Posts Tagged ‘Innovation’

Current events in metrology for fun, profitable, and self-sustaining sustainability impacts

September 18, 2018

At the main event I attended last week at the Global Climate Action Summit in San Francisco, the #giveyouthachance philanthropic gathering at the Aquarium of the Bay, multiple people independently spoke to aligning social and environmental values with financial values, and explicitly stated that economic growth does not automatically entail environmental degradation.

As my new buddy David Traub (introduced as a consequence of the New Algorithm event in Stockholm in June with Angelica Lips da Cruz) was the MC, he put me on the program at the last minute, and gave me five minutes to speak my piece in a room of 30 people or so. A great point of departure was opened up when Carin Winter of MissionBe.org spoke to her work in mindfulness education and led a guided meditation. So I conveyed the fact that the effects of mindfulness practice are rigorously measurable, and followed that up with the analogy from music (tuning instruments to harmonize relationships),  with the argument against merely shouldering the burden of costs because it is the right thing to do, with the counter-argument for creating efficient competitive markets for sustainable impacts, and with info on the previous week’s special session on social and psychological metrology at IMEKO in Belfast. It appeared that the message of metrology as a means for making sustainability self-sustaining, fun, and profitable got through!

Next up: Unify.Earth has developed their own new iteration on blockchain, which will be announced Monday, 24 September, at the UN SDG Media Center (also see here) during the World Economic Forum’s Sustainable Development Impact Summit. The UEX (Unify Earth Exchange) fills the gap for human capital stocks left by the Universal Commons‘ exclusive focus on social and natural capital.

So I’ve decided to go to NY and have booked my travel.

Back in February, Angelica Lips da Cruz recounted saying six months before that it would take two years to get to where we were at that time. Now another seven months have passed and I am starting to feel that the acceleration is approaching Mach 1! At this rate, it’ll be the speed of light in the next six months….

Creative Commons License
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.

Advertisements

Metrics, Stocks, Shares, and Secure Ledger Accounts for Living Capital: Getting the Information into the Hands of Individual Decision Makers

August 30, 2018

Individual investments in, and returns from, shares of various kinds of human, social, and natural capital stocks will be tracked in secure online accounting ledgers, often referred to generically using the Blockchain brand name. A largely unasked and unanswered question is just what kind of data would best be tracked in secure ledgers. To be meaningful, entries in such accounts will have to stand for something real in the world that is represented in a common language interpretable to anyone capable of reading the relevant signs and symbols. Since we are talking about amounts of things that vary, measurement will unavoidably be a factor.

High quality measurement is essential to the manageability and profitability of investments of all kinds, whether in manufactured capital and property, or in literacy, numeracy, mental and physical health, sociability, and environmental quality (human, social, and natural capital). The measurability and manageability of these intangible factors has achieved significant levels of scientific precision and rigor over the last 90 and more years.

This development is of increasing interest to economists and accountants who have long envisioned ways of reinventing capitalism that do not assume the only alternative is some form of socialism or communism (see references listed below). Many of today’s economic problems may follow from capitalism’s incompleteness. More specifically, we may be suffering from the way in which manufactured capital alone has been been brought to life, economically speaking, while human, social, and natural capital have not (Fisher, 2002, 2007, 2009a/b, 2010a/b, 2011a/b, 2012ab, 2014, etc.).

One in particular who speaks directly to an essential issue that must be addressed in creating an economy of authentic wealth and genuine productivity is Paul Hawken (2007, pp. 21-22), who says that Friedrich Hayek foresaw

“a remedy for the basic expression of the totalitarian impulse: ensuring that information and the right to make decisions are co-located. To achieve this, one can either move the information to the decision makers, or move decision making rights to the information. The movement strives to do both. The earth’s problems are everyone’s problems, and what modern technology and the movement can achieve together is to distribute problem solving tools.”

Hayek (1945, 1948, 1988; Frantz & Leeson, 2013) is well known for his focus on a distinction between a mechanical definition of individuals as uniform and homogenous, and a more vital sense of economic “true individuals” as complex and interdependent. To create efficient markets for the production of authentic wealth, we need to figure out how to extend the “true individuals” of manufactured capital markets into new markets for human, social, and natural capital (Fisher, 2014).

The distributed problem solving tools we need to support the decision making of “true” individuals are secure online ledgers accounting for investments in measured amounts of authentic wealth. Efficient markets are functions of individual processes that create wholes greater than their sums. The multiplier effect that makes this possible depends on transparent communication. Words, including number words, have to mean something specific and distinct. This is where the value of systematic measurement and metrology comes to bear. This is why we need an Intangible Assets Metric System.

For as long as economists have been concerned with markets, philosophers have been pointing out that society is an effect of shared symbol systems. In both cases, economists and philosophers are focused on the fact that it is only when people have a common language that an idea, a meme, can go viral, that a market can seem to have a mind of its own, and science can maintain an ever-increasing pace of technical innovation.

Our aim is to create the information that will populate the entries in the secure ledger accounts people use to track and manage their investments in literacy, numeracy, health, social, and natural capital. These entries will be posted right alongside their existing entries for investments in manufactured capital and property, which includes everything from groceries to autos to electronics to homes.

But the new ledger accounts will be different from today’s in important ways. Many current accounting entries are ultimately written off as costs producing untracked and unaccountable returns. We simply spend the money on groceries or school tuition or a doctor visit. The income is logged, and so are the expenses. We can see that, yes, buying groceries is an investment of a kind, since we profit from it by enjoying the processes of cooking, sharing, and eating tasty food, by avoiding hunger, and by sustaining good health.

Investments are tracked in a different way, though. Money is not just spent and kissed goodbye. Instead, investment funds are loaned to or leased by someone else who is expected to be able to increase the value of those funds. There are often no guarantees of an increase, but the invested value is associated with a proportionate share in the total value of the business. As the business grows or fails, so does the investment.

In much the same way, if we had the information available to us, we could track the returns on the investments we make in food, education, or health care. If we track the impacts of our dietary choices, we would be able to see if and when the investments we make result in healthy outcomes. The information brought to bear will have to include systematic advice relevant to one’s age, sex, pre-existing conditions, genetic propensities, etc. Additional information on the returns on one’s investments in a healthy diet should also be made available, as might be found in the expected income or expenses associated with the consequences of what is eaten, and how much of it. Sometimes there will be room for improvement, for example, if the foods we eat are too sugary or fatty, or if we eat too much. Other times, maintaining a healthy, varied diet may be all that is needed to see a consistent positive return on investment.

Public reports will allow us all to learn from one another. The ability to communicate in a common language and to see what has worked for others will enable everyone to experiment with new ways of doing things. People with common food interests or problems, for instance, will be able quickly evaluate the relevance and benefits of other people’s approaches or solutions. Because of the ways in which communication and community go together, it may be reasonable to hope that new levels of innovation, diversity, tolerance, and respect will follow.

Many aspects of work, education and health care are already undergoing transformations that move their processes out of the usual office, school and hospital environments. These changes will be accelerated as distributed network effects take hold in each of these various markets.

It is easy to see how the Internet of things may evolve to be the medium in which we manage relationships of all kinds, from education and school to health and safety to work and career. Secure ledgers immune from hacking will be essential. And an important health factor will be to know how much relationship management is enough, and when it’s time to get out into the world. That balancing factor will be a key aspect of a successful approach to connecting information on authentic wealth with the individual decision makers growing it and living it.

References

Andriessen, D. (2003). Making sense of intellectual capital: Designing a method for the valuation of intangibles. Oxford, England: Butterworth-Heinemann.

Anielski, M. (2007). The economics of happiness: Building genuine wealth. Gabriola, British Columbia: New Society Publishers.

Cadman, D. (1986). Money as if people mattered. In P. Ekins &  Staff of The Other Economic Summit (Eds.), The living economy: A new economics in the making (pp. 204-210). London: Routledge & Kegan Paul.

Eisler, R. (2007). The real wealth of nations: Creating a caring economics. San Francisco, California: Berrett-Koehler Publishers, Inc.

Ekins, P. (1992). A four-capital model of wealth creation. In P. Ekins & M. Max-Neef (Eds.), Real-life economics: Understanding wealth creation (pp. 147-155). London: Routledge.

Ekins, P. (1999). Economic growth and environmental sustainability: The prospects for green growth. New York: Routledge.

Ekins, P., Dresner, S., & Dahlstrom, K. (2008, March/April). The four-capital method of sustainable development evaluation. European Environment, 18(2), 63-80.

Ekins, P., Hillman, M., & Hutchison, R. (1992). The Gaia atlas of green economics (Foreword by Robert Heilbroner). New York: Anchor Books.

Ekins, P., & Max-Neef, M. A. (Eds.). (1992). Real-life economics: Understanding wealth creation. London: Routledge.

Ekins, P., & Voituriez, T. (2009). Trade, globalization and sustainability impact assessment: A critical look at methods and outcomes. London, England: Earthscan Publications Ltd.

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. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-1093 [http://www.rasch.org/rmt/rmt211.pdf].

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

Fisher, W. P., Jr. (2009b). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (Tech. Rep., http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology.

Fisher, W. P., Jr. (2010a). Measurement, reduced transaction costs, and the ethics of efficient markets for human, social, and natural capital., Bridge to Business Postdoctoral Certification, Freeman School of Business, Tulane University (p. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2340674).

Fisher, W. P., Jr. (2010b, 13 January). Reinventing capitalism: Diagramming living capital flows in a green, sustainable, and responsible economy. Retrieved from LivingCapitalMetrics.com: https://livingcapitalmetrics.wordpress.com/2010/01/13/reinventing-capitalism/.

Fisher, W. P., Jr. (2011a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 12(1), 49-66.

Fisher, W. P., Jr. (2011b). Measuring genuine progress by scaling economic indicators to think global & act local: An example from the UN Millennium Development Goals project. LivingCapitalMetrics.com. Retrieved 18 January 2011, from Social Science Research Network: http://ssrn.com/abstract=1739386.

Fisher, W. P., Jr. (2012a). Measure and manage: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. 43-63). New York: Palgrave Macmillan.

Fisher, W. P., Jr. (2012b, May/June). What the world needs now: A bold plan for new standards [Third place, 2011 NIST/SES World Standards Day paper competition]. Standards Engineering, 64(3), 1 & 3-5 [http://ssrn.com/abstract=2083975].

Fisher, W. P., Jr. (2014, Autumn). The central theoretical problem of the social sciences. Rasch Measurement Transactions, 28(2), 1464-1466.

Frantz, R., & Leeson, R. (Eds.). (2013). Hayek and behavioral economics. (Archival Insights Into the Evolution of Economics). New York: Palgrave Macmillan.

Gleeson-White, J. (2015). Six capitals, or can accountants save the planet? Rethinking capitalism for the 21st century. New York: Norton.

Greider, W. (2003). The soul of capitalism: Opening paths to a moral economy. New York: Simon & Schuster.

Griliches, Z. (1994, March). Productivity, R&D, and the data constraint. American Economic Review, 84(1), 1-23.

Grootaert, C. (1998). Social capital: The missing link? (Vol. 3). Social Capital Intiative Working Paper). Washington, D.C.: The World Bank.

Hand, J. R. M., & Lev, B. (Eds.). (2003). Intangible assets: Values, measures, and risks. Oxford Management Readers). Oxford, England: Oxford University Press.

Hart, S. L. (2005). (2007). Capitalism at the crossroads: Aligning business, earth, and humanity (Foreword by Al Gore) (2nd ed.). Wharton School Publishing.

Hawken, P. (1993). The ecology of commerce: A declaration of sustainability. New York: HarperCollins Publishers.

Hawken, P. (2007). Blessed unrest: How the largest movement in the world came into being and why no one saw it coming. New York: Viking Penguin.

Hayek, F. A. (1945, September). The use of knowledge in society. American Economic Review, 35, 519-530. (Rpt. in Individualism and economic order (pp. 77-91). Chicago: University of Chicago Press.)

Hayek, F. A. (1955). The counter revolution of science. Glencoe, Illinois: Free Press.

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.

Korten, D. (2009). Agenda for a new economy: From phantom wealth to real wealth. San Francisco: Berret-Koehler Publishing.

Krueger, A. B. (Ed.). (2009). Measuring the subjective well-being of nations: National accounts of time use and well-being. National Bureau of Economic Research Conference Reports). Chicago, Illinois: University of Chicago Press.

Swann, G. M. P. (2001). “No Wealth But Life”: When does conventional wealth create Ruskinian wealth. European Research Studies, 4(3-4), 5-18.

Vemuri, A. W., & Costanza, R. (2006, 10 June). The role of human, social, built, and natural capital in explaining life satisfaction at the country level: Toward a National Well-Being Index. Ecological Economics, 58(1), 119-133.

Creative Commons License
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.

Self-Sustaining Sustainability, Once Again, Already

August 12, 2018

The urgent need for massive global implementations of sustainability policies and practices oddly and counterproductively has not yet led to systematic investments in state of the art sustainability metric standards. My personal mission is to contribute to meeting this need. Longstanding, proven resources in the art and science of precision instrumentation calibration and explanatory theory are available to address these problems. In the same way technical standards for measuring length, mass, volume, time, energy, light, etc. enable the coordination of science and commerce for manufactured capital and property, so, too, will a new class of standards for measuring human, social, and natural capital.

This new art and science contradicts common assumptions in three ways. First, contrary to popular opinion that measuring these things is impossible, over 90 years of research and practice support a growing consensus among weights and measures standards engineers (metrologists) and social and psychological measurement experts that relevant unit standards are viable, feasible, and desirable.

Common perceptions are contradicted in a second way in that measurement of this kind does not require reducing human individuality to homogenized uniform sameness. Instead of a mechanical metaphor of cogs in a machine, the relevant perspective is an organic or musical one. The goal is to ensure that local uniqueness and creative improvisations are freely expressed in a context informed by shared standards (like DNA, or a musical instrument tuning system).

The third way in which much of what we think we know is mistaken concerns how to motivate adoption of sustainability policies and practices. Many among us are fearful that neither the general population nor its leaders in government and business care enough about sustainability to focus on implementing solutions. But finding the will to act is not the issue. The problem is how to create environments in which new sustainable forms of life multiply and proliferate of their own accord. To do this, people need means for satisfying their own interests in life, liberty, and the pursuit of happiness. The goal, therefore, is to organize knowledge infrastructures capable of informing and channeling the power of individual self-interest. The only way mass scale self-sustaining sustainable economies will ever happen is by tapping the entrepreneurial energy of the profit motive, where profit is defined not just in financial terms but in the quality of life and health terms of authentic wealth and genuine productivity.

We manage what we measure. If we are to collectively, fluidly, efficiently, and innovatively manage the living value of our human, social, and natural capital, we need, first, high quality information expressed in shared languages communicating that value. Second, we need, to begin with, new scientific, legal, economic, financial, and governmental institutions establishing individual rights to ownership of that value, metric units expressing amounts of that value, conformity audits for ascertaining the accuracy and precision of those units, financial alignments of the real value measured with bankable dollar amounts, and investment markets to support entrepreneurial innovations in creating that value.

The end result of these efforts will be a capacity for all of humanity to pull together in common cause to create a sustainable future. We will each be able to maximize our own personal potential at the same time we contribute to the greater good. We will not only be able to fulfill the potential of our species as stewards of the earth, we will have fun doing it! For technical information resources, see below. PDFs are available on request, and can often be found freely available online.

Self-Sustaining Sustainability

Relevant Information Resources

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

Barney, M., & Fisher, W. P., Jr. (2016). Adaptive measurement and assessment. Annual Review of Organizational Psychology and Organizational Behavior, 3, 469-490.

Fisher, W. P., Jr. (1997). Physical disability construct convergence across instruments: Towards a universal metric. Journal of Outcome Measurement, 1(2), 87-113.

Fisher, W. P., Jr. (1999). Foundations for health status metrology: The stability of MOS SF-36 PF-10 calibrations across samples. Journal of the Louisiana State Medical Society, 151(11), 566-578.

Fisher, W. P., Jr. (2000). Objectivity in psychosocial measurement: What, why, how. Journal of Outcome Measurement, 4(2), 527-563.

Fisher, W. P., Jr. (2002). “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. (2003). The mathematical metaphysics of measurement and metrology: Towards meaningful quantification in the human sciences. In A. Morales (Ed.), Renascent pragmatism: Studies in law and social science (pp. 118-153). Brookfield, VT: Ashgate Publishing Co.

Fisher, W. P., Jr. (2004). Meaning and method in the social sciences. Human Studies: A Journal for Philosophy & Social Sciences, 27(4), 429-454.

Fisher, W. P., Jr. (2007). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-1093 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2009, November 19). Draft legislation on development and adoption of an intangible assets metric system. Living Capital Metrics blog: https://livingcapitalmetrics.wordpress.com/2009/11/19/draft-legislation/.

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

Fisher, W. P., Jr. (2009). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology.

Fisher, W. P., Jr. (2010, 22 November). Meaningfulness, measurement, value seeking, and the corporate objective function: An introduction to new possibilities. LivingCapitalMetrics.com, Sausalito, California.

Fisher, W. P., Jr. (2010). Measurement, reduced transaction costs, and the ethics of efficient markets for human, social, and natural capital. Bridge to Business Postdoctoral Certification, Freeman School of Business, Tulane University (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2340674).

Fisher, W. P., Jr. (2010). The standard model in the history of the natural sciences, econometrics, and the social sciences. Journal of Physics Conference Series, 238(1), 012016.

Fisher, W. P., Jr. (2011). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In N. Brown, B. Duckor, K. Draney & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 2 (pp. 1-27). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2012). Measure and manage: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. 43-63). New York: Palgrave Macmillan.

Fisher, W. P., Jr. (2012). What the world needs now: A bold plan for new standards [Third place, 2011 NIST/SES World Standards Day paper competition]. Standards Engineering, 64(3), 1 & 3-5 [http://ssrn.com/abstract=2083975].

Fisher, W. P., Jr. (2015). A probabilistic model of the law of supply and demand. Rasch Measurement Transactions, 29(1), 1508-1511 [http://www.rasch.org/rmt/rmt291.pdf].

Fisher, W. P., Jr. (2015). Rasch measurement as a basis for metrologically traceable standards. Rasch Measurement Transactions, 28(4), 1492-1493 [http://www.rasch.org/rmt/rmt284.pdf].

Fisher, W. P., Jr. (2015). Rasch metrology: How to expand measurement locally everywhere. Rasch Measurement Transactions, 29(2), 1521-1523.

Fisher, W. P., Jr. (2017, September). Metrology, psychometrics, and new horizons for innovation. 18th International Congress of Metrology, Paris, 10.1051/metrology/201709007.

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

Fisher, W. P., Jr. (2018). How beauty teaches us to understand meaning. Educational Philosophy and Theory, in review.

Fisher, W. P., Jr. (2018). Separation theorems in econometrics and psychometrics: Rasch, Frisch, two Fishers, and implications for measurement. Scandinavian Economic History Review, in review.

Fisher, W. P., Jr., Harvey, R. F., & Kilgore, K. M. (1995). New developments in functional assessment: Probabilistic models for gold standards. NeuroRehabilitation, 5(1), 3-25.

Fisher, W. P., Jr., Harvey, R. F., Taylor, P., Kilgore, K. M., & Kelly, C. K. (1995). Rehabits: A common language of functional assessment. Archives of Physical Medicine and Rehabilitation, 76(2), 113-122.

Fisher, W. P., Jr., & Stenner, A. J. (2011, January). Metrology for the social, behavioral, and economic sciences (Social, Behavioral, and Economic Sciences White Paper Series).National Science Foundation: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36.

Fisher, W. P., Jr., & Stenner, A. J. (2011, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium, http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf, Jena, Germany.

Fisher, W. P., Jr., & Stenner, A. J. (2016). Theory-based metrological traceability in education: A reading measurement network. Measurement, 92, 489-496.

Fisher, W. P., Jr., & Wilson, M. (2015). Building a productive trading zone in educational assessment research and practice. Pensamiento Educativo: Revista de Investigacion Educacional Latinoamericana, 52(2), 55-78.

Pendrill, L., & Fisher, W. P., Jr. (2013). Quantifying human response: Linking metrological and psychometric characterisations of man as a measurement instrument. Journal of Physics Conference Series, 459, 012057.

Pendrill, L., & Fisher, W. P., Jr. (2015). Counting and quantification: Comparing psychometric and metrological perspectives on visual perceptions of number. Measurement, 71, 46-55.

 

Creative Commons License
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.

Self-Sustaining Sustainability

August 6, 2018

After decades of efforts and massive resources expended in trying to create a self-sustaining sustainable economy, perhaps it is time to wonder if we are going about it the wrong way. There seems to be truly significant and widespread desire for change, but the often inspiring volumes of investments and ingenuity applied to the problem persistently prove insufficient to the task. Why?

I’ve previously and repeatedly explained how finding the will to change is not the issue. This time I’ll approach my proposed solution in a different way.

Q: How do we create a self-sustaining sustainable economy?

A: By making sustainability profitable in monetary terms as well as in the substantive real terms of the relationships we live out with each other and the earth. Current efforts in this regard focus solely on reducing energy costs enough to compensate for investments in advancing the organizational mission. We need far more comprehensively designed solutions than that.

Q: How do we do that?

A: By financially rewarding improved sustainability at every level of innovation, from the individual to the community to the firm.

Q: How do we do that?

A: By instituting rights to the ownership of human, social, and natural capital properties, and by matching the demand for sustainability with the supply of it in a way that will inform arbitrage and pricing.

Q: How do we do that?

A: By lowering the cost of the information needed to be able to know how many shares of human, social, and natural capital stocks are owned, and to match demand with supply.

Q: How could that be done?

A: By investing as a society in improving the quality and distribution of the available information.

Q: What does that take?

A: Creating dependable and meaningful tools for ascertaining the quantity, quality, and type of sustainability impacts on human, social, and natural capital being offered.

Q: Can that be done?

A: The technical art and science of measurement needed for creating these tools is well established, having been in development for almost 100 years.

Q: How do we start?

A: An important lesson of history is that building the infrastructure and its array of applications follows in the wake of, and cannot precede, the institution of the constitutional ideals. We must know what the infrastructure and applications will look like in their general features, but nothing will ever be done if we think we have to have them in place before instantiating the general frame of reference. The most general right to own legal title to human, social, and natural capital can be instituted, and the legal status of new metric system units can be established, before efforts are put into unit standards, traceability processes, protocols for intralaboratory ruggedness tests and interlaboratory round robin trials, conformity assessments, etc.

Q: It sounds like an iterative process.

A: Yes, one that must attend from the start to the fundamental issues of information coherence and complexity, as is laid out in my recent work with Emily Oon, Spencer Benson, Jack Stenner, and others.

Q: This sounds highly technical, utilitarian, and efficient. But all the talk of infrastructure, standards, science, and laboratories sounds excessively technological. Is there any place in this scheme for ecological values, ethics, and aesthetics? And how are risk and uncertainty dealt with?

A: We can take up each of these in turn.

Ecological values: To use an organic metaphor, we know the DNA of the various human, social, and natural capital forms of life, or species, and we know their reproductive and life cycles, and their ecosystem requirements. What we have not done is to partner with each of these species in relationships that focus on maximizing the quality of their habitats, their maturation, and the growth of their populations. Social, psychological, and environmental relationships are best conceived as ecosystems of mutual interdependencies. Being able to separate and balance within-individual, between-individual, and collective levels of complexity in these interdependencies will be essential to the kinds of steward leadership needed for creating and maintaining new sociocognitive ecosystems. Our goal here is to become the change we want to institute, since caterpillar to butterfly metamorphoses come about only via transformations from within.

Ethics: The motivating intention is to care simultaneously and equally effectively for both individual uniqueness and global humanity. In accord with the most fundamental ethical decision, we choose discourse over violence, and we do so by taking language as the model for how things come into words. Language is itself alive in the sense of the collective processes by which new meanings come into it. Language moreover has the remarkable capacity of supporting local concrete improvisations and creativity at the same time that it provides navigable continuity and formal ideals. Care for the unity and sameness of meaning demands a combination of rigorous conceptual determinations embodied in well-defined words with practical applications of those words in local improvisations. That is how we support the need to make decisions with inevitably incomplete and inconsistent information while not committing the violence of the premature conclusion. The challenge is one of finding a balance between openness and boundaries that allows language and our organizational cultures to be stable while also evolving. Our technical grasp of complex adaptive systems, autopoiesis, and stochastic measurement information models is advanced enough to meet these ethical requirements of caring for ourselves, each other, and the earth.

Aesthetics: An aesthetic desire for and love of beauty roots the various forms of life inhabiting diverse niches in the proposed knowledge ecosystem and information infrastructure, and does so in the ground of the ethical choice of discourse and meaning over violence. The experience of beauty teaches us how to understand meaning. The attraction to beauty is a unique human phenomenon because it combines apparent opposites into a single complex feeling. Even when the object of desire is possessed as fully as possible, desire is not eliminated, and even when one feels the object of desire to be lost or completely out of touch, its presence and reality is still felt. So, too, with meaning: no actual instance of anything in the world ever embodies the fullness of an abstract conceptual ideal. This lesson of beauty is perhaps most plainly conveyed in music, where artists deliberately violate the standards of instrument tuning to create fascinating and absorbing combinations of harmony and dissonance from endlessly diverse ensembles. Some tunings persist beyond specific compositions to become immediately identifiable trademark sounds. In taking language as a model, the aesthetic combination of desire and possession informs the ethics of care for the unity and sameness of meaning, and vice versa. And ecological values, ethics, and aesthetics stand on par with the technical concerns of calibration and measurement.

Risk and uncertainty: Calibrating a tool relative to a unit standard is by itself already a big step toward reducing uncertainty and risk. Instead of the chaos of dozens of disconnected sustainability indicators, or the cacophony of hundreds or thousands of different tests, assessments, or surveys measuring the same things, we will have data and theory supporting interpretation of reproducible patterns. These patterns will be, and in many cases already are, embodied in instruments that further reduce risk by defining an invariant unit of comparison, simplifying interpretation, reducing opportunities for mistakes, by quantifying uncertainty, and by qualifying it in terms of the anomalous exceptions that depart from expectations. Each of these is a special feature of rigorously defined measurement that will eventually become the expected norm for information on sustainability.

For more on these themes, see my other blog posts here, my various publications, and my SSRN page.

 

Creative Commons License
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.

Convergence, Divergence, and the Continuum of Field-Organizing Activities

March 29, 2014

So what are the possibilities for growing out green shoots from the seeds and roots of an ethical orientation to keeping the dialogue going? What kinds of fruits might be expected from cultivating a common ground for choosing discourse over violence? What are the consequences for practice of planting this seed in this ground?

The same participant in the conversation earlier this week at Convergence XV who spoke of the peace building processes taking place around the world also described a developmental context for these issues of mutual understanding. The work of Theo Dawson and her colleagues (Dawson, 2002a, 2002b, 2004; Dawson, Fischer, and Stein, 2006) is especially pertinent here. Their comparisons of multiple approaches to cognitive and moral development have provided clear and decisive theory, evidence, and instrumentation concerning the conceptual integrations that take place in the evolution of hierarchical complexity.

Conceptual integrations occur when previously tacit, unexamined, and assumed principles informing a sphere of operations are brought into conscious awareness and are transformed into explicit objects of new operations. Developmentally, this is the process of discovery that takes place from the earliest stages of life, in utero. Organisms of all kinds mature in a process of interaction with their environments. Young children at the “terrible two” stage, for instance, are realizing that anything they can detach from, whether by throwing or by denying (“No!”), is not part of them. Only a few months earlier, the same children will have been fascinated with their fingers and toes, realizing these are parts of their own bodies, often by putting them in their mouths.

There are as many opportunities for conceptual integrations between the ages of 21 to 99 as there are between birth and 21. Developmental differences in perspectives can make for riotously comic situations, and can also lead to conflicts, even when the participants agree on more than they disagree on. And so here we arrive at a position from which we can get a grip on how to integrate convergence and divergence in a common framework that follows from the prior post’s brief description of the ontological method’s three moments of reduction, application, and deconstruction.

Image

Woolley and colleagues (Woolley, et al., 2010; Woolley and Fuchs, 2011) describe a continuum of five field-organizing activities categorizing the types of information needed for effective collective intelligence (Figure 1). Four of these five activities (defining, bounding, opening, and bridging) vary in the convergent versus divergent processes they bring to bear in collective thinking. Defining and bounding are convergent processes that inform judgment and decision making. These activities are especially important in the emergence of a new field or organization, when the object of interest and the methods of recognizing and producing it are in contention. Opening and bridging activities, in contrast, diverge from accepted definitions and transgress boundaries in the creative process of pushing into new areas. Undergirding the continuum as a whole is the fifth activity, grounding, which serves as a theory- and evidence-informed connection to meaningful and useful results.

There are instances in which defining and bounding activities have progressed to the point that the explanatory power of theory enables the calibration of test items from knowledge of the component parts included in those items. The efficiencies and cost reductions gained from computer-based item generation and administration are significant. Research in this area takes a variety of approaches; for more information, see Daniel and Embretson (2010), DeBoeck and Wilson (2004), Stenner, et al. (2013), and others.

The value of clear definitions and boundaries in this context stems in large part from the capacity to identify exceptions that prove (test) the rules, and that then also provide opportunities for opening and bridging. Kuhn (1961, p. 180; 1977, p. 205) noted that

To the extent that measurement and quantitative technique play an especially significant role in scientific discovery, they do so precisely because, by displaying significant anomaly, they tell scientists when and where to look for a new qualitative phenomenon.

Rasch (1960, p. 124) similarly understood that “Once a law has been established within a certain field then the law itself may serve as a tool for deciding whether or not added stimuli and/or objects belong to the original group.” Rasch gives the example of mechanical force applied to various masses with resulting accelerations, introducing idea that one of the instruments might exert magnetic as well as mechanical force, with noticeable effects on steel masses, but not on wooden masses. Rasch suggests that exploration of these anomalies may result in the discovery of other similar instruments that vary in the extent to which they also exert the new force, with the possible consequence of discovering a law of magnetic attraction.

There has been an intense interest in the assessment of divergent inconsistencies in measurement research and practice following in the wake of Rasch’s early work in psychological and social measurement (examples from a very large literature in this area include Karabatsos and Ulrich, 2002, and Smith and Plackner, 2009). Andrich, for instance, makes explicit reference to Kuhn (1961), saying, “…the function of a model for measurement…is to disclose anomalies, not merely to describe data” (Andrich, 2002, p. 352; also see Andrich, 1996, 2004, 2011). Typical software for applying Rasch models (Andrich, et al., 2013; Linacre, 2011, 2013; Wu, et al., 2007) thus accordingly provides many more qualitative numbers evaluating potential anomalies than quantitative measuring numbers. These qualitative numbers (digits that do not stand for something substantive that adds up in a constant unit) include uncertainty and confidence indicators that vary with sample size; mean square and standardized model fit statistics; and principal components analysis factor loadings and eigenvalues.

The opportunities for divergent openings onto new qualitative phenomena provided by data consistency evaluations are complemented in Rasch measurement by a variety of bridging activities. Different instruments intended to measure the same or closely related constructs may often be equated or co-calibrated, so they measure in a common unit (among many publications in this area, see Dawson, 2002a, 2004; Fisher, 1997; Fisher, et al., 1995; Massof and Ahmadian, 2007; Smith and Taylor, 2004). Similarly, the same instrument calibrated on different samples from the same population may exhibit consistent properties across those samples, offering further evidence of a potential for defining a common unit (Fisher, 1999).

Other opening and bridging activities include capacities (a) to drop items or questions from a test or survey, or to add them; (b) to adaptively administer subsets of custom-selected items from a large bank; and (c) to adjust measures for the leniency or severity of judges assigning ratings, all of which can be done, within the limits of the relevant definitions and boundaries, without compromising the unit of comparison. For methodological overviews, see Bond and Fox (2007), Wilson (2005), and others.

The various field-organizing activities spanning the range from convergence to divergence are implicated not only in research on collective thinking, but also in the history and philosophy of science. Galison and colleagues (Galison, 1997, 1999; Galison and Stump, 1996) closely examine positivist and antipositivist perspectives on the unity of science, finding their conclusions inconsistent with the evidence of history. A postpositivist perspective (Galison, 1999, p. 138), in contrast, finds “distinct communities and incommensurable beliefs” between and often within the areas of theory, experiment, and instrument-making. But instead of finding these communities “utterly condemned to passing one another without any possibility of significant interaction,” Galison (1999, p. 138) observes that “two groups can agree on rules of exchange even if they ascribe utterly different significance to the objects being exchanged; they may even disagree on the meaning of the exchange process itself.” In practice, “trading partners can hammer out a local coordination despite vast global differences.”

In accord with Woolley and colleagues’ work on convergent and divergent field-organizing activities, Galison (1999, p. 137) concludes, then, that “science is disunified, and—against our first intuitions—it is precisely the disunification of science that underpins its strength and stability.” Galison (1997, pp. 843-844) concludes with a section entitled “Cables, Bricks, and Metaphysics” in which the postpositivist disunity of science is seen to provide its unexpected coherence from the simultaneously convergent and divergent ways theories, experiments, and instruments interact.

But as Galison recognizes, a metaphor based on the intertwined strands in a cable is too mechanical to support the dynamic processes by which order arises from particular kinds of noise and chaos. Not cited by Galison is a burgeoning literature on the phenomenon of noise-induced order termed stochastic resonance (Andò  and Graziani 2000, Benzi, et al., 1981; Dykman and McClintock, 1998; Fisher, 1992, 2011; Hess and Albano, 1998; Repperger and Farris, 2010). Where the metaphor of a cable’s strands breaks down, stochastic resonance provides multiple ways of illustrating how the disorder of finite and partially independent processes can give rise to an otherwise inaccessible order and structure.

Stochastic resonance involves small noisy signals that can be amplified to have very large effects. The noise has to be of a particular kind, and too much of it will drown out rather than amplify the effect. Examples include the interaction of neuronal ensembles in the brain (Chialvo, Lontin, and Müller-Gerking, 1996), speech recognition (Moskowitz and Dickinson, 2002), and perceptual interpretation (Rianni and Simonotto, 1994). Given that Rasch’s models for measurement are stochastic versions of Guttman’s deterministic models (Andrich, 1985), the question has been raised as to how Rasch’s seemingly weaker assumptions could lead to a measurement model that is stronger than Guttman’s (Duncan, 1984, p. 220). Stochastic resonance may provide an essential clue to this puzzle (Fisher, 1992, 2011).

Another description of what might be a manifestation of stochastic resonance akin to that brought up by Galison arises in Berg and Timmermans’ (2000, p. 56) study of the constitution of universalities in a medical network. They note that, “Paradoxically, then, the increased stability and reach of this network was not due to more (precise) instructions: the protocol’s logistics could thrive only by parasitically drawing upon its own disorder.” Much the same has been said about the behaviors of markets (Mandelbrot, 2004), bringing us back to the topic of the day at Convergence XV earlier this week. I’ll have more to say on this issue of universalities constituted via noise-induced order in due course.

References

Andò, B., & Graziani, S. (2000). Stochastic resonance theory and applications. New York: Kluwer Academic Publishers.

Andrich, D. (1985). An elaboration of Guttman scaling with Rasch models for measurement. In N. B. Tuma (Ed.), Sociological methodology 1985 (pp. 33-80). San Francisco, California: Jossey-Bass.

Andrich, D. (1996). Measurement criteria for choosing among models with graded responses. In A. von Eye & C. Clogg (Eds.), Categorical variables in developmental research: Methods of analysis (pp. 3-35). New York: Academic Press, Inc.

Andrich, D. (2002). Understanding resistance to the data-model relationship in Rasch’s paradigm: A reflection for the next generation. Journal of Applied Measurement, 3(3), 325-359.

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

Andrich, D. (2011). Rating scales and Rasch measurement. Expert Reviews in Pharmacoeconomics Outcome Research, 11(5), 571-585.

Andrich, D., Lyne, A., Sheridan, B., & Luo, G. (2013). RUMM 2030: Rasch unidimensional models for measurement. Perth, Australia: RUMM Laboratory Pty Ltd [www.rummlab.com.au].

Benzi, R., Sutera, A., & Vulpiani, A. (1981). The mechanism of stochastic resonance. Journal of Physics. A. Mathematical and General, 14, L453-L457.

Berg, M., & Timmermans, S. (2000). Order and their others: On the constitution of universalities in medical work. Configurations, 8(1), 31-61.

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

Chialvo, D., Longtin, A., & Müller-Gerking, J. (1996). Stochastic resonance in models of neuronal ensembles revisited [Electronic version].

Daniel, R. C., & Embretson, S. E. (2010). Designing cognitive complexity in mathematical problem-solving items. Applied Psychological Measurement, 34(5), 348-364.

Dawson, T. L. (2002a, Summer). A comparison of three developmental stage scoring systems. Journal of Applied Measurement, 3(2), 146-89.

Dawson, T. L. (2002b, March). New tools, new insights: Kohlberg’s moral reasoning stages revisited. International Journal of Behavioral Development, 26(2), 154-66.

Dawson, T. L. (2004, April). Assessing intellectual development: Three approaches, one sequence. Journal of Adult Development, 11(2), 71-85.

Dawson, T. L., Fischer, K. W., & Stein, Z. (2006). Reconsidering qualitative and quantitative research approaches: A cognitive developmental perspective. New Ideas in Psychology, 24, 229-239.

De Boeck, P., & Wilson, M. (Eds.). (2004). Explanatory item response models: A generalized linear and nonlinear approach. Statistics for Social and Behavioral Sciences). New York: Springer-Verlag.

Duncan, O. D. (1984). Notes on social measurement: Historical and critical. New York: Russell Sage Foundation.

Dykman, M. I., & McClintock, P. V. E. (1998, January 22). What can stochastic resonance do? Nature, 391(6665), 344.

Fisher, W. P., Jr. (1992, Spring). Stochastic resonance and Rasch measurement. Rasch Measurement Transactions, 5(4), 186-187 [http://www.rasch.org/rmt/rmt54k.htm].

Fisher, W. P., Jr. (1997). Physical disability construct convergence across instruments: Towards a universal metric. Journal of Outcome Measurement, 1(2), 87-113.

Fisher, W. P., Jr. (1999). Foundations for health status metrology: The stability of MOS SF-36 PF-10 calibrations across samples. Journal of the Louisiana State Medical Society, 151(11), 566-578.

Fisher, W. P., Jr. (2011). Stochastic and historical resonances of the unit in physics and psychometrics. Measurement: Interdisciplinary Research & Perspectives, 9, 46-50.

Fisher, W. P., Jr., Harvey, R. F., Taylor, P., Kilgore, K. M., & Kelly, C. K. (1995, February). Rehabits: A common language of functional assessment. Archives of Physical Medicine and Rehabilitation, 76(2), 113-122.

Galison, P. (1997). Image and logic: A material culture of microphysics. Chicago: University of Chicago Press.

Galison, P. (1999). Trading zone: Coordinating action and belief. In M. Biagioli (Ed.), The science studies reader (pp. 137-160). New York: Routledge.

Galison, P., & Stump, D. J. (1996). The disunity of science: Boundaries, contexts, and power. Palo Alto, California: Stanford University Press.

Hess, S. M., & Albano, A. M. (1998, February). Minimum requirements for stochastic resonance in threshold systems. International Journal of Bifurcation and Chaos, 8(2), 395-400.

Karabatsos, G., & Ullrich, J. R. (2002). Enumerating and testing conjoint measurement models. Mathematical Social Sciences, 43, 487-505.

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

Linacre, J. M. (2011). A user’s guide to WINSTEPS Rasch-Model computer program, v. 3.72.0. Chicago, Illinois: Winsteps.com.

Linacre, J. M. (2013). A user’s guide to FACETS Rasch-Model computer program, v. 3.71.0. Chicago, Illinois: Winsteps.com.

Mandelbrot, B. (2004). The misbehavior of markets. New York: Basic Books.

Massof, R. W., & Ahmadian, L. (2007, July). What do different visual function questionnaires measure? Ophthalmic Epidemiology, 14(4), 198-204.

Moskowitz, M. T., & Dickinson, B. W. (2002). Stochastic resonance in speech recognition: Differentiating between /b/ and /v/. Proceedings of the IEEE International Symposium on Circuits and Systems, 3, 855-858.

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

Repperger, D. W., & Farris, K. A. (2010, July). Stochastic resonance –a nonlinear control theory interpretation. International Journal of Systems Science, 41(7), 897-907.

Riani, M., & Simonotto, E. (1994). Stochastic resonance in the perceptual interpretation of ambiguous figures: A neural network model. Physical Review Letters, 72(19), 3120-3123.

Smith, R. M., & Plackner, C. (2009). The family approach to assessing fit in Rasch measurement. Journal of Applied Measurement, 10(4), 424-437.

Smith, R. M., & Taylor, P. (2004). Equating rehabilitation outcome scales: Developing common metrics. Journal of Applied Measurement, 5(3), 229-42.

Stenner, A. J., Fisher, W. P., Jr., Stone, M. H., & Burdick, D. S. (2013, August). Causal Rasch models. Frontiers in Psychology: Quantitative Psychology and Measurement, 4(536), 1-14 [doi: 10.3389/fpsyg.2013.00536].

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

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010, 29 October). Evidence for a collective intelligence factor in the performance of human groups. Science, 330, 686-688.

Woolley, A. W., & Fuchs, E. (2011, September-October). Collective intelligence in the organization of science. Organization Science, 22(5), 1359-1367.

Wu, M. L., Adams, R. J., Wilson, M. R., Haldane, S.A. (2007). ACER ConQuest Version 2: Generalised item response modelling software. Camberwell: Australian Council for Educational Research.

Outline of the Efficient Markets for Living Capital Project

February 24, 2014

If I’m invited to submit a full application to the Data-Driven Discovery program at the Moore Foundation, I intend to suggest a five-year plan for launching various experiments in self-organizing market-making processes. Yesterday I posted a Mission, Vision, Values, and Goals statement for this process. Here now is the current state of the plan, whether or not it goes to the Moore Foundation.

  • The first year will involve planning a conference, to be held in the second year, gathering together Rasch, IMEKO, Criterion Institute Making Markets leaders, and others, to focus on devising a technology road mapping process applicable in any field.
    • This is not to say there is any expectation of uniformity across or even within fields, but only that a forum embodying awareness of the various necessary facets of the process could be a vital catalyst.
    • The overarching theme is one of mutually adapting multilevel information systems and multilevel forms of social organization by managing multilevel models and measures.
    • Better measurement practices will be more widely adopted when our forms of social organization are adapted to our forms of quantification, and vice versa.
      • Examples will contrast
        • the top down command and control reductionism of individuals presumed identical and interchangeable versus
        • the multilevel modeling of unique individuals sharing a common group-level construct orientation; and
        • an HLM example will illustrate relations of unique individuals (micro-level) to varying common construct orientations (meso-level) within a larger ecology (macro-level).
      • Recommendations for policy and action will be based on evidence of what happens when unexamined default assumptions about organizations are not modified and adapted to the form of the concepts and models in use, and when they are.
  • The second year will see the conference and the formation of several working groups.
    • The conference will be recorded using multiple cameras; a composite video will be produced.
    • An experienced facilitator will be in charge to foster productive dialogue.
    • There will be multiple assistants taking notes on whiteboards or large tablet easels, one for each major group of stakeholders (end users, researchers, accountants, psychometricians, metrologists, economists, etc.)
  • The third year will involve synthesizing and publishing the results of that conference (book, video, interactive web site, mobile app, new working groups, etc.).
  • The third through fifth years will focus on initiating multiple replicable market-making processes in various fields.
    • It will be important to identify and select major market sectors in education, health care, social services, natural resource management.
    • Natural variations in concept-model-information-organization assemblages will be documented and compared within and across fields.
    • Instead of funding a one-time research or demonstration project, investors will expect innovation and entrepreneurial teams to document capital growth and provide appreciable returns.
    • The goal will be for all projects to result in self-sustaining markets enabling consumer comparison shopping, systematic quality improvements, appropriate rewards and punishments for value to price relations, and more rapid gains in research productivity.

Background reading:

Akrich, M., Callon, M., & Latour, B. (2002). The key to success in innovation Part I: The art of interessement. International Journal of Innovation Management, 6(2), 187-206 [doi: 10.1142/S1363919602000550].
Akrich, M., Callon, M., & Latour, B. (2002). The key to success in innovation Part II: The art of choosing a good spokesperson. International Journal of Innovation Management, 6(2), 207-225.
Fisher, W. P., Jr. (2010, June 13-16). Rasch, Maxwell’s method of analogy, and the Chicago tradition. In  G. Cooper (Chair), Https://conference.cbs.dk/index.php/rasch/Rasch2010/paper/view/824. Probabilistic models for measurement in education, psychology, social science and health: Celebrating 50 years since the publication of Rasch’s Probabilistic Models, University of Copenhagen School of Business, FUHU Conference Centre, Copenhagen, Denmark.
Garfinkel, A. (1991). Reductionism. In R. Boyd, P. Gasper & J. D. Trout (Eds.), The philosophy of science (pp. 443-459). Cambridge, Mass.: MIT Press.
Hutchins, E. (2012). Concepts in practice as sources of order. Mind, Culture, and Activity, 19, 314-323.
Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-734.
Nersessian, N. J. (2002). Maxwell and “the method of physical analogy”: Model-based reasoning, generic abstraction, and conceptual change. In D. Malament (Ed.), Reading natural philosophy: Essays in the history and philosophy of science and mathematics (pp. 129-166). Lasalle, Illinois: Open Court.
Stenner, A. J., Fisher, W. P., Jr., Stone, M. H., & Burdick, D. S. (2013, August). Causal Rasch models. Frontiers in Psychology: Quantitative Psychology and Measurement, 4(536), 1-14 [doi: 10.3389/fpsyg.2013.00536].
Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

The New Information Platform No One Sees Coming

December 6, 2012

I’d like to draw your attention to a fundamentally important area of disruptive innovations no one seems to see coming. The biggest thing rising in the world of science today that does not appear to be on anyone’s radar is measurement. Transformative potential beyond that of the Internet itself is available.

Realizing that potential will require an Intangible Assets Metric System. This system will connect together all the different ways any one thing is measured, bringing common languages for representing human, social, and economic value into play everywhere. We need these metrics on the front lines of education, health care, social services, and in human, reputation, and natural resource management, as well as in the economic models and financial spreadsheets informing policy, and in the scientific research conducted in dozens of fields.

All reading ability measures, for instance, should be transparently, inexpensively, and effortlessly expressed in a universally uniform metric, in the same way that standardized measures of weight and volume inform grocery store purchasing decisions. We have made starts at such systems for reading, writing, and math ability measures, and for health status, functionality, and chronic disease management measures. There oddly seems to be, however, little awareness of the full value that stands to be gained from uniform metrics in these areas, despite the overwhelming human, economic, and scientific value derived from standardized units in the existing economy. There has accordingly been virtually no leadership or investment in this area.

Measurement practice in business is woefully out of touch with the true paradigm shift that has been underway in psychometrics for years, even though the mantra “you manage what you measure” is repeated far and wide. In a fascinating twist, practically the only ones who notice the business world’s conceptual shortfall in measurement practice are the contrarians who observe that quantification can often be more of a distraction from management than the medium of its execution—but this is true only when measures are poorly conceived, designed, and implemented.

Demand for better measurement—measurement that reduces data volume not only with no loss of information but with the addition of otherwise unavailable interstitial information; that supports mass customized comparability for informed purchasing and quality improvement decisions; and that enables common product definitions for outcomes-based budgeting—is growing hand in hand with the spread of resilient, nimble, lean, and adaptive business models, and with the ongoing geometrical growth in data volume.

An even bigger source of demand for the features of advanced measurement is the increasing dependence of the economy on intangible assets, those forms of human, social, and natural capital that comprise 90% or more of the total capital under management. We will bring these now economically dead forms of capital to life by systematically standardizing representations of their quality and quantity. The Internet is the planetary nervous system through which basic information travels, and the Intangible Assets Metric System will be the global cerebrum, where higher order thinking takes place.

It will not be possible to realize the full potential of lean thinking in the information- and service-based economy without an Intangible Assets Metric System. Given the long-proven business value of standards and the role of measurement in management, it seems self-evident that our ongoing economic difficulties stem largely from our failure to develop and deploy an Intangible Assets Metric System providing common currencies for the exchange of authentic wealth. The future of sustainable and socially responsible business practices must surely depend extensively on universal access to flexible and practical uniform metrics for intangible assets.

Of course, for global intangible assets standards to be viable, they must be adaptable to local business demands and conditions without compromising their comparability. And that is just what is most powerfully disruptive about contemporary measurement methods: they make mass customization a reality. They’ve been doing so in computerized testing since the 1970s. Isn’t it time we started putting this technology to systematic use in a wide range of applications, from human and environmental resource management to education, health care, and social services?

Measuring/Managing Social Value

August 28, 2012

From my December 1, 2008 personal journal, written not long after the October 2008 SoCap conference. I’ve updated a few things that have changed in the intervening years.

Over the last month, I’ve been digesting what I learned at the Social Capital Markets conference at Fort Mason in San Francisco, and at the conference I attended just afterward, Bioneers, in Marin county. Bioneers (www.Bioneers.org) could be called Natural Capital Markets. It was quite like the Social Capital Markets conference with only a slight shift in emphasis, and lots of discussion of social value.

The main thing that impressed me at both of these conferences, apart from what I already knew about the caring passion I share with so many, is the huge contrast between that passion and the quality of the data that so many are basing major decisions on. Seeing this made me step back and think harder about how to shape my message.

First, though it may not seem like it initially, there is incredible practical value to be gained from taking the trouble to construct good measures. We do indeed manage what we measure. So whatever we measure becomes what we manage. If we’re not measuring anything that has anything to do with our mission, vision, or values, then what we’re managing won’t have anything to do with those, either. And when the numbers we use as measures do not actually represent a constant unit amount that adds up the way the numbers do, then we don’t have a clue what we’re measuring and we could be managing just about anything.

This is not the way to proceed. First take-away: ask for more from your data. Don’t let it mislead you with superficial appearances. Dig deeper.

Second, to put it a little differently, percentages, scores, and counts per capita, etc. are not measures that have the same meaning or quality that measures of height, weight, time, temperature, or volts have. However, for over 50 years, we have been constructing measures mathematically equivalent to physical measures from ability tests, surveys, assessments, checklists, etc. The technical literature on this is widely available. The methods have been mainstream at ETS, ACT, state and national departments of education globally, etc for decades.

Second take-away: did I say you should ask for more from your data? You can get it. A lot of people already are, though I don’t think they’re asking for nearly as much as they could get.

Third, though the massive numbers of percentages, scores, and counts per capita are not the measures we seek, they are indeed exactly the right place to start. I have seen over and over again, in education, health care, sociology, human resource management, and most recently in the UN Millennium Development Goals data, that people do know exactly what data will form a proper basis for the measurement systems they need.

Third take-away: (one more time!) ask for more from your data. It may conceal a wealth beyond what you ever guessed.

So what are we talking about? There are methods for creating measures that give you numbers that verifiably stand for a substantive unit amount that adds up in the same way one-inch blocks do (probabilistically, and within a range of error). If the instrument is properly calibrated and administered, the unit size and meaning will not change across individuals or samples measured. You can reduce data volume dramatically, not only with no loss of information but also with false appearances of information either indicated as error or flagged for further attention. You can calibrate a continuum of less to more that is reliably and reproducibly associated with, annotated by, and interpreted through your own indicators. You can equate different collections of indicators that measure the same thing so that they do so in the same unit.

Different agencies using the same, different, or mixed collections of indicators in different countries or regions could assess their measures for comparability, and if they are of satisfactory quality, equate them so they measure in the same unit. That is, well-designed instruments written and administered in different languages routinely have their items calibrate in the same order and positions, giving the same meaning to the same unit of measurement. For instance, see the recent issue of the Journal of Applied Measurement ([link]) devoted to reports on the OECD’s Programme for International Student Assessment.

This is not a data analysis strategy. It is an instrument calibration strategy. Once calibrated, the instrument can be deployed. We need to monitor its structure, but the point is to create a tool people can take out into the world and use like a thermometer or clock.

I’ve just been looking at the Charity Navigator (for instance, [link]) and the UN’s Millenium Development Goals ([link]), and the databases that have been assembled as measures of progress toward these goals ([link]). I would suppose these web sites show data in forms that people are generally familiar with, so I’m working up analyses to use as teaching tools from the UN data.

You don’t have to take any of this at my word. It’s been documented ad nauseum in the academic literature for decades. Those interested can find out more than they ever wanted to know at http://www.Rasch.org, in the Wikipedia Rasch entry, in the articles and books at JAMPress.com, or in dozens of academic journals and hundreds of books. Though I’ve done my share of it, I’m less interested in continuing to add to that than I am in making a tangible contribution to improving people’s lives.

Sorry to go on like this. I meant to keep this short. Anyway, there it is.

PS, for real geeks: For those of you serious about learning about measurement as it is rigorously and mathematically defined, look into taking Everett Smith’s measurement course at Statistics.com ([link]) or David Andrich’s academic units at the University of Western Australia ([link]). Available software includes Mike Linacre’s Winsteps, Andrich’s RUMM, and Mark Wilson’s, at UC Berkeley, Conquest.

The methods Ev, Mike, David, and Mark teach have repeatedly been proven, both in mathematical theory and in real life, to be both necessary and sufficient in the construction of meaningful, practical measurement. Any number of ways of defining objectivity in measurement have been shown to reduce to the mathematical models they use. Why all the Chicago stuff? Because of Ben Wright. I’m helping (again) to organize a conference in his honor, to be held in Chicago next March. His work won him a Career Achievement Award from the Association of Test Publishers, and the coming conference will celebrate his foundational contributions to computerized measurement in health care.

As a final note, for those of you fearing reductionistic meaninglessness, look into my philosophical work.  But enough…

Review of “Advancing Social Impact Investments Through Measurement”

August 24, 2012

Over the last few days, I have been reading several of the most recent issues of the Community Development Investment Review, especially volume 7, number 2, edited by David Erickson of the Federal Reserve Bank of San Francisco, reporting the proceedings of the March 21, 2011 conference in Washington, DC on advancing social impact investments through measurement. I am so excited to see this work that I am (truly) fairly trembling with excitement. I feel as though I’ve finally made my way home. There are so many points of contact, it’s hard to know where to start. After several days of concentrated deep breathing and close study of the CDIR, it’s now possible to formulate some coherent thoughts to share.

The CDIR papers start to sort out the complex issues involved in clarifying how measurement might contribute to the integration of impact investing and community development finance. I am heartened by the statement that “The goal of the Review is to bridge the gap between theory and practice and to enlist as many viewpoints as possible—government, nonprofits, financial institutions, and beneficiaries.” On the other hand, the omission of measurement scientists from that list of viewpoints adds another question to my long list of questions as to why measurement science is so routinely ignored by the very people who proclaim its importance. The situation is quite analogous to demanding more frequent conversational interactions from colleagues while ignoring the invention of the telephone and not providing them with the tools and network connections.

The aims shared by the CDIR contributors and myself are evident in the fact that David Erickson opens his summary of the March 21, 2011 conference with the same quote from Robert Kennedy that I placed at the end of my 2009 article in Measurement (see references below; all papers referenced are available by request if they are not already online). In that 2009 paper, in others I’ve published over the last several years, in presentations I’ve made to my measurement colleagues abroad and at home, and in various entries in my blog, I take up virtually all of the major themes that arose in the DC conference: how better measurement can attract capital to needed areas, how the cost of measurement repels many investors, how government can help by means of standard setting and regulation, how diverse and ambiguous investor and stakeholder interests can be reconciled and/or clarified, etc.

The difference, of course, is that I present these issues from the technical perspective of measurement and cannot speak authoritatively or specifically from the perspectives represented by the community development finance and impact investing fields. The bottom line take-away message for these fields from my perspective is this: unexamined assumptions may unnecessarily restrict assessments of problems and their potential solutions. As Salamon put it in his remarks in the CDIR proceedings from the Washington meeting (p. 43), “uncoordinated innovation not guided by a clear strategic concept can do more than lose its way: it can do actual harm.”

A clear strategic concept capable of coordinating innovations in social impact measurement is readily available. Multiple, highly valuable, and eminently practical measurement technologies have proven themselves in real world applications over the last 50 years. These technologies are well documented in the educational, psychological, sociological, and health care research literatures, as well as in the practical experience of high stakes testing for professional licensure and certification, for graduation, and for admissions.

Numerous reports show how to approach problems of quantification and standards with new degrees of rigor, transparency, meaningfulness, and flexibility. When measurement problems are not defined in terms of these technologies, solutions that may offer highly advantageous features are not considered. When the area of application is as far reaching and fundamental as social impact measurement, not taking new technologies into account is nothing short of tragic. I describe some of the new opportunities for you in a Technical Postscript, below.

In his Foreword to the CDIR proceedings issue, John Moon mentions having been at the 2009 SoCap event bringing together stakeholders from across the various social capital markets arenas. I was at the 2008 SoCap, and I came away from it with much the same impression as Moon, feeling that the palpable excitement in the air was more than tempered by the evident fact that people were often speaking at cross purposes, and that there did not seem to be a common object to the conversation. Moon, Erickson, and their colleagues have been in one position to sort out the issues involved, and I have been in another, but we are plainly on converging courses.

Though the science is in place and has been for decades, it will not and cannot amount to anything until the people who can best make use of it do so. The community development finance and impact investing fields are those people. Anyone interested in getting together for an informal conversation on topics of mutual interest should feel free to contact me.

Technical Postscript

There are at least six areas in efforts to advance social impact investments via measurement that will be most affected by contemporary methods. The first has to do with scale quality. I won’t go into the technical details, but numbers do not automatically stand for something that adds up the way they do. Mapping a substantive construct onto a number line requires specific technical expertise; there is no evidence of that expertise in any of the literature I’ve seen on social impact investing, or on measuring intangible assets. This is not an arbitrary bit of philosophical esoterica or technical nicety. This is one of those areas where the practical value of scientific rigor and precision comes into its own. It makes all the difference in being able to realize goals for measurement, investment, and redefining profit in terms of social impacts.

A second area in which thinking on social impact measurement will be profoundly altered by current scaling methods concerns the capacity to reduce data volume with no loss of information. In current systems, each indicator has its own separate metric. Data volume quickly multiplies when tracking separate organizations for each of several time periods in various locales. Given sufficient adherence to data quality and meaningfulness requirements, today’s scaling methods allow these indicators to be combined into a single composite measure—from which each individual observation can be inferred.

Elaborating this second point a bit further, I noted that some speakers at the 2011 conference in Washington thought reducing data volume is a matter of limiting the number of indicators that are tracked. This strategy is self-defeating, however, as having fewer independent observations increases uncertainty and risk. It would be far better to set up systems in which the metrics are designed so as to incorporate the amount of uncertainty that can be tolerated in any given decision support application.

The third area I have in mind deals with the diverse spectrum of varying interests and preferences brought to the table by investors, beneficiaries, and other stakeholders. Contemporary approaches in measurement make it possible to adapt the content of the particular indicators (counts or frequencies of events, or responses to survey questions or test items) to the needs of the user, without compromising the comparability of the resulting quantitative measure. This feature makes it possible to mass customize the content of the metrics employed depending on the substantive nature of the needs at that time and place.

Fourth, it is well known that different people judging performances or assigning numbers to observations bring different personal standards to bear as they make their ratings. Contemporary measurement methods enable the evaluation and scaling of raters and judges relative to one another, when data are gathered in a manner facilitating such comparisons. The end result is a basis for fair comparisons, instead of scores that vary depending more on which rater is observing than on the quality of the performance.

Fifth, much of the discussion at the conference in Washington last year emphasized the need for shared data formatting and reporting standards. As might be guessed from the prior four areas I’ve described, significant advances have occurred in standard setting methods. It is suggested in the CDIR proceedings that the Treasury Department should be the home to a new institute for social impact measurement standards. In a series of publications over the last few years, I have suggested a need for an Intangible Assets Metric System to NIST and NSF (see below for references and links; all papers are available on request). That suggestion comes up again in my third-prize winning entry in the 2011 World Standards Day paper competition, sponsored by NIST and SES (the Society for Standards Professionals), entitled “What the World Needs Now: A Bold Plan for New Standards.” (See below for link.)

Sixth, as noted by Salamon (p. 43), “metrics are not neutral. They not only measure impact, they can also shape it.” Though this is not likely exactly what Salamon meant, one of the most exciting areas in measurement applications in education in recent years, one led in many ways by my colleague, Mark Wilson, and his group at UC Berkeley, concerns exactly this feedback loop between measurement and impact. In education, it has become apparent that test scaling reveals the order in which lessons are learned. Difficult problems that require mastery of easier problems are necessarily answered correctly less often than the easier problems. When the difficulty order of test questions in a given subject remains constant over time and across thousands of students, one may infer that the scale reveals the path of least resistance. Individualizing instruction by targeting lessons at the student’s measure has given rise to a concept of formative assessment, distinct from the summative assessment of accountability applications. I suspect this kind of a distinction may also prove of value in social impact applications.

Relevant Publications and Presentations

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, Thursday, January 22). Bringing capital to life via measurement: A contribution to the new economics. In  R. Smith (Chair), Session 3.3B. Rasch Models in Economics and Marketing. Second International Conference on Measurement in Health, Education, Psychology, and Marketing: Developments with Rasch Models, The International Laboratory for Measurement in the Social Sciences, School of Education, Murdoch University, Perth, Western Australia.

Fisher, W. P., Jr. (2005, August 1-3). Data standards for living human, social, and natural capital. In Session G: Concluding Discussion, Future Plans, Policy, etc. Conference on Entrepreneurship and Human Rights [http://www.fordham.edu/economics/vinod/ehr05.htm], Pope Auditorium, Lowenstein Bldg, Fordham University.

Fisher, W. P., Jr. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-3 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2008, 3-5 September). New metrological horizons: Invariant reference standards for instruments measuring human, social, and natural capital. Presented at the 12th International Measurement Confederation (IMEKO) TC1-TC7 Joint Symposium on Man, Science, and Measurement, Annecy, France: University of Savoie.

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

Fisher, W. P.. Jr. (2009). NIST Critical national need idea White Paper: Metrological infrastructure for human, social, and natural capital (Tech. Rep., http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology.

Fisher, W. P., Jr. (2010). The standard model in the history of the natural sciences, econometrics, and the social sciences. Journal of Physics: Conference Series, 238(1), http://iopscience.iop.org/1742-6596/238/1/012016/pdf/1742-6596_238_1_012016.pdf.

Fisher, W. P., Jr. (2011). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In N. Brown, B. Duckor, K. Draney & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 2 (pp. 1-27). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2011). Measuring genuine progress by scaling economic indicators to think global & act local: An example from the UN Millennium Development Goals project. LivingCapitalMetrics.com. Retrieved 18 January 2011, from Social Science Research Network: http://ssrn.com/abstract=1739386.

Fisher, W. P., Jr. (2012). Measure and manage: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. 43-63). New York: Palgrave Macmillan.

Fisher, W. P., Jr. (2012, May/June). What the world needs now: A bold plan for new standards. Standards Engineering, 64(3), 1 & 3-5 [http://ssrn.com/abstract=2083975].

Fisher, W. P., Jr., & Stenner, A. J. (2011, January). Metrology for the social, behavioral, and economic sciences (Social, Behavioral, and Economic Sciences White Paper Series). Retrieved 25 October 2011, from National Science Foundation: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36.

Fisher, W. P., Jr., & Stenner, A. J. (2011, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium, http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf, Jena, Germany.

Creative Commons License
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.

HEY GREECE!!! One more time through the basics

May 10, 2012

As the battle between austerity and growth mindsets threatens to freeze into a brittle gridlock, it seems time once again to simplify and repeat some painfully obvious observations.

1. Human, social, and natural capital make up at least 90 percent of the capital under management in the global economy.

2. There is no system of uniform weights and measures for these forms of capital.

3. We manage what we measure; so, lacking proper measures for 90 percent of the capital in the economy, we cannot possibly manage it properly.

4. Measurement theory and practice have advanced to the point that the technical viability of a meaningful, objective, and precise system of uniform units for human, social, and natural capital is no longer an issue.

5. A metric system for intangible assets (human, social, and natural capital) is the infrastructural capacity building project capable of supporting sustainable and responsible growth we are looking for.

6. Individual citizens, philanthropists, entrepreneurs, corporations, NGOs, educators, health care advocates, innovators, researchers, and governments everywhere ought to be focusing intensely on building systems of consensus measures that take full advantage of existing technical means for instrument scaling, equating, adaptive administration, mass customization, growth modeling, data quality assessment, and diagnostic individualized reporting.

7. Uniform impact measurement will make it possible to price outcomes in ways that allow market forces to inform consumers as to where they can obtain the best cost/value relation for the money. In other words, the profit motive will be directly harnessed in growing human, social, and natural capital.

8. Happiness indexes and gross national or domestic authentic wealth products will not obtain any real practical utility until individuals, firms, NGOs, and governments can directly manage their own intangible asset bottom lines.

See other posts in this blog or the links below for more information.

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

Research Associate
BEAR Center
Graduate School of Education
University of California, Berkeley
Principal
LivingCapitalMetrics Consulting

We are what we measure.

It’s time we measured what we want to be.

Connect with me on LinkedIn: http://www.linkedin.com/in/livingcapitalmetrics
View my research on my SSRN Author page: http://ssrn.com/author=1090685
Read my blog at https://livingcapitalmetrics.wordpress.com.
See my web site at http://www.livingcapitalmetrics.com.
http://www.rasch.org
Creative Commons License
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.