LARS P. SYLL

Central bank independence — a neoliberal con trick

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[h/t Jan Milch]

Central bank independence

Central banks wield near-unlimited power over monetary policy, a policy which largely governs inflation, employment, and economic stability. This power ought to be subject to greater democratic oversight to ensure it aligns with the interests of us, the citizens. Over time, central banks have developed a sort of ‘policy bias’ where inflation control is prioritised over unemployment and welfare, which to broad sections of society appears deeply offensive.

If, through a constitutionally sound and democratically taken decision, we conclude that instead of a given inflation target we wish to prioritise fairness, welfare, and jobs, it is difficult to see why we should be bound by institutions that impose a more narrowly ‘economicist’ choice upon us via a framework designed precisely for that purpose. We must not forget that economics is not always a trump card in policy matters. This really ought to be obvious, but not least economists have a tendency to forget it. From an economic perspective, one could certainly argue, for example, that it is more economically efficient to let the elderly jump off a cliff than to expand costly elderly care. But who would seriously advocate or accept such a thing? Other considerations sometimes carry more weight than purely economic ones.

Beyond the problems with the ‘democratic aspects’ of central banks’ independence, there are also — especially given the remarkable failures to meet inflation targets over the last two decades — good reasons to question central banks’ competence. International research has also convincingly shown that the empirical evidence for the argument that independent central banks benefit the economy is almost non-existent.

For anyone naive enough to believe that central bank governors’ work is based on solid evidence-based science — forget it! As the Swedish documentary Skuldfeber convincingly demonstrates, the work of central bank governors is little more than subtle, fairy-tale charlatanism — which they themselves admit!

Ergodicity — an introduction

Ergodicity often hides behind a veil of mathematical complexity, yet at its core, it offers a profoundly simple and insightful lens through which to view and understand probability and time. At its heart, it challenges us to distinguish between two distinct types of averages: the ensemble average and the time average.

To grasp this distinction, let us look at a simple, everyday example: to determine which is the most popular shop in a city. How would we proceed? We are faced with two distinct methods.

The first method is the ensemble average. We freeze time for a single moment, and we calculate a statistical average across a large population. We count the number of people in the bakery, the coffee shop, and the hardware store, and divide by the total population. We find that the ensemble average for being in the bakery is, say, 10%. This conclusion is drawn from a cross-section of the population at a single point in time. It is a photograph of the collective.

The second method is the time average. Here, we shift our focus from the breadth of the crowd to the depth of a single life. We choose one person, yours truly, and we follow him for a year. We record the total time he spends in each shop and divide it by the total time of the study. We discover that his time average for being in the bakery is only 0.5%, while his time average for his local grocery store is much higher. This conclusion is a longitudinal study of a single trajectory.

When we compare these two averages, we arrive at the critical juncture. The ensemble average tells us the bakery is a crowd-puller. The time average tells us the grocery store is the cornerstone of yours truly’s routine. The two calculations yield different results, which is typical of a non-ergodic system.

A system is ergodic if, and only if, the time average equals the ensemble average for a given observable. In such a world, the cross-sectional snapshot would perfectly mirror the long-term experience of any one individual within it. Yours truly’s annual time averages would align precisely with the city’s instantaneous ensemble averages. But the real world is not so uniform or predictable. It is full of variation, shaped by diverse preferences and random events. The two measures diverge.

The implications of this insight are profound. It forces us to scrutinise the statistics we use to understand our lives. When we hear about an ‘average’ outcome, we must ask: is this an ensemble average or a time average? In finance, for example, a risky investment might have a positive ensemble average return (the expected value across all possible investors looks good), but could lead to total ruin for a single investor who follows that path over time (yours truly has elaborated on this here), resulting in a negative time average of their wealth growth. The ensemble looks promising. The individual’s trajectory is catastrophic.

Ultimately, understanding ergodicity is about understanding the critical difference between these two averages. It emphasises the ontological reality that an individual life constitutes a singular, continuous time average unfolding within a shifting landscape of ensemble averages. Recognising this distinction enables us to formulate more precise questions, critically interrogate aggregate data, and approach the probabilistic complexity of life with greater analytical acuity.

Over the years, some of us have tried to teach our economics students something of the importance of questioning the common ergodicity assumption. This assumption, often left unstated, underpins most mainstream economic models concerning preferences and expected utility.

One of the problems has been the lack of an accessible textbook on ergodicity economics. However, this has now been remedied!

Ole Peters’ and Alexander Adamou’s newly published An Introduction to Ergodicity Economics provides an excellent introduction to a rapidly expanding field of research within economics.

Paul Samuelson once famously claimed that the ‘ergodic hypothesis’ is essential for advancing economics from the realm of history to the realm of science. But is it really tenable to assume — as Samuelson and most other mainstream economists — that ergodicity is essential to economics?

Sometimes ergodicity is mistaken for stationarity. But although all ergodic processes are stationary, they are not equivalent.

Let’s say we have a stationary process. That does not guarantee that it is also ergodic. The long-run time average of a single output function of the stationary process may not converge to the expectation of the corresponding variables — and so the long-run time average may not equal the probabilistic (expectational) average.

Say we have two coins, where coin A has a probability of 1/2 of coming up heads, and coin B has a probability of 1/4 of coming up heads. We pick either of these coins with a probability of 1/2 and then toss the chosen coin over and over again. Now let H1, H2, … be either one or zero as the coin comes up heads or tales. This process is obviously stationary, but the time averages — [H1 + … + Hn]/n — converges to 1/2 if coin A is chosen, and 1/4 if coin B is chosen. Both these time averages have a probability of 1/2 and so their expected average is 1/2 x 1/2 + 1/2 x 1/4 = 3/8, which obviously is not equal to 1/2 or 1/4. The time averages depend on which coin you happen to choose, while the probabilistic (expectational) average is calculated for the whole “system” consisting of both coin A and coin B.

Instead of arbitrarily assuming that people have a certain type of utility function — as in mainstream theory — time average considerations show that we can obtain a less arbitrary and more accurate picture of real people’s decisions and actions by basically assuming that time is irreversible. When our assets are gone, they are gone. The fact that in a parallel universe, it could conceivably have been refilled, is of little comfort to those who live in the one and only possible world that we call the real world.

Time average considerations show that because we cannot go back in time, we should not take excessive risks. High leverage increases the risk of bankruptcy. This should also be a warning for the financial world, where the constant quest for greater and greater leverage — and risks — creates extensive and recurrent systemic crises.

Suppose I want to play a game. Let’s say we are tossing a coin. If heads come up, I win a dollar, and if tails come up, I lose a dollar. Suppose further that I believe I know that the coin is asymmetrical and that the probability of getting heads (p) is greater than 50% – say 60% (0.6) – while the bookmaker assumes that the coin is totally symmetric. How much of my bankroll (T) should I optimally invest in this game?

A strict mainstream utility-maximising economist would suggest that my goal should be to maximise the expected value of my bankroll (wealth), and according to this view, I ought to bet my entire bankroll.

Does that sound rational? Most people would answer ‘no’. The risk of losing is so high that after just a few games — the expected time until the first loss is 1/(1-p), which in this case is 2.5 — one would, in all likelihood, have lost and gone bankrupt. The expected-value maximising economist does not seem to have a particularly compelling approach.

While I share many of the critiques that Peters and Adamou level against mainstream expected utility theory, I diverge from their conclusion regarding human decision-making. In their book, Peters and Adamou write:

We will do decision theory by using mathematical models … The wealth process and the decision criterion may or may not remind you of the real world. We will not worry too much about the accuracy of these reminiscences. Instead we will ‘shut up and calculate’ — we will let the mathematical model create its world … Importantly, we need not resort to psychology to generate a host of behaviours, such as preference reversal as time passes or impatience of poorer individuals … Unlike utility functions, treated in mainstream decision theory as encoding psychological risk preferences, wealth dynamics are something about which we can reason mechanistically …

Contrary to their position, I contend that psychological factors are not merely incidental but are fundamental. Any framework that seeks to describe or predict human action must place them at its core.

When evaluating decisions, the way we measure ‘growth’ changes the story dramatically. Consider two very different processes. In the first (Gamble 1), an investor begins with $10,000 and passes through three rounds of wealth reduction, ending with just half a cent. In the second (Gamble 2), the investor faces a single gamble: a 99.9% chance of walking away with $10,000,000 and a 0.1% chance of ending with nothing.

In Gamble 1, the deterministic shrinking process is straightforward: each round reduces the investor’s wealth by a constant proportion. Mathematically, the wealth after three rounds is $0.005.The investor loses about 99% of wealth per round. The average per-round growth rate is about −99%. The result is a guaranteed catastrophe.

In Gamble 2, the investor risks everything on a single binary outcome — a 99.9% chance of $10,000,000, and a 0.1% chance of nothing. The expected value is huge — on average, the gamble turns $10,000 into $9,990,000. However, if the gamble were repeated many times with the entire bankroll at stake, ruin would be inevitable. Since there is always some probability of hitting zero, the long-run geometric growth rate is negative infinity. Once the investor reaches zero wealth, no recovery is possible.

Which gamble is regarded as superior depends on the objective. If the goal is maximising expected wealth from a one-off decision, Gamble 2 dominates, offering huge expected gains. But if the goal is preserving wealth over repeated plays, Gamble 2 is disastrous. Gamble 1 is equally unappealing — it guarantees destruction without the possibility of recovery.

The metric we use — arithmetic or geometric averages and growth rates — can give entirely different conclusions. From an expected value perspective, one would favour Gamble 1, since it offers a higher average growth rate. Yet I suspect very few investors would actually share that preference.

When it comes to human decision-making, psychological factors are paramount. This is especially true when confronting uncertainty. The models and examples presented often operate, either explicitly or implicitly, within the realm of quantifiable risk. On this point, it is wise to recall the crucial distinction made by Keynes a century ago: measurable risk is fundamentally different from unmeasurable uncertainty. In the latter domain, where probabilistic calculations break down, psychology inevitably plays a decisive role.

Consequently, while ‘optimal growth rates’ may serve as a useful decision criterion in specific, well-defined contexts, they cannot be considered the sole or universally best guide for human action. A comprehensive theory of decision-making must account for the full spectrum of human cognition and attitude, particularly when navigating the unquantifiable unknowns of the real world.

Economics of uncertainty

The disregard of radical uncertainty by a generation of economists condemned modern macroeconomics to near irrelevance … Keynes’ critique of ‘getting on the job’ without asking ‘whether the job is worth getting on with’ would prove to be as true of the new macroeconomic theorising as of the older econometric modeling …

Over forty years, the authors have watched the bright optimism of a new, rigorous approach to economics dissolve into the failures of prediction and analysis which were seen in the global financial crisis of 2007-08. And it is the pervasive nature of radical uncertainty which is the source of the problem.

Kay and King’s challenge to contemporary economics resonates with a much older intellectual tradition — one that recognises the limits of human foresight and the complexity of economic life. In this respect, Radical Uncertainty is a sophisticated rearticulation of concerns voiced by earlier thinkers such as Frank Knight, John Maynard Keynes, and Friedrich Hayek, each of whom, in different ways, emphasised the irreducible uncertainty inherent in socio-economic systems.

Frank Knight famously distinguished between measurable risk and unmeasurable uncertainty. Risk can be insured against or incorporated into actuarial tables; uncertainty cannot. For Knight, entrepreneurial judgement — not optimisation — was at the heart of economic activity because it required navigating precisely those situations that could not be captured by known numerical probabilities.

John Maynard Keynes developed a similar, though more expansive, view. His concept of ‘weight of argument’ and his insistence that economic decisions are shaped by expectations, conventions, and ‘animal spirits’ reflect a recognition that the future is genuinely unknowable. In Keynes’s world, probabilities are not merely difficult to calculate; they may not exist in any meaningful sense. Kay and King explicitly situate their claims within this Keynesian insight, arguing that modern macroeconomics has lost sight of the epistemic humility that Keynes regarded as essential.

[Although Kay and King more or less equate Knight’s and Keynes’ views, I think there exists a fundamental distinction between the uncertainty concepts of Knight and Keynes: Knight’s is rooted primarily in epistemology, while Keynes’s is decidedly ontological.

The most profound implication of this difference is that subscribing to the epistemological (Knightian) view can foster the mistaken belief that with superior information and greater computational power, we might eventually be able to calculate probabilities and describe the world as an ergodic system. However, as Keynes convincingly argued, this is ontologically impossible. For Keynes, the wellspring of uncertainty lies in the intrinsic nature of a non-ergodic reality.

Keynes’ concern was not merely, or primarily, the epistemological fact that we currently lack knowledge of certain things. Instead, it addressed a deeper and more far-reaching ontological fact: in most ‘large world’ contexts, there simply is no firm or stable basis upon which to form any quantifiable probabilities or expectations at all.

Often we do not know because we cannot know.]

Friedrich Hayek, from a quite different intellectual tradition, likewise emphasised the decentralised, dispersed nature of knowledge and the impossibility of capturing it within a single model. Whilst Hayek’s critique was aimed at central planning, the underlying point — that no system of economic calculation can fully grasp the complexities of an evolving society — echoes strongly in Kay and King’s work.

Nassim Nicholas Taleb has in his analysis of ‘Black Swan’ events and ‘fat-tailed’ distributions highlighted the fragility of systems built on the assumption of stable probabilistic structures. Taleb focuses on the statistical dangers of small-world modelling; Kay and King broaden the argument to show that these dangers are not merely technical but conceptual. Where Taleb stresses tail risk, Kay and King stress the impossibility of enumerating the relevant distribution in the first place.

Thus, Radical Uncertainty sits at a crossroads of multiple intellectual traditions: Knightian and Keynesian genuine uncertainty, Hayekian epistemic limits, and Talebian systemic fragility. What unifies these perspectives is the argument that modern economic modelling often betrays a profound misunderstanding of the world it seeks to describe.

The implications of Kay and King’s critique extend far beyond academic debates over modelling. If the economy is truly a ‘large world’ system, then policies designed for a ‘small world’ — those predicated on precise forecasts and supposedly robust risk management — are doomed to fail.

Central banks, for instance, increasingly rely on sophisticated models to forecast inflation, output, and financial stability risks. These models typically embed assumptions of rational expectations, representative agents, and stable distributions. Yet, as the crisis of 2008 revealed, such models can be dangerously blind to systemic interactions, feedback loops, and shifts in expectations. The belief that interest rates can be fine-tuned in response to forecasts gives policymakers a false sense of control — one that evaporates when the economy behaves in ways that lie outside their model’s conceptual universe.

Kay and King argue that central banking must adopt a radically different orientation — one rooted in scenario thinking, robustness, and institutional resilience rather than the pursuit of optimality. The Bank of England, the ECB, and the Federal Reserve have all, to varying degrees, started to move in this direction, but the legacy of small-world modelling remains deeply embedded.

Financial regulation likewise suffers when based on risk-weighted models that underestimate the interdependence of financial institutions. When regulators rely on models that assume a normal distribution of shocks or stable correlations between asset classes, they fail to anticipate cascading failures — precisely the form that systemic crises typically take. The result is a regulatory architecture that appears rigorous but is, in fact, brittle.

Radical uncertainty implies that what matters is not numerical precision but the capacity to absorb surprise. This suggests a shift away from micro-prudential rules towards a macro-prudential philosophy focused on diversity, redundancy, and buffers — concepts far closer to engineering or ecology than to traditional mainstream ideas of optimisation.

At a broader societal level, Kay and King advocate for institutions that promote resilience, adaptability, and distributed decision-making. A world of radical uncertainty rewards institutions that can learn, revise, and evolve.

Optimisation, as they point out, is a fair-weather strategy. It performs brilliantly in stable, predictable environments but fails catastrophically when confronted with shocks. Resilient systems, by contrast, appear ‘inefficient’ in conventional economic terms — they hold spare capacity, maintain buffers, and decentralise authority. Yet these very features make them capable of weathering the unpredictable.

A further implication concerns decision theory itself. Kay and King reject the view that decision-making should revolve around probabilistic calculus. Instead, they argue for the centrality of narrative reasoning: constructing coherent stories about how the world works, how events may unfold, and what actions are prudent. This is not irrationality; it is a practical and context-sensitive mode of reasoning that mirrors how successful individuals, firms, and policymakers actually navigate complex environments.

Judgement is elevated above optimisation. Experience and contextual awareness become more important than the ability to manipulate formal models.

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?’

Taken together, these arguments amount to a call for a more realistic and humble discipline — echoing Keynes’ wish that economists should “manage to get themselves thought of as humble, competent people, on the level with dentists” — one that accepts the limits of knowledge rather than obscuring them. Economics, in Kay and King’s vision, would become more interdisciplinary, drawing on history, psychology, political economy, and sociology. It would resist the temptation to convert uncertainty into pseudo-certainty. And it would recognise that, in an open world filled with novelty and genuine unpredictability, the primary task of economic reasoning is not to forecast the future but to equip us to cope with whatever the future brings.

The world is not a casino or a laboratory. It is a place of surprise, creativity, and human fallibility. By acknowledging this, economists can abandon the false comfort of the small world and embrace the challenges — and opportunities — of the large one.

Kenneth Boulding on economists and madmen

Behavioural economics — a theory-induced blindness

Although discounting empirical evidence cannot be the right way to solve economic issues, there are still, in my view, a couple of weighty reasons why we perhaps shouldn’t be too enthusiastic about the so-called ‘empirical revolution’ that behavioural economics has brought about in mainstream economics.

Behavioural experiments and laboratory research face the same fundamental problem as theoretical models — they are built on often rather artificial conditions and have difficulties with the ‘trade-off’ between internal and external validity. The more artificial the conditions, the greater the internal validity, but the less the external validity. The more we rig up experiments to avoid ‘confounding factors’, the less the conditions resemble the real ‘target system.’ The nodal issue is how economists using different isolation strategies in different ‘nomological machines’ attempt to learn about causal relationships. One may have justified doubts about the generalisability of this research strategy, since the probability is high that causal mechanisms are different in different contexts and that a lack of homogeneity and invariance doesn’t give us warranted export licenses to ‘real’ societies or economies.

If we see experiments or laboratory research as theory tests or models that ultimately aspire to say something about the real ‘target system,’ then the problem of external validity is central (and for a long time was also a key reason why behavioural economists had trouble getting their research results published).

A standard procedure in behavioural economics — think of, for example, dictator or ultimatum games — is to set up a situation where one induces people to act according to the standard microeconomic — homo oeconomicus — benchmark model. In most cases, the results show that people do not behave as one would have predicted from the benchmark model, in spite of the setup almost invariably being ‘loaded’ for that purpose. [And in those cases where the result is consistent with the benchmark model, one, of course, has to remember that this in no way proves the benchmark model to be right or ‘true,’ since there, as a rule, may be many outcomes that are consistent with that model.]

For most heterodox economists, this is just one more reason for giving up on the standard model. But not so for mainstreamers and many behaviouralists. To them, the empirical results are not reasons for abandoning their preferred hardcore axioms. So they set out to ‘save’ or ‘repair’ their model and try to ‘integrate’ the empirical results into mainstream economics. Instead of accepting that the homo oeconomicus model has zero explanatory real-world value, one puts lipstick on the pig and hopes to carry on with business as usual. Why we should keep on using that model as a benchmark when everyone knows it is false is something we are never told. Instead of using behavioural economics and its results as building blocks for a progressive alternative research programme, the ‘save and repair’ strategy immunises a hopelessly false and irrelevant model.

By this, I do not mean to say that empirical methods per se are so problematic that they can never be used. On the contrary, I am basically — though not without reservations — in favour of the increased use of behavioural experiments and laboratory research within economics. Not least as an alternative to completely barren ‘bridge-less’ axiomatic-deductive theory models. My criticism is more about aspiration levels and what we believe that we can achieve with our mediational epistemological tools and methods in the social sciences.

The increasing use of natural and quasi-natural experiments in economics during the last couple of decades has led several prominent economists to triumphantly declare it as a major step on a recent path toward empirics, where instead of being a deductive philosophy, economics is now increasingly becoming an inductive science.

Limiting model assumptions in economic science always have to be closely examined, since if we are going to be able to show that the mechanisms or causes that we isolate and handle in our models are stable in the sense that they do not change when we ‘export’ them to our ‘target systems,’ we have to be able to show that they do not only hold under ceteris paribus conditions and a fortiori are only of limited value to our understanding, explanations or predictions of real economic systems.

‘Ideally controlled experiments’ tell us with certainty what causes what effects — but only given the right ‘closures.’ Making appropriate extrapolations from (ideal, accidental, natural or quasi) experiments to different settings, populations or target systems, is not easy. ‘It works there’ is no evidence for ‘it will work here.’ Causes deduced in an experimental setting still have to show that they come with an export warrant to the target system. The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods is despairingly small.

So — although it is good that people like Kahneman and Thaler have been rewarded with ‘Nobel prizes’ and that much of their research has vastly undermined the lure of axiomatic-deductive mainstream economics, there is still a long way to go before economics has become a truly empirical science. The great challenge for the future of economics is not to develop methodologies and theories for well-controlled laboratories, but to develop relevant methodologies and theories for the messy world in which we happen to live.

Most of the observed ‘biases’ in behavioural economics are not the result of errors in beliefs or logic, although some are. Most are the product of a reality in which decisions must be made in the absence of a precise and complete description of the world in which people live, in contrast to the small worlds in which the students whose choices are studied in experimental economics are asked to participate …

Economists who label certain types of behaviour as cognitive illusions may be missing the point that the people they observe are not living in the small world which they themselves inhabit (or the small world they model) …

Kahneman offers an explanation of why earlier and inadequate theories of choice persisted for so long — a ‘theory-induced blindness: once you have accepted a theory and used it as a tool in your thinking, it is extraordinarily difficult to notice its flaws’. We might say the same about behavioural economics. We believe that it is time to move beyond judgemental taxonomies of ‘biases’ derived from a benchmark which is a normative model of human behaviour deduced from implausible a priori principles. And ask instead how humans do behave in large worlds of which they can only ever have imperfect knowledge …

There is an alternative story to that told by behavioural economics. It is that many of the characteristics of human reasoning which behavioural economics describes as biases are in fact adaptive — beneficial to success — in the large real worlds in which people live, even if they are sometimes misleading in the small worlds created for the purposes of economic modelling and experimental psychology. It is an account which substitutes evolutionary rationality for axiomatic rationality.

John Kay & Mervyn King

Theory of science books for economists

• Harré, Rom (1960). An introduction to the logic of the sciences. London: Macmillan

• Bhaskar, Roy (1978). A realist theory of science. Hassocks: Harvester

• Garfinkel, Alan (1981). Forms of explanation: rethinking the questions in social theory. New Haven: Yale U.P.

• Lieberson, Stanley (1987). Making it count: the improvement of social research and theory. Berkeley: Univ. of California Press

• Miller, Richard (1987). Fact and method: explanation, confirmation and reality in the natural and the social sciences. Princeton, N.J.: Princeton Univ. Press

• Archer, Margaret (1995). Realist social theory: the morphogenetic approach. Cambridge: Cambridge University Press

• Lawson, Tony (1997). Economics and reality. London: Routledge

• Lipton, Peter (2004). Inference to the best explanation. 2. ed. London: Routledge

• Cartwright, Nancy (2007). Hunting causes and using them. Cambridge: Cambridge University Press

• Kay, John & King, Mervyn (2020). Radical uncertainty. The Bridge Street Press, London