1947 that led to his appreciation for an explicit theory of interpretation. He had been completely perplexed by Aristotle’s account of motion, in which Aristotle writes a great many things that appear blatantly absurd. Kuhn was very puzzled and disturbed by this, as Aristotle made many astute observations in other areas, such as biology and political behavior. He eventually came to see what Aristotle was in fact talking about, and he then came to routinely offer the following maxim to his students:

“When reading the works of an important thinker [or anyone else who usually seems to have a modicum of coherence], look first for the apparent absurdities in the text and ask yourself how a sensible person could have written them. When you find an answer, I continue, when those passages make sense, then you may find that more central passages, ones you previously thought you understood, have changed their meaning.”

As Kuhn goes on to say, if his book was addressed primarily to historians, this point wouldn’t be worth making, as historians are in the business of precisely this kind of interpretive back-and-forth, as are many philosophers, literary critics, writers, social scientists, educators, and artists. But as a physicist, Kuhn says that the discovery of hermeneutics not only made history seem consequential, it changed his view of science. As is well known, his skill in practicing hermeneutics changed a great many people’s views of science.

In my personal experience, however, one does not need to be a physicist to be guilty of dismissing apparent absurdities. In a classic article, Paul Ricoeur (1974) refers to uncontrolled submission to prejudices as “the violence of the premature conclusion.” He (Ricoeur, 1974, p. 96) agrees with Gadamer about the inevitability of prejudice at some level, saying, “There can be no philosophy without presuppositions.”

And agreement with this general attitude is shared even by someone as apparently unlikely as Jacques Derrida, reviled by some (for instance, Bloom, 1987, p. 387, among many others) for appearing to hold that reason is futile, precisely because it is inevitably tied to the interests that shape our presuppositions. Derrida was perplexed by these reactions to his work, strenuously objecting and pointing out that

“…people who read me and think I’m playing with or transgressing norms–which I do, of course–usually don’t know what I know: that all of this has not only been made possible by but is constantly in contact with very classical, rigorous, demanding discipline in writing, in ‘demonstrating,’ in rhetoric. …the fact that I’ve been trained in and that I am at some level true to this classical teaching is essential. … When I take liberties, it’s always by measuring the distance from the standards I know or that I’ve been rigorously trained in” (Derrida, 2003, pp. 62-63).

Contrary to what many of his readers presume, Derrida considered himself true to philosophy (1989b, p. 218), agreeing that mathematically ideal objects are the “absolute model for any object whatsoever” (1989a, p. 66), and that metaphysical presuppositions are unavoidable (Derrida, 1978, pp. 280-281). What we have in this extreme case, then, is an ironic example of becoming subject to prejudices concerning the role of those prejudices in shaping understanding. Bloom (1987), for his part, is also tragically ironic in taking deconstruction to be a closing of the American mind when it actually represents ways of opening further than ever before, as but one moment in cycling through the ontological method (Heidegger, 1982, pp. 19-23, 320-330; Fisher, 2010) from (1) reducing experience to words to (2) applying what has been said in practice to (3) creatively destroying our routines to uncover hidden prejudices via deconstruction, which then informs a return to new reductions.

What happened in Derrida’s case gives a good context for considering the smaller everyday ways in which we counter-productively dismiss apparent nonsense, commit small acts of violence against others and ourselves, and fail to appreciate as well as we could the opportunities with which we are presented. There seem to be a lot of ways in which we build up a righteous sense of ourselves over against the madness of the world by projecting inanities on others instead of asking, as Kuhn found he had to ask, how a reasonable person could arrive at such a position.

Of course, it is simply easier to assume other people are not reasonable, or that their methods of reasoning are insufficient, unnecessary, or both. And, of course, it takes a lot of time to try to understand how others might be reasonable in ways that we have not conceived. Anyone who has experienced close but difficult relationships with others knows how much effort can be expended in achieving even small gains in mutual understanding.

So is there really very little or nothing that can be done to find other ways besides leaping to conclusions to get our exercise? We need something more than patience and tolerance, valuable though these are over the long term for allowing new learning to unfold in its own time. But simply allowing others the method of their madness does nothing to advance the general state of things, when we have so many pressing demands to learn from each other.

What we really need is a science that systematically tests our preconceptions and checks them for internal consistency and productive potential, via the checks and balances of mutually mediated theory, data, and instruments (Ackermann, 1985;  Ihde, 1991). In our specialized world, we wind up living in closed micro-societies with others of like mind who do little to challenge the boundaries of new thinking. Though old ethnic prejudices persist to the point of tribal wars in many parts of the world, they are more subtle today those of the past in other places. For instance, in the United States, Poles, Italians, and Irish previously found each other mutually distasteful, and despite ongoing institutional racism, Barack Obama symbolizes a significant shift in focus.

A broader concern with prejudice in general would be an example of the tide that lifts all boats. No one is exempted from culpability, and everyone would benefit from the removal of their own and others’ blinders. Many significant obstacles to social progress are based in unexamined prejudices.

  • Is the conduct of business inherently immoral? Many academics seem to think so, though they themselves participate in the larger economy, though no one has ever proposed a better way of improving the overall quality of life for society at large, and though universities, too, are driven by profits of various kinds.
  • Are soldiers inherently immoral? Though killing is absolutely immoral, and the training of young people to kill and to be insensitive to killing is abhorrent, would it be better to allow malicious evil to run rampant? If not, should we not do a better job of honoring and respecting those willing to give their lives? More fundamentally, are we ever going to own up as a society to the trade-offs in the calculus of lives saved vs those sacrificed? If not, how will we ever effectively oppose unjust wars or unsafe consumer products?
  • Is government inherently obstructionist and wasteful? Or does society require that its will be embodied in independent representation and balanced legislative, judicial, and executive powers? Is not the optimal role of government found in providing the infrastructural media for the fair and just expression of the collective social will? If we want to restrict the role of government in our lives, should we not then be investing our resources in uniform metrics for the efficient and effective management of human, social, and natural capital so that we can take control of education, health care, social services, and environmental quality directly?
  • Does the market need to be controlled by external mechanisms? Few would say any longer that it always knows best, though the extent that its behavior is a function of the information available is still unknown. Could the available information be improved in significant ways, perhaps by creating the highest possible quality information for each significant form of capital?
  • Is science an inherent good? Can we somehow slow or stop it, or, like democracy, can we improve it only by applying it to itself?
  • Is the measurement of human qualities inherently reductionistic, always and everywhere an immoral and meaningless categorization? Is psychosocial measurement mathematically equivalent with physical measurement in quality and in its potential for fostering scientific, humanistic, and economic revolutions impossible? Or might it already be in hand, and only our prejudices are preventing us from seeing it and using it?
  • Is addressing environmental concerns completely at odds with business interests, or are there in fact many business people who recognize that long-term profitability requires close attention to sustainability?
  • Are academics who focus on class oppression, sexism, racism, and the constant play of power as expressions of vested interests necessarily always wrong?
  • Instead of dismissing the excesses of the consumer culture as inherently devoid of any redeeming value, what is the message we need to learn that is being conveyed in this medium?
  • Are unreligious people automatically going to hell? Are those who believe their way is the only way automatically going to heaven? Is it possible to find and build on the elements of forgiveness and redemption found in all religions?

How might we find the germ of truth that gives life to each perspective? How might we reconcile and heal our own internal differences so that we can do more to accept the differences between us, and build on them in ways that brings out the real value of e pluribus unum, “out of many one”?

Far from being locked by these questions into a permanent analysis paralysis, there are concrete things that can be done to examine, test, and overcome our prejudices. I’m looking forward to engaging in this work with any and all willing to take it on. There just has to be a better way for us to get our exercise!

References

Ackermann, J. R. (1985). Data, instruments, and theory: A dialectical approach to understanding science. Princeton, New Jersey: Princeton University Press.

Bloom, A. (1987). The closing of the American mind: How higher education has failed democracy and impoverished the souls of today’s students. New York: Simon & Schuster.

Derrida, J. (1978). Structure, sign and play in the discourse of the human sciences. In Writing and difference (pp. 278-93). Chicago: University of Chicago Press.

Derrida, J. (1989a). Edmund Husserl’s Origin of Geometry: An introduction. Lincoln: University of Nebraska Press.

Derrida, J. (1989b). On colleges and philosophy: An interview conducted by Geoffrey Bennington. In L. Appignanesi (Ed.), Postmodernism: ICA documents (pp. 209-28). London, England: Free Association Books.

Derrida, J. (2003). Interview on writing. In G. A. Olson & L. Worsham (Eds.), Critical intellectuals on writing (pp. 61-9). Albany, New York: State University of New York Press.

Fisher, W. P., Jr. (2010). Reducible or irreducible? Mathematical reasoning and the ontological method. Journal of Applied Measurement, p. in press.

Gadamer, H.-G. (1989). Truth and method (J. Weinsheimer & D. G. Marshall, Trans.) (Rev. ed.). New York: Crossroad (Original work published 1960).

Heidegger, M. (1982). The basic problems of phenomenology (J. M. Edie, Ed.) (A. Hofstadter, Trans.). Studies in Phenomenology and Existential Philosophy. Bloomington, Indiana: Indiana University Press (Original work published 1975).

Ihde, D. (1991). Instrumental realism: The interface between philosophy of science and philosophy of technology. The Indiana Series in the Philosophy of Technology). Bloomington, Indiana: Indiana University Press.

Kuhn, T. S. (1977). The essential tension: Selected studies in scientific tradition and change. Chicago, Illinois: University of Chicago Press.

Ricoeur, P. (1974). Violence and language. In D. Stewart & J. Bien (Eds.), Political and social essays by Paul Ricoeur (pp. 88-101). Athens, Ohio: Ohio University Press.

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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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Just posted on the LinkedIn Human Performance Discussion on the art and science of measurement

December 16, 2009

Great question and discussion!

Business performance measurement and management ought to be a blend of art and science akin to music–the most intuitive and absorbing of the arts and simultaneously reliant on some of the most high tech precision instrumentation available.

Unfortunately, the vast majority of the numbers used in HR and marketing are not scientific. Despite the fact that highly scientific  instruments for intangibles measurement have been available for decades, this is generally true in two ways. First, measures of some qualitative substance that really adds up the way numbers do have to be read off a calibrated instrument. Most surveys and assessments used in business are not calibrated. Second, once instruments measuring a particular thing are calibrated, to be fully scientific they all have to be linked together in a metric system so that everyone everywhere thinks and acts together in a common language.

The advantages of taking the trouble to calibrate and link instruments are numerous. The history of industry is the history of the ways we have capitalized on standardized technologies. A whole new economy is implied by our capacity to vastly improve the measurement and management of human, social, and natural capital.

The research on the integration of qualitative substance and quantitative precision in meaningful measurement is extensive. My most recent publication appeared in the November 2009 issue of Measurement (Elsevier): doi:10.1016/j.measurement.2009.03.014.

For more information, see some of my published papers and the references cited in them at http://www.livingcapitalmetrics.com/researchpapers.html.

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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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Information and Leadership: New Opportunities for Advancing Strategy, Engaging Customers, and Motivating Employees

December 9, 2009

Or, What’s a Mathematical Model a Model Of, After All?
Or, How to Build Scale Models of Organizations and Use Them to Learn About Organizational Identity, Purpose, and Mission

William P. Fisher, Jr., Ph.D.

The greatest opportunity and most significant challenge to leadership in every area of life today is the management of information. So says Carol Bartz, CEO of Yahoo! in her entry in The Economist’s annual overview of world events, “The World in 2010.” Information can be both a blessing and a curse. The right information in the right hands at the right time is essential to effectiveness and efficiency. But unorganized and incoherent information can be worse than none at all. Too often leaders and managers are faced with deciding between gut instincts based in unaccountable intuitions and facts that are potentially seriously flawed, or that are merely presented in such overwhelming volumes as to be useless.

This situation is only going to get worse as information volumes continue to increase. The upside is that solutions exist, solutions that not only reduce data volume by factors as high as hundreds to one with no loss of information, but which also distinguish between merely apparent and really reliable information. What we have in these solutions are the means of following through on Carol Bartz’s information leadership warnings and recommendations.

Clearly communicating what matters, for instance, requires leaders to find meaning in new facts and the changing scene. They have to be able to use their vision of the organization, its mission, and its place in the world to tell what’s important and what isn’t, to put each event or opportunity in perspective. And what’s more is that the vision of the organization has to be dynamic. It, too, has to be able to change with the changing circumstances.

And this is where a whole new class of useful information solutions comes to bear. It may seem odd to say so, but leadership is fundamentally mathematical. You can begin to get a sense of what I mean in the ambiguity of the way leaders can be calculating. Making use of people’s skills and talents is a challenge that requires being able to assess facts and potentials in a way that intuitively gauges likelihoods of success. It is possible to lead, of course, without being manipulative; the point is that leadership requires an ability to envision and project an abstract heuristic ideal as a fundamental principle for focusing attention and separating the wheat from the chaff. A leader who dithers and wastes time and resources on irrelevancies is a contradiction in terms. An organization is supposed to have an identity, a purpose, and a mission in life independent of the local particulars of who its actual employees, customers, and suppliers are, and independent of the opportunities and challenges that arise in different times and places.

Of course, every organization is colored and shaped to some extent by every different person that comes into contact with it, and by the times and places it finds itself in. No one wants to feel like an interchangeable part in machine, but neither does anyone want to feel completely out of place, with no role to play. If an organization was entirely dependent on the particulars of who, what, when, and where, it’s status as a coherent organization with an identifiable presence would be compromised. So what we need is to find the right balance between the ideal and the real, the abstract and the concrete, and, as the philosopher Paul Ricoeur put it, between belonging and distanciation.

And indeed, scientists often note that no mathematical model ever holds in every detail in the real world. That isn’t what they’re intended to do, in fact. Mathematical models serve the purpose of being guides to creating meaningful, useful relationships. One of the leading lights of measurement theory, Georg Rasch, said it well over 50 years ago: models aren’t meant to be true, but to be useful.

Rasch accordingly also pointed out that, if we measure mass, force, and acceleration with enough precision, we see that even Newton’s laws of motion are not perfectly true. Measured to the nth decimal place, what we find is that observed amounts of mass, force, and acceleration form probability distributions that do indeed satisfy Newton’s laws. Even in classical physics, then, measurement models are best conceived probabilistically.

Over the last several decades, use of Rasch’s probabilistic measurement models in scaling tests, surveys, and assessments has grown exponentially. As has been explored at length in previous posts in this blog, most applications of Rasch’s models mistakenly treat them as statistical models, as so their real value and importance is missed. But even those actively engaged in using the models appropriately often do not engage with the basic question concerning what the model is a model of, in their particular application of it. The basic assumption seems to be that the model is a mathematical representation of relations between observations recorded in a data set, but this is an extremely narrow and unproductive point of view.

Let’s ask ourselves, instead, how we would model an organization. Why would we want to do that? We would want to do that for the same reasons we model anything, such as creating a safe and efficient way of experimenting with different configurations, and of coming to new understandings of basic principles. If we had a standard model of organizations of a certain type, or of organizations in a particular industry, we could use it to see how different variations on the basic structure and processes cause or are associated with different outcomes. Further, given that such models could be used to calibrate scales meaningfully measuring organizational development, industry-wide standards could be brought to bear in policy, decision making, and education, effecting new degrees of efficiency and effectiveness.

So, we’d previously said that the extent to which an organization finds its identity, realizes its purpose, and advances its mission (i.e., develops) is, within certain limits, a function of its capacity to be independent from local particulars. What we mean by this is that we expect employees to be able to perform their jobs no matter what day of the week it is, no matter who the customer is, no matter which particular instance of a product is involved, etc. Though no amount of skill, training, or experience can prepare someone for every possible contingency, people working in a given job description prepare themselves for a certain set of tasks, and are chosen by the organization for their capacities in that regard.

Similarly, we expect policies, job descriptions, work flows, etc. to function in similar fashions. Though the exact specifics of each employee’s abilities and each situation’s demands cannot be known in advance, enough is known that the defined aims will be achieved with high degrees of success. Of course, this is the point at which the interchangeability of employee ability and task difficulty can become demeaning and alienating. It will be important that we allow room for some creative play, and situate each level of ability along a continuum that allows everyone to see a developmental trajectory personalized to their particular strengths and needs.

So, how do we mathematically model the independence of the organization from its employees, policies, customers, and challenges, and scientifically evaluate that independence?

One way to begin is to posit that organizational development is equal to the differences between the abilities of the people employed, the efficiencies of the policies, alignments, and linkages implemented; and the challenges presented by the market. If we observe the abilities, efficiencies, and challenges in by means of a rating scale, the resulting model could be written as:

ln(Pmoas/(1-Pmoas)) = bm – fo – ca – rs

which hypothesizes that the natural logarithm of the response odds (the response probabilities divided by one minus themselves) is equal to the ability b of employee m minus the efficiency f of policy o minus the challenge c of market a minus the difficulty r of obtaining rating in category s. This model has the form of a multifaceted Rasch model (Linacre, 1989; others), used in academic research, rehabilitative functional assessments, and medical licensure testing.

What does it take for each of these model parameters to be independent of the others in the manner that we take for granted in actual practice? Can we frame our observations of the members of each facet in the model in ways that will clearly show us when we have failed to obtain the desired independence? Can we do that in a way that simultaneously provides us with a means for communicating information about individual employees, policies, and challenges efficiently in a common language?

Can that common language be expressed in words and numbers that capitalize on the independence of the model parameters and so mean the same thing across local particulars? Can we set up a system for checking and maintaining the meaning of the parameters over time? Can we build measures of employee abilities, policy efficiencies, and market challenges into our information systems in useful ways? Can we improve the overall quality, efficiency, and meaningfulness of our industry by collaborating with other firms, schools, non-profits, and government agencies in the development of reference standard metrics?

These questions all have the same answer: Yes, we can. These questions set the stage for understanding how effective leadership depends on effective information management. If, as Yahoo! CEO Bartz says, leadership has become more difficult in the age of blogospherical second-guessing and “opposition research,” why not tap all of that critical energy as a resource and put it to work figuring out what differences make a difference? If critics think they have important questions that need to be answered, the independence and consistency, or lack thereof, of their and others’ responses gives real heft to a “put-up-or-shut-up” criterion for distinguishing signal from noise.

This kind of a BS-detector supports leadership in two ways, by focusing attention on meaningful information, and by highlighting significant divergences from accepted opinion. The latter might turn out to be nothing more than exceptionally loud noise, but it might also signal something very important, a contrary opinion sensitive to special information available only from a particular perspective.

Bartz is right on, then, in saying that the central role of information in leadership has made listening and mentoring more important than ever. Modeling the organization and experimenting with it makes it possible to listen and mentor in completely new ways. Testing data for independent model parameters is akin to tuning the organization like an instrument. When independence is achieved, everything harmonizes. The path forward is clear, since the ratings delineate the range in which organizational performance consistently varies.

Variation in the measures is illustrated by the hierarchy of the policy and market items rated, which take positions in their distributions showing what consistently comes first, and what precedents have to be set for later challenges to be met successfully. By demanding that the model parameters be independent of one another, we have set ourselves up to learn something from the past that can be used to predict the future.

Further and quite importantly, as experience is repeatedly related to these quantitatively-scaled hierarchies, the factors that make policies and challenges take particular positions on the ruler come to be understood, theory is refined, and leadership gains an edge. Now, it is becoming possible to predict where new policies and challenges will fall on the measurement continuum, making it possible for more rapid responses and earlier anticipations of previously unseen opportunities.

It’s a different story, though, when dependencies emerge, as when one or more employees in a particular area unexpectedly disagree with otherwise broadly accepted policy efficiencies or market challenges, or when a particular policy provokes anomalous evaluations relative to some market challenges but not others. There’s a qualitatively different kind of learning that takes place when expectations are refuted. Instead of getting an answer to the question we asked, we got an answer to one we didn’t ask.

It might just be noise or error, but it is imperative to ask and find out what question the unexpected answer responds to. Routine management thrives on learning how to ever more efficiently predict quantitative results; its polar opposite, innovation, lives on the mystery of unexpected anomalies. If someone hadn’t been able to wonder what value hardened rubber left on a stove might have, what might have killed bacteria in a petri dish, or why an experimental effect disappeared when a lead plate was moved, Vulcanized tires, Penicillin, and X-ray devices might never have come about.

We are on the cusp of the information analogues of these ground-breaking innovations. Methods of integrating rigorously scientific quantities with qualitative creative grist clarify information in previously unimagined ways, and in so doing make it more leveragable than ever before for advancing strategy, engaging customers, and motivating employees.

The only thing in Carol Bartz’s article that I might take issue with comes in the first line, with the words “will be.” The truth is that information already is our greatest opportunity.

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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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