(From The Archives) In Times Of Radical Uncertainty. The Delhi Smog Causal Chain

Two Books Critiquing Modern Economics. And, What Causes Delhi Smog?

Programming Note: We are on a short ‘writing’ break. Normal service will resume from Oct 24. Meanwhile, here are two essays from our archives.

India Policy Watch: Tackling Radical Uncertainty

Insights on burning policy issues in India

— RSJ

From our edition #57 on Aug 5, 2020

We throw the term ‘economic reasoning’ around here a lot. We use models with their assumptions and approximation of the real world to explain why a phenomenon that seems non-intuitive on the surface is a helpful representation of how things work. We like explaining these things here.

For example, Ricardo’s model of comparative advantage suggests you should trade with another country even when you are more efficient in making a product than it is. This doesn’t seem right till you study the model. We often get questions where economists are blamed for not predicting global financial crisis (GFC) or not getting GDP projections right. We parry them away with our usual response. Economists attach probabilities to events, weigh risks and project certain outcomes. They are right more often than not. It is unfair to expect them to be right on all occasions. Our general view is understanding utility function of entities, thinking through likely scenarios and applying probabilistic thinking to them in a rigorous and repetitive manner should lead to fewer ‘surprises’ in future.

Like one of our intellectual heroes, Gary Becker, put it:

“All human behaviour can be regarded as involving participants who maximize their utility from a stable set of preferences and accumulate an optimal amount of information and other inputs in a variety of markets. If this argument is correct, the economic approach provides a unified framework for understanding behaviour that has long been sought by and eluded Bentham, Comte, Marx, and others.”

“The combined assumptions of maximizing behaviour, market equilibrium, and stable preferences, used relentlessly and unflinchingly, form the heart of the economic approach.”

That would be a good summary of the frame through which this newsletter views the issues confronting us. However, this view has been challenged. Most notably in the works of behavioural economists like Thaler, Kahneman and Tversky who questioned the assumption of a rational actor who is free of biases and takes cold, calculating decisions maximising her utility. But behavioural economics doesn’t question the models themselves; instead, it casts its doubts on the individual using the model.

A very different critique of the ‘Beckerian’ approach has emerged in a recent book Radical Uncertainty: Decision Making For An Unknowable Future by John Kay and Mervyn King. John Kay taught at LBS and Said Business School (University of Oxford) while running a consulting firm while Mervyn King taught at LSE before he joined Bank of England where he rose to become its Governor and held its reins during the GFC. These are academic economists who ventured out to the world of policy making and business to test the models they taught or researched. And they have come back convinced those models, those assumptions of utility maximisation and rationality aren’t meant for real world. In their view, neoclassical economics works but in a very ‘small world’, as statistician Jimmie Savage once called it.

Apparently, real world is different.

That must have been a surprise for no one except career academic economists!

Yet, it is a kind of book I like. There is a deep understanding of the edifice of received economic wisdom they are attacking. They lay out the history of how the current narrative became dominant with important economic concepts, papers and models explained. The arguments against the dominant narrative are well constructed drawing evidence from multiple disciplines, using their experience in managing real crisis and questioning our priors. The breadth of their scholarship is evident in the multiple references to seminal papers in logic, philosophy, anthropology, sociology and public policy all serving the central thrust of their argument.

There are many diversions into famous thought experiments, puzzles, pop culture references and apocryphal stories that don’t let the pace flag. In one chapter the authors question the lessons we learnt from Akerlof’s ‘market for lemons’ paper. In another we are treated to Arrow-Debreu’s mathematical explanation of efficient market and Smith’s ‘invisible hand’. There’s the two-child problem, Monty Hall problem, the co-operative principle of linguistics, how to hold a Mad Hatter’s tea party, Dynamic Stochastic General Equilibrium (DSGE), parallels with Lee Smolin’s critique of the string theory community and many such references that you least expect in a book on economics.

There are rabbit holes everywhere and I dived into many of them. It took me over two months to finish the book. It could have taken two years. I had to abandon going down many tantalising garden paths because I wanted to finish the book. It is an engaging book like few I have read. This despite my reservation about the central premise of the book. In that sense, it was a strange and provocative book. You shake your head in disagreement every few pages, but you don’t want it to end. I have been thinking of writing on the book for a few weeks now. This week’s EconTalk (hosted by Russ Roberts) episode where the authors featured got me going.

Kay and King have four central points in the book. I will use the relevant extracts to give you a sense of their arguments for these points.

Risk and Uncertainty Are Different Concepts

This was understood by Keynes and Frank Knight (the founder of the Chicago school of economics) but they lost the battle on this. Friedman and the later day Chicago school economists wrapped these two concepts together which led to the belief in magical predictive ability of economics.

Kay and King quote Keynes on this.

Keynes made a similar distinction. In an article summarising his magnum opus, The General Theory of Employment, Interest and Money, he wrote: By ‘uncertain’ knowledge, let me explain, I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty; nor is the prospect of a Victory bond being drawn. Or, again, the expectation of life is only slightly uncertain. Even the weather is only moderately uncertain.

The sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention, or the position of private wealth-owners in the social system in 1970. About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know.

They describe how the use of subjective probabilities turned ‘mysteries’ into ‘calculable puzzles’ but there would be consequences to this simplification.

The title of this book, and its central concept, is radical uncertainty. Uncertainty is the result of our incomplete knowledge of the world, or about the connection between our present actions and their future outcomes. Depending on the nature of the uncertainty, such incomplete knowledge may be distressing or pleasurable.

The result was that the concept of radical uncertainty virtually disappeared from the mainstream of economics for more than half a century. The use of subjective probabilities, and the associated mathematics, seemed to turn the mysteries of radical uncertainty into puzzles with calculable solutions. And it would be at the University of Chicago that the triumph of subjective probability over radical uncertainty would be most enthusiastically celebrated.

….Friedman’s Price Theory – a Provisional Text may be regarded as the primer of the doctrines of the Chicago School. In it he wrote: in his seminal work, Frank Knight drew a sharp distinction between risk, as referring to events subject to a known or knowable probability distribution, and uncertainty, as referring to events for which it was not possible to specify numerical probabilities. I’ve not referred to this distinction because I do not believe it is valid . . . We may treat people as if they assigned numerical probabilities to every conceivable event (emphasis ours).

Friedman’s followers distanced themselves – at least in this respect – from Knight’s legacy. They even explained that the revered founder of the school could not have meant what he said.

People Don’t Optimise Their Utility

Nobody has all the details to be able to maximise this function. Most people just ‘cope’. But the idea of rational actors taking actions to optimise actions is fundamental to economic thinking.

Kay and King experienced a different ‘real’ world when they left their academic chambers.

This ‘optimising’ description of behaviour was well suited to the growing use of mathematical techniques in the social sciences. If the problems facing businesses, governments and families could be expressed in terms of well-defined models, then behaviour could be predicted by evaluating the ‘optimal’ solution to those problems.

Although much can be learnt by thinking in this way, our own practical experience was that none of these economic actors were trying to maximise anything at all. This was not because they were stupid, although sometimes they were, nor because they were irrational, although sometimes they were. It was because an injunction to maximise shareholder value, or social welfare, or household utility, is not a coherent guide to action. Business people, policy-makers and families could not even imagine having the information needed to determine the actions that would maximise shareholder value, social welfare or household utility.

This way of thinking we will describe as ‘axiomatic rationality’. It has the logical consequence that there is something which might be described as ‘subjective expected utility’ which individuals who are ‘rational’ are maximising. Obedience to these axioms, it was claimed, defined ‘rational’ behaviour. This is not a particularly obvious way to define ‘rationality’ and it is certainly not the only possible approach. It is, however, one which has come to dominate economics.

Further Kay and King add that our ability to choose not to maximise utility is actually a smart behaviour. We understand the large world works differently from any ‘small world’ model and we use our intelligence to cope with complex problems in an uncertain world. We wouldn’t have survived otherwise:

If we do not act in accordance with axiomatic rationality and maximise our subjective expected utility, it is not because we are stupid but because we are smart. And it is because we are smart that humans have become the dominant species on Earth. Our intelligence is designed for large worlds, not small. Human intelligence is effective at understanding complex problems within an imperfectly defined context…

Non-Stationarity Impacts Forecasting

Economics and social sciences deal with human behaviour. These social systems influence human behaviour and, in turn, are influenced by it. The sociologist Robert Merton called this phenomenon reflexivity. This makes forecasting difficult. In a time of radical uncertainty, human behaviour can be very different (non-stationary) from usual and starts impacting the system itself.

“Reflexivity undermines stationarity. This was the essence of ‘Goodhart’s Law’ – any business or government policy which assumed stationarity of social and economic relationships was likely to fail because its implementation would alter the behaviour of those affected and therefore destroy that stationarity.”

We don’t live in a ‘stationary’ world while all our social science models are built on that fundamental assumptions. Therefore, our ability to solve for or optimise the models that have been built for the ‘small world’ are bound to fail in the ‘big world’. These models should therefore be read like parables. There’s truth in what they offer which is important but you can’t take decisions in everyday life based on what you learn from them. Kay and King conclude:

Radical uncertainty and non-stationarity go hand in hand. There is no stable structure of the world about which we could learn from past experience and use to extrapolate future behaviour. We live in a world of incomplete markets in which there are simply no price signals to guide us back to an efficient equilibrium. There are times when expectations have a life of their own. As a result, the models used by central banks perform quite well when nothing very much is happening and fail dramatically when something big occurs – precisely the moment when the model might have something to offer beyond mere extrapolation of the past.

Economics As Practical Knowledge

The starting point to using economics as a problem-solving tool is to acknowledge the uncertainty and our inability to fully grasp it. As the authors put it:

A great deal of strategy work is trying to figure out what is going on. Not just deciding what to do, but the more fundamental problem of comprehending the situation. The question ‘What is going on here?’ sounds banal, but it is not. In our careers we have seen repeatedly how people immersed in technicalities, engaged in day-to-day preoccupations, have failed to stand back and ask, ‘What is going on here?’ We have often made that mistake ourselves.

Once you assess the situation without trying to fit it into any existing model or preconceived economic notion, you are ready to use the tools of economics like a dentist, plumber or a firefighter who doesn’t begin with an overarching theory of what they are dealing with. They are interested in solving the problem than proving a theory right:

The role of the practical economist, like that of the firefighter, the doctor, the dentist and the engineer, is to be a problem-solver. These other competent professionals – foxes, not hedgehogs – do not begin from a set of axioms or an overarching theory. A major part of the reason medicine was of little practical use before the twentieth century is that its practitioners did begin from theories which dominated medical thinking but contributed little to real understanding – most notoriously, the Greek physician Galen’s notion, from the second century ad, that illness was caused by imbalances between the humours. Modern scientific medicine has been built through the piecemeal accretion of knowledge about details, making full use of inductive, deductive and abductive reasoning, a process which is still contributing to an understanding of human anatomy and physiology.  

Possibly, this is the part where I most agreed with Kay and King. They advocate the role of an economist as that of a problem-framer who helps policymaker think using their tools and then provide them with relevant information to make decisions in the face of radical uncertainty.

Economists cannot tell policy-makers what decisions to make. But they can help them think about their problems and provide relevant information. The social scientist’s narrative is akin to that of the professional practitioner – the diagnosis of the doctor, the project specification of the engineer, the lawyer’s statement of case. The selection of relevant narratives is problem- and context-specific, so that the choice of fictions, numbers and models requires the exercise of judgement in relation to both problem and context. The narratives we seek to construct are neither true nor false, but helpful or unhelpful.

We are suspicious of all ‘schools’ which claim to provide a wide range of answers to problems based on a priori assertions of a general kind about the world. A mystery cannot be solved as a puzzle can. Reasoning through mysteries requires us to acknowledge ambiguities and to resolve them sufficiently to clarify our thinking. But even to frame a problem requires skill and judgement. That is one of the most important contributions that economists can make. A mystery must first be framed, well or badly, to aid people in reaching the decisions they have to make in conditions of radical uncertainty. Framing begins by identifying critical factors and assembling relevant data. It involves applying experience of how these factors have interacted in the past, and making an assessment of how they might interact in the future.

…The role of the economist, like that of other social scientists, is to frame the economic and social issues that political and business leaders face when confronted by radical uncertainty.

Where I Differ

Radical Uncertainty is a rambling but delightfully original work packed with anecdotes, trivia and eclectic references that showcase the wide intellectual landscape its authors inhabit. I have differences with the core idea of the book that we have erred in assigning probabilities and sizing the many risks of our uncertain future. The authors believe we have become slaves to these models that don’t stand to scrutiny in the real world. I see the progress of economic thought and the use of data and better modelling techniques as a net positive.

So, for instance, the stress tests that banks learnt to do following the GFC are about future scenarios with probabilities assigned to them. This is the kind of model that the authors find futile in much of the book. It is true these tests can never account for that one chance event that will make a bank insolvent in future. But should that mean banks don’t do the stress tests? I would think not. In fact, the current economic crisis in the pandemic is a good example of how stress tests actually worked for US banks. The banking system didn’t buckle. It has weathered this ‘radically uncertain’ event well because it used economic models and theories of probability to anticipate a lot of this stress.

Our desire to predict the future to control the direction of our present is primal to us. We understand the futility of it all when radically uncertain events hit us like the current pandemic. But that won’t stop our belief in our will and our pursuit to be masters of our destiny.

That said I will unreservedly recommend the book.  


Share Anticipating The Unintended


Addendum

— Pranay Kotasthane

Coincidentally, I also read a book that makes light of economics. I am referring to Rutger Bregman’s Humankind: A Hopeful History. Written by a historian, the book delves into ideas from economics, politics, philosophy, and psychology to come to one simple — yet a somewhat startling one for our times — conclusion: human beings are pretty decent. Not selfishness, not courage, not superior intelligence, homo sapiens’ superpower is friendliness.

What explains our grim view of humanity then? Bregman says its a nocebo. We become what we believe. In his words:

“If we believe most people can’t be trusted, that’s how we’ll treat each other, to everyone’s detriment. Few ideas have as much power to shape the world as our view of other people. Because ultimately, you get what you expect to get. If we want to tackle the greatest challenges of our times – from the climate crisis to our growing distrust of one another – then I think the place we need to start is our view of human nature.

To be clear: this book is not a sermon on the fundamental goodness of people. Obviously, we’re not angels. We’re complex creatures, with a good side and a not-so-good side. The question is which side we turn to. My argument is simply this: that we – by nature, as children, on an uninhabited island, when war breaks out, when crisis hits – have a powerful preference for our good side. I will present the considerable scientific evidence showing just how realistic a more positive view of human nature is. At the same time, I’m convinced it could be more of a reality if we’d start to believe it.”

Bregman, Rutger. Humankind (p. 9). Bloomsbury Publishing. Kindle Edition.

Using this framework, the book takes a potshot at economics by saying that economics is premised on the Hobbesian notion of human nature, which sees us as rational, self-serving individuals. However, Bregman goes on to say, people are just too kind, too decent.

“Less amusing is that this dim view of human nature has worked as a nocebo for decades now. In the 1990s, economics professor Robert Frank wondered how viewing humans as ultimately egotistical might affect his students. He gave them a range of assignments designed to gauge their generosity. The outcome? The longer they’d studied economics, the more selfish they’d become. ‘We become what we teach,’ Frank concluded.

The doctrine that humans are innately selfish has a hallowed tradition in the western canon. Great thinkers like Thucydides, Augustine, Machiavelli, Hobbes, Luther, Calvin, Burke, Bentham, Nietzsche, Freud and America’s Founding Fathers each had their own version of the veneer theory of civilisation.

Bregman, Rutger. Humankind (p. 17). Bloomsbury Publishing. Kindle Edition.

While it is true that the assumption that “humans are selfish” is a powerful intuition across many religions and philosophies, the author gets it wrong when it comes to economics. Economic reasoning isn’t based on the assumption that people are selfish. Rather, it assumes that people act in self-interest.

Economic reasoning tells us that people try to maximise their own utility. Yes, it’s true that some people try to maximise their utility solely by exploiting others. At the same time, many others maximise their own utility by donating money to NGOs, helping others in need, and by just being decent.

Economics tells us that we try to maximise our benefits and minimise our costs. Also that not all costs and benefits are explicit and monetary. This is what many people get wrong about economics — it’s not always about money. Homo economicus is not a selfish, calculating robot. If anything, one of the most fundamental lessons of economics is that not everything is a zero-sum game.

Despite this dim view of economics, this book is an excellent read. If nothing else, it might just cheer you up.



PolicyWTF: Paddy Minimum Support Prices cause Delhi Smog

This section looks at egregious public policies. Policies that make you go: WTF, Did that really happen?

— Pranay Kotasthane

From our edition #7 on Nov 13, 2019

We are nearing that time of the year. Delhi smog is around the corner. This thought-letter had discussed a few articles that present Coasean solutions to address the crop residue burning problem. I now realise that even these solutions will only fix symptoms rather than address the root cause — the minimum support price for paddy (!). This causal chain is interesting and let me trace it backwards.

  1. Delhi Smog is partially caused by crop residue being burnt on many farms simultaneously in a short time frame to clear the farms for sowing wheat in the first week of November. 

  2. Now why can’t the burning of crop residue be staggered in time? That’s because of delayed sowing. The kharif crop (paddy) is sowed after June 15. This leads to a delayed output leaving farmers with very little time to clear the field for the next crop.

  3. But why is the sowing delayed? That’s where the government comes in. Punjab passed a Punjab Preservation of Subsoil Water Act in 2009 which prohibits paddy transplantation before June 15th. If this rule is violated, paddy nurseries can be destroyed. Repeated violation can also lead to disconnection of power supply.

  4. But why does the government prohibit paddy cultivation before June 15? To save water during the peak-summer season. Paddy farms apparently need 4500 litres/hectare water in April as against 3000 litlres/hectare in June because of evaporation in the summer months. 

  5. Then why are farmers growing rice in the water scarce area? One answer, Minimum. Support. Price. Assured procurement of rice by the government on behalf of the Food Corporation of India incentivises over-production of rice even in areas not well-suited. 

Amazing, isn’t it? Another unintended consequence of a seemingly well-intentioned policy. To tackle this root cause of the Delhi pollution, renowned agrieconomist Ashok Gulati has a few solutions in this Financial Express article


Share