Posts Tagged ‘impact metrics’

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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Dispelling Myths about Measurement in Psychology and the Social Sciences

August 27, 2013

Seven common assumptions about measurement and method in psychology and the social sciences stand as inconsistent anomalies in the experience of those who have taken the trouble to challenge them. As evidence, theory, and instrumentation accumulate, will we see a revolutionary break and disruptive change across multiple social and economic levels and areas as a result? Will there be a slower, more gradual transition to a new paradigm? Or will the status quo simply roll on, oblivious to the potential for new questions and new directions? We shall see.

1. Myth: Qualitative data and methods cannot really be integrated with quantitative data and methods because of opposing philosophical assumptions.

Fact: Qualitative methods incorporate a critique of quantitative methods that leads to a more scientific theory and practice of measurement.

2. Myth: Statistics is the logic of measurement.

Fact: Statistics did not emerge as a discipline until the 19th century, while measurement, of course, has been around for millennia. Measurement is modeled at the individual level within a single variable whereas statistics model at the population level between variables. Data are fit to prescriptive measurement models using the Garbage-In, Garbage-Out (GIGO) Principle, while descriptive statistical models are fit to data.

3. Myth: Linear measurement from ordinal test and survey data is impossible.

Fact: Ordinal data have been used as a basis for invariant linear measures for decades.

4. Myth: Scientific laws like Newton’s laws of motion cannot be successfully formulated, tested, or validated in psychology and the social sciences.

Fact: Mathematical laws of human behavior and cognition in the same form as Newton’s laws are formulated, tested, and validated in numerous Rasch model applications.

5. Myth: Experimental manipulations of psychological and social phenomena are inherently impossible or unethical.

Fact: Decades of research across multiple fields have successfully shown how theory-informed interventions on items/indicators/questions can result in predictable, consistent, and substantively meaningful quantitative changes.

6. Myth: “Real” measurement is impossible in psychology and the social sciences.

Fact: Success in predictive theory, instrument calibration, and in maintaining stable units of comparison over time are all evidence supporting the viability of meaningful uniform units of measurement in psychology and the social sciences.

7. Myth: Efficient economic markets can incorporate only manufactured and liquid capital, and property. Human, social, and natural capital, being intangible, have permanent status as market externalities as they cannot be measured well enough to enable accountability, pricing, or transferable representations (common currency instruments).

Fact: The theory and methods necessary for establishing an Intangible Assets Metric System are in hand. What’s missing is the awareness of the scientific, human, social, and economic value that would be returned from the admittedly very large investments that would be required.

References and examples are available in other posts in this blog, in my publications, or on request.