John Kay and Mervyn King, Radical Uncertainty: Decision-making for an unknowable future. The Bridge Street Press, 2020. 544 pages.
John Kay and Mervyn King’s book Radical Uncertainty: Decision-making for an unknowable future is a wonderfully practical book about how to deal with uncertainty in economics, finance, and public policy. The book’s core message is troubling: much of modern economic and technical advice is bogus quantification or misapplication of predictive models in uncertain domains based on ill-conceived ideas of quantifying risk and uncertainty under the cloak of positivistic science. In modern public policy, invented numbers and a deceitful sense of quantification and measurement offer a false sense of security and control, yet in some areas plagued with radical uncertainty and ‘unknown unknowns,’ precise quantifications of risks and probabilities are, at best, implausible, and at worst dangerous. The authors draw on broad practical experience in central banking and the financial world and borrow from a wide range of research to show how we make decisions in contexts of deep uncertainty that need profound rethinking.
The main takeaway of this book is that some uncertainties are ultimately unresolvable and, hence, unable to become quantifiable and expressible in terms of well-defined probability distributions or mathematical models. For instance, the insurance industry’s actuarial tables and the gambler’s roulette wheel are both expressions of well-defined and measurable risks that are manageable, contained, and expressible in models based on the tools of probability theory and clearly defined distributions. However, most situations in macroeconomics, finance, politics, and social phenomena involve a deeper kind of uncertainty, a radical and unpredictable uncertainty for which historical data, regressions, and probability distributions provide no practical guidance to future outcomes and how we should deal with unimaginable risks (see also Ostrom, 1982). Radical uncertainty concerns unimaginable and unlikely events whose determinants are insufficiently understood for probabilities to be known or for reliable forecasting to be possible.
Kay and King’s book argues that in most critical economic and political decisions, there can be no accurate forecasts or clear probability distributions on which we might sensibly rely to predict future outcomes; believing otherwise, they argue, is pseudoscience and a fatal conceit (see also Paniagua, 2023). Instead of inventing probabilities and predictive models under the cloak of quantification and certainty, we should recognize the profound limits of our knowledge rooted in the inevitable pervasiveness of radical uncertainty. Based on that crucial point, we should build businesses, banking systems, societies, and public policy strategies that will become robust and flexible to future unexpected outcomes and resilient to unpredictable events (Pennington, 2011; Taleb, 2013). Using robust and resilient reference narratives and heuristics, they argue that uncertainty can be embraced and leveraged because it is the source of creativity, excitement, and prosperity.
The core themes of this book, which permeate the entire 23 chapters, are three: i) the notion of radical uncertainty and how it completely differs from measurable risk and quantifiable notions of probability; ii) given that uncertainty is such a pervasive element in economics, finance, and public policy there is the need to construct robust narratives and heuristics that give us the confidence to manage uncertainty rather than assuming it away through predictive models, and iii) the severe limitations of mathematics, formal modelling, and quantification in economics, which provides strong reasons to become ‘intellectually humble’ concerning what we can achieve with social sciences and public policy. Hence, the book follows a long—albeit disregarded—tradition in economics that deals with radical uncertainty and the limitations of mathematics and probability theory in providing a correct assessment of the risks and plausible events involved in a complex and uncertain world. In other words, the book carries forward the ideas of thinkers such as Frank Knight (1921), G.L.S. Shackle (1972), Hayek (1989), Keynes (1921), Romer (2015, 2016), and Taleb (2008, 2013), among others.
Radical Uncertainty makes a timely contribution to the current public policy debate, particularly in a ‘positivist era’ plagued by the failure of experts to use models to predict and improve society in macroeconomic policy, climate policy, and pandemics (Koppl, 2018). The book uses the concept of radical uncertainty to challenge the role of technocratic experts in decision-making, deeply questioning the mathematical and predictive models they use to map and predict events in a radically uncertain world in which probabilities and outcomes are ill-defined at best. Thus, it questions the ‘Nostradamus-like’ pretensions of social scientists, epidemiologists, environmental scientists, and policymakers attempting to predict and control an uncertain future. Today, in a world that in almost a decade has experienced the financial crisis and Great Recession of 2008—which revealed the deep fragilities and limits of financial and macroeconomic models—and, subsequently, the global COVID-19 pandemic—which revealed the severe limitations of epidemiological models and the lockdown policies in which they were based—this book should be a much welcome balm of common sense and intellectual humility to economists, epidemiologists, and all social scientists at large.
Given the context in which the world is faced, in which experts have failed miserably in misapplying and abusing the quantification of risk and ill-based predictions based on bogus probabilities and statistics into numerous areas plagued with uncertainty, this is a timely book that practitioners, policymakers, and economists alike should read. The book is comprehensive and accessible as it provides an exciting and broad range of narratives and applied cases that span economics, finance, public policy, the history of mathematics and probability theory, war strategy, city planning, and the financial crisis of 2008, among other cases. The applied and real-life examples are both exciting and relevant. The book uses plain narrative explanations and common sense to drive home complex concepts and issues related to decision theory, risk management, and economics. However, at the same time, the book does not shy away from technical subjects either: an exciting part of the book is when the authors explain the birth, history, and limitations of statistical models, rational choice theory, probability theory, homo economicus reasoning, expected utility theories, financial models, etc. Based on a critical assessment of these theories, the authors remind us that, in the realm of complex phenomena, we must stop relying on probabilistic reasoning and mathematical predictive models in order to embrace a more humble “I do not know” and hence to put far less attention to models, mathematics, and predictions and more on narratives, institutions, and heuristics to build more robust decision-making systems (see also Paniagua, 2023).
In conclusion, this alternative approach should lead to a new emphasis on evolutionary explanations, institutional analysis, robust decision-making systems, and bounded rationality models. In times when pseudoscience, misguided applications of probability and simulations, and fancy models full of ‘mathiness’ but of little value prevail (Romer, 2015), it is unsurprising that there are not enough books available on this critical and timely topic. Therefore, this book is a valuable and necessary contribution. Now, after the COVID-19 pandemic, is the time to recognize radical uncertainty as a critical factor in our economic and political life. It should lead us to recognize the limitations and failures of mathematical models and statistical simulations in some areas where radical uncertainty and complexity are too pervasive (Paniagua, 2023). It should make us stop demanding the impossible of leaders and policymakers who know very little and, consequently, can control only fractions of our uncertain and complex world. As Hayek (1988) once reminded us: “The curious task of economics is to demonstrate to men how little they really know about what they imagine they can design.” We should thank Kay and King for bringing this timely Hayekian message to life in our post-pandemic and highly pseudoscientific world.
Pablo Paniagua
Universidad del Desarrollo (UDD), Núcleo Humanidades y Ciencias Sociales Faro UDD. Av. Plaza 680, Las Condes, Santiago, Chile.
References
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Hayek, F. A. (1988). The Fatal Conceit: The Errors of Socialism. Chicago: Chicago University Press.
Keynes, J. M. (1921). A Treatise on Probability. London: Macmillan and Co.
Knight, F. H. (1921). Risk, Uncertainty and Profit. New York: Houghton Mifflin.
Koppl, R. (2018). Expert Failure. Cambridge: CUP.
Ostrom, E. (1982). “Beyond positivism: an introduction to this volume”. In Ostrom, E. (ed.), Strategies of Political Inquiry, Beverly Hills, pp. 11–28. CA: Sage Publications.
Paniagua, P. (2023). “Complexity defying macroeconomics”. Cambridge Journal of Economics, 47(3), 575-592. https://doi.org/10.1093/cje/bead002.
Pennington, M. (2011). Robust political economy: Classical liberalism and the future of public policy. Northampton: Edward Elgar Publishing Inc.
Romer, P. M. (2015). “Mathiness in the Theory of Economic Growth”. American Economic Review, 105 (5): 89–93.
Romer, P. (2016). “The Trouble with Macroeconomics”. Commons Memorial Lecture of the Omicron Delta Epsilon Society, The American Economist, forthcoming.
Shackle, G. L. S. (1972). Epistemics and Economics: A Critique of Economic Doctrines. Cambridge: CUP.
Taleb, N. N. (2013). Antifragile: Things that Gain from Disorder. London: Penguin.
Taleb, N. N. (2008). The Black Swan: The Impact of the Highly Improbable. London: Penguin.
