In a previous post, I asserted that “the existence of irreducible uncertainty is sufficient to justify an evolutionary approach for any social system, whether it be an organization or a macro-economy.” This is not a controversial statement – Nelson and Winter introduced their seminal work on evolutionary economics as follows: “Our evolutionary theory of economic change…is not an interpretation of economic reality as a reflection of supposedly constant “given data” but a scheme that may help an observer who is sufficiently knowledgeable regarding the facts of the present to see a little further through the mist that obscures the future.”

In microeconomics, irreducible uncertainty implies a world of bounded rationality where many heuristics become not signs of irrationality but a rational and effective tool of decision-making. But it is the implications of human action under uncertainty for macro-economic outcomes that is the focus of this blog – In previous posts (1,2) I have elaborated upon the resilience-stability tradeoff and its parallels in economics and ecology. This post focuses on another issue critical to the functioning of all complex adaptive systems: the relationship between evolvability and robustness.

Evolvability and Robustness Defined

Hiroaki Kitano defines robustness as follows: “Robustness is a property that allows a system to maintain its functions despite external and internal perturbations….A system must be robust to function in unpredictable environments using unreliable components.” Kitano makes it explicit that robustness is concerned with the maintenance of functionality rather than specific components: “Robustness is often misunderstood to mean staying unchanged regardless of stimuli or mutations, so that the structure and components of the system, and therefore the mode of operation, is unaffected. In fact, robustness is the maintenance of specific functionalities of the system against perturbations, and it often requires the system to change its mode of operation in a flexible way. In other words, robustness allows changes in the structure and components of the system owing to perturbations, but specific functions are maintained.”

Evolvability is defined as the ability of the system to generate novelty and innovate thus enabling the system to “adapt in ways that exploit new resources or allow them to persist under unprecedented environmental regime shifts” (Whitacre 2010). At first glance, evolvability and robustness appear to be incompatible: Generation of novelty involves a leap into the dark, an exploration rather than an act of “rational choice” and the search for a beneficial innovation carries with it a significant risk of failure. It’s worth noting that in social systems, this dilemma vanishes in the absence of irreducible uncertainty. If all adaptations are merely a realignment to a known systemic configuration (“known” in either a deterministic or a probabilistic sense), then an inability to adapt needs other explanations such as organisational rigidity.

Evolvability, Robustness and Resilience

Although it is typical to equate resilience with robustness, resilient complex adaptive systems also need to possess the ability to innovate and generate novelty. As Allen and Holling put it : “Novelty and innovation are required to keep existing complex systems resilient and to create new structures and dynamics following system crashes”. Evolvability also enables the system to undergo fundamental transformational change – it could be argued that such innovations are even more important in a modern capitalist economic system than they are in the biological or ecological arena. The rest of this post will focus on elaborating upon how macro-economic systems can be both robust and evolvable at the same time – the apparent conflict between evolvability and robustness arises from a fallacy of composition where macro-resilience is assumed to arise from micro-resilience, when in fact it arises from the very absence of micro-resilience.

EVOLVABILITY, ROBUSTNESS AND RESILIENCE IN MACRO-ECONOMIC SYSTEMS

The pre-eminent reference on how a macro-economic system can be both robust and evolvable at the same time is the work of Burton Klein in his books “Dynamic Economics” and “Prices, Wages and Business Cycles: A Dynamic Theory”. But as with so many other topics in evolutionary economics, no one has summarised it better than Brian Loasby: “Any economic system which is to remain viable over a long period must be able to cope with unexpected change. It must be able to revise or replace policies which have worked well. Yet this ability is problematic. Two kinds of remedy may be tried, at two different system levels. One is to try to sensitize those working within a particular research programme to its limitations and to possible alternatives, thus following Menger’s principle of creating private reserves against unknown but imaginable dangers, and thereby enhancing the capacity for internal adaptation….But reserves have costs; and it may be better , from a system-wide perspective, to accept the vulnerability of a sub-system in order to exploit its efficiency, while relying on the reserves which are the natural product of a variety of sub-systems….
Research programmes, we should recall, are imperfectly specified, and two groups starting with the same research programme are likely to become progressively differentiated by their experience, if there are no strong pressures to keep them closely aligned. The long-run equilibrium of the larger system might therefore be preserved by substitution between sub-systems as circumstances change. External selection may achieve the same overall purpose as internal adaptation – but only if the system has generated adequate variety from which the selection may be made. An obvious corollary which has been emphasised by Klein (1977) is that attempts to preserve sub-system stability may wreck the larger system. That should not be a threatening notion to economists; it also happens to be exemplified by Marshall’s conception of the long-period equilibrium of the industry as a population equilibrium, which is sustained by continued change in the membership of that population. The tendency of variation is not only a chief cause of progress; it is also an aid to stability in a changing environment (Eliasson, 1991). The homogeneity which is conducive to the attainment of conventional welfare optima is a threat to the resilience which an economy needs.”

Uncertainty can be tackled at the micro-level by maintaining reserves and slack (liquidity, retained profits) but this comes at the price of slack at the macro-level in terms of lost output and employment. Note that this is essentially a Keynesian conclusion, similar to how individually rational saving decisions can lead to collectively sub-optimal outcomes. From a systemic perspective, it is more preferable to substitute the micro-resilience with a diverse set of micro-fragilities. But how do we induce the loss of slack at firm-level? And how do we ensure that this loss of micro-resilience occurs in a sufficiently diverse manner?

The “Invisible Foot”

The concept of the “Invisible Foot” was introduced by Joseph Berliner as a counterpoint to Adam Smith’s “Invisible Hand” to explain why innovation was so hard in the centrally planned Soviet economy: “Adam Smith taught us to think of competition as an “invisible hand” that guides production into the socially desirable channels….But if Adam Smith had taken as his point of departure not the coordinating mechanism but the innovation mechanism of capitalism, he may well have designated competition not as an invisible hand but as an invisible foot. For the effect of competition is not only to motivate profit-seeking entrepreneurs to seek yet more profit but to jolt conservative enterprises into the adoption of new technology and the search for improved processes and products. From the point of view of the static efficiency of resource allocation, the evil of monopoly is that it prevents resources from flowing into those lines of production in which their social value would be greatest. But from the point of view of innovation, the evil of monopoly is that it enables producers to enjoy high rates of profit without having to undertake the exacting and risky activities associated with technological change. A world of monopolies, socialist or capitalist, would be a world with very little technological change.” To maintain an evolvable macro-economy, the invisible foot needs to be “applied vigorously to the backsides of enterprises that would otherwise have been quite content to go on producing the same products in the same ways, and at a reasonable profit, if they could only be protected from the intrusion of competition.”

Entry of New Firms and the Invisible Foot

Burton Klein’s great contribution along with other dynamic economists of the time (notably Gunnar Eliasson) was to highlight the critical importance of entry of new firms in maintaining the efficacy of the invisible foot. Klein believed that “the degree of risk taking is determined by the robustness of dynamic competition, which mainly depends on the rate of entry of new firms. If entry into an industry is fairly steady, the game is likely to have the flavour of a highly competitive sport. When some firms in an industry concentrate on making significant advances that will bear fruit within several years, others must be concerned with making their long-run profits as large as possible, if they hope to survive. But after entry has been closed for a number of years, a tightly organised oligopoly will probably emerge in which firms will endeavour to make their environments highly predictable in order to make their environments highly predictable in order to make their short-run profits as large as possible….Because of new entries, a relatively concentrated industry can remain highly dynamic. But, when entry is absent for some years, and expectations are premised on the future absence of entry, a relatively concentrated industry is likely to evolve into a tight oligopoly. In particular, when entry is long absent, managers are likely to be more and more narrowly selected; and they will probably engage in such parallel behaviour with respect to products and prices that it might seem that the entire industry is commanded by a single general!”

Again, it can’t be emphasised enough that this argument does not depend on incumbent firms leaving money on the table – on the contrary, they may redouble their attempts at static optimisation. From the perspective of each individual firm, innovation is an incredibly risky process even though the result of such dynamic competition from the perspective of the industry or macro-economy may be reasonably predictable. Of course, firms can and do mitigate this risk by various methods but this argument only claims that any single firm, however dominant cannot replicate the “risk-free” innovation dynamics of a vibrant industry in-house.

Micro-Fragility as the Hidden Hand of Macro-Resilience

In an environment free of irreducible uncertainty, evolvability suffers leading to reduced macro-resilience. “If firms could predict each others’ advances they would not have to insure themselves against uncertainty by taking risks. And no smooth progress would occur” (Klein 1977). Conversely, “because firms cannot predict each other’s discoveries, they undertake different approaches towards achieving the same goal. And because not all of the approaches will turn out to be equally successful, the pursuit of parallel paths provides the options required for smooth progress.”

The Aftermath of the Minsky Moment: A Problem of Micro-Resilience

Within the context of the current crisis, the pre-Minsky moment system was a homogeneous system with no slack which enabled the attainment of “conventional welfare optima” but at the cost of an incredibly fragile and unevolvable condition. The logical evolution of such a system post the Minsky moment is of course still a homogeneous system but with significant firm-level slack built in which is equally unsatisfactory. In such a situation, the kind of macro-economic intervention matters as much as the force of intervention. For example, in an ideal world, monetary policy aimed at reducing borrowing rates of incumbent banks and corporates will flow through into reduced borrowing rates for new firms. In a dynamically uncompetitive world, such a policy will only serve the interests of the incumbents.

The “Invisible Foot” and Employment

Vivek Wadhwa argues that startups are the main source of net job growth in the US economy and Mark Thoma links to research that confirms this thesis. Even if one disagrees with this thesis, the “invisible foot” thesis argues that if the old guard is to contribute to employment, they must be forced to give up their “slack” by the strength of dynamic competition and dynamic competition is maintained by preserving conditions that encourage entry of new firms.

MICRO-EVOLVABILITY AND MACRO-RESILIENCE IN BIOLOGY AND ECOLOGY

Note: The aim of this section is not to draw any false precise equivalences between economic resilience and ecological or biological resilience but simply to highlight the commonality of the micro-macro fallacy of composition across complex adaptive systems – a detailed comparison will hopefully be the subject of a future post. I have tried to keep the section on biological resilience as brief and simple as possible but an understanding of the genotype-phenotype distinction and neutral networks is essential to make sense of it.

Biology: Genotypic Variation and Phenotypic Robustness

In the specific context of biology, evolvability can be defined as “the capacity to generate heritable, selectable phenotypic variation. This capacity may have two components: (i) to reduce the potential lethality of mutations and (ii) to reduce the number of mutations needed to produce phenotypically novel traits” (Kirschner and Gerhart 1998). The apparent conflict between evolvability and robustness can be reconciled by distinguishing between genotypic and phenotypic robustness and evolvability. James Whitacre summarises Andrew Wagner’s work on RNA genotypes and their structure phenotypes as follows: “this conflict is unresolvable only when robustness is conferred in both the genotype and the phenotype. On the other hand, if the phenotype is robustly maintained in the presence of genetic mutations, then a number of cryptic genetic changes may be possible and their accumulation over time might expose a broad range of distinct phenotypes, e.g. by movement across a neutral network. In this way, robustness of the phenotype might actually enhance access to heritable phenotypic variation and thereby improve long-term evolvability.”

Ecology: Species-Level Variability and Functional Stability

The notion of micro-variability being consistent with and even being responsible for macro-resilience is an old one in ecology as Simon Levin and Jane Lubchenco summarise here: “That the robustness of an ensemble may rest upon the high turnover of the units that make it up is a familiar notion in community ecology. MacArthur and Wilson (1967), in their foundational work on island biogeography, contrasted the constancy and robustness of the number of species on an island with the ephemeral nature of species composition. Similarly, Tilman and colleagues (1996) found that the robustness of total yield in high-diversity assemblages arises not in spite of, but primarily because of, the high variability of individual population densities.”

The concept is also entirely consistent with the “Panarchy” thesis which views an ecosystem as a nested hierarchy of adaptive cycles: “Adaptive cycles are nested in a hierarchy across time and space which helps explain how adaptive systems can, for brief moments, generate novel recombinations that are tested during longer periods of capital accumulation and storage. These windows of experimentation open briefly, but the results do not trigger cascading instabilities of the whole because of the stabilizing nature of nested hierarchies. In essence, larger and slower components of the hierarchy provide the memory of the past and of the distant to allow recovery of smaller and faster adaptive cycles.”

Misc. Notes

1. It must be emphasised that micro-fragility is a necessary, but not a sufficient condition for an evolvable and robust macro-system. The role of not just redundancy but degeneracy is critical as is the size of the population.

2. Many commentators use resilience and robustness interchangeably. I draw a distinction primarily because my definitions of robustness and evolvability are borrowed from biology and my definition of resilience is borrowed from ecology which in my opinion defines a robust and evolvable system as a resilient one.

An over-reliance on models over discretion cripples the adaptive capabilities of the firm: “No contract can anticipate all contingencies. But securitized financing makes ongoing adaptations infeasible; because of the great difficulty of renegotiating terms, borrowers and lenders must adhere to the deal that was struck at the outset. Securitized mortgages are more likely than mortgages retained by banks to be foreclosed if borrowers fall behind on their payments, as recent research shows.” But why would firms choose such rigid and inflexible solutions? There are many answers to this question but all of them depend on the obvious fact that adaptable solutions entail a higher cost than rigid solutions. It is far less expensive to analyse the creditworthiness of mortgages with standardised models than with people on the ground.

This increased efficiency comes at the cost of catastrophic losses in a crisis but long periods of stability inevitably select for efficient and rigid solutions rather than adaptable and flexible solutions. This may be a consequence of moral hazard or principal-agent problems as I have analysed many times on this blog but it does not depend on either. A preference for rigid routines may be an entirely rational response to a long period of stability under uncertainty – both from an individual’s perspective and an organisation’s perspective. Probably the best exposition of this problem was given by Brian Loasby in his book “Equilibrium and Evolution” (pages 56-7): “Success has its opportunity costs. People who know how to solve their problems can get to work at once, without considering whether some other method might be more effective; they thereby become increasingly efficient, but also increasingly likely to encounter problems which are totally unexpected and which are not amenable to their efficient routines…The patterns which people impose on phenomena have necessarily a limited range of application, and the very success with which they exploit that range tends to make them increasingly careless about its limits. This danger is likely to be exacerbated by formal information systems, which are typically designed to cope with past problems, and which therefore may be worse than useless in signalling new problems. If any warning messages do arrive, they are likely to be ignored, or force-fitted into familiar categories; and if a crisis breaks, the information needed to deal with it may be impossible to obtain.”

Now it is obvious why banks stuck with such rigid models during the “Great Moderation” but it is less obvious why banks don’t discard them voluntarily post the “Minsky Moment”. The answer lies in the difficulty that organisations and other social systems face in making dramatic systemic U-turns even when the logic for doing so is clear, thus the importance of mitigating the TBTF problem and enabling entry of new firms. As I have asserted before: “A crony capitalist economic system that protects the incumbent firms hampers the ability of the system to innovate and adapt to novelty. It is obvious how the implicit subsidy granted to our largest financial institutions via the Too-Big-To-Fail doctrine represents a transfer of wealth from the taxpayer to the financial sector. It is also obvious how the subsidy encourages a levered, homogenous and therefore fragile financial sector that is susceptible to collapse. What is less obvious is the paralysis that it induces in the financial sector and by extension the macroeconomy long after the bailouts and the Minsky moment have passed.”

Much like he accused the Austrians, Paul Krugman accuses Rajan of being a “liquidationist”. This is not a coincidence – Rajan and Diamond’s thesis is quite explicit about its connections to Austrian Business Cycle Theory: “a central bank that promises to cut interest rates conditional on stress, or that is biased towards low interest rates favouring entrepreneurs, will induce banks to promise higher payouts or take more illiquid projects. This in turn can make the illiquidity crisis more severe and require a greater degree of intervention, a view reminiscent of the Austrian theory of cycles.” But as the summary hints, Rajan and Diamond’s thesis is fundamentally different from ABCT. The conventional Austrian story identifies excessive credit inflation and interest rates below the “natural” rate of interest as the driver of the boom/bust cycle but Rajan and Diamond’s thesis identifies the anticipation by economic agents of low rates and “liquidity” facilities every time there is an economic downturn as the driver of systemic fragility. The adaptation of banks and other market players to this regime makes the eventual bust all the more likely. As Rajan and Diamond note: “If the authorities are expected to reduce interest rates when liquidity is at a premium, banks will take on more short-term leverage or illiquid loans, thus bringing about the very states where intervention is needed.”

Rajan and Diamond’s thesis is limited to the impact of such policies on banks but as I noted in a previous post, market players also adapt to this implicit commitment from the central bank to follow easy money policies at the first hint of economic trouble. This thesis is essentially a story of the Greenspan-Bernanke era and the damage that the Greenspan Put has caused. It also explains the dramatically diminishing returns inherent in the Greenspan Put strategy as the stabilising policies of the central bank become entrenched in the expectations of market players and crucially banks – in each subsequent cycle, the central bank has to do more and more (lower rates, larger liquidity facilities) to achieve less and less.

Normal, Pre-Crisis and Crisis Regimes

In a post on market crises, Rick Bookstaber identified three regimes that any model of the market must represent (normal, pre-crisis and crisis) and analysed the statistical properties (volatility,correlation etc) of each of these regimes. The framework below however characterises each regime by the varying combinations of positive and negative feedback processes and the variations and regime shifts are determined by the adaptive and evolutionary processes operating within the system.

1. Normal regimes are resilient regimes. They are characterised by a balanced and diverse mix of positive and negative feedback processes. For every momentum trader who bets on the continuation of a trend, there is a contrarian who bets the other way.

2. Pre-crisis regimes are characterised by an increasing dominance of positive feedback processes. An unusually high degree of stability or a persistent trend progressively weeds out negative feedback processes from the system thus leaving it vulnerable to collapse even as a result of disturbances that it could easily absorb in its previously resilient normal state. Such regimes can arise from bubbles but this is not necessary. Pre-crisis only implies that a regime change into the crisis regime is increasingly likely – in ecological terms, the pre-crisis regime is fragile and has suffered a significant loss of resilience.

3. Crisis regimes are essentially transitional  - the disturbance has occurred and the positive feedback processes that dominated the previous regime have now reversed direction. However, the final destination of this transition is uncertain – if the system is left alone, it will undergo a discontinuous transition to a normal regime. However, if sufficient external stabilisation pressures are exerted upon the system, it may revert to the pre-crisis regime or even stay in the crisis regime for a longer period. It’s worth noting that I define a normal regime only by its resilience and not by its desirability – even a state of civilizational collapse can be incredibly resilient.

“Critical Transitions” from the Pre-Crisis to the Crisis Regime

In fragile systems even a minor disturbance can trigger a discontinuous move to an alternative regime – Marten Scheffer refers to such moves as “critical transitions”. Figures a,b,c and d below represent a continuum of ways in which the system can react to changing external conditions (ref Scheffer et al) . Although I will frequently refer to “equilibria” and “states” in the discussion below, these are better described as “attractors” and “regimes” given the dynamic nature of the system – the static terminology is merely a simplification.

In Figure a, the system state reacts smoothly to perturbations – for example, a large external change will trigger a large move in the state of the system. The dotted arrows denote the direction in which the system moves when it is not on the curve i.e. in equilibrium.  Any move away from equilibrium triggers forces that bring it back to the curve. In Figure b, the transition is non-linear and a small perturbation can trigger a regime shift – however a reversal of conditions of an equally small magnitude can reverse the regime shift. Clearly, such a system does not satisfactorily explain our current economic predicament where monetary and fiscal intervention far in excess of the initial sub-prime shock have failed to bring the system back to its previous state.

Figure c however may be a more accurate description of the current state of the economy and the market – for a certain range of conditions, there exist two alternative stable states separated by an unstable equilibrium (marked by the dotted line). As the dotted arrows indicate, movement away from the unstable equilibrium can carry the system to either of the two alternative stable states. Figure d illustrates how a small perturbation past the point F2 triggers a “catastrophic” transition from the upper branch to the lower branch – moreover, unless conditions are reversed all the way back to the point F1, the system will not revert back to the upper branch stable state. The system therefore exhibits “hysteresis” – i.e. the path matters. The forward and backward switches occur at different points F2 and F1 respectively, which implies that reversing such transitions is not easy. A comprehensive discussion of the conditions that will determine the extent of hysteresis is beyond the scope of this post – however it is worth mentioning that cognitive and organisational rigidity in the absence of sufficient diversity is a sufficient condition for hysteresis in the macro-system.

Before I apply the above framework to some events in the market, it is worth clarifying how the states in Figure d correspond to those chosen by Rick Bookstaber. The “normal” regime refers to the parts of the upper and lower branch stable states that are far from the points F1 and F2 i.e. the system is resilient to a change in external conditions. As I mentioned earlier, normal does not equate to desirable – the lower branch could be a state of collapse. If we designate the upper branch as a desirable normal state and the lower branch as an undesirable one, then the zone close to point F2 on the upper branch is the pre-crisis regime. The crisis regime is the short catastrophic transition from F2 to the lower branch if the system is left alone. If forces external to the system are applied to prevent a transition to the lower branch, then the system could either revert back to the upper branch or even stay in the crisis regime on the dotted line unstable equilibrium for a longer period.

The Magnetar Trade revisited

In an earlier post, I analysed how the infamous Magnetar Trade could be explained with a framework that incorporates catastrophic transitions between alternative stable states. As I noted: “The Magnetar trade would pay off in two scenarios – if there were no defaults in any of their CDOs, or if there were so many defaults that the tranches that they were short also defaulted alongwith the equity tranche. The trade would likely lose money if there were limited defaults in all the CDOs and the senior tranches did not default. Essentially, the trade was attractive if one believed that this intermediate scenario was improbable…Intermediate scenarios are unlikely when the system is characterised by multiple stable states and catastrophic transitions between these states. In adaptive systems such as ecosystems or macroeconomies, such transitions are most likely when the system is fragile and in a state of low resilience. The system tends to be dominated by positive feedback processes that amplify the impact of small perturbations, with no negative feedback processes present that can arrest this snowballing effect.”

In the language of critical transitions, Magnetar calculated that the real estate and MBS markets were in a fragile pre-crisis state and no intervention would prevent the rapid critical transition from F2 to the lower branch.

“Schizophrenic” Markets and the Long Crisis

Recently, many commentators have noted the apparently schizophrenic nature of the markets, turning from risk-on to risk-off at the drop of a hat. For example, John Kemp argues that the markets are “trapped between euphoria and despair” and notes the U-shaped distribution of Bank of England’s inflation forecasts (table 5.13). Although at first glance this sort of behaviour seems irrational, it may not be – As PIMCO’s Richard Clarida notes: “we are in a world in which average outcomes – for growth, inflation, corporate and sovereign defaults, and the investment returns driven by these outcomes – will matter less and less for investors and policymakers. This is because we are in a New Normal world in which the distribution of outcomes is flatter and the tails are fatter. As such, the mean of the distribution becomes an observation that is very rarely realized”

Richard Clarida’s New Normal is analogous to the crisis regime (the dotted line unstable equilibrium in Figures c and d). Any movement in either direction is self-fulfilling and leads to either a much stronger economy or a much weaker economy. So why is the current crisis regime such a long one? As I mentioned earlier, external stabilisation (in this case monetary and fiscal policy) can keep the system from collapsing down to the lower branch normal regime – the “schizophrenia” only indicates that the market may make a decisive break to a stable state sooner rather than later.

The capital constraint that is an obvious empirical reality for individual banks’ does not imply that bank bailouts are the only way to prevent a collapse of the monetary transmission channel. Although individual banks are capital constrained, the argument that an impairment in capital will induce the bank to turn away profitable lending opportunities assumes that the bank is unable to attract a fresh injection of capital. Again, this is not far from the truth: As I have explained many times on this blog, banks are motivated to minimise capital and given the “liquidity” support extended to them by the central bank during the crisis, they are incentivised to turn away offers for recapitalisation and instead slowly recapitalise by borrowing from the central bank and lending out to low-risk ventures such as T-Bonds or AAA Bonds. This of course means that they are able to avoid injecting new capital unless forced to do so by their regulator. Potential investors know of this incentive structure facing the bank and are wary of offering new equity. Moreover, injecting new capital into existing banks can be a riskier proposition than capitalising a new bank due to the opacity of bank balance sheets.

So the bank capital “limitation” that faces individual banks is real, in no small part due to the incestuous nature of their relationship with the central bank. But does this imply that the banking sector as a whole is capital constrained? The financial intermediation channel as a whole is capital constrained only if there is no entry of new firms into the banking sector despite the presence of profitable lending opportunities. Again this is empirically true but I would argue that changing this empirical reality is critical if we want to achieve a resilient financial system. The opacity of bank balance sheets means that even in the most perfectly competitive of markets, it is unlikely that old banks will find willing new investors when dramatic financial crises hit. However, investors most certainly can and should start up new unimpaired financial intermediary firms if the opportunity is profitable enough.

The onerous regulations and the time required to set up a new bank clearly discourage new entry – see for example the experience of potential new banks in the UK here. But even if we accelerate the regulatory approval process, the fundamental driver that discourages the entry of startup new banks is the Too-Big-To-Fail(TBTF) subsidy extended to the large incumbent banks that ensures that startup banks are forced to operate with significantly higher funding costs than the TBTF banks. This may be the most damaging aspect of TBTF – not only does it discriminate against existing small banks, it discourages new entry into the sector thus crippling the monetary transmission mechanism via the bank capital constraint.

Before exploring exactly what the DMU study and Gigerenzer’s work implies, it is worth emphasizing what it does not imply. First, as both DMU and Gigerenzer stress, the purpose of this post is not to argue for the superiority of the 1/N Rule over all other asset-allocation strategies. The aim is just to illustrate how simple heuristics can outperform apparently complex optimisation strategies under certain circumstances. Second, the 1/N Rule does not apply when allocating across securities with excessive idiosyncratic risk e.g. single stocks. In the DMU study for example, the N assets are equity portfolios constructed on the basis of industry classification, countries, firm characteristics etc.

So in what circumstances does the 1/N Rule outperform? Gigerenzer provides a few answers here as do DMU in the above-mentioned study but in my opinion, all of them come down to “the predictive uncertainty of the problem“. When faced with significant irreducible uncertainty, the robustness of the approach is more relevant to its future performance than its optimality. As Gigerenzer notes, this is not about computational intractability – indeed, a more uncertain environment requires a simpler approach, not a more complex one. In his words: “The optimization models performed better than the simple heuristic in data fitting but worse in predicting the future.”

Again, it’s worth reiterating that both studies do not imply that we should abandon all attempts at asset allocation – the DMU study essentially evaluates the 1/N Rule and all other strategies based on their risk-adjusted returns as defined under MPT i.e. by their Sharpe Ratio. Given that most active asset management implies a certain absence of faith in the canonical assumptions underlying MPT, some strategies could outperform if evaluated differently. Nevertheless, the fundamental conclusion regarding the importance of a robust approach holds and a robust asset allocation can be achieved in other ways. For example, when allocating across 20 asset categories, any preferred asset-allocation algorithm could be used with a constraint that the maximum allocation to any category cannot exceed 10%. Such “hard limits” are commonly used by fund managers and although they may not have any justifying rationale under MPT, this does not mean that they are “irrational”.

The need to increase robustness over optimisation when faced with uncertainty is also one of the reasons why the Kelly Criterion is so often implemented in practise as a “Fractional Kelly” strategy. The Kelly Criterion is used to determine the optimal size of sequential bets/investments that maximises the expected growth rate of the portfolio. It depends crucially upon the estimate of the “edge” that the trader possesses. In an uncertain environment, this estimate is less reliable and as Ed Thorp explains here, the edge will most likely be overestimated. In Ed Thorp’s words: “Estimates….in the stock market have many uncertainties and, in cases of forecast excess return, are more likely to be too high than too low. The tendency is to regress towards the mean….The economic situation can change for companies, industries, or the economy as a whole. Systems that worked may be partly or entirely based on data mining….Systems that do work attract capital, which tends to push exceptional [edge] down towards average values.”

A More Realistic View of Rationality and Human Cognition, Not Irrationality

Much of the debate on rationality in economics focuses on whether we as human beings are rational in the “homo economicus” sense. The “heuristics and biases” program pioneered by Daniel Kahneman and Amos Tversky argues that we are not “rational” – however, it does not question whether the definition of rationality implicit in “rational choice theory” is valid or not. Many researchers in the neural and cognitive sciences now believe that the conventional definition of rationality needs to be radically overhauled.

Most heuristics/biases are not a sign of irrationality but an entirely rational form of decision-making when faced with uncertainty. In an earlier post, I explained how Ronald Heiner’s framework can explain our neglect of tail events as a logical response to an uncertain environment, but the best exposition of this viewpoint can be seen in Gerd Gigerenzer’s work which itself is inspired by Herbert Simon’s ideas on “bounded rationality”. In his aptly named book “Rationality for Mortals: How People Cope with Uncertainty”, Gigerenzer explains the two key building blocks of “the science of heuristics”:

• The Adaptive Toolbox: “the building blocks for fast and frugal heuristics that work in real-world environments of natural complexity, where an optimal strategy is often unknown or computationally intractable”
• Ecological Rationality: “the environmental structures in which a given heuristic is successful” and the “coevolution between heuristics and environments”

The irony of course is that many classical economists had a more accurate definition of rationality than the one implicit in “rational choice theory” (See Brian Loasby’s book which I discussed here). Much of the work done in the neural sciences confirms the more nuanced view of human cognition espoused in Hayek’s “The Sensory Order” or Ken Boulding’s “The Image” (See Joaquin Fuster on Hayek or the similarities between Ken Boulding’s views and V.S. Ramachandran’s work discussed here).

Macro-Rationality is consistent with Micro-Irrationality

Even a more realistic definition of rationality doesn’t preclude individual irrationality. However, as Michael Mauboussin pointed out: “markets can still be rational when investors are individually irrational. Sufficient investor diversity is the essential feature in efficient price formation. Provided the decision rules of investors are diverse—even if they are suboptimal—errors tend to cancel out and markets arrive at appropriate prices. Similarly, if these decision rules lose diversity, markets become fragile and susceptible to inefficiency. So the issue is not whether individuals are irrational (they are) but whether they are irrational in the same way at the same time. So while understanding individual behavioral pitfalls may improve your own decision making, appreciation of the dynamics of the collective is key to outperforming the market.”

Economies as Complex Adaptive Systems: Behavioural Heterogeneity, Selection Pressures and Emphasis on System Dynamics

In my view, the ecological approach to macroeconomics is essentially a systems approach with the emphasis on the “adaptive” nature of the system i.e. incentives matter and the actors in a system tend to find ways to work around imposed rules that try to fight the impact of misaligned incentives. David Merkel explained it well when he noted: “People hate having their freedom restrained, and so when arbitrary rules are imposed, even smart rules, they look for means of escape.” And many of the posts on this blog have focused on how rules can be subverted even when economic agents don’t actively intend to do so.

The ecological approach emphasises the diversity of behavioural preferences and the role of incentives/institutions/rules in “selecting” from this pool of possible agent behaviours or causing agent behaviour to adapt in reaction to these incentives. When a behaviourally homogeneous pool of agents is observed, the ecological approach focuses on the selection pressures and incentives that could have caused this loss of diversity rather than attempting to lay the blame on some immutable behavioural trait. Again, as Rajiv Sethi puts it here: “human behavior differs substantially across career paths because of selection both into and within occupations….[Regularities] identified in controlled laboratory experiments with standard subject pools have limited application to environments in which the distribution of behavioral propensities is both endogenous and psychologically rare. This is the case in financial markets, which are subject to selection at a number of levels. Those who enter the profession are unlikely to be psychologically typical, and market conditions determine which behavioral propensities survive and thrive at any point in historical time.”

Fisher utilises the “incentives” argument but the same argument could also be made via the language of natural selection and Hannan and Freeman did exactly that in their seminal paper that launched the field of “Organizational Ecology”. Hannan and Freeman wrote the below in the context of the bailout of Lockheed in 1971 but it is as relevant today as it has ever been: “we must consider what one anonymous reader, caught up in the spirit of our paper, called the anti-eugenic actions of the state in saving firms such as Lockheed from failure. This is a dramatic instance of the way in which large dominant organizations can create linkages with other large and powerful ones so as to reduce selection pressures. If such moves are effective, they alter the pattern of selection. In our view, the selection pressure is bumped up to a higher level. So instead of individual organizations failing, entire networks fail. The general consequence of a large number of linkages of this sort is an increase in the instability of the entire system and therefore we should see boom and bust cycles of organizational outcomes.”

The Dynamics of the TBTF Problem

In Fisher’s words: “Big banks that took on high risks and generated unsustainable losses received a public benefit: TBTF support. As a result, more conservative banks were denied the market share that would have been theirs if mismanaged big banks had been allowed to go out of business. In essence, conservative banks faced publicly backed competition…..It is my view that, by propping up deeply troubled big banks, authorities have eroded market discipline in the financial system.

The system has become slanted not only toward bigness but also high risk…..if the central bank and regulators view any losses to big bank creditors as systemically disruptive, big bank debt will effectively reign on high in the capital structure. Big banks would love leverage even more, making regulatory attempts to mandate lower leverage in boom times all the more difficult…..

It is not difficult to see where this dynamic leads—to more pronounced financial cycles and repeated crises.”

Fisher correctly notes that TBTF support damages system resilience not only by encouraging higher leverage amongst large banks, but by disadvantaging conservative banks that would otherwise have gained market share during the crisis. As I have noted many times on this blog, the dynamic, evolutionary view of moral hazard focuses not only on the protection provided to destabilising positive feedback forces, but on how stabilising negative feedback forces that might have flourished in the absence of the stabilising actions are selected against and progressively weeded out of the system.

Regulatory Discretion and the Time Consistency Problem

Fisher: “Language that includes a desire to minimize moral hazard—and directs the FDIC as receiver to consider “the potential for serious adverse effects”—provides wiggle room to perpetuate TBTF.” Fisher notes that it’s difficult to credibly commit ex-ante not to bail out TBTF creditors – as long as the regulator retains any amount of discretion with the purpose of maintaining systemic stability, they will be tempted to use it.

On the Ineffectiveness of Regulation Alone

Fisher: “While it is certainly true that ineffective regulation of systemically important institutions—like big commercial banking companies—contributed to the crisis, I find it highly unlikely that such institutions can be effectively regulated, even after reform…Simple regulatory changes in most cases represent a too-late attempt to catch up with the tricks of the regulated—the trickiest of whom tend to be large. In the U.S. financial system, what passed as “innovation” was in large part circumvention, as financial engineers invented ways to get around the rules of the road. There is little evidence that new regulations, involving capital and liquidity rules, could ever contain the circumvention instinct.”

This is a sentiment I don’t often hear expressed by a regulator – As I have opined before on this blog, regulations alone just don’t work. The history of banking is one of repeated circumvention of regulations by banks, a process that has only accelerated with the increased completeness of markets. The question is not whether deregulation accelerated the process of banks’ maximising the moral hazard subsidy – it almost certainly did and this was understood even by the Fed as early as 1983. As John Kareken noted, “Deregulation Is the Cart, Not the Horse”. The question is whether re-regulation has any chance of succeeding without fixing the incentives guiding the actors in the system – it does not.

Bailouts Come in Many Shapes and Sizes

Fisher: “Even if an effective resolution regime can be written down, chances are it might not be used. There are myriad ways for regulators to forbear. Accounting forbearance, for example, could artificially boost regulatory capital levels at troubled big banks. Special liquidity facilities could provide funding relief. In this and similar manners, crisis-related events that might trigger the need for resolution could be avoided, making resolution a moot issue.”

A watertight resolution regime may only encourage regulators to aggressively utilise other forbearance mechanisms. Fisher mentions accounting and liquidity relief but fails to mention the most important “alternative bailout mechanism” – the “Greenspan Put” variant of monetary policy.

Preventing Systemic Risk perpetuates the Too-Big-To-Fail Problem

Fisher: “Consider the idea of limiting any and all financial support strictly to the system as a whole, thus preventing any one firm from receiving individual assistance….If authorities wanted to support a big bank in trouble, they would need only institute a systemwide program. Big banks could then avail themselves of the program, even if nobody else needed it. Systemwide programs are unfortunately a perfect back door through which to channel big bank bailouts.”

“System-wide” programs by definition get activated only when big banks and non-banking financial institutions such as GE Capital are in trouble. Apart from perpetuating TBTF, they encourage smaller banks to mimic big banks and take on similar tail risk thus reducing system diversity.

Shrink the TBTF Banks?

Fisher clearly prefers that the big banks be shrunk as a “second-best” solution to the incentive problems that both regulators and banks face in our current system. Although I’m not convinced that shrinking the banks is a sufficient response, even a “free market” solution to the crisis will almost certainly imply a more dispersed banking sector, due to the removal of the TBTF subsidy. The gist of the problem is not size but insufficient diversity. Fisher argues “there is considerable diversity in strategy and performance among banks that are not TBTF.” This is the strongest and possibly even the only valid argument for breaking up the big banks. My concern is that even a more dispersed banking sector will evolve towards a tightly coupled and homogenous outcome due to the protection against systemic risk provided by the “alternative bailout mechanisms”, particularly the Greenspan Put.

The fact that Richard Fisher’s comments echo themes popular with both left-wing and right-wing commentators is not a coincidence. In the fitness landscape of our financial system, our current choice is not so much a local peak as a deep valley – tinkering will get us nowhere and a significant move either to the left or to the right is likely to be an improvement.