Archive for the ‘economy’ Category

New Opportunities for Job Creation and Prosperity

August 17, 2011

What can be done to create jobs and revive the economy? There is no simple, easy answer to this question. Creating busywork is nonsense. We need fulfilling occupations that meet the world’s demand for products and services. It is not easy to see how meaningful work can be systematically created on a broad scale. New energy efficiencies may lead to the cultivation of significant job growth, but it may be unwise to put all of our eggs in this one basket.

So how are we to solve this puzzle? What other areas in the economy might be ripe for the introduction of a new technology capable of supporting a wave of new productivity, like computers did in the 1980s, or the Internet in the 1990s? In trying to answer this question, simplicity and elegance are key factors in keeping things at a practical level.

For instance, we know we accomplish more working together as a team than as disconnected individuals. New jobs, especially new kinds of jobs, will have to be created via innovation. Innovation in science and industry is a team sport. So the first order of business in teaming up for job creation is to know the rules of the game. The economic game is played according to the rules of law embodied in property rights, scientific rationality, capital markets, and transportation/communications networks (see William Bernstein’s 2004 book, The Birth of Plenty). When these conditions are met, as they were in Europe and North America at the beginning of the nineteenth century, the stage is set for long term innovation and growth on a broad scale.

The second order of business is to identify areas in the economy that lack one or more of these four conditions, and that could reasonably be expected to benefit from their introduction. Education, health care, social services, and environmental management come immediately to mind. These industries are plagued with seemingly interminable inflationary spirals, which, no doubt, are at least in part caused by the inability of investors to distinguish between high and low performers. Money cannot flow to and reward programs producing superior results in these industries because they lack common product definitions and comparable measures of their results.

The problems these industries are experiencing are not specific to each of them in particular. Rather, the problem is a general one applicable across all industries, not just these. Traditionally, economic thinking focuses on three main forms of capital: land, labor, and manufactured products (including everything from machines, roads, and buildings to food, clothing, and appliances). Cash and credit are often thought of as liquid capital, but their economic value stems entirely from the access they provide to land, labor, and manufactured products.

Economic activity is not really, however, restricted to these three forms of capital. Land is far more than a piece of ground. What are actually at stake are the earth’s regenerative ecosystems, with the resources and services they provide. And labor is far more than a pair of skilled hands; people bring a complex mix of abilities, motivations, and health to bear in their work. Finally, this scheme lacks an essential element: the trust, loyalty, and commitment required for even the smallest economic exchange to take place. Without social capital, all the other forms of capital (human, natural, and manufactured, including property) are worthless. Consistent, sustainable, and socially responsible economic growth requires that all four forms of capital be made accountable in financial spreadsheets and economic models.

The third order of business, then, is to ask if the four conditions laying out the rules for the economic game are met in each of the four capital domains. The table below suggests that all four conditions are fully met only for manufactured products. They are partially met for natural resources, such as minerals, timber, fisheries, etc., but not at all for nature’s air and water purification systems or broader genetic ecosystem services.

 Table

Existing Conditions Relevant to Conceiving a New Birth of Plenty, by Capital Domains

Human

Social

Natural

Manufactured

Property rights

No

No

Partial

Yes

Scientific rationality

Partial

Partial

Partial

Yes

Capital markets

Partial

Partial

Partial

Yes

Transportation & communication networks

Partial

Partial

Partial

Yes

That is, no provisions exist for individual ownership of shares in the total available stock of air and water, or of forest, watershed, estuary, and other ecosystem service outcomes. Nor do any individuals have free and clear title to their most personal properties, the intangible abilities, motivations, health, and trust most essential to their economic productivity. Aggregate statistics are indeed commonly used to provide a basis for policy and research in human, social, and natural capital markets, but falsifiable models of individually applicable unit quantities are not widely applied. Scientifically rational measures of our individual stocks of intangible asset value will require extensive use of these falsifiable models in calibrating the relevant instrumentation.

Without such measures, we cannot know how many shares of stock in these forms of capital we own, or what they are worth in dollar terms. We lack these measures, even though decades have passed since researchers first established firm theoretical and practical foundations for them. And more importantly, even when scientifically rational individual measures can be obtained, they are never expressed in terms of a unit standardized for use within a given market’s communications network.

So what are the consequences for teams playing the economic game? High performance teams’ individual decisions and behaviors are harmonized in ways that cannot otherwise be achieved only when unit amounts, prices, and costs are universally comparable and publicly available. This is why standard currencies and exchange rates are so important.

And right here we have an insight into what we can do to create jobs. New jobs are likely going to have to be new kinds of jobs resulting from innovations. As has been detailed at length in recent works such as Surowiecki’s 2004 book, The Wisdom of Crowds, innovation in science and industry depends on standards. Standards are common languages that enable us to multiply our individual cognitive powers into new levels of collective productivity. Weights and measures standards are like monetary currencies; they coordinate the exchange of value in laboratories and businesses in the same way that dollars do in the US economy.

Applying Bernstein’s four conditions for economic growth to intangible assets, we see that a long term program for job creation then requires

  1. legislation establishing human, social, and natural capital property rights, and an Intangible Assets Metrology System;
  2. scientific research into consensus standards for measuring human, social, and natural capital;
  3. venture capital educational and marketing programs; and
  4. distributed information networks and computer applications through which investments in human, social, and natural capital can be tracked and traded in accord with the rule of law governing property rights and in accord with established consensus standards.

Of these four conditions, Bernstein (p. 383) points to property rights as being the most difficult to establish, and the most important for prosperity. Scientific results are widely available in online libraries. Capital can be obtained from investors anywhere. Transportation and communications services are available commercially.

But valid and verifiable means of representing legal title to privately owned property is a problem often not yet solved even for real estate in many Third World and former communist countries (see De Soto’s 2000 book, The Mystery of Capital). Creating systems for knowing the quality and quantity of educational, health care, social, and environmental service outcomes is going to be a very difficult process. It will not be impossible, however, and having the problem identified advances us significantly towards new economic possibilities.

We need leaders able and willing to formulate audacious goals for new economic growth from ideas such as these. We need enlightened visionaries able to see our potentials from a new perspective, and who can reflect our new self-image back at us. When these leaders emerge—and they will, somewhere, somehow—the imaginations of millions of entrepreneurial thinkers and actors will be fired, and new possibilities will unfold.

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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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Science, Public Goods, and the Monetization of Commodities

August 13, 2011

Though I haven’t read Philip Mirowski’s new book yet (Science-Mart: Privatizing American Science. Cambridge, MA: Harvard University Press, 2011), a statement in the cover blurb given at Amazon.com got me thinking. I can’t help but wonder if there is another way of interpreting neoliberal ideology’s “radically different view of knowledge and discovery: [that] the fruits of scientific investigation are not a public good that should be freely available to all, but are commodities that could be monetized”?

Corporations and governments are not the only ones investing in research and new product development, and they are not the only ones who could benefit from the monetization of the fruits of scientific investigation. Individuals make these investments as well, and despite ostensible rights to private ownership, no individuals anywhere have access to universally comparable, uniformly expressed, and scientifically valid information on the quantity or quality of the literacy, health, community, or natural capital that is rightfully theirs. They accordingly also then do not have any form of demonstrable legal title to these properties. In the same way that corporations have successfully advanced their economic interests by seeing that patent and intellectual property laws were greatly strengthened, so, too, ought individuals and communities advance their economic interests by, first, expanding the scope of weights and measures standards to include intangible assets, and second, by strengthening laws related to the ownership of privately held stocks of living capital.

The nationalist and corporatist socialization of research will continue only as long as social capital, human capital, and natural capital are not represented in the universally uniform common currencies and transparent media that could be provided by an intangible assets metric system. When these forms of capital are brought to economic life in fungible measures akin to barrels, bushels, or kilowatts, then they will be monetized commodities in the full capitalist sense of the term, ownable and purchasable products with recognizable standard definitions, uniform quantitative volumes, and discernable variations in quality. Then, and only then, will individuals gain economic control over their most important assets. Then, and only then, will we obtain the information we need to transform education, health care, social services, and human and natural resource management into industries in which quality is appropriately rewarded. Then, and only then, will we have the means for measuring genuine progress and authentic wealth in ways that correct the insufficiencies of the GNP/GDP indexes.

The creation of efficiently functioning markets for all forms of capital is an economic, political, and moral necessity (see Ekins, 1992 and others). We say we manage what we measure, but very little effort has been put into measuring (with scientific validity and precision in universally uniform and accessible aggregate terms) 90% of the capital resources under management: human abilities, motivations, and health; social commitment, loyalty, and trust; and nature’s air and water purification and ecosystem services (see Hawken, Lovins, & Lovins, 1999, among others). All human suffering, sociopolitical discontent, and environmental degradation are rooted in the same common cause: waste (see Hawken, et al., 1999). To apply lean thinking to removing the wasteful destruction of our most valuable resources, we must measure these resources in ways that allow us to coordinate and align our decisions and behaviors virtually, at a distance, with no need for communicating and negotiating the local particulars of the hows and whys of our individual situations. For more information on these ideas, search “living capital metrics” and see works like the following:

Ekins, P. (1992). A four-capital model of wealth creation. In P. Ekins & M. Max-Neef (Eds.), Real-life economics: Understanding wealth creation (pp. 147-15). London: Routledge.

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

Hawken, P., Lovins, A., & Lovins, H. L. (1999). Natural capitalism: Creating the next industrial revolution. New York: Little, Brown, and Co.

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.

Miller, P., & O’Leary, T. (2007). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-34.

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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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Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Debt, Revenue, and Changing the Way Washington Works: The Greatest Entrepreneurial Opportunity of Our Time

July 30, 2011

“Holding the line” on spending and taxes does not make for a fundamental transformation of the way Washington works. Simply doing less of one thing is just a small quantitative change that does nothing to build positive results or set a new direction. What we need is a qualitative metamorphosis akin to a caterpillar becoming a butterfly. In contrast with this beautiful image of natural processes, the arguments and so-called principles being invoked in the sham debate that’s going on are nothing more than fights over where to put deck chairs on the Titanic.

What sort of transformation is possible? What kind of a metamorphosis will start from who and where we are, but redefine us sustainably and responsibly? As I have repeatedly explained in this blog, my conference presentations, and my publications, with numerous citations of authoritative references, we already possess all of the elements of the transformation. We have only to organize and deploy them. Of course, discerning what the resources are and how to put them together is not obvious. And though I believe we will do what needs to be done when we are ready, it never hurts to prepare for that moment. So here’s another take on the situation.

Infrastructure that supports lean thinking is the name of the game. Lean thinking focuses on identifying and removing waste. Anything that consumes resources but does not contribute to the quality of the end product is waste. We have enormous amounts of wasteful inefficiency in many areas of our economy. These inefficiencies are concentrated in areas in which management is hobbled by low quality information, where we lack the infrastructure we need.

Providing and capitalizing on this infrastructure is The Greatest Entrepreneurial Opportunity of Our Time. Changing the way Washington (ha! I just typed “Wastington”!) works is the same thing as mitigating the sources of risk that caused the current economic situation. Making government behave more like a business requires making the human, social, and natural capital markets more efficient. Making those markets more efficient requires reducing the costs of transactions. Those costs are determined in large part by information quality, which is a function of measurement.

It is often said that the best way to reduce the size of government is to move the functions of government into the marketplace. But this proposal has never been associated with any sense of the infrastructural components needed to really make the idea work. Simply reducing government without an alternative way of performing its functions is irresponsible and destructive. And many of those who rail on and on about how bad or inefficient government is fail to recognize that the government is us. We get the government we deserve. The government we get follows directly from the kind of people we are. Government embodies our image of ourselves as a people. In the US, this is what having a representative form of government means. “We the people” participate in our society’s self-governance not just by voting, writing letters to congress, or demonstrating, but in the way we spend our money, where we choose to live, work, and go to school, and in every decision we make. No one can take a breath of air, a drink of water, or a bite of food without trusting everyone else to not carelessly or maliciously poison them. No one can buy anything or drive down the street without expecting others to behave in predictable ways that ensure order and safety.

But we don’t just trust blindly. We have systems in place to guard against those who would ruthlessly seek to gain at everyone else’s expense. And systems are the point. No individual person or firm, no matter how rich, could afford to set up and maintain the systems needed for checking and enforcing air, water, food, and workplace safety measures. Society as a whole invests in the infrastructure of measures created, maintained, and regulated by the government’s Department of Commerce and the National Institute for Standards and Technology (NIST). The moral importance and the economic value of measurement standards has been stressed historically over many millennia, from the Bible and the Quran to the Magna Carta and the French Revolution to the US Constitution. Uniform weights and measures are universally recognized and accepted as essential to fair trade.

So how is it that we nonetheless apparently expect individuals and local organizations like schools, businesses, and hospitals to measure and monitor students’ abilities; employees’ skills and engagement; patients’ health status, functioning, and quality of care; etc.? Why do we not demand common currencies for the exchange of value in human, social, and natural capital markets? Why don’t we as a society compel our representatives in government to institute the will of the people and create new standards for fair trade in education, health care, social services, and environmental management?

Measuring better is not just a local issue! It is a systemic issue! When measurement is objective and when we all think together in the common language of a shared metric (like hours, volts, inches or centimeters, ounces or grams, degrees Fahrenheit or Celsius, etc.), then and only then do we have the means we need to implement lean strategies and create new efficiencies systematically. We need an Intangible Assets Metric System.

The current recession in large part was caused by failures in measuring and managing trust, responsibility, loyalty, and commitment. Similar problems in measuring and managing human, social, and natural capital have led to endlessly spiraling costs in education, health care, social services, and environmental management. The problems we’re experiencing in these areas are intimately tied up with the way we formulate and implement group level decision making processes and policies based in statistics when what we need is to empower individuals with the tools and information they need to make their own decisions and policies. We will not and cannot metamorphose from caterpillar to butterfly until we create the infrastructure through which we each can take full ownership and control of our individual shares of the human, social, and natural capital stock that is rightfully ours.

We well know that we manage what we measure. What counts gets counted. Attention tends to be focused on what we’re accountable for. But–and this is vitally important–many of the numbers called measures do not provide the information we need for management. And not only are lots of numbers giving us low quality information, there are far too many of them! We could have better and more information from far fewer numbers.

Previous postings in this blog document the fact that we have the intellectual, political, scientific, and economic resources we need to measure and manage human, social, and natural capital for authentic wealth. And the issue is not a matter of marshaling the will. It is hard to imagine how there could be more demand for better management of intangible assets than there is right now. The problem in meeting that demand is a matter of imagining how to start the ball rolling. What configuration of investments and resources will start the process of bursting open the chrysalis? How will the demand for meaningful mediating instruments be met in a way that leads to the spreading of the butterfly’s wings? It is an exciting time to be alive.

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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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The Moral Implications of the Concept of Human Capital: More on How to Create Living Capital Markets

March 22, 2011

The moral reprehensibility of the concept of human capital hinges on its use in rationalizing impersonal business decisions in the name of profits. Even when the viability of the organization is at stake, the discarding of people (referred to in some human resource departments as “taking out the trash”) entails degrees of psychological and economic injury no one should have to suffer, or inflict.

There certainly is a justified need for a general concept naming the productive capacity of labor. But labor is far more than a capacity for work. No one’s working life should be reduced to a job description. Labor involves a wide range of different combinations of skills, abilities, motivations, health, and trustworthiness. Human capital has then come to be broken down into a wide variety of forms, such as literacy capital, health capital, social capital, etc.

The metaphoric use of the word “capital” in the phrase “human capital” referring to stocks of available human resources rings hollow. The traditional concept of labor as a form of capital is an unjustified reduction of diverse capacities in itself. But the problem goes deeper. Intangible resources like labor are not represented and managed in the forms that make markets for tangible resources efficient. Transferable representations, like titles and deeds, give property a legal status as owned and an economic status as financially fungible. And in those legal and economic terms, tangible forms of capital give capitalism its hallmark signification as the lifeblood of the cycle of investment, profits, and reinvestment.

Intangible forms of capital, in contrast, are managed without the benefit of any standardized way of proving what is owned, what quantity or quality of it exists, and what it costs. Human, social, and natural forms of capital are therefore managed directly, by acting in an unmediated way on whomever or whatever embodies them. Such management requires, even in capitalist economies, the use of what are inherently socialistic methods, as these are the only methods available for dealing with the concrete individual people, communities, and ecologies involved (Fisher, 2002, 2011; drawing from Hayek, 1948, 1988; De Soto, 2000).

The assumption that transferable representations of intangible assets are inconceivable or inherently reductionist is, however, completely mistaken. All economic capital is ultimately brought to life (conceived, gestated, midwifed, and nurtured to maturity) as scientific capital. Scientific measurability is what makes it possible to add up the value of shares of stock across holdings, to divide something owned into shares, and to represent something in a court or a bank in a portable form (Latour, 1987; Fisher, 2002, 2011).

Only when you appreciate this distinction between dead and living capital, between capital represented on transferable instruments and capital that is not, then you can see that the real tragedy is not in the treatment of labor as capital. No, the real tragedy is in the way everyone is denied the full exercise of their rights over the skills, abilities, health, motivations, trustworthiness, and environmental resources that are rightly their own personal, private property.

Being homogenized at the population level into an interchangeable statistic is tragic enough. But when we leave the matter here, we fail to see and to grasp the meaning of the opportunities that are lost in that myopic world view. As I have been at pains in this blog to show, statistics are not measures. Statistical models of interactions between several variables at the group level are not the same thing as measurement models of interactions within a single variable at the individual level. When statistical models are used in place of measurement models, the result is inevitably numbers without a soul. When measurement models of individual response processes are used to produce meaningful estimates of how much of something someone possesses, a whole different world of possibilities opens up.

In the same way that the Pythagorean Theorem applies to any triangle, so, too, do the coordinates from the international geodetic survey make it possible to know everything that needs to be known about the location and disposition of a piece of real estate. Advanced measurement models in the psychosocial sciences are making it possible to arrive at similarly convenient and objective ways of representing the quality and quantity of intangible assets. Instead of being just one number among many others, real measures tell a story that situates each of us relative to everyone else in a meaningful way.

The practical meaning of the maxim “you manage what you measure” stems from those instances in which measures embody the fullness of the very thing that is the object of management interest. An engine’s fuel efficiency, or the volume of commodities produced, for instance, are things that can be managed less or more efficiently because there are measures of them that directly represent just what we want to control. Lean thinking enables the removal of resources that do not contribute to the production of the desired end result.

Many metrics, however, tend to obscure and distract from what need to be managed. The objects of measurement may seem to be obviously related to what needs to be managed, but dealing with each of them piecemeal results in inefficient and ineffective management. In these instances, instead of the characteristic cycle of investment, profit, and reinvestment, there seems only a bottomless pit absorbing ever more investment and never producing a profit. Why?

The economic dysfunctionality of intangible asset markets is intimately tied up with the moral dysfunctionality of those markets. Drawing an analogy from a recent analysis of political freedom (Shirky, 2010), economic freedom has to be accompanied by a market society economically literate enough, economically empowered enough, and interconnected enough to trade on the capital stocks issued. Western society, and increasingly the entire global society, is arguably economically literate and sufficiently interconnected to exercise economic freedom.

Economic empowerment is another matter entirely. There is no economic power without fungible capital, without ways of representing resources of all kinds, tangible and intangible, that transparently show what is available, how much of it there is, and what quality it is. A form of currency expressing the value of that capital is essential, but money is wildly insufficient to the task of determining the quality and quantity of the available capital stocks.

Today’s education, health care, human resource, and environmental quality markets are the diametric opposite of the markets in which investors, producers, and consumers are empowered. Only when dead human, social, and natural capital is brought to life in efficient markets (Fisher, 2011) will we empower ourselves with fuller degrees of creative control over our economic lives.

The crux of the economic empowerment issue is this: in the current context of inefficient intangibles markets, everyone is personally commodified. Everything that makes me valuable to an employer or investor or customer, my skills, motivations, health, and trustworthiness, is unjustifiably reduced to a homogenized unit of labor. And in the social and environmental quality markets, voting our shares is cumbersome, expensive, and often ineffective because of the immense amount of work that has to be done to defend each particular living manifestation of the value we want to protect.

Concentrated economic power is exercised in the mass markets of dead, socialized intangible assets in ways that we are taught to think of as impersonal and indifferent to each of us as individuals, but which is actually experienced by us as intensely personal.

So what is the difference between being treated personally as a commodity and being treated impersonally as a commodity? This is the same as asking what it would mean to be empowered economically with creative control over the stocks of human, social, and natural capital that are rightfully our private property. This difference is the difference between dead and living capital (Fisher, 2002, 2011).

Freedom of economic communication, realized in the trade of privately owned stocks of any form of capital, ought to be the highest priority in the way we think about the infrastructure of a sustainable and socially responsible economy. For maximum efficiency, that freedom requires a common meaningful and rigorous quantitative language enabling determinations of what exactly is for sale, and its quality, quantity, and unit price. As I have ad nauseum repeated in this blog, measurement based in scientifically calibrated instrumentation traceable to consensus standards is absolutely essential to meeting this need.

Coming in at a very close second to the highest priority is securing the ability to trade. A strong market society, where people can exercise the right to control their own private property—their personal stocks of human, social, and natural capital—in highly efficient markets, is more important than policies, regulations, and five-year plans dictating how masses of supposedly homogenous labor, social, and environmental commodities are priced and managed.

So instead of reacting to the downside of the business cycle with a socialistic safety net, how might a capitalistic one prove more humane, moral, and economically profitable? Instead of guaranteeing a limited amount of unemployment insurance funded through taxes, what we should have are requirements for minimum investments in social capital. Instead of employment in the usual sense of the term, with its implications of hiring and firing, we should have an open market for fungible human capital, in which everyone can track the price of their stock, attract and make new investments, take profits and income, upgrade the quality and/or quantity of their stock, etc.

In this context, instead of receiving unemployment compensation, workers not currently engaged in remunerated use of their skills would cash in some of their accumulated stock of social capital. The cost of social capital would go up in periods of high demand, as during the recent economic downturns caused by betrayals of trust and commitment (which are, in effect, involuntary expenditures of social capital). Conversely, the cost of human capital would also fluctuate with supply and demand, with the profits (currently referred to as wages) turned by individual workers rising and falling with the price of their stocks. These ups and downs, being absorbed by everyone in proportion to their investments, would reduce the distorted proportions we see today in the shares of the rewards and punishments allotted.

Though no one would have a guaranteed wage, everyone would have the opportunity to manage their capital to the fullest, by upgrading it, keeping it current, and selling it to the highest bidder. Ebbing and flowing tides would more truly lift and drop all boats together, with the drops backed up with the social capital markets’ tangible reassurance that we are all in this together. This kind of a social capitalism transforms the supposedly impersonal but actually highly personal indifference of flows in human capital into a more fully impersonal indifference in which individuals have the potential to maximize the realization of their personal goals.

What we need is to create a visible alternative to the bankrupt economic system in a kind of reverse shock doctrine. Eleanor Roosevelt often said that the thing we are most afraid of is the thing we most need to confront if we are to grow. The more we struggle against what we fear, the further we are carried away from what we want. Only when we relax into the binding constraints do we find them loosened. Only when we channel overwhelming force against itself or in a productive direction can we withstand attack. When we find the courage to go where the wild things are and look the monsters in the eye will we have the opportunity to see if their fearful aspect is transformed to playfulness. What is left is often a more mundane set of challenges, the residuals of a developmental transition to a new level of hierarchical complexity.

And this is the case with the moral implications of the concept of human capital. Treating individuals as fungible commodities is a way that some use to protect themselves from feeling like monsters and from being discarded as well. Those who find themselves removed from the satisfactions of working life can blame the shortsightedness of their former colleagues, or the ugliness of the unfeeling system. But neither defensive nor offensive rationalizations do anything to address the actual problem, and the problem has nothing to do with the morality or the immorality of the concept of human capital.

The problem is the problem. That is, the way we approach and define the problem delimits the sphere of the creative options we have for solving it. As Henry Ford is supposed to have said, whether you think you can or you think you cannot, you’re probably right. It is up to us to decide whether we can create an economic system that justifies its reductions and actually lives up to its billing as impersonal and unbiased, or if we cannot. Either way, we’ll have to accept and live with the consequences.

References

DeSoto, H. (2000). The mystery of capital: Why capitalism triumphs in the West and fails everywhere else. New York: Basic Books.

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. (2011, Spring). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 12(1), in press.

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

Hayek, F. A. (1988). The fatal conceit: The errors of socialism (W. W. Bartley, III, Ed.) The Collected Works of F. A. Hayek. Chicago: University of Chicago Press.

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

Shirky, C. (2010, December 20). The political power of social media: Technology, the public sphere, and political change. Foreign Affairs, 90(1), http://www.foreignaffairs.com/articles/67038/clay-shirky/the-political-power-of-social-media.

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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.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

A Second Simple Example of Measurement’s Role in Reducing Transaction Costs, Enhancing Market Efficiency, and Enables the Pricing of Intangible Assets

March 9, 2011

The prior post here showed why we should not confuse counts of things with measures of amounts, though counts are the natural starting place to begin constructing measures. That first simple example focused on an analogy between counting oranges and measuring the weight of oranges, versus counting correct answers on tests and measuring amounts of ability. This second example extends the first by, in effect, showing what happens when we want to aggregate value not just across different counts of some one thing but across different counts of different things. The point will be, in effect, to show how the relative values of apples, oranges, grapes, and bananas can be put into a common frame of reference and compared in a practical and convenient way.

For instance, you may go into a grocery store to buy raspberries and blackberries, and I go in to buy cantaloupe and watermelon. Your cost per individual fruit will be very low, and mine will be very high, but neither of us will find this annoying, confusing, or inconvenient because your fruits are very small, and mine, very large. Conversely, your cost per kilogram will be much higher than mine, but this won’t cause either of us any distress because we both recognize the differences in the labor, handling, nutritional, and culinary value of our purchases.

But what happens when we try to purchase something as complex as a unit of socioeconomic development? The eight UN Millennium Development Goals (MDGs) represent a start at a systematic effort to bring human, social, and natural capital together into the same economic and accountability framework as liquid and manufactured capital, and property. But that effort is stymied by the inefficiency and cost of making and using measures of the goals achieved. The existing MDG databases (http://data.un.org/Browse.aspx?d=MDG), and summary reports present overwhelming numbers of numbers. Individual indicators are presented for each year, each country, each region, and each program, goal by goal, target by target, indicator by indicator, and series by series, in an indigestible volume of data.

Though there are no doubt complex mathematical methods by which a philanthropic, governmental, or NGO investor might determine how much development is gained per million dollars invested, the cost of obtaining impact measures is so high that most funding decisions are made with little information concerning expected returns (Goldberg, 2009). Further, the percentages of various needs met by leading social enterprises typically range from 0.07% to 3.30%, and needs are growing, not diminishing. Progress at current rates means that it would take thousands of years to solve today’s problems of human suffering, social disparity, and environmental quality. The inefficiency of human, social, and natural capital markets is so overwhelming that there is little hope for significant improvements without the introduction of fundamental infrastructural supports, such as an Intangible Assets Metric System.

A basic question that needs to be asked of the MDG system is, how can anyone make any sense out of so much data? Most of the indicators are evaluated in terms of counts of the number of times something happens, the number of people affected, or the number of things observed to be present. These counts are usually then divided by the maximum possible (the count of the total population) and are expressed as percentages or rates.

As previously explained in various posts in this blog, counts and percentages are not measures in any meaningful sense. They are notoriously difficult to interpret, since the quantitative meaning of any given unit difference varies depending on the size of what is counted, or where the percentage falls in the 0-100 continuum. And because counts and percentages are interpreted one at a time, it is very difficult to know if and when any number included in the sheer mass of data is reasonable, all else considered, or if it is inconsistent with other available facts.

A study of the MDG data must focus on these three potential areas of data quality improvement: consistency evaluation, volume reduction, and interpretability. Each builds on the others. With consistent data lending themselves to summarization in sufficient statistics, data volume can be drastically reduced with no loss of information (Andersen, 1977, 1999; Wright, 1977, 1997), data quality can be readily assessed in terms of sufficiency violations (Smith, 2000; Smith & Plackner, 2009), and quantitative measures can be made interpretable in terms of a calibrated ruler’s repeatedly reproducible hierarchy of indicators (Bond & Fox, 2007; Masters, Lokan, & Doig, 1994).

The primary data quality criteria are qualitative relevance and meaningfulness, on the one hand, and mathematical rigor, on the other. The point here is one of following through on the maxim that we manage what we measure, with the goal of measuring in such a way that management is better focused on the program mission and not distracted by accounting irrelevancies.

Method

As written and deployed, each of the MDG indicators has the face and content validity of providing information on each respective substantive area of interest. But, as has been the focus of repeated emphases in this blog, counting something is not the same thing as measuring it.

Counts or rates of literacy or unemployment are not, in and of themselves, measures of development. Their capacity to serve as contributing indications of developmental progress is an empirical question that must be evaluated experimentally against the observable evidence. The measurement of progress toward an overarching developmental goal requires inferences made from a conceptual order of magnitude above and beyond that provided in the individual indicators. The calibration of an instrument for assessing progress toward the realization of the Millennium Development Goals requires, first, a reorganization of the existing data, and then an analysis that tests explicitly the relevant hypotheses as to the potential for quantification, before inferences supporting the comparison of measures can be scientifically supported.

A subset of the MDG data was selected from the MDG database available at http://data.un.org/Browse.aspx?d=MDG, recoded, and analyzed using Winsteps (Linacre, 2011). At least one indicator was selected from each of the eight goals, with 22 in total. All available data from these 22 indicators were recorded for each of 64 countries.

The reorganization of the data is nothing but a way of making the interpretation of the percentages explicit. The meaning of any one country’s percentage or rate of youth unemployment, cell phone users, or literacy has to be kept in context relative to expectations formed from other countries’ experiences. It would be nonsense to interpret any single indicator as good or bad in isolation. Sometimes 30% represents an excellent state of affairs, other times, a terrible one.

Therefore, the distributions of each indicator’s percentages across the 64 countries were divided into ranges and converted to ratings. A lower rating uniformly indicates a status further away from the goal than a higher rating. The ratings were devised by dividing the frequency distribution of each indicator roughly into thirds.

For instance, the youth unemployment rate was found to vary such that the countries furthest from the desired goal had rates of 25% and more(rated 1), and those closest to or exceeding the goal had rates of 0-10% (rated 3), leaving the middle range (10-25%) rated 2. In contrast, percentages of the population that are undernourished were rated 1 for 35% or more, 2 for 15-35%, and 3 for less than 15%.

Thirds of the distributions were decided upon only on the basis of the investigator’s prior experience with data of this kind. A more thorough approach to the data would begin from a finer-grained rating system, like that structuring the MDG table at http://mdgs.un.org/unsd/mdg/Resources/Static/Products/Progress2008/MDG_Report_2008_Progress_Chart_En.pdf. This greater detail would be sought in order to determine empirically just how many distinctions each indicator can support and contribute to the overall measurement system.

Sixty-four of the available 336 data points were selected for their representativeness, with no duplications of values and with a proportionate distribution along the entire continuum of observed values.

Data from the same 64 countries and the same years were then sought for the subsequent indicators. It turned out that the years in which data were available varied across data sets. Data within one or two years of the target year were sometimes substituted for missing data.

The data were analyzed twice, first with each indicator allowed its own rating scale, parameterizing each of the category difficulties separately for each item, and then with the full rating scale model, as the results of the first analysis showed all indicators shared strong consistency in the rating structure.

Results

Data were 65.2% complete. Countries were assessed on an average of 14.3 of the 22 indicators, and each indicator was applied on average to 41.7 of the 64 country cases. Measurement reliability was .89-.90, depending on how measurement error is estimated. Cronbach’s alpha for the by-country scores was .94. Calibration reliability was .93-.95. The rating scale worked well (see Linacre, 2002, for criteria). The data fit the measurement model reasonably well, with satisfactory data consistency, meaning that the hypothesis of a measurable developmental construct was not falsified.

The main result for our purposes here concerns how satisfactory data consistency makes it possible to dramatically reduce data volume and improve data interpretability. The figure below illustrates how. What does it mean for data volume to be drastically reduced with no loss of information? Let’s see exactly how much the data volume is reduced for the ten item data subset shown in the figure below.

The horizontal continuum from -100 to 1300 in the figure is the metric, the ruler or yardstick. The number of countries at various locations along that ruler is shown across the bottom of the figure. The mean (M), first standard deviation (S), and second standard deviation (T) are shown beneath the numbers of countries. There are ten countries with a measure of just below 400, just to the left of the mean (M).

The MDG indicators are listed on the right of the figure, with the indicator most often found being achieved relative to the goals at the bottom, and the indicator least often being achieved at the top. The ratings in the middle of the figure increase from 1 to 3 left to right as the probability of goal achievement increases as the measures go from low to high. The position of the ratings in the middle of the figure shifts from left to right as one reads up the list of indicators because the difficulty of achieving the goals is increasing.

Because the ratings of the 64 countries relative to these ten goals are internally consistent, nothing but the developmental level of the country and the developmental challenge of the indicator affects the probability that a given rating will be attained. It is this relation that defines fit to a measurement model, the sufficiency of the summed ratings, and the interpretability of the scores. Given sufficient fit and consistency, any country’s measure implies a given rating on each of the ten indicators.

For instance, imagine a vertical line drawn through the figure at a measure of 500, just above the mean (M). This measure is interpreted relative to the places at which the vertical line crosses the ratings in each row associated with each of the ten items. A measure of 500 is read as implying, within a given range of error, uncertainty, or confidence, a rating of

  • 3 on debt service and female-to-male parity in literacy,
  • 2 or 3 on how much of the population is undernourished and how many children under five years of age are moderately or severely underweight,
  • 2 on infant mortality, the percent of the population aged 15 to 49 with HIV, and the youth unemployment rate,
  • 1 or 2 the poor’s share of the national income, and
  • 1 on CO2 emissions and the rate of personal computers per 100 inhabitants.

For any one country with a measure of 500 on this scale, ten percentages or rates that appear completely incommensurable and incomparable are found to contribute consistently to a single valued function, developmental goal achievement. Instead of managing each separate indicator as a universe unto itself, this scale makes it possible to manage development itself at its own level of complexity. This ten-to-one ratio of reduced data volume is more than doubled when the total of 22 items included in the scale is taken into account.

This reduction is conceptually and practically important because it focuses attention on the actual object of management, development. When the individual indicators are the focus of attention, the forest is lost for the trees. Those who disparage the validity of the maxim, you manage what you measure, are often discouraged by the the feeling of being pulled in too many directions at once. But a measure of the HIV infection rate is not in itself a measure of anything but the HIV infection rate. Interpreting it in terms of broader developmental goals requires evidence that it in fact takes a place in that larger context.

And once a connection with that larger context is established, the consistency of individual data points remains a matter of interest. As the world turns, the order of things may change, but, more likely, data entry errors, temporary data blips, and other factors will alter data quality. Such changes cannot be detected outside of the context defined by an explicit interpretive framework that requires consistent observations.

-100  100     300     500     700     900    1100    1300
|-------+-------+-------+-------+-------+-------+-------|  NUM   INDCTR
1                                 1  :    2    :  3     3    9  PcsPer100
1                         1   :   2    :   3            3    8  CO2Emissions
1                    1  :    2    :   3                 3   10  PoorShareNatInc
1                 1  :    2    :  3                     3   19  YouthUnempRatMF
1              1   :    2   :   3                       3    1  %HIV15-49
1            1   :   2    :   3                         3    7  InfantMortality
1          1  :    2    :  3                            3    4  ChildrenUnder5ModSevUndWgt
1         1   :    2    :  3                            3   12  PopUndernourished
1    1   :    2   :   3                                 3    6  F2MParityLit
1   :    2    :  3                                      3    5  DebtServExpInc
|-------+-------+-------+-------+-------+-------+-------|  NUM   INDCTR
-100  100     300     500     700     900    1100    1300
                   1
       1   1 13445403312323 41 221    2   1   1            COUNTRIES
       T      S       M      S       T

Discussion

A key element in the results obtained here concerns the fact that the data were about 35% missing. Whether or not any given indicator was actually rated for any given country, the measure can still be interpreted as implying the expected rating. This capacity to take missing data into account can be taken advantage of systematically by calibrating a large bank of indicators. With this in hand, it becomes possible to gather only the amount of data needed to make a specific determination, or to adaptively administer the indicators so as to obtain the lowest-error (most reliable) measure at the lowest cost (with the fewest indicators administered). Perhaps most importantly, different collections of indicators can then be equated to measure in the same unit, so that impacts may be compared more efficiently.

Instead of an international developmental aid market that is so inefficient as to preclude any expectation of measured returns on investment, setting up a calibrated bank of indicators to which all measures are traceable opens up numerous desirable possibilities. The cost of assessing and interpreting the data informing aid transactions could be reduced to negligible amounts, and the management of the processes and outcomes in which that aid is invested would be made much more efficient by reduced data volume and enhanced information content. Because capital would flow more efficiently to where supply is meeting demand, nonproducers would be cut out of the market, and the effectiveness of the aid provided would be multiplied many times over.

The capacity to harmonize counts of different but related events into a single measurement system presents the possibility that there may be a bright future for outcomes-based budgeting in education, health care, human resource management, environmental management, housing, corrections, social services, philanthropy, and international development. It may seem wildly unrealistic to imagine such a thing, but the return on the investment would be so monumental that not checking it out would be even crazier.

A full report on the MDG data, with the other references cited, is available on my SSRN page at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1739386.

Goldberg, S. H. (2009). Billions of drops in millions of buckets: Why philanthropy doesn’t advance social progress. New York: Wiley.

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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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How bad will the financial crises have to get before…?

April 30, 2010

More and more states and nations around the world face the possibility of defaulting on their financial obligations. The financial crises are of epic historical proportions. This is a disaster of the first order. And yet, it is so odd–we have the solutions and preventative measures we need at our finger tips, but no one knows about them or is looking for them.

So,  I am persuaded to once again wonder if there might now be some real interest in the possibilities of capitalizing on

  • measurement’s well-known capacity for reducing transaction costs by improving information quality and reducing information volume;
  • instruments calibrated to measure in constant units (not ordinal ones) within known error ranges (not as though the measures are perfectly precise) with known data quality;
  • measures made meaningful by their association with invariant scales defined in terms of the questions asked;
  • adaptive instrument administration methods that make all measures equally precise by targeting the questions asked;
  • judge calibration methods that remove the person rating performances as a factor influencing the measures;
  • the metaphor of transparency by calibrating instruments that we really look right through at the thing measured (risk, governance, abilities, health, performance, etc.);
  • efficient markets for human, social, and natural capital by means of the common currencies of uniform metrics, calibrated instrumentation, and metrological networks;
  • the means available for tuning the instruments of the human, social, and environmental sciences to well-tempered scales that enable us to more easily harmonize, orchestrate, arrange, and choreograph relationships;
  • our understandings that universal human rights require universal uniform measures, that fair dealing requires fair measures, and that our measures define who we are and what we value; and, last but very far from least,
  • the power of love–the back and forth of probing questions and honest answers in caring social intercourse plants seminal ideas in fertile minds that can be nurtured to maturity and Socratically midwifed as living meaning born into supportive ecologies of caring relations.

How bad do things have to get before we systematically and collectively implement the long-established and proven methods we have at our disposal? It is the most surreal kind of schizophrenia or passive-aggressive avoidance pathology to keep on tormenting ourselves with problems for which we have solutions.

For more information on these issues, see prior blogs posted here, the extensive documentation provided, and http://www.livingcapitalmetrics.com.

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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.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Parameterizing Perfection: Practical Applications of a Mathematical Model of the Lean Ideal

April 2, 2010

To properly pursue perfection, we need to parameterize it. That is, taking perfection as the ideal, unattainable standard against which we judge our performance is equivalent to thinking of it as a mathematical model. Organizations are intended to realize their missions independent of the particular employees, customers, suppliers, challenges, products, etc. they happen to engage with at any particular time. Organizational performance measurement (Spitzer, 2007) ought to then be designed in terms of a model that posits, tests for, and capitalizes on the always imperfectly realized independence of those parameters.

Lean thinking (Womack & Jones, 1996) focuses on minimizing waste and maximizing value. At every point at which resources are invested in processes, services, or products, the question is asked, “What value is added here?” Resources are wasted when no value is added, when they can be removed with no detrimental effect on the value of the end product. In their book, Natural Capitalism: Creating the Next Industrial Revolution, Hawken, Lovins, and Lovins (1999, p. 133) say

“Lean thinking … changes the standard for measuring corporate success. … As they [Womack and Jones] express it: ‘Our earnest advice to lean firms today is simple. To hell with your competitors; compete against perfection by identifying all activities that are muda [the Japanese term for waste used in Toyota’s landmark quality programs] and eliminating them. This is an absolute rather than a relative standard which can provide the essential North Star for any organization.”

Further, every input should “be presumed waste until shown otherwise.” A constant, ongoing, persistent pressure for removing waste is the basic characteristic of lean thinking. Perfection is never achieved, but it aptly serves as the ideal against which progress is measured.

Lean thinking sounds a lot like a mathematical model, though it does not seem to have been written out in a mathematical form, or used as the basis for calibrating instruments, estimating measures, evaluating data quality, or for practical assessments of lean organizational performance. The closest anyone seems to have come to parameterizing perfection is in the work of Genichi Taguchi (Ealey, 1988), which has several close parallels with Rasch measurement (Linacre, 1993).  But meaningful and objective quantification, as required and achieved in the theory and practice of fundamental measurement (Andrich, 2004; Bezruczko, 2005; Bond & Fox 2007; Smith & Smith, 2004; Wilson, 2005; Wright, 1999), in fact asserts abstract ideals of perfection as models of organizational, social, and psychological processes in education, health care, marketing, etc. These models test the extent to which outcomes remain invariant across examination or survey questions, across teachers, students, schools, and curricula, or across treatment methods, business processes, or policies.

Though as yet implemented only to a limited extent in business (Drehmer, Belohlav, James, & Coye, 2000; Drehmer & Deklava, 2001;  Lunz & Linacre, 1998; Salzberger, 2009), advanced measurement’s potential rewards are great. Fundamental measurement theory has been successfully applied in research and practice thousands of times over the last 40 years and more, including in very large scale assessments and licensure/certification applications (Adams, Wu, & Macaskill, 1997; Masters, 2007; Smith, Julian, Lunz, et al., 1994). These successes speak to an opportunity for making broad improvements in outcome measurement that could provide more coherent product definition, and significant associated opportunities for improving product quality and the efficiency with which it is produced, in the manner that has followed from the use of fundamental measures in other industries.

Of course, processes and outcomes are never implemented or obtained with perfect consistency. This would be perfectly true only in a perfect world. But to pursue perfection, we need to parameterize it. In other words, to raise the bar in any area of performance assessment, we have to know not only what direction is up, but we also need to know when we have raised the bar far enough. But we cannot tell up from down, we do not know how much to raise the bar, and we cannot properly evaluate the effects of lean experiments when we have no way of locating measures on a number line that embodies the lean ideal.

To think together collectively in ways that lead to significant new innovations, to rise above what Jaron Lanier calls the “global mush” of confused and self-confirming hive thinking, we need the common languages of widely accepted fundamental measures of the relevant processes and outcomes, measures that remain constant across samples of customers, patients, employees, students, etc., and across products, sales techniques, curricula, treatment processes, assessment methods, and brands of instrument.

We are all well aware that the consequences of not knowing where the bar is, of not having product definitions, can be disastrous. In many respects, as I’ve said previously in this blog, the success or failure of health care reform hinges on getting measurement right. The Institute of Medicine report, To Err is Human, of several years ago stresses the fact that system failures pose the greatest threat to safety in health care because they lead to human errors. When a system as complex as health care lacks a standard product definition, and product delivery is fragmented across multiple providers with different amounts and kinds of information in different settings, the system becomes dangerously cumbersome and over-complicated, with unacceptably wide variations and errors in its processes and outcomes, not to even speak of its economic inefficiency.

In contrast with the widespread use of fundamental measures in the product definitions of other industries, health care researchers typically implement neither the longstanding, repeatedly proven, and mathematically rigorous models of fundamental measurement theory nor the metrological networks through which reference standard metrics are engineered. Most industries carefully define, isolate, and estimate the parameters of their products, doing so in ways 1) that ensure industry-wide comparability and standardization, and 2) that facilitate continuous product improvement by revealing multiple opportunities for enhancement. Where organizations in other industries manage by metrics and thereby keep their eyes on the ball of product quality, health care organizations often manage only their own internal processes and cannot in fact bring the product quality ball into view.

In his message concerning the Institute for Healthcare Improvement’s Pursuing Perfection project a few years ago, Don Berwick, like others (Coye, 2001; Coye & Detmer, 1998), observed that health care does not yet have an organization setting new standards in the way that Toyota did for the auto industry in the 1970s. It still doesn’t, of course. Given the differences between the auto and health care industries uses of fundamental measures of product quality and associated abilities to keep their eyes on the quality ball, is it any wonder then, that no one in health care has yet hit a home run? It may well be that no one will hit a home run in health care until reference standard measures of product quality are devised.

The need for reference standard measures in uniform data systems is crucial, and the methods for obtaining them are widely available and well-known. So what is preventing the health care industry from adopting and deploying them? Part of the answer is the cost of the initial investment required. In 1980, metrology comprised about six percent of the U.S. gross national product (Hunter, 1980). In the period from 1981 to 1994, annual expenditures on research and development in the U.S. were less than three percent of the GNP, and non-defense R&D was about two percent (NIST Subcommittee on Research, National Science and Technology Council, 1996). These costs, however, must be viewed as investments from which high rates of return can be obtained (Barber, 1987; Gallaher, Rowe, Rogozhin, et al., 2007; Swann, 2005).

For instance, the U.S. National Institute of Standards and Technology estimated the economic impact of 12 areas of research in metrology, in four broad areas including semiconductors, electrical calibration and testing, optical industries, and computer systems (NIST, 1996, Appendix C; also see NIST, 2003). The median rate of return in these 12 areas was 147 percent, and returns ranged from 41 to 428 percent. The report notes that these results compare favorably with those obtained in similar studies of return rates from other public and private research and development efforts. Even if health care metrology produces only a small fraction of the return rate produced in physical metrology, its economic impact could still amount to billions of dollars annually. The proposed pilot projects therefore focus on determining what an effective health care outcomes metrology system should look like. What should its primary functions be? What should it cost? What rates of return could be expected from it?

Metrology, the science of measurement (Pennella, 1997), requires 1) that instruments be calibrated within individual laboratories so as to isolate and estimate the values of the required parameters (Wernimont, 1978); and 2) that individual instruments’ capacities to provide the same measure for the same amount, and so be traceable to a reference standard, be established and monitored via interlaboratory round-robin trials (Mandel, 1978).

Fundamental measurement has already succeeded in demonstrating the viability of reference standard measures of health outcomes, measures whose meaningfulness does not depend on the particular samples of items employed or patients measured. Though this work succeeds as far as it goes, it being done in a context that lacks any sense of the need for metrological infrastructure. Health care needs networks of scientists and technicians collaborating not only in the first, intralaboratory phase of metrological work, but also in the interlaboratory trials through which different brands or configurations of instruments intended to measure the same variable would be tuned to harmoniously produce the same measure for the same amount.

Implementation of the two phases of metrological innovation in health care would then begin with the intralaboratory calibration of existing and new instruments for measuring overall organizational performance, quality of care, and patients’ health status, quality of life, functionality, etc.  The second phase takes up the interlaboratory equating of these instruments, and the concomitant deployment of reference standard units of measurement throughout a health care system and the industry as a whole. To answer questions concerning health care metrology’s potential returns on investment, the costs for, and the savings accrued from, accomplishing each phase of each pilot will be tracked or estimated.

When instruments measuring in universally uniform, meaningful units are put in the hands of clinicians, a new scientific revolution will occur in medicine. It will be analogous to previous ones associated with the introduction of the thermometer and the instruments of optometry and the clinical laboratory. Such tools will multiply many times over the quality improvement methods used by Brent James, touted as holding the key to health care reform in a recent New York Times profile. Instead of implicitly hypothesizing models of perfection and assessing performance relative to them informally, what we need is a new science that systematically implements the lean ideal on industry-wide scales. The future belongs to those who master these techniques.

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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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How to Trade “Global Mush” for Beauty, Meaning, and Value: Reflections on Lanier’s New Book

January 15, 2010

Implicit in many of my recent posts here is the idea that we must learn how to follow through on the appropriation of meaning to proper ownership of the properties characteristic of our own proprietary capital resources: the creativities, abilities, skills, talents, health, motivations, trust, etc.  that make us each reliable citizens and neighbors, and economically viable in being hireable, promotable, productive, and retainable. Individual control of investment in, income from, and returns on our own shares of human, social, and natural capital ought to be a fundamental constitutional human right.

But, just as property rights are constitutionally guaranteed by nations around the world that don’t take the trouble to enforce them or even to provide their necessary infrastructural capacities, so, too, are human rights to equal opportunities widely guaranteed without being properly provided for or enforced. And now in the Internet age, we have succeeded in innovating ever more fluid media for the expression of our individual capacities for making original cultural, technical, and human contributions, but we have yet to figure out how to exert effective control over the returns and income generated by these contributions.

Jaron Lanier’s new book, “You Are Not a Gadget,” is taking up this theme in interesting ways. In his recent Wall Street Journal article, Lanier says:

“There’s a dominant dogma in the online culture of the moment that collectives make the best stuff, but it hasn’t proven to be true. The most sophisticated, influential and lucrative examples of computer code—like the page-rank algorithms in the top search engines or Adobe’s Flash— always turn out to be the results of proprietary development. Indeed, the adored iPhone came out of what many regard as the most closed, tyrannically managed software-development shop on Earth.

Actually, Silicon Valley is remarkably good at not making collectivization mistakes when our own fortunes are at stake. On the one hand we want to avoid physical work and instead benefit from intellectual property. On the other hand, we’re undermining intellectual property so that information can roam around for nothing, or more precisely as bait for advertisements. That’s a formula that leaves no way for our nation to earn a living in the long term.
The “open” paradigm rests on the assumption that the way to get ahead is to give away your brain’s work—your music, writing, computer code and so on—and earn kudos instead of money. You are then supposedly compensated because your occasional dollop of online recognition will help you get some kind of less cerebral work that can earn money. For instance, maybe you can sell custom branded T-shirts.
We’re well over a decade into this utopia of demonetized sharing and almost everyone who does the kind of work that has been collectivized online is getting poorer. There are only a tiny handful of writers or musicians who actually make a living in the new utopia, for instance. Almost everyone else is becoming more like a peasant every day.”
Lanier’s suggestions of revised software structures and micropayment systems in an extension of intellectual property rights correctly recognizes the scope of the challenges we face. He also describes the motivations driving the ongoing collectivization process, saying that “youthful fascination with collectivism is in part simply a way to address perceived ‘unfairness’.” This radical way of enforcing a very low lowest common denominator points straight at the essential problem, and that problem is apparent in the repeated use of the key word, collective.

It was not so long ago that it was impossible to use that word without immediately evoking images of Soviet central planning and committees. The “global mush” of mediocrity Lanier complains about as a direct result of collective thinking is a very good way of describing the failures of socialism that brought down the Soviet Union by undercutting its economic viability. Lanier speaks of growing up and enthusiastically participating various forms of collective life, like food co-ops and shared housing. I, too, have shared those experiences. I saw, as Lanier sees and as the members of communes in the U.S. during the 1960s saw, that nothing gets done when no one owns the process and stands to reap the rewards: when housekeeping is everyone’s responsibility, no one does it.

Further and more to the point, nothing goes right when supply and demand are dictated by a central committee driven by ideological assumptions concerning exactly what does and does not constitute the greater good.  On the contrary, innovation is stifled, inefficiencies are rampant, and no one takes the initiative to do better because there are no incentives for doing so. Though considerable pain is experienced in allowing the invisible hand to coordinate the flux and flows of markets, no better path to prosperity has yet been found. The current struggle is less one of figuring out how to do without markets than it is one of figuring out how to organize them for greater long term stability. As previous posts in this blog endeavor to show, we ought to be looking more toward bringing all forms of capital into the market instead of toward regulating some to death while others ravage the economy, scot-free.

Friedrich von Hayek (1988, 1994) is an economist and philosopher often noted for his on-target evaluations of the errors of socialism. He tellingly focused on the difference between the laborious micromanagement of socialism’s thought police and the wealth-creating liberation of capital’s capacity for self-organization. It is interesting that Lanier describes the effects of demonetized online sharing as driving most of us toward peasant status, as Hayek (1994) describes socialism as a “road to serfdom.” Of course, capitalism itself is far from perfect, since private property, and manufactured and liquid capital, have enjoyed a freedom of movement that too often recklessly tramples human rights, community life, and the natural environment. But as is described in a previous blog I posted on re-inventing capitalism, we can go a long way toward rectifying the errors of capitalism by setting up the rules of law that will lubricate and improve the efficiency of human, social, and natural capital markets.

Now, I’ve always been fascinated with the Latin root shared in words like property, propriety, proprietary, appropriation, proper, and the French propre (which means both clean and one’s own, or belonging to oneself, depending on whether it comes before or after the noun; une maison propre = a clean house and sa propre maison = his/her own house). I was then happy to encounter in graduate school Ricoeur’s (1981) theory of text interpretation, which focuses on the way we create meaning by appropriating it. Real understanding requires that we must make a text our own if we are to be able to give proper evidence of understanding it by restating or summarizing it in our own words.

Such restating is, of course, also the criterion for demonstrating that a scientific theory of the properties of a phenomenon is adequate to the task of reproducing its effects on demand. As Ricoeur (1981, p. 210) says, situating science in a sphere of signs puts the human and natural sciences together on the same footing in the context of linguistically-mediated social relations. This unification of the sciences has profound consequences, not just for philosophy, the social sciences, or economics, but for the practical task of transforming the current “global mush” into a beautiful, meaningful, and effective living creativity system. So, there is real practical significance in realizing what appropriation is and how its processes feed into our conceptualizations of property, propriety, and ownership.

When we can devise a new instrument or measuring method that gives the same results as an existing instrument or method, we have demonstrated theoretical control over the properties of the phenomenon (Heelan, 1983, 2001; Ihde, 1991; Ihde & Selinger, 2003; Fisher, 2004, 2006, 2010b). The more precisely the effects are reproduced, the purer they become, the clearer their representation, and the greater their independence from the local contingencies of sample, instrument, observer, method, etc. When we can package a technique for reproducing the desired effects (radio or tv broadcast/reception, vibrating toothbrushes, or what have you), we can export the phenomenon from the laboratory via networks of distribution, supply, sales, marketing, manufacture, repair, etc. (Latour, 1987). Proprietary methods, instruments, and effects can then be patented and ownership secured.

What we have in the current “global mush” of collective aggregations are nothing at all of this kind. There are specific criteria for information quality and network configuration (Akkerman, et al., 2007; Latour, 1987, pp. 247-257; Latour, 1995; Magnus, 2007; Mandel, 1978; Wise, 1995) that have to be met for collective cognition to realize its potential in the manner described by Surowiecki (2004) or Brafman and Beckstrom (2006), for instance.  The difference is the difference between living and dead capital, between capitalism and socialism, and between scientific measurement and funny numbers that don’t stand for the repetitive additivity of a constant unit (Fisher, 2002, 2009, 2010a). As Lanier notes, Silicon Valley understands very well the nature of this difference, and protects its own interests by vigilantly ensuring that its collective cognitions are based in properly constructed information and networks.

And here we find the crux of the lesson to be learned. We need to focus very carefully on the details of how we create meaningful relationships, of how things come into words, of how instruments are calibrated and linked together in shared systems of signification, and of how economies thrive on the productive efficiencies of well-lubricated markets. Everything we need to turn things around is available, though seeing things for what they are is one of the most daunting and difficult tasks we can undertake.

The postmodern implications of the way appropriation is more a letting-go than a possessing (Ricoeur, 1981, p. 191) will be taken up another time, in the context of the playful flow of signification we are always already caught up within. For now, it is enough to point the way toward the issues raised and examined in other posts in this blog as to how capital is brought to life. We are well on the way toward a convergence of efforts that may well result in exactly the kind of fierce individuals and competing teams able to reap their just due, as Lanier envisions.

References

Akkerman, S., Van den Bossche, P., Admiraal, W., Gijselaers, W., Segers, M., Simons, R.-J., Kirschnerd, P. (2007, February). Reconsidering group cognition: From conceptual confusion to a boundary area between cognitive and socio-cultural perspectives? Educational Research Review, 2, 39-63.
Brafman, O., & Beckstrom, R. A. (2006). The starfish and the spider: The unstoppable power of leaderless organizations. New York: Portfolio (Penguin Group).

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. (2006). Meaningfulness, sufficiency, invariance, and conjoint additivity. Rasch Measurement Transactions, 20(1), 1053 [http://www.rasch.org/rmt/rmt201.htm].

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

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].

Fisher, W. P., Jr. (2010)b. Reducible or irreducible? Mathematical reasoning and the ontological method. Journal of Applied Measurement, 11, in press.

von Hayek, F. A. (1988). The fatal conceit: The errors of socialism (W. W. Bartley, III, Ed.) (Vol. I). The Collected Works of F. A. Hayek. Chicago: University of Chicago Press.

von Hayek, F. A. (1994/1944). The road to serfdom (Fiftieth Anniversary Edition; Introduction by Milton Friedman). Chicago: University of Chicago Press.

Heelan, P. A. (1983, June). Natural science as a hermeneutic of instrumentation. Philosophy of Science, 50, 181-204.

Heelan, P. A. (2001). The lifeworld and scientific interpretation. In S. K. Toombs (Ed.), Handbook of phenomenology and medicine (pp. 47-66). Chicago: University of Chicago Press.

Ihde, D., & Selinger, E. (Eds.). (2003). Chasing technoscience: Matrix for materiality. (Indiana Series in Philosophy of Technology). Bloomington, Indiana: Indiana University 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.

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

Mandel, J. (1978, December). Interlaboratory testing. ASTM Standardization News, 6, 11-12.

Ricoeur, P. (1981). Hermeneutics and the human sciences: Essays on language, action and interpretation (J. B. Thompson, Ed. & Trans). Cambridge, England: Cambridge University Press.

Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York: Doubleday.
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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.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Universal Rights and Universal Measures: Advancing Science, Economics, and Democracy Simultaneously

January 14, 2010

Art historians and political theorists often remark on the way the columns in Greek temples symbolize the integration of individuals and society in democracies. The connection of architecture and forms of government is well enough known that at least one theater critic was compelled to include it in a review of a World War II-themed musical (Wonk, 2002). With an eye to illuminating the victory over fascism, he observed that Greek temple pillars

“are unique, curved, each one slightly different. They are harmonized in a united effort. They are a democracy. Whereas, the temples of the older, Eastern empires are supported by columns that are simply straight sticks, interchangeable. The phalanx of individual citizens was stronger than the massed army of slaves [and so 9,000 Greek citizen soldiers could defeat 50,000 Persian mercenaries and slaves at the Battle of Marathon in the fifth century BCE].”

Wonk makes this digression in a review of a musical, The 1940’s Radio Hour, to set the stage for his point that

“while listening to the irrepressible and irresistible outpourings of Tin Pan Alley, I understood that the giant fascist war machine, with its mechanical stamp, stamp, stamp of boots was defeated, in a sense, by American syncopation. ‘Deutscheland Deutscheland Uber Alles’ ran aground and was wrecked on the shoals of ‘The Boogie Woogie Bugle Boy of Company B.'”

Of course, the same thing has been said before (the Beatles’ “Back in the USSR” brought down the Berlin Wall, etc.), but the sentiment is right on target. The creativity and passion of free people will ultimately always win out over oppressive regimes that kill joy and try to control innovation. As Emma Goldman is famously paraphrased, a revolution that bans dancing isn’t worth having. What we see happening here is a way in which different sectors of life are co-produced as common values resonate across the social, political, economic, and scientific spheres (Jasanoff, 2004; Jasanoff and Martello, 2004; Wise, 1995).

So how does science come to bear? Consider Ken Alder’s (2002, pp. 2, 3) perspective on the origins of the metric system:

“Just as the French Revolution had proclaimed universal rights for all people, the savants argued, so too should it proclaim universal measures.”
“…the use a society makes of its measures expresses its sense of fair dealing. That is why the balance scale is a widespread symbol of justice. … Our methods of measurement define who we are and what we value.”

As I’ve been saying in the signature line of my emails for many years, “We are what we measure. It’s time we measured what we want to be.” The modern world’s alienating consumer culture is fundamentally characterized by they way it compromises our ability to relate our experiences as individuals to shared stories that are true of us all, even if they actually never happened in their specific details to any of us. Being able to recognize the pattern of our own lives in the stories that we tell is what makes for science and technology’s universal applicability, as well as for great literature, powerful historical accounts, poetry that resonates across the centuries, as well as political and religious convictions strong enough to rationalize war and totalitarian repression.

In traditional cultures, ancient myths tell the stories that shape the world and enable everyone to find and value their place in it. Because these stories were transmitted from generation to generation orally, they could change a little with each retelling without anyone noticing. This allowed the myths to remain current and relevant as history unfolded in times with a slower pace of change.

But modern Western culture is blessed and cursed with written records that remain fixed. Instead of the story itself slowly changing with the times in every retelling, now new interpretations of the story emerge more quickly in the context of an overall faster pace of change, opening the door to contentious differences in the way the text is read. We’re now in the untenable and tense situation of some of us (relativists) feeling that all interpretations are legitimate, and others of us (fundamentalists) feeling that our interpretation is the only valid one.

Contrary to the way it often seems, rampant relativism and fundamentalist orthodoxy are not our only alternatives. As Paul Ricoeur (1974, p. 291-292) put it,

“…for each of the historical societies, the developing as well as those advanced in industrialization, the task is to exercise a kind of permanent arbitration between technical universalism and the personality constituted on the ethico-political plane. All the struggles of decolonization and liberation are marked by the double necessity of entering into the global technical society and being rooted in the cultural past.”

Without going into an extensive analysis of the ways in which the metaphors embedded in each culture’s language, concepts and world view structure meaning in universally shared ways, suffice it to say that what we need is a way of mediating between the historical past and a viable future.

We obtain mediations of this kind when we are able to identify patterns in our collective behaviors consistent enough to be considered behavioral laws. Such patterns are revealed in Rasch measurement instrument calibration studies by the way that every individual’s pattern of responses to the questions asked might be unique but still in probabilistic conformity with the overall pattern in the data as a whole. What we have in Rasch measurement is directly analogous with the pillars of ancient Greek temples: unique individuals harmonized and coordinated in common interpretations, collective effort and shared purpose.

The difficulty is in balancing respect for individual differences with capitalizing on the aggregate pattern. This is, as Gadamer (1991, pp. 7-8) says, the

“systematic problem of philosophy itself: that the part of lived reality that can enter into the concept is always a flattened version-like every projection of a living bodily existence onto a surface. The gain in unambiguous comprehensibility and repeatable certainty is matched by a loss in stimulating multiplicity of meaning.”

The problem is at least as old as Plato’s recognition of the way that (a) the technology of writing supplants and erases the need for detailed memories, and (b) counting requires us to metaphorically abstract something in common from what are concretely different entities. In social measurement, justice and respect for individual dignity requires that we learn to appreciate uniqueness while taking advantage of shared similarities (Ballard, 1978, p. 189).

Rasch’s models for measurement represent a technology essential to achieving this balance between the individual and society (Fisher, 2004, 2010). In contrast with descriptive statistical models that focus on accounting for as much variation as possible within single data sets, prescriptive measurement models focus on identifying consistent patterns across data sets. Where statistical models are content to conceive of individuals as interchangeable and structurally identical, measurement models conceive of individuals as unique and seek to find harmonious patterns of shared meanings across them. When such patterns are in hand, we are able to deploy instruments embodying shared meanings to the front lines of applications in education, health care, human resource management, organizational performance assessment, risk management, etc.

The consistent data patterns observed over several decades of Rasch applications (for examples, see Bond, 2008; Stenner, Burdick, Sanford, & Burdick, 2006) document and illustrate self-organizing forms of our collective life. They are, moreover, evidence of capital resources of the first order that we are only beginning to learn about and integrate into our institutions and social expectations. Wright (1999, p. 76) recognized that we need to “reach beyond the data in hand to what these data might imply about future data, still unmet, but urgent to foresee.” When repeated observations, tests, experiments, and practices show us unequivocally that our abilities, attitudes, behaviors, health, social relationships, etc. are structured in ways that we can rely on as objective constants across the particulars of who, when, where, and what, as the burgeoning scientific literature shows, we will create a place in which we will again feel at home in a larger community of shared values.

To take one example, everyone is well aware that “it’s who you know, not what you know” that matters most in finding a job, making sales, or in generally creating a place for oneself in the world. The phenomenon of online social networking has only made the truth of this platitude more evident. Culturally, we have evolved ways of adapting to the unfairness of this, though it still rankles and causes discontent.

But what if we capitalized on the general consensus on the structure of abilities, motivations, productivity, health, and trustworthiness that is emerging in the research literature? What if we actually created an Intangible Assets Metric System (see my 2009 blog on this issue) that would provide a basis of comparison integrating individual perspectives with the collective social perspective? Such an integration is what is implied in every successful Rasch measurement instrument calibration. Following through on these successes to the infrastructure of rights to our own human, social, and natural capital would not only advance economic prosperity and scientific learning on a whole new scale of magnitude, but democratic institutions themselves would also be renewed in fundamental ways.

The convergence of political revolutions, the Industrial Revolution, and the Second Scientific revolution in the late 18th and early 19th centuries was, after all, not just a coincidence. In the same way that the metric system simultaneously embodied the French Revolution’s political values of universal rights, equal representation, fairness and justice; scientific values of universal comparability; and capitalist values of efficient, open markets, so, too, will an Intangible Assets Metric System expand and coordinate these values as we once again reinvent who we are and what we want to be.

Alder, K. (2002). The measure of all things: The seven-year odyssey and hidden error that transformed the world. New York: The Free Press.

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

Bond, T. (2008). Invariance and item stability. Rasch Measurement Transactions, 22(1), 1159 [http://www.rasch.org/rmt/rmt221h.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. (2010). Reducible or irreducible? Mathematical reasoning and the ontological method. Journal of Applied Measurement, 11, in press.

Gadamer, H.-G. (1991). Plato’s dialectical ethics: Phenomenological interpretations relating to the Philebus (R. M. Wallace, Trans.). New Haven, Connecticut: Yale University Press.

Jasanoff, S. (2004). States of knowledge: The co-production of science and social order. International Library of Sociology). New York: Routledge.

Jasanoff, S., & Martello, M. L. ((Eds.)). (2004). Earthly politics: Local and global in environmental governance. Politics, Science, and the Environment). Cambridge, MA: MIT Press.

Ricoeur, P. (1974). Political and social essays (D. Stewart & J. Bien, Eds.). Athens, Ohio: Ohio University Press.

Stenner, A. J., Burdick, H., Sanford, E. E., & Burdick, D. S. (2006). How accurate are Lexile text measures? Journal of Applied Measurement, 7(3), 307-22.

Wise, M. N. (Ed.). (1995). The values of precision. Princeton, New Jersey: Princeton University Press.

Wonk, D. (2002, June 11). Theater review: Looking back. Gambit Weekly, 32. Retrieved 20 November 2009, from http://bestofneworleans.com/gyrobase/PrintFriendly?oid=oid%3A28341.

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.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Al Gore: Marshalling the Collective Will is NOT the Problem–The Problem is the Problem!

November 22, 2009

In his new book, former vice-president Al Gore says we have in hand all the tools we need to solve the climate change crises, except the collective will to do anything about them. I respectfully beg to differ. Finding the will is not the problem. We already have it and we have it volumes sufficient to the task. Gore is also wrong in claiming we have the tools we need. There are entire classes of scientific and economic tools that we are missing. It is because we lack the right tools that we are unable to focus and channel our will for solutions.

The short version of my argument is that we don’t have scientific, universally uniform, and ubiquitously used metrics for measuring overall environmental quality. Because we don’t have the measures, we can’t and don’t effectively and efficiently manage our natural capital and environmental assets. Without metrics akin to barrels of oil or bushels of grain, we don’t have markets for matching environmental quality supply with demand for it.

Without tools as essential as metrics and markets, we can’t harness our existing will to improve our relationship with the earth. What will do we have, you might ask? Our collective will is expressed in the profit motive. What we need to do is set up metrics and markets to harness the energy of the profit motive. We need to create systems for trading natural capital (and human and social capital) so that we generate real wealth and drive happiness indexes north by realizing human potential, building thriving communities, and nurturing sustainable environments. The profit motive is not our enemy. It is the source of energy we need to deal with the multiple crises we face: human, social, and environmental.

Now for the long version of my argument. The problem is the problem. We restrict our options for solving problems by the way we frame the issue. Einstein supposedly pointed out that big problems, ones framed at a level where they define the entire paradigmatic orientation to a class of smaller, solvable problems, cannot be solved from within the paradigm they emerge from. We tend to define problems from the modern point of view, in a Cartesian fashion, from the point of view of a subject that is separate from, and in no way involved in the construction of, the objects it encounters. What I want to point out is that it is this Cartesian orientation to problem definition that is itself the problem!

Set aside your opinions on the basic issues concerning climate change, and think about what’s going on. It is undeniable that human activities are implicated in changes to the environment, and that we have to learn to manage our effects on the planet, or they will feed back on us in potentially harmful ways. This is the nature of life in the flux and flow of ecological relationships. It is one of many ways in which observers are inherently implicated in constructing what is observed, which is recognized as holding true as much in physics as in anthropology. These are uncontroversial facts, quite apart from any concern with climate change.

And what these feedback loops imply, as has indeed already been pointed out by generations of scholars and thinkers, is that there is no such thing as a pure Cartesian subject separate from its objects. We shape the things in our world, and those things, in turn, shape us. Subjects and objects are mutually implicated. All observers are participant observers. It is inevitable that what we do and think will change the world, and the new world will require us to think and act differently.

The plethora of environmental crises we face are therefore situated in a new non-Cartesian paradigm. It is a fundamental error of the first order to approach a non-Cartesian problem as though it were merely another variation on the usual kind of thing that can be addressed fairly well from the Cartesian dualist perspective. When we think, as Al Gore does, that we should be socialistically organizing resources for a centrally-organized 5-year plan of attack on environmental problems, we are missing the point.

This approach can be put to work only in terms of an authoritarian form of control directed by a dictatorial panel of experts, a military junta, or a self-appointed czar. Framed from a Cartesian point of view, no democratic process will ever compel voters to do what needs to be done. As was illustrated so dramatically by the fall of Communism, the socialistic manipulation of the concrete particulars of human, social, and environmental problems is unsustainable and socially irresponsible.

The fact is that non-Cartesian problems are only made worse when we try to solve them with Cartesian solutions. This is why non-Cartesian problems are often described by philosophers as “hermeneutic,” a word that derives from the name of the Greek god Hermes, known by the ancient Romans as Mercury. Like liquid mercury, non-Cartesian problems merely split and multiply when we grasp at them clumsily ignoring our own involvement in the creation of the problem.

So we can go on trying to herd cats or nail jello to the wall, but to be part of the solution and not just another way of being part of the problem, we need to set up systems of thought and behavior that are not internally inconsistent and self-contradictory. No matter what we do, if we keep on marshalling resources to attack problems in deliberate and systematic ignorance of this cross-paradigmatic dissonance, we can only make matters worse.

What else can be done? Just what does it mean to go with the flow of the mutual implication of subject and object? How can we explicitly model the problem to include the participant observer?

“The medium is the message,” to quote Marshall McLuhan. As was pointed out so humorously by Woody Allen in his film, “Annie Hall,” this expression is often repeated and often misunderstood. Though all can see that the news and entertainment media are ubiquitous, the meaning of our captivation with the media of creative expression has not yet been clarified sufficiently well for generalized understanding.

Significant advances have occurred in recent years, however. The media we are captivated by define and limit not only how and what we communicate, but who and what we have been, are, and could be. Depending on the quality of their transparency and of the biases that color them, media convey moral, human, and economic values of various kinds. The media through which we express values include every conceivable technology, from alphabets and phonemes to buildings, clothing, and food preparation, to musical instruments, and the creations of art and science.

Media are at the crux of the lesson we have to learn if we are to frame the problems of environmental management so that we are living solutions, not exacerbating problems. Media of all kinds, from pen and paper to television to the Internet, are fundamentally technical. In fact, media are the original technologies. The words “text,” “textile,” and “technique” all derive from the Greek “techne,” to make, and have even deeper roots in the Sanskrit “TEK.” Technology is our primary medium of shared meaning. Technology embodies the meanings we create and distributes their values across society and around the world.

What we need to do to effect non-Cartesian solutions then is to dwell deeply with our shared meanings and values, and find new ways of living them out, ways that embody the unity of subject and object, problem and solution. Nice rhetoric, you might say, but what does it mean? What is its practical consequence?

Put in academic terms, the pragmatic issue concerns the nature of technology and how it provides measures of reality serving as the media through which we experience the world in terms of shared universals. Primary sources here include the works of writers like Latour, Wise, Jasanoff, Knorr-Cetina, Schaffer, Ihde, Heidegger, and others cited in previous posts in this blog, and in my published work.

To do more to cut to the chase, we can start to think of language and technology as embodying problem-solution unities. Words and tools are situated within ecologies of relationships that define their meanings and functions. We need to be more sensitive to the way meanings and values become embodied in language and technologies, and then are distributed across far-flung networks to coordinate collectively harmonized thought and action.

To get right down to where this all is leading, though it is probably far from obvious, the appropriate non-Cartesian orientation to the problems of environmental management raised in Al Gore’s new book ultimately culminates in creation of the technical networks through which we distribute measures of what we want to manage. These networks comprise the ecologies of meaning and values that we inhabit. Not coincidentally, they also create the markets in which human, social, and natural capital can be efficiently and effectively traded.

When these networks and markets are created, finding the collective will to deal with the environmental challenges we face will be the least of our problems. The profit motive is an exceptionally strong force. What we ought to be doing is figuring out how to harness it as the engine of social change. This contrasts diametrically with Al Gore’s perspective, which treats the profit motive as part of the problem.

Technical networks of instruments traceable to reference standards, and markets for the exchange of the values measured by those instruments, are what we ought to be focusing on. The previous post in this blog proposes an Intangible Assets Metric System, and is related to earlier posts on the role of common currencies for the exchange of meaningful quantitative values in creating functional markets for human, social, and natural capital. What we need are these infrastructural supports for creating the efficient markets in which demand for environmental solutions can be matched the supply of those solutions. The failure of socialism is testimony to the futility of trying to man-handle our way forward by brute force.

Of course, I will continue living out my life’s mission and passion by continuing to elaborate variations, explanations, and demonstrations of how this could be so….

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