Archive for the ‘measurement’ Category

Psychology and the social sciences: An atheoretical, scattered, and disconnected body of research

February 16, 2019

A new article in Nature Human Behaviour (NHB) points toward the need for better theory and more rigorous mathematical models in psychology and the social sciences (Muthukrishna & Henrich, 2019). The authors rightly say that the lack of an overarching cumulative theoretical framework makes it very difficult to see whether new results fit well with previous work, or if something surprising has come to light. Mathematical models are especially emphasized as being of value in specifying clear and precise expectations.

The point that the social sciences and psychology need better theories and models is painfully obvious. But there are in fact thousands of published studies and practical real world applications that not only provide, but indeed often surpass, the kinds of predictive theories and mathematical models called for in the NHB article. The article not only makes no mention of any of this work, its argument is framed entirely in a statistical context instead of the more appropriate context of measurement science.

The concept of reliability provides an excellent point of entry. Most behavioral scientists think of reliability statistically, as a coefficient with a positive numeric value usually between 0.00 and 1.00. The tangible sense of reliability as indicating exactly how predictable an outcome is does not usually figure in most researchers’ thinking. But that sense of the specific predictability of results has been the focus of attention in social and psychological measurement science for decades.

For instance, the measurement of time is reliable in the sense that the position of the sun relative to the earth can be precisely predicted from geographic location, the time of day, and the day of the year. The numbers and words assigned to noon time are closely associated with the Sun being at the high point in the sky (though there are political variations by season and location across time zones).

That kind of a reproducible association is rarely sought in psychology and the social sciences, but it is far from nonexistent. One can discern different degrees to which that kind of association is included in models of measured constructs. Though most behavioral research doesn’t mention the connection between linear amounts of a measured phenomenon and a reproducible numeric representation of it (level 0), quite a significant body of work focuses on that connection (level 1). The disappointing thing about that level 1 work is that the relentless obsession with statistical methods prevents most researchers from connecting a reproducible quantity with a single expression of it in a standard unit, and with an associated uncertainty term (level 2). That is, level 1 researchers conceive measurement in statistical terms, as a product of data analysis. Even when results across data sets are highly correlated and could be equated to a common metric, level 1 researchers do not leverage that source of potential value for simplified communication and accumulated comparability.

And then, for their part, level 2 researchers usually do not articulate theories about the measured constructs, by augmenting the mathematical data model with an explanatory model predicting variation (level 3). Level 2 researchers are empirically grounded in data, and can expand their network of measures only by gathering more data and analyzing it in ways that bring it into their standard unit’s frame of reference.

Level 3 researchers, however, have come to see what makes their measures tick. They understand the mechanisms that make their questions vary. They can write new questions to their theoretical specifications, test those questions by asking them of a relevant sample, and produce the predicted calibrations. For instance, reading comprehension is well established to be a function of the difference between a person’s reading ability and the complexity of the text they encounter (see articles by Stenner in the list below). We have built our entire educational system around this idea, as we deliberately introduce children first to the alphabet, then to the most common words, then to short sentences, and then to ever longer and more complicated text. But stating the construct model, testing it against data, calibrating a unit to which all tests and measures can be traced, and connecting together all the books, articles, tests, curricula, and students is a process that began (in English and Spanish) only in the 1980s. The process still is far from finished, and most reading research still does not use the common metric.

In this kind of theory-informed context, new items can be automatically generated on the fly at the point of measurement. Those items and inferences made from them are validated by the consistency of the responses and the associated expression of the expected probability of success, agreement, etc. The expense of constant data gathering and analysis can be cut to a very small fraction of what it is at levels 0-2.

Level 3 research methods are not widely known or used, but they are not new. They are gaining traction as their use by national metrology institutes globally grows. As high profile critiques of social and psychological research practices continue to emerge, perhaps more attention will be paid to this important body of work. A few key references are provided below, and virtually every post in this blog pertains to these issues.

References

Baghaei, P. (2008). The Rasch model as a construct validation tool. Rasch Measurement Transactions, 22(1), 1145-6 [http://www.rasch.org/rmt/rmt221a.htm].

Bergstrom, B. A., & Lunz, M. E. (1994). The equivalence of Rasch item calibrations and ability estimates across modes of administration. In M. Wilson (Ed.), Objective measurement: Theory into practice, Vol. 2 (pp. 122-128). Norwood, New Jersey: Ablex.

Cano, S., Pendrill, L., Barbic, S., & Fisher, W. P., Jr. (2018). Patient-centred outcome metrology for healthcare decision-making. Journal of Physics: Conference Series, 1044, 012057.

Dimitrov, D. M. (2010). Testing for factorial invariance in the context of construct validation. Measurement & Evaluation in Counseling & Development, 43(2), 121-149.

Embretson, S. E. (2010). Measuring psychological constructs: Advances in model-based approaches. Washington, DC: American Psychological Association.

Fischer, G. H. (1973). The linear logistic test model as an instrument in educational research. Acta Psychologica, 37, 359-374.

Fischer, G. H. (1983). Logistic latent trait models with linear constraints. Psychometrika, 48(1), 3-26.

Fisher, W. P., Jr. (1992). Reliability statistics. Rasch Measurement Transactions, 6(3), 238 [http://www.rasch.org/rmt/rmt63i.htm].

Fisher, W. P., Jr. (2008). The cash value of reliability. Rasch Measurement Transactions, 22(1), 1160-1163 [http://www.rasch.org/rmt/rmt221.pdf].

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

Green, S. B., Lissitz, R. W., & Mulaik, S. A. (1977). Limitations of coefficient alpha as an index of test unidimensionality. Educational and Psychological Measurement, 37(4), 827-833.

Hattie, J. (1985). Methodology review: Assessing unidimensionality of tests and items. Applied Psychological Measurement, 9(2), 139-64.

Hobart, J. C., Cano, S. J., Zajicek, J. P., & Thompson, A. J. (2007). Rating scales as outcome measures for clinical trials in neurology: Problems, solutions, and recommendations. Lancet Neurology, 6, 1094-1105.

Irvine, S. H., Dunn, P. L., & Anderson, J. D. (1990). Towards a theory of algorithm-determined cognitive test construction. British Journal of Psychology, 81, 173-195.

Kline, T. L., Schmidt, K. M., & Bowles, R. P. (2006). Using LinLog and FACETS to model item components in the LLTM. Journal of Applied Measurement, 7(1), 74-91.

Lunz, M. E., & Linacre, J. M. (2010). Reliability of performance examinations: Revisited. In M. Garner, G. Engelhard, Jr., W. P. Fisher, Jr. & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 1 (pp. 328-341). Maple Grove, MN: JAM Press.

Mari, L., & Wilson, M. (2014). An introduction to the Rasch measurement approach for metrologists. Measurement, 51, 315-327.

Markward, N. J., & Fisher, W. P., Jr. (2004). Calibrating the genome. Journal of Applied Measurement, 5(2), 129-141.

Maul, A., Mari, L., Torres Irribarra, D., & Wilson, M. (2018). The quality of measurement results in terms of the structural features of the measurement process. Measurement, 116, 611-620.

Muthukrishna, M., & Henrich, J. (2019). A problem in theory. Nature Human Behaviour, 1-9.

Obiekwe, J. C. (1999, August 1). Application and validation of the linear logistic test model for item difficulty prediction in the context of mathematics problems. Dissertation Abstracts International: Section B: The Sciences & Engineering, 60(2-B), 0851.

Pendrill, L. (2014). Man as a measurement instrument [Special Feature]. NCSLi Measure: The Journal of Measurement Science, 9(4), 22-33.

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

Pendrill, L., & Petersson, N. (2016). Metrology of human-based and other qualitative measurements. Measurement Science and Technology, 27(9), 094003.

Sijtsma, K. (2009). Correcting fallacies in validity, reliability, and classification. International Journal of Testing, 8(3), 167-194.

Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107-120.

Stenner, A. J. (2001). The necessity of construct theory. Rasch Measurement Transactions, 15(1), 804-5 [http://www.rasch.org/rmt/rmt151q.htm].

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

Stenner, A. J., & Horabin, I. (1992). Three stages of construct definition. Rasch Measurement Transactions, 6(3), 229 [http://www.rasch.org/rmt/rmt63b.htm].

Stenner, A. J., Stone, M. H., & Fisher, W. P., Jr. (2018). The unreasonable effectiveness of theory based instrument calibration in the natural sciences: What can the social sciences learn? Journal of Physics Conference Series, 1044(012070).

Stone, M. H. (2003). Substantive scale construction. Journal of Applied Measurement, 4(3), 282-297.

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

Wilson, M. R. (2013). Using the concept of a measurement system to characterize measurement models used in psychometrics. Measurement, 46, 3766-3774.

Wright, B. D., & Stone, M. H. (1979). Chapter 5: Constructing a variable. In Best test design: Rasch measurement (pp. 83-128). Chicago, Illinois: MESA Press.

Wright, B. D., & Stone, M. H. (1999). Measurement essentials. Wilmington, DE: Wide Range, Inc. [http://www.rasch.org/measess/me-all.pdf].

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

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Making sustainability impacts universally identifiable, individually owned, efficiently exchanged, and profitable

February 2, 2019

Sustainability impacts plainly and obviously lack common product definitions, objective measures, efficient markets, and associated capacities for competing on improved quality. The absence of these landmarks in the domain of sustainability interests is a result of inattention and cultural biases far more than it is a result of the inherent characteristics or nature of sustainability itself. Given the economic importance of these kinds of capacities and the urgent need for new innovations supporting sustainable development, it is curious how even those most stridently advocating new ways of thinking seem to systematically ignore well-established opportunities for advancing their cause. The wealth of historical examples of rapidly emerging, transformative, disruptive, and highly profitable innovations would seem to motivate massive interest in how extend those successes in new directions.

Economists have long noted how common currencies reduce transaction costs, support property rights, and promote market efficiencies (for references and more information, see previous entries in this blog over the last ten years and more). Language itself is well known for functioning as an economical labor-saving device in the way that useful concepts representing things in the world as words need not be re-invented by everyone for themselves, but can simply be copied. In the same ways that common languages ease communication, and common currencies facilitate trade, so, too, do standards for common product definitions contribute to the creation of markets.

Metrologically traceable measurements make it possible for everyone everywhere to know how much of something in particular there is. This is important, first of all, because things have to be identifiable in shared ways if we are to be able to include them in our lives, socially. Anyone interested in obtaining or producing that kind of thing has to be able to know it and share information about it as something in particular. Common languages capable of communicating specifically what a thing is, and how much of it there is, support claims to ownership and to the fruits of investments in entrepreneurial innovations.

Technologies for precision measurement key to these communications are one of the primary products of science. Instruments measuring in SI units embody common currencies for the exchange of scientific capital. The calibration and distribution of such instruments in the domain of sustainability impact investing and innovation ought to be a top-level priority. How else will sustainable impacts be made universally identifiable, individually owned, efficiently exchanged, and profitable?

The electronics, computer, and telecommunications industries provide ample evidence of precision measurement’s role in reducing transaction costs, establishing common product definitions, and reaping huge profits. The music industry’s use of these technologies combines the science and economics of precision measurement with the artistic creativity of intensive improvisations constructed from instruments tuned to standardized scales that achieve wholly unique levels of individual innovation.

Much stands to be learned, and even more to be gained, in focusing sustainability development on ways in which we can harness the economic power of the profit motive by combining collective efforts with individual imaginations in the domains of human, social, and natural capital. Aligning financial, monetary wealth with the authentic wealth and genuine productivity of gains in human, community, and environmental value ought to be the defining mission of this generation. The time to act is now.

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So you say knowledge wants to be free?

January 26, 2019

If knowledge wants to be free, why do we work so hard keeping it trapped in scores and ratings whose meanings change depending on which questions were asked and who answered them?

Why don’t we liberate knowledge from its many prisons by embodying it in measurement systems that mean the same thing (within the range of uncertainty) no matter which questions on a topic are asked and no matter who answers them?

We routinely share knowledge quickly and easily when it’s about time, length, temperature, energy, mass, etc. Methods, theories, models, and tools developed over the last 90+ years show how we could be doing the same thing for literacy, health, functionality, environmental management, and every other major area of concern in the UN Sustainability Development Goals.

There’s a lot of talk among sustainability advocates about how urgent the need is for transformative efforts, investments, and technologies. It seems to me that sense of urgency will never be more than empty talk as long as we go on willfully ignoring the fact that we hold the keys to the chains that bind us.

 

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Remembering Elie Wiesel’s teaching

November 21, 2018

Paraphrasing the last paragraph from a recent USA Today story on a new book about Elie Wiesel (linked in below):

Language does more than just provide a means of sharing ideas and memories. Language embodies the human desire for meaning, for the infinite, for surpassing limits, for community and the communication of transcendent experiences of beauty.

But language cuts two ways. It provides a medium for transcending limits while it nonetheless has its own limits. Wiesel says language can be corrupted and contaminated by human cruelty. I think this allows human will more latitude and agency than it actually has. I think broad scale corruption and cruelty are artifacts or by-products of the limits of language.

Corruption and cruelty scale up when human experience encounters new challenges for which the available language is inadequate and new conceptual frameworks are slow to emerge. Dark fears and destructive hate go on a rampage when ignorance rules and understanding is scarce. Seeing these times in human history as products of individual will is a point of view that is itself situated within the overly narrow limits of a language that has long outlived its usefulness.

Our challenge is how to find our way to new languages better able to inspire confident cooperation and communication across our diverse differences. On the face of it, this may seem to be an insurmountable barrier demanding we look elsewhere for creative opportunities. I beg to differ.

Root metaphors captivate the imaginations of millions, as with the ‘Love is a rose’ metaphor of romance, the Christian ‘God is Love’ metaphor, or the ‘clockwork universe’ metaphor of Newtonian physics. But in today’s age of global humanity, a new poetics capable of making general sense of individual experience seems perpetually out of reach.

The complexity of the problem is truly staggering. In fact, the way we define the problem is the crux of the matter. As I’ve tried to explain before, the problem is the problem.

That is, today we face a meta-problem asking, what is the metaphor for metaphor? How do we transform implicit background assumptions about the limits of language into explicit objects of operations on language? How do we attain that complex level of understanding where we do not look upon corruption and cruelty simply as matters of individual human will but as the logical consequence of our failure to create institutions modeled on language’s complex combination of navigable continuity and local improvisation?

Can we go beyond the often impossibly out of reach but still inadequate compassion for those who commit atrocities, who are twisted into grotesque forms by hate? Can we actually come to understand the multilevel complexity, limits, and processes of language and metaphor well enough to intentionally cultivate new organic social and cognitive ecosystems? Can we see language as a collectively projected knowledge technology? Can we learn how to foster meaningful conceptual determinations embodied in words that stand for real things in the world? Can we apply language itself as a model for transforming our educational, health care, market, social service, and government institutions?

I say not only that yes, we can; I say yes, we have. This new level of complexity in approaching language has been taking shape for decades, and in some respects for centuries. It is emerging now on multiple fronts across a wide range of fields. This is the focus of my recent work on the multilevel complexity of sociocognitive ecosystems, and on the developmental, horizontal, and vertical coherence of integrated assessment and instruction. It’s a huge challenge, but having seen clearly that the biggest problem is how we define the problem, I am cautiously optimistic humanity will find a way.

Meyer, Zlati. 2018. New book shares Elie Wiesel’s powerful classroom lessons from the Holocaust. USA Today, 14 November.

Print version appeared in Life section, pp. 1D, 6D. Tuesday, 20 November, titled Elie Wiesel’s classroom lessons resonate.

https://www.usatoday.com/story/life/books/2018/11/14/elie-wiesels-classroom-lessons-holocaust-book-witness-ariel-burger-book-review/1897795002/

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On the recent Pew poll contrasting differences as to the “very big” problems we face today

October 20, 2018

An online news item appearing on 15 October 2018 proclaims that “Americans don’t just disagree on the issues. They disagree on what the issues are.” The article, by Dylan Scott on the Vox website, reports on a poll conducted by the Pew Research Center, involving registered voters in the U.S., between 24 September and 7 October. Polarizing disagreement is a recurring theme in the world, and keeping the tension up sells ads, so it is not surprising to see the emphasis in both the article and in the Pew report on major differences in people’s perceptions of what counts as a “very big” problem in the U.S. today. But a closer look at the data gives hope for finding ways to communicate across barriers that may look more significant than they actually are.

There’s no mention in the article of the sampling error, uncertainty, or confidence level, but the Pew site indicates that, overall, sampling error is 1.5%. But the Vox article mentions only the total sample size and fails to say that the registered voter portion of the respondents is smaller by a couple of thousand. Further, the sampling error jumps up to 2.6% for respondents indicating support for a Republican candidate, and to 2.3% for respondents supporting a Democrat. Again, the differences being played up are quite large, so there’s little risk of making too much out of a small difference. It’s good to know just how much of a difference makes a difference, though.

That said, neither Pew nor the Vox story mentions the very strong agreement between the different groups supporting opposing party candidates when the focus is on the relative magnitudes of agreement on aligned issues. Survey research typically focuses, of course, on percentages of responses to individual questions. Only measurement geeks like me wonder whether questions addressing a common theme could be related in a way that might convey more information. My curiosity was piqued, even though it is impossible to properly evaluate a model of this kind from the mere summary percentages. I knew if I found any correspondences they might just be accidents or coincidences, but I wanted to see what would happen.

So I typed up the text of the 18 issues concerning the seriousness of the problems being confronted in the US today, along with the percentages of registered voters saying each is a “very big” problem today. I put it all into SPSS and made a few technical checks to see if any major problems of interpretation would emerge from the nonlinear and ordinal percentages. The plots and correlations I did indicated that the same general results could be inferred from both the Pew percentages and their logit transformations.

While I was looking at a scatter plot of the Republican vs Democrat agreement percentages I noticed something interesting. I had been wondering if perhaps the striking differences in the groups’ willingness to say problems were serious might be a matter of relative emphases. Might the Republican supporters be less willing to find anything a big problem, but to nonetheless rank the issues in the same order as the Democrat supporters? This is, after all, exactly the kind of pattern commonly found in data from various surveys, assessments, and tests. No matter whether a respondent scores low overall, or scores high, the relative order of things stays the same.

Now, this is true in the kind of data I work with because considerable care is invested in composing questions that are intended to hang together like that. The idea is to deliberately vary the agreeability or difficulty of the questions so they all tap the same basic construct and demonstrably measure the same thing. When these kind of data are obtained, different questions measuring the same thing can be asked of different people without compromising the unit of measurement. That is, each different examinee or respondent can answer a unique set of questions and still have a measure comparable with anyone else’s. Like I said, this does not just happen by itself, but has to come about through a careful process of design and calibration. But the basic principles are well-established as being of longstanding and proven value across wide areas of research and practice.

So I was wondering if there might be one or more subsets of questions in the Pew data that would define the same problem magnitude dimension for supporters of both Republican and Democratic candidates. And as soon as I looked at the scatterplot of the percentages from the two groups, I saw that yes, indeed, there appeared to be four groups of issues that lined up along shared slopes. A color-coded version of that plot is in Figure 1.

The one statistical inference problem that emerged in examining these ordinal data concerns the yellow dot that is lowest and furthest to the left. At 8% agreement from the Republican supporters it was pulled away from the linear relation further than the other correspondences. When transformed into a log-odds unit, that single problematic difference lines up well with the other yellow dots further to the right.

The identity line in the figure shows where exact agreement between the two groups would be. That line marks out the connection between the same percentages of respondents agreeing an issue is a “very big” problem. We can see that the three green dots very nearly fall on that identity line. Just below them is a row of blue dots almost parallel with the identity line. Then there’s a third row of yellow dots further down, indicating more absolute disagreement between the two groups on these issues, but also showing a quite strong agreement as to their relative magnitudes within that group. Finally, there is another, red, line of dots in the lower right corner of the figure that marks out a more extreme range of absolute disagreement, but is also quite parallel to the identity line.

Fisher2018PewFig1

Figure 1 Initial plot of Republican vs Democrat Percentages agreement as to “Very Big” problems

Figures 2-5 below illustrate each of these groups of issues separately, giving further information on the problems and showing the regression lines and correlations for each contrast. The same colors have been retained to aid in seeing which groups of issues in Figure 1 are being shown.

The four areas of problems seem to me to correspond to issues of perceived major threats (Figure 2), accountability and access issues (Figure 3), equal opportunity issues (Figure 4), and systemic problems (Figure 5). Each of these content areas could be explored conceptually and qualitatively to assess whether some initial sense of a measured construct can be formed. If the by-person individual response data could be analyzed for fit to a proper measurement model, a much better job of determining the presence of invariant structure could be done.

But even without undertaking that work, these results already suggest a basis for productive conversations between the supposedly polarized groups. To start from the low-hanging fruit, the three problems the two groups agree on to within a couple of sampling errors (Figure 2) present topics of common agreement. Both Democrats and Republicans identify violent crime, the federal budget deficit, and drug addiction as matters of equally shared concern. The point is not that these are the highest rated problems for either group, but, rather, that they agree within the limits of statistical precision as to the extent that these are “very big” problems. It may be that setting shared priorities for addressing these problems could ground new relationships in that experience of having accomplished something productive together.

This new approach to building social capital might then proceed by taking up progressively more difficult areas of disagreement as to what “very big” problems are. Even though Republicans rate each area as less likely to be a “very big” problem, within each of the four groups of issues, they agree with Democrats as to their relative magnitudes. News like this might not sell a lot of ads, but it does offer hope for finding new ways of approaching relationships and crossing divides.

Fisher2018PewFig2

Figure 2.Republican vs Democrat areas of agreement as to “Very Big” problems

Fisher2018PewFig3

Figure 3 Republican vs Democrat areas of some disagreement as to “Very Big” problems

Fisher2018PewFig4

Figure 4 Republican vs Democrat areas of marked disagreement as to “Very Big” problems

Fisher2018PewFig5

Figure 5 Republican vs Democrat areas of fundamental disagreement as to “Very Big” problems

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Why economic growth can and inevitably will be green

October 1, 2018

So, approaching matters once again from yet another point of view, we have Jason Hickel explaining a couple of weeks ago “Why Growth Can’t Be Green.” This article provides yet another example of how the problem is the problem. That is, the way we define problems sets up particular kinds of solutions in advance, and sometimes, as Einstein famously pointed out, problems cannot be solved from within the same conceptual framework that gave rise to them. I’ve expanded on this theme in a number of previous posts, for instance, here.

Hickel takes up the apparent impossibility of aligning economic growth with environmental values. He speaks directly to what he calls the rebound effect, the way that “improvements in resource efficiency drive down prices and cause demand to rise—thus canceling out some of the gains.” But that rebound can happen only as long as the economy remains defined and limited by the alignment of manufactured capital and finance, ignoring the largely unexamined and unconsidered possibility that human, social, and natural capital could be measured well enough to be also aligned with finance.

Hence, as I say, the problem is the problem. Broadening one’s conceptualization of the problem opens up new opportunities that otherwise never come into view.

The Hickel article’s entire focus is then on top-down policy impositions like taxes or a Genuine Progress Index. These presume human, social, and natural capital can only ever exist in dead formations that have to be micromanaged and concretely manipulated, and that efficient markets bringing them to life are inherently and literally unthinkable. (See a short article here for an explanation of the difference between dead and living capital. There’s a lot more where that came from, as is apparent in the previous posts here in this blog.)

The situation could be vastly different than what Hickel imagines. If we could own, buy, and sell products in efficient markets we could reward the production of human, social, and environmental value. In that scenario, when improvements in environmental resource efficiency are obtained, demand for that new environmental value will rise and its price will go down, not the resource’s price.

We ought to be creative enough to figure out how to configure markets so that prices for environmental resources (oil, farmland, metals, etc.) can stay constant or fall without increasing demand for them, as could happen if that demand is counterbalanced and absorbed by rising human, social, and environmental quality capital values.

The question is how to absorb the rebound effect in other forms of capital that grow in demand while holding demand for the natural resource base in check. The vital conceptual distinction is between socialistic centralized planning and control of actual physical entities (people, communities, the environment, and manufactured items), on the one hand, and capitalistic decentralized distributed network effects on abstract transferable representations, on the other. Everyone defaults to the socialist scenario without ever considering there might be a whole other arena in which fruitful possibilities might be imagined.

What if, for instance, we could harness the profit motive to promote growth in genuine human, social, and environmental value? What if we were able to achieve qualitatively meaningful increases in authentic wealth that were economically contingent on reduced natural resource consumption? What if the financial and substantive value profits that could be had meant that resource consumption could be reduced by the same kinds of factors as have been realized in the context of Moore’s Law? What if a human economics of genuine value could actually result in humanity being able to adjust the global thermostat up or down in small increments by efficiently rewarding just the right combinations of policies and practices at the right times and places in the right volumes?

The only way that could ever happen is if people are motivated to do the right thing for the earth and for humanity because it is the right thing for them and their families. They have to be able to own their personal shares of their personal stocks of human, social, and natural capital. They have to be able to profit from investments in their own and others’ shares. They will not act on behalf of the earth and humanity only because it is the right thing to do. There has to be evidence and explanations of how everyone is fairly held accountable to the same standards, and has the same opportunities for profit and loss as anyone else. Then, and only then, it seems, will human, social, and environmental value become communicable in a viral contagion of good will.

Socialism has been conclusively proven unworkable, for people, communities, and the environment, as well as financially. But a human, social, and natural capitalism has hardly even been articulated, much less tried out. How do we make human, social, and natural capital fungible? How might the economy transcend its traditional boundaries and expand itself beyond the existing alignment of manufactured capital and finance?

It’s an incredibly complex proposal, but also seems like such a simple thing. The manufactured capital economy uses the common language of good measurement to improve quality, to simplify management communications, and to lower transaction costs in efficient markets. So what should we do if we want to correct the imbalanced negative impacts on people, communities, and the environment created by the misplaced emphasis on aligning only manufactured capital and financial capital?

As has been repeatedly proposed for years in this blog, maybe we should use the manufactured capital markets as a model and use good measurement to improve the quality of human, social, and environmental capital, to simplify communications and management, to lower transaction costs, and to align the genuine human, social, and environmental value created with financial value in efficient markets.

Of course, grasping that as viable, feasible, and desirable requires understanding that substantively meaningful precision measurement is something quite different from what usually passes for quantification. And that is an entirely different story, though one taken up repeatedly in previous entries in this blog, of course….

 

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Common currencies for the exchange of human, social, and environmental value

October 1, 2018

I was just now reading an article (see link below) that underscores my conviction that the secure ledger platforms are the real message, and that cryptocurrencies are intuitions of the need for fungible expressions of scientifically and meaningfully measured human, social, and environmental value.

We absolutely need efficient markets to be able to buy, invest in, profit from, and scale up UN SDG sustainability products like carbon sequestration, reduced violence, and improved literacy rates. Linking real value with common product definitions and financial value will be complex, multilevel, and multifaceted, but it can be done. It will be expensive to create efficient markets for economically self-sustaining sustainability impacts, but doing so will pay returns many times greater than what’s invested. And neither doing nothing nor continuing as we are can stand as viable options.

When the right combination is hit, watch out! This event will reveal the Internet’s true purpose. Some will say it fulfills humanity’s destiny as stewards of the earth.

The economic transformation that follows will make everything that’s happened to date pale in comparison. It will become impossible to generate financial profits while destroying human, social, or environmental value. The alignment of financial and genuine wealth will even out monetary flows in such a way as to make a guaranteed minimum income nothing more than an obvious and inarguable consequence of economic reality. Critical engagement will lead sometimes to systemic improvements, sometimes to clear refutation of the critic, sometimes to a recognized need for more information, and more often to a new tolerance and respect for differences of opinion as our capacities to learn from one another grow.

All that to say I remain unconvinced by stories like this one that seem blind to the poetry, beauty, meaning, and power of taking living language as a model for adaptive, intelligent institutions capable of improving the human condition:

She prosecuted Bitcoin crimes. Now she’s a major cryptocurrency investor. – FORTUNE

For more on these matters, see previous posts here, my web site, my publications, etc.


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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New Ideas on How to Realize the Purpose of Capital

September 20, 2018

I’d like to offer the following in reply to James Militzer, at https://nextbillion.net/deciphering-emersons-tears-time-impact-investing-lower-expectations/.

Rapid advances toward impact investing’s highest goals of social transformation are underway in quiet technical work being done in places no one is looking. That work shares Jed Emerson’s sentiments expressed at the 2017 Social Capital Markets conference, as he is quoted in Militzer’s NextBillion.net posting, that “The purpose of capital is to advance a more progressively free and just experience of life for all.” And he is correct in what Militzer reported he said the year before, that we need a “real, profound critique of current practices within financial capitalism,” one that would “require real change in our own behavior aside from adding a few funds to our portfolios here or augmenting a reporting process there.”

But the efforts he and others are making toward fulfilling that purpose and articulating that critique are incomplete, insufficient, and inadequate. Why? How? Language is the crux of the matter, and the issues involved are complex and technical. The challenge, which may initially seem simplistic or naive, is how to bring human, social, and environmental values into words. Not just any words, but meaningful words in a common language. What is most challenging is that this language, like any everyday language, has to span the range from abstract theoretical ideals to concrete local improvisations.

That means it cannot be like our current languages for expressing human, social, and environmental value. If we are going to succeed in aligning those forms of value with financial value, we have a lot of work to do.

Though there is endless talk of metrics for managing sustainable impacts, and though the importance of these metrics for making sustainability manageable is also a topic of infinite discussion, almost no one takes the trouble to seek out and implement the state of the art in measurement science. This is a crucial way, perhaps the most essential way, in which we need to criticize current practices within financial capitalism and change our behaviors. Oddly, almost no one seems to have thought of that.

That is, one of the most universally unexamined assumptions of our culture is that numbers automatically stand for quantities. People who analyze numeric data are called quants, and all numeric data analysis is referred to as quantitative. That is the case, but almost none of these quants and quantitative methods involve actually defining, modeling, identifying, evaluating, or applying an substantive unit of something real in the world that can be meaningfully represented by numbers.

There is, of course, an extensive and longstanding literature on exactly this science of measurement. It has been a topic of research, philosophy, and practical applications for at least 90 years, going back to the work of Thurstone at the University of Chicago in the 1920s. That work continued at the University of Chicago with Rasch’s visit there in 1960, with Wright’s adoption and expansion of Rasch’s theory and methods, and with the further work done by Wright’s students and colleagues in the years since.

Most importantly, over the last ten years, metrologists, the physicists and engineers who maintain and improve the SI units, the metric system, have taken note of what’s been going on in research and practice involving the approaches to measurement developed by Rasch, Wright, and their students and colleagues (for just two of many articles in this area, see here and here). The most recent developments in this new metrology include

(a) initiatives at national metrology institutes globally (Sweden and the UK, Portugal, Ukraine, among others) to investigate potentials for a new class of unit standards;

(b) a special session on this topic at the International Measurement Confederation (IMEKO) World Congress in Belfast on 5 September 2018;

(c) the Journal of Physics Conference Series proceedings of the 2016 IMEKO Joint Symposium hosted by Mark Wilson and myself at UC Berkeley;

(d) the publication of a 2017 book on Ben Wright edited by Mark Wilson and myself in Springer’s Series on Measurement Science and Technology; and

(e) the forthcoming October 2018 special issue of Elsevier’s Measurement journal edited by Wilson and myself, and a second one currently in development.

There are profound differences between today’s assumptions about measurement and how a meaningful art and science of precision measurement proceeds. What passes for measurement in today’s sustainability economics and accounting are counts, percentages, and ratings. These merely numeric metrics do not stand for anything that adds up the way they do. In fact, it’s been repeatedly demonstrated over many years that these kinds of metrics measure in a unit that changes size depending on who or what is measured, who is measuring, and what tool is used to measure. What makes matters even worse is that the numbers are usually taken to be perfectly precise, as uncertainty ranges, error terms, and confidence intervals are only sporadically provided and are usually omitted.

Measurement is not primarily a matter of data analysis. Measurement requires calibrated instruments that can be read as standing for a given amount of something that stays the same, within the uncertainty range, no matter who is measuring, no matter what or who is measured, and no matter what tool is used. This is, of course, quite an accomplishment when it can be achieved, but it is not impossible and has been put to use in large scale practical ways for several decades (for instance, see here, here, and here). Universally accessible instruments calibrated to common unit standards are what make society in general, and markets in particular, efficient in the way of projecting distributed network effects, turning communities into massively parallel stochastic computers (as W. Brian Arthur put it on p. 6 of his 2014 book, Complexity Economics).

These are not unexamined assumptions or overly ideal theoretical demands. They are pragmatic ways of adapting to emergent patterns in various kinds of data that have repeatedly been showing themselves around the world for decades. Our task is to literally capitalize on these nonhuman forms of life by creating multilevel, complex ecosystems of relationships with them, letting them be what they are in ways that also let us represent ourselves to each other. (Emerson quotes Bruno Latour to this effect on page 136 in his new book, The Purpose of Capital; those familiar with my work will know I’ve been reading and citing Latour since the early 1980s).

So it seems to me that, however well-intentioned those promoting impact investing may be, there is little awareness of just how profound and sweeping the critique of current practices needs to be, or of just how much our own behaviors are going to have to change. There are, however, truly significant reasons to be optimistic and hopeful. The technical work being done in measurement and metrology points toward possibilities for extending everyday language into a pragmatic idealism that does not require caving in to either varying local circumstances or to authoritarian dictates.

The upside of the situation is that, as so often happens in the course of human history, this critique and the associated changes are likely to have that peculiar quality captured in the French expression, “plus ça change, plus c’est la même chose” (the more things change, the more they stay the same). The changes in process are transformative, but will also be recognizable repetitions of human scale patterns.

In sum, what we are doing is tuning the instruments of the human, social, and environmental sciences to better harmonize relationships. Just as jazz, folk, and world music show that creative improvisation is not constrained by–but is facilitated by–tuning standards and high tech solutions, so, too, can we make that the case in other areas.

For instance, in my presentation at the IMEKO World Congress in Belfast on 5 September, I showed that the integration of beauty and meaning we have within our grasp reiterates principles that date back to Plato. The aesthetics complement the mathematics, with variations on the same equations being traceable from the Pythagorean theorem to Newton’s laws to Rasch’s models for measurement (see, for instance, Fisher & Stenner, 2013). In many ways, the history of science and philosophy continues to be a footnote to Plato.

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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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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….

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A Yet Simpler Take on Making Sustainability Self-Sustaining

September 1, 2018

The point of focusing on sustainability is to balance human interests with a long term view of life on earth. Depleting resources as though they will be always available plainly is no way to plan for a safe and pleasant future. But it seems to me something is missing in the way we approach sustainability. Every time I see any efforts aimed at rebalancing resource usage with a long term view of the Earth’s capacity to support us, what do I see? I see solutions that cost a lot, and people saying that the costs are the price we have to pay for the mistakes that have been made, and for a viable future. And so I also see a lot of procrastination, delays, and reluctance to commit to sustainable policies and practices.

Why? Because, first, there are a great many people who cannot afford to live in the world as it is, right now, simply bearing their existing day-to-day costs. Even in the richest countries, huge proportions of people live hand to mouth, or very nearly so. Second, it’s hard to detect and punish freeloaders. Many people, companies, and governments are willing to hold off committing to sustainability in the hope that some technological fix will come along and spare them avoidable costs.

So, my question is, and I do not say this at all in jest or with any sense of irony or sarcasm: how do we make sustainability fun and profitable? How can we make sustainability economically self-sustaining? How can we make sustainability into a growth industry?

My answer to those questions is, by improving the quality of information on sustainability impacts. What does that mean? Why should that have anything to do with making sustainability fun and profitable? What improving the quality of information on sustainability impacts means is measuring it well, using methods and models that have been used in research and practice for more than 90 years. What we need is a Human, Social, and Natural Capital Metric System. or an International System of Units for Human, Social, and Natural Capital.

As we all know from the existing SI (metric system) units, high quality information makes it much easier to communicate value. Easier communication means lower transaction costs, and lower transaction costs mean that it becomes very inexpensive to find out how much of a sustainability impact is available, and what quality it is. High quality information enables grassroots bottom up efforts coordinating the decisions and behaviors of everyone everywhere. Managers would be able to dramatically improve quality in domains of human, social, and environmental value the way they do now for manufactured value. And investors would be able to reward innovation in those areas in ways they currently cannot.

For instance, with high quality sustainability impact measures, you’d be able to buy shares of stock in a new global carbon reduction effort that realistically projects it is on track to reverse climate change back its 1980 status. If someone came out with a better carbon reduction product that would make it possible to get the job done faster or at lower cost, we would have the information we need to quickly shift the flow of resources to the better product.

Speaking to other components of the UN’s Sustainability Development Goals, maybe people need to wonder why they cannot go buy 250 units of additional literacy right now? Why can’t you get a good price on a specific amount of literacy gain for your ten-year-old child from a few minutes of competitive shopping? And while you’re at it, maybe you could catch a special sale on 470 units of improved physical functionality for your great aunt who just had a hip replacement. Oh, she doesn’t need it because she’s got herself listed in a health capital investment bond likely to pay a 6% return? Well, maybe you should sink some funds into one of those contracts!

To take up the SDG 16.1 issue, if efforts to reduce armed violence were measured with the same level of information quality as kilowatt hours, that form of social capital product would be available in market transactions just the same way manufactured capital products like electricity are now. Conversely, your personal efforts at reducing armed violence, or improving someone’s literacy, or helping your great aunt with gains in physical functionality—all of these are investments of your skills and abilities that will pay back cash value to you. And because having fun with the kids, and getting out for recreational activities, are healthful things to do, enjoyment also should pay dividends.

Maybe this focus on fun and profit in making sustainability economically self-sustaining might finally find some traction for efforts in this area. Sustainability commerce could be a way of talking about these issues that will speak to matters more directly and practically. We’ll see how that works out as I try it on people in the near future.

 

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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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