Illusions and Delusions

I recently read a bit about the Buddhist concept of “pratitya samutpada,” translated literally or liberally as “in dependence, things rise up,” “interdependent co-arising,” or simply “dependent rising” (Hahn 1998; Namgyel 2018). There seem to be two main aspects of the concept. The first is that what we perceive as separate entities are only so at a superficial level. In truth, they are part of a whole and, as part of that whole, they are connected, mutually affect each other, rather than one entity being the cause of the other or being independent of the other. The second aspect of the concept of pratitya samutpada is that those entities that we perceive as separate are constantly changing, morphing into other and new aspects of the whole. The consequence of this concept is that, if we focus on the separate entities that we perceive, we can fall into a kind of delusion, where we do not see the dynamic interdependence that governs the entities we perceive.

This concept seems to have similarities with other concepts in Asian philosophy such as that of yin and yang, where opposites are part of a whole, but also with common ideas in western science and philosophy: from Lavoisier’s formulation that in nature “nothing is lost, nothing is created, everything is transformed,” to Hegel’s dialectics and the concept of “aufheben,” often translated as “self-sublation,” a process that simultaneously negates and preserves forms or concepts that previously seemed well defined and stable (Maybee 2020; Wikipedia Contributors 2021)¹. How often and to what extent does this idea of a dynamic, interdependent world lead to conclusions about our capacity to see through the temporary, perhaps time and space specific formations, and grasp the whole of what is actually going on? How often are we “deluded” into thinking that the temporary and time-specific reality that we perceive is more permanent than it is or that it is all there is? How often does it matter?

The dictionary distinguishes between the terms illusion and delusion in subtle ways. Merriam-Websters definitions:

Illusion:

    1. something that looks or seems different from what it is : something that is false or not real but that seems to be true or real
    2.  an incorrect idea : an idea that is based on something that is not true

[Merriam-Webster. Undated (a)]

Delusion:

    1. a belief that is not true : a false idea
    2. a false idea or belief that is caused by mental illness

[Merriam-Webster. Undated (b)]

The definitions above seem to suggest illusion happens in the realm of perception and ideas; delusion is closer to beliefs and mental illness. One of my Buddist references for this post distinguishes between illusion and delusion by stating that “illusion refers to seeing through appearances by recognizing their independent nature. Delusion, on the other hand, refers to misapprehending things to have an independent reality from  their own side” (Namygel 2018, p. 25). In other words, illusions do not necessarily fool you into beliefs, delusions do.

Joni Mitchel’s beautifully mesmerizing song “Both sides now” uses the term illusion similarly, in the sense that the composer is aware that her recollections are illusions, whether they be about clouds, love or life, and concludes that she knows nothing about them at all. E.g.:

I've looked at life from both sides now
From win and lose and still somehow
It's life's illusions I recall
I really don't know life at all

The distinction between illusion and delusion brings to mind (for me, at least) the challenge of translating social science modeling into public policy without losing sight of model limitations. 

Social science models are often able to represent mathematically the two aspects of “pratitya samutpada:” interdependence and dynamics. But, as with all models, simplifications are needed for tractability and the consequences of the model will depend on those simplifications made, assumptions about what variables are more or less important, functional relationships, bounding of magnitudes, temporal lags. These assumptions can be informed or rejected by empirical work, to some extent. What exactly is that extent, how much certainty academics attribute to their models is, based on my humble experience, influenced early on by human flaws. Whether it is an overemphasis on quickly thinking within the confines of established methodological approaches that leads to a poor understanding of the limitations of those approaches themselves, or whether it is the difficulty of living with uncertainty, or perhaps just plain vanity, it is my impression that academics themselves often lose sight of the limitations of their models and fall into the temptation of making grand but unsupported statements about the world they live in.

When the next step is taken (whether by academics themselves. policy makers, or by mere practitioners like me) to translate conclusions of limited validity to policy that needs to be developed for a specific time and space, it seems like the assumptions, limitations and caveats of academic discourse are further forgotten. Before we know it, the illusion of general principles, guidelines, best practices and rules of thumb, that we would hope to be well understood as the illusions they are, morph into the delusion of ideological constructs, over-simplified, over-generalized, distorted by the influence of a kaleidoscope of interest groups, and imbued by a certainty they do not merit. 

In a world of unmerited certainty, Joni Mitchell’s illusions, the awareness of them, seems something to strive for, to appreciate in its melancholic beauty, and to sing in a song.

Footnote:

  1. Antoine Lavoisier, French chemist, and Georg Wilhelm Friedrich Hegel, German philosopher, were contemporaries during the late 18th century.

References

Hanh, Thich Nhat. 1998. The Heart of Buddha’s Teaching: Transforming Suffering into Peach, Joy, and Liberation. Harmony Books.

Maybee, Julie E., Hegel’s Dialectics. In: The Stanford Encyclopedia of Philosophy (Winter 2020 Edition), Edward N. Zalta (ed.). Available: https://plato.stanford.edu/entries/hegel-dialectics/. Accessed: February 13, 2022

Merriam-Webster. Undated (a). Illusion. In Merriam-Webster.com dictionary. Available: https://www.merriam-webster.com/dictionary/illusion. Accessed: February 13, 2022

Merriam-Webster. Undated (b). Delusion. In Merriam-Webster.com dictionary. Available: https://www.merriam-webster.com/dictionary/delusion. Accessed: February 13, 2022

Namgyel, Elizabeth Mattis. 2018. The Logic of Faith: A Buddhist Approach to Finding Certainty Beyond Belief and Doubt. Shambhala Publications. 

Wikipedia contributors. 2021. Aufheben. In Wikipedia, The Free Encyclopedia. Available: https://en.wikipedia.org/w/index.php?title=Aufheben&oldid=1050479001. Accessed: February 13, 2022

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On Data and Evidence in the Social Sciences

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On a recent trip to the local public library I happened to find a copy of a book of collected works by Bertrand Russel that I used to own but that, as many other books, had been a victim of my international moves. I always admired Bertrand Russel’s clear, simple and straightforward way of discussing not so simple topics without distorting them (at least not in ways that were obvious to me). The library was selling the book as part of a used book sale and I bought it, together with a copy of Bertrand Russel’s “The Scientific Outlook.”

In reading this latter book, I found myself sucked into a web of interrelated methodological discussions; some old ones (e.g. how scientific the social sciences are or can be) and some newer ones (e.g. whether the huge amount and speed of data availability, and easy access to it, brought by information technology, has challenged traditional scientific methodology and put correlation – with no theory – front and center on the research agenda). I remember delving into economic methodological discussions some thirty years ago as an economics student but have distanced myself from economic theory since.  After getting lost in the rabbit hole, I decided I did not have enough time to dig deep enough into these discussions, but thought I would register what I found, perhaps for continuing/revisiting at a later date.

So here goes.

Both Mlodinow (2009) and Russel (1962) place the origins of the scientific revolution in the late sixteenth and early seventeenth century, pretty much on the shoulders of Galileo Galilei (1564-1642), his contemporaries and those coming soon after him (e.g. Isaac Newton – 1643-1727). They also both characterize the scientific revolution as being centered on induction and experimentation, as opposed to deduction, as a source of knowledge. Both deduction (theory) and induction (evidence) have a role in science and Russel describes the scientific method as including three stages:

  • Observing significant facts

  • Arriving at a hypothesis, which, if true, would account for these facts

  • Deducing from this hypothesis consequences which can be tested by observation (some, quoting Karl Popper, would say “refuted” by evidence)

This characterization of the scientific method (and its variations) seems to have been criticized over time as not adequately portraying how science evolves. The idea that science progresses by refuting hypotheses empirically, for example, seems to have been criticized repeatedly over time. A recent opinion article in Scientific American (Singham 2020) claims that it must be abandoned for good for at least two reasons: first, because empirical experiments are framed by many theories themselves making its results more reflective of comparisons between theories than between theory and evidence; second, because this is not really how science has advanced historically. Rather, the author claims, “It is the single-minded focus on finding what works that gives science its strength, not any philosophy.“ Similar arguments have been made by various philosophers of science, including Thomas Kuhn (Wikipedia 2021).

The use of empirical evidence may vary from one branch of science or research program to another. I particularly looked for discussions among economists, because that is an area I have more of a background in and because of its relevance to international development. In a well known paper, Larry Summers (1991) argues that elaborated statistical tests aimed at estimating model parameters have had little consequence to advance economic thinking, that most papers remembered as having advanced economic theory have little empirical content at all, and that successful empirical research in economics have relied mostly on attempts to gauge strength of association and on persuasiveness. He criticizes models that have been overspecified to enable testing under the argument that results tend be of little worth and, comparing economics to natural sciences he states that “The image of an economic theorist nervously awaiting the result of a decisive econometric test does not ring true.”

In general, the Popperian criteria of falsifiability through testing seems to be simultaneously nominally accepted and yet in practice not met in economics, with theory moving forward anyway, based on the use of empirical evidence to support argumentation. Hausman (2018) summarizes the challenges of application of Popperian criteria to economics (presumably applicable to the social sciences more generally) and how several authors have abandoned completely the criteria to argue that economics (and, again, presumably the social sciences more generally) advances by using a more comprehensive blend of theory and empirical evidence. Durlauf (2012) states that “while some empirical economics involves the full delineation of an economic environment, so that empirical analysis is conducted through the prism of a fully specified general equilibrium model, other forms of empirical work use economic theory in order to guide, as opposed to determine, statistical model specification. Further, a distinct body of empirical economics is explicitly atheoretical, employing so-called natural experiments to evaluate economic propositions and to measure objects such as incentives. 

A more recent discussion on the use of evidence to advance our knowledge of society gained traction with the rapid growth of “big data. ” Some claimed that data in large volume would make the scientific method obsolete and the correlation would suffice to advance our knowledge, even if these claims may come from outside the academic community. For example, an article in WIRED magazine, written by its Editor in Chief, claimed that “Petabytes allow us to say: ‘correlation is enough.’ We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot(Anderson 2000). These kinds of claims have been countered by others (e.g. Mazzochi 2015) and it is hard for me to imagine how computerized analysis of data would not be imbued with human theorizing, no matter how much one tries to step aside and let “data speak.” In addition, for my purposes, much of the data in international development is not “big data.” Even if it were, it is not clear to me how we would separate the many variables that in international development tend to move together (in the same or opposite direction) with just correlation as a criteria (and no theory). 

There is a large literature to review on this topic and I have not even looked at the random control trial based research that gave Esther Duflo, Abhijit Banerjee and Michael Kremer the 2019 Nobel prize in economics, and what that line of research means for the discussion above. But I am thinking (for now) that there may not be a clear rule in the use of evidence and theory for discussing international development knowledge, and I am satisfied (for now) with looking for the reasonable use of theory, evidence, skepticism and caution in thinking of development policy and practice. I am sure I will come back to this discussion at a later date.

References:

Anderson, Chris. 2000. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. WIRED. June. Available: https://www.wired.com/2008/06/pb-theory/. Accessed: October 30, 2021.

Boland, Lawrence. 2006. Seven decades of economic methodology: a Popperian perspective. In: Karl Popper: a Centenary Assessment: Science, I. Jarvie, K. Milford and D. Miller (Eds), 2006, 219–27. Available: http://www.sfu.ca/~boland/wien02.pdf. Accessed: October 30, 2021

Durlauf, Steven. 2012. Complexity, Economics, and Public Policy. Politics, Philosophy & Economics 11(1) 45–75. Sage. Available: http://home.uchicago.edu/sdurlauf/includes/pdf/Durlauf%20-%20Complexity%20Economics%20and%20Public%20Policy.pdf. Accessed: October 30, 2021.

Hausman, Daniel. 2018. Philosophy of Economics. Stanford Encyclopedia of Philosophy. Available: https://plato.stanford.edu/entries/economics/#RhetEcon. Accessed: October 30, 2021

Mazzocchi, Fulvio. 2015. Could Big Data be the end of theory in science? A few remarks on the epistemology of data-driven science. EMBO reports. EMBO Press. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766450/pdf/EMBR-16-1250.pdf. Accessed: October 30, 2021. 

Mlodinow, Leonard. 2009. The Drunkard’s Walk. How Randomness Rules Our Lives. New York: Vintage Books. A Division of Random House.

Russel, Bertrand. 1962 (first copyrighted in 1931). The Scientific Outlook. The Norton Library. W.W. Norton & Company.

Singham, Manu. 2020. The Idea That a Scientific Theory Can Be ‘Falsified’ Is a Myth. It’s time we abandoned the notion. In Scientific American, September 2020. Available: https://www.scientificamerican.com/article/the-idea-that-a-scientific-theory-can-be-falsified-is-a-myth/. Accessed: October 30, 2021

Summers, Larry. 1991. The Scientific Illusion in Empirical Macroeconomics. The Scandinavian Journal of Economics. Vol. 93, No. 2, Proceedings of a Conference on New Approaches to Empirical Macroeconomics (Jun., 1991), pp. 129-148 (20 pages). Wiley. Available: http://faculty.econ.ucdavis.edu/faculty/kdsalyer/LECTURES/Ecn200e/summers_illusion.pdf. Accessed: October 30, 2021.

Wikipedia contributors. 2021. Scientific Method. Available: https://en.wikipedia.org/wiki/Scientific_method. Accessed: October 30, 2021

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