Macroeconomic Resilience

This post is the first in a series that takes an ecological and dynamic approach to analysing market/macroeconomic regimes and transitions between these regimes.

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.

In a recent post, Rajiv Sethi questions the tendency to find behavioural explanations for financial crises and argues for an ecological approach instead – a sentiment that I agree with and have touched upon in previous posts on this blog. This post expands upon some of these themes.

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.”

In a previous post, I quoted Richard Fisher’s views on how bailouts cause business cycles and financial crises: “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 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.”

When the WSJ provides us with the least plausible explanation of the “Crash of 2:45 p.m.”, it is only fitting that Jon Stewart provides us with the most succinct and accurate diagnosis of the crash.

Most explanations of the crash either focus on the proximate cause of the crash or blame it all on the “perfect storm”. The “perfect storm” explanation absolves us from analysing the crash too closely, the implicit conclusion being that such an event doesn’t occur too often and not much needs to or can be done to prevent its recurrence. There are two problems with this explanation. For one, it violates Occam’s Razor – it is easy to construct an ex-post facto explanation that depends upon a confluence of events that have not occurred together before. And more crucially, perfect storms seem to occur all too often. As Jon Stewart put it: “Why is it that whenever something happens to the people that should’ve seen it coming didn’t see coming, it’s blamed on one of these rare, once in a century, perfect storms that for some reason take place every f–king two weeks. I’m beginning to think these are not perfect storms. I’m beginning to think these are regular storms and we have a sh–ty boat.”

The focus on proximate causes ignores the complexity and nonlinearity of market systems. Michael Mauboussin explained it best when he remarked: “Cause and effect thinking is dangerous. Humans like to link effects with causes, and capital markets activities are no different. For example, politicians created numerous panels after the market crash in 1987 to identify its “cause.” A nonlinear approach, however, suggests that large-scale changes can come from small-scale inputs. As a result, cause-and-effect thinking can be both simplistic and counterproductive.” The true underlying causes may be far removed from the effect, both in time and in space and the proximate cause may only be the “straw that broke the camel’s back”.

So what is the true underlying cause of the crash? In my opinion, the crash was the inevitable consequence of a progressive loss of system resilience. Why and how has the system become fragile? A static view of markets frequently attributes loss of resilience to the presence of positive feedback processes such as margin calls on levered bets, stop-loss orders, dynamic hedging of short-gamma positions and even just plain vanilla momentum trading strategies – Laura Kodres‘ paper here has an excellent discussion on “destabilizing” hedge fund strategies. However, in a dynamic conception of markets, a resilient market is characterised not by the absence of positive feedback processes but by the presence of a balanced and diverse mix of positive and negative feedback processes.

Policy measures that aim to stabilise the system by countering the impact of positive feedback processes select against and weed out negative feedback processes – Stabilisation reduces system resilience. The decision to cancel errant trades is an example of such a measure. It is critical that all market participants who implement positive feedback strategies (such as stop-loss market orders) suffer losses and those who step in to buy in times of chaos i.e. the negative-feedback providers are not denied of the profits that would accrue to them if markets recover. This is the real damage done by policy paradigms such as the “Greenspan/Bernanke Put” that implicitly protect asset markets. They leave us with a fragile market prone to collapse even with a “normal storm”, unless there is further intervention as we saw from the EU/ECB. Of course, every subsequent intervention that aims to stabilise the system only further reduces its resilience.

As positive feedback processes become increasingly dominant, even normal storms that were easily absorbed earlier will cause a catastrophic transition in the system. There are many examples of the loss of system resilience being characterised by its vulnerability to a “normal” disturbance, such as in Minsky’s Financial Instability Hypothesis or Buzz Holling’s conception of ecological resilience, both of which I have discussed earlier.

The Role of Waddell & Reed

In the framework I have outlined above, the appropriate question to ask of the Waddell & Reed affair is whether their sell order was a “normal” storm or an “abnormal” storm? More specifically, pinning the blame on a single order requires us to prove that each time in the past an order of this size was executed, the market crashed in a similar manner. It is also probable that the sell order itself was a component of a positive feedback hedging strategy and Waddell’s statement that it was selling the futures to “protect fund investors from downside risk” confirms this assessment. In this case, the Waddell sell order was an endogenous event in the framework and not an exogenous shock. Mitigating the impact of such positive feedback strategies only makes the system less resilient in the long run.

As Taleb puts it: “When a bridge collapses, you don’t look at the last truck that was on it, you look at the engineer. You’re looking for the straw that broke the camel’s back. Let’s not worry about the straw, focus on the back.” Or as Jon Stewart would say, let’s figure out why we have a sh–ty boat.

In a previous post, I outlined why cognitive rigidity is not necessarily irrational even though it may lead to a loss of resilience. However, if the universe of agent strategies is sufficiently diverse, a macro-system comprising of fragile, inflexible agents can be incredibly resilient. So a simple analysis of micro-fragility does not enable us to reach any definitive conclusions about macro-resilience - organisations and economies may retain significant resilience and an ability to cope with novelty despite the fragility of their component agents.

Yet, there is significant evidence that organisations exhibit rigidity and although some of this rigidity can be perceived as irrational or perverse, much of it arises as a rational response to uncertainty. In Hannan and Freeman’s work on “Organizational Ecology”, the presence of significant organisational rigidity is the basis of a selection-based rather than an adaptation-based explanation of organisational diversity. There are many factors driving organisational inertia, some of which have been summarised in this paper by Hannan and Freeman. These include internal considerations such as sunk costs, informational constraints, political constraints etc as well as external considerations such as barriers to entry and exit. In a later paper, Hannan and Freeman also justify organisational inertia as a means to an end, the end being “reliability”. Just as was the case in Ronald Heiner’s and V.S. Ramachandran’s framework discussed previously, inertia is a perfectly logical response to an uncertain environment.

Hannan and Freeman also hypothesise that older and larger organizations are more structurally inert and less capable of adapting to novel situations. In his book “Dynamic Economics”, Burton Klein analysed the historical record and found that advances that “resulted in new S-shaped curves in relatively static industries” do not come from the established players in an industry. In an excellent post, Sean Park summarises exactly why large organizations find it so difficult to innovate and also points to the pre-eminent reference in the management literature on this topic – Clayton Christensen’s “The Innovator’s Dilemma”. Christensen’s work is particularly relevant as it elaborates how established firms can fail not because of any obvious weaknesses, but as a direct consequence of their focus on core clients’ demands.

The inability of older and larger firms to innovate and adapt to novelty can be understood within the framework of the exploration-exploitation tradeoff as an inability to “explore” in an effective manner. As Levinthal and March put it, “past exploitation in a given domain makes future exploitation in the same domain even more efficient….As they develop greater and greater competence at a particular activity, they engage in that activity more, thus further increasing competence and the opportunity cost of exploration.” Exploration is also anathema to large organisations as it seems to imply a degree of managerial indecision. David Ellerman captures the essence of this thought process: “The organization’s experts will decide on the best experiment or approach—otherwise the organization would appear “not to know what it’s doing.”"

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.

We shouldn’t conflate this paralysis with an absence of competition between the incumbents – the competition between the incumbents may even be intense enough to ensure that they retain only a small portion of the rents that they fight so desperately to retain. What the paralysis does imply is a fierce and unified defence of the local peak that they compete for. Their defence is directed not so much against new entrants who want to play the incumbents at their own game, but at those who seek to change the rules of the game.

The best example of this is the OTC derivatives market which is the benefits of TBTF to the big banks are most evident. Bob Litan notes that clients “wanted the comfort of knowing that they were dealing with large, well-capitalized financial institutions” when dealing in CDS and this observation holds for most other OTC derivative markets. He also correctly identifies that the crucial component of effective reform is removing the advantage that the “Derivative Dealers’ Club” currently possess: “Systemic risk also would be reduced with true derivatives market reforms that would have the effect of removing the balance sheet advantage of the incumbent dealers now most likely regarded as TBTF. If end-users know that when their trades are completed with a clearinghouse, they are free to trade with any market maker – not just the specific dealer with whom they now customarily do business – that is willing to provide the right price, the resulting trades are more likely to be the end-users’ advantage. In short, in a reformed market, the incumbent dealers would face much greater competition.”

Innovation in the financial sector is also hampered because of the outsized contribution it already makes to economic activity in the United States, which makes market-broadening innovations extremely unlikely. James Utterback identified how difficult it is for new entrants to immediately substitute incumbent players: “Innovations that broaden a market create room for new firms to start. Innovation-inspired substitutions may cause established firms to hang on all the more tenaciously, making it extremely difficult for an outsider to gain a foothold along with the cash flow needed to expand and become a player in the industry.” Of course, the incumbents may eventually break away from the local peak but an extended period of stagnation is more likely.

Sustaining an environment conducive to the entry of new firms is critical to the maintenance of a resilient macroeconomy that is capable of innovating and dealing with novelty. The very least that financial sector reform must achieve is to eliminate the benefits of TBTF that currently make it all but impossible for a new entrant to challenge the status quo.

The Magnetar Trade according to ProPublica’s recent article is a long-short strategy that worked due to the perverse incentives operating in the CDO market during the boom. According to Jesse Eisinger and Jake Bernstein, Magnetar went long the equity tranche and short the senior tranches and used their position as the buyer of the equity tranche to ensure that the asset quality of the CDO was poorer than it would otherwise be. If ProPublica’s account is true, then this is a moral hazard trade i.e. Magnetar buys insurance against the burning down of a house and uses its influence as an equity buyer to significantly improve the odds of the house burning.

However, there are some hints in Magnetar’s response to the story that cast significant doubt on the accuracy of ProPublica’s narrative. To understand why this is the case, we need to understand what exactly the Magnetar trade as described in the story would look like. Magnetar’s portfolio was most likely a “close to carry neutral” portfolio consisting of long equity tranche positions and short senior/mezzanine tranche positions. In order to be carry-neutral, the notional value of senior tranches that are shorted needs to be an order of magnitude higher than the notional value of equity tranches purchased. In option parlance, this is equivalent to a zero-premium strategy consisting of short ATM options and long OTM options.

There are two reasons to execute such a strategy – one, simply to fund a “short options” strategy and the second, to execute a market-neutral “arbitrage” strategy. The significant advantage that such a long-short strategy has over a “naked short” strategy a la John Paulson is the absence of negative carry. As Taleb explains: “A butterfly position allows you to wait a lot longer for the wings to become profitable. In other words, a strategy that involves a butterfly allows you to be far more aggressive [when buying out-of-the-money options]. When you short near-the-money options, they bring in a lot of cash, so you can afford to spend more on out-of-the-money options. You can do a lot better as a spread trader.”

However, Magnetar describe their portfolio as market-neutral and “designed to have a positive return whether housing performed well or did poorly”.This implies that the portfolio was carry-positive i.e. the coupons on the long-equity positions exceeded the running-premium cost of buying protection on the senior tranches. This ensures that the portfolio will be profitable in the event that there are no defaults in the portfolio.

If the Magnetar Trade was based upon moral hazard, then it would have to short the senior tranches of the same CDO that it bought equity in and the notional of this short position would have to be multiples of the notional value of the equity position. However, Magnetar in their response to ProPublica explicitly deny this and state: “focusing solely on the group of CDOs in which Magnetar was the initial purchaser of the equity, Magnetar had a net long notional position. To put this into perspective, Magnetar would earn materially more money if these CDOs in aggregate performed well than if these CDOs performed poorly.” The operative term here is “net long notional position” as opposed to “net long position”. A net long position measured in delta terms could easily imply a net short notional position in which case the portfolio would outperform if all the tranches in the CDO were wiped out. But Magnetar seem to make it clear in their response that in the deals where they were the initial purchaser of equity, the notional of the equity positions exceeded the notional of the senior positions that they were short. They also assert that “the majority of the notional value of Magnetar’s hedges referenced CDOs in which Magnetar had no long investment” i.e. of course the notional value of their short positions exceeded that of their long positions, but these short positions were in other CDOs in which they did not have a long position.

But what about the fact that Magnetar seemed to be influencing the portfolio composition of these CDOs to include riskier assets in them? Surely this proves conclusively that Magnetar would profit if the CDOs collapsed? To understand why this may not necessarily be true, we need to examine the payoff profile of the Magnetar trade.

As with most market-neutral “arbitrage” trades, it is unlikely that the trade would deliver a positive return in every conceivable scenario. Rather, it would deliver a positive return in every scenario that Magnetar deemed probable. 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.

A distribution where intermediate scenarios are improbable can arise from many underlying processes but there is one narrative that is particularly relevant to complex adaptive systems such as financial markets. 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.

It turns out that such a framework was extremely well-suited to describing the housing market before the crash. Once house prices started falling and refinancing was no longer an option, the initial wave of defaults triggered a vicious cycle of house price declines and further defaults. Similarly, collateral requirements on leveraged investors, mark-to-market pressures and other positive feedback processes in the market created a vicious cycle of price declines in the market for mortage-backed securities and CDOs.

So what does all this have to do with Magnetar’s desire to include riskier assets in their long equity portfolios? If one believes that only a small perturbation is required to tip the market over into a state of collapse, then the long position should be weighted towards the riskiest possible asset portfolio. Essentially, the above framework implies that there is no benefit to having “safer” long positions in the long-short portfolio. The fragility of the system means that either there is no perturbation and all assets perform no matter how low-quality they are, or there is a perturbation and even “high quality” assets default.

The above framework of catastrophic shifts between multiple stable states is not uncommon, especially in fixed income markets. In fact, the Greek funding situation is a perfect example. If one had to sketch out a distribution of the yield on Greek debt, it is likely that intermediate levels are the least likely scenarios. In other words, either Greece funds at low sustainable rates or it moves rapidly to a state of default – it is unlikely that Greece raises say 50 billion Euros at an interest rate of 10%. The situation is of course made even more stark by Greece’s inability to inflate away its debt via the printing press. Of course, the bifurcation exists in fiat currency issuing countries as well, but at the point when hyperinflation kicks in.

Bank incentives are the real problem

Even if my arguments are valid, it is nevertheless obvious that even if Magnetar may not have executed the moral hazard trade, someone else could quite easily have done so. But the moral hazard trade was only possible because there was sufficient investor demand for the rated tranches of the CDO and even more crucially, because the originating bank was willing to hold onto the super-senior tranche. As I have discussed many times earlier in detail, bank demand for super-senior tranches is a logical consequence of the cheap leverage that they are afforded via the moral hazard subsidy of the TBTF doctrine. If banks were less levered, many of these deals would not have been issued at all.

In fact, two of the hedging strategies that we know were implemented in banks – UBS’ “AMPS” strategy and Howie Hubler’s trade in Morgan Stanley – were mirror images of the Magnetar trade. It is not a coincidence that bank traders chose the negatively skewed payoff distribution and Magnetar chose the positively skewed one.

Disclaimer: The above note is just my analysis of the facts and assertions in ProPublica’s article. I have no additional knowledge of the facts of the case and it is entirely possible that Magnetar are being less than fully forthright in their responses to the story. The above analysis is more useful as an illustration of how the facts as described in the article can be reconciled to a narrative that does not imply moral hazard.

Before assessing whether a resilience approach is relevant to macro-economic analysis, we need to define resilience. Resilience is best defined as “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks.”

The assertion that an ecosystem can lose resilience and become fragile is not controversial. To claim that the same can occur in social systems such as macro-economies is nowhere near as obvious, not least due to our ability to learn, forecast the future and adapt to changes in our environment. Any analysis of how social systems can lose resilience is open to the objection that loss of resilience implies systematic error on the part of economic actors in assessing the economic conditions accurately and an inability to adapt to the new reality. For example, one of the common objections to Minsky’s Financial Instability Hypothesis (FIH) is that it requires irrational behaviour on the part of economic actors. Rajiv Sethi’s post has a summary of this debate with a notable objection coming from Bernanke’s paper on the subject which insists that “Hyman Minsky and Charles Kindleberger have in several places argued for the inherent instability of the financial system, but in doing so have had to depart from the assumption of rational behavior.”

One response to this objection is “So What?” and indeed the stability-resilience trade-off can be explained within the Kahneman-Tversky framework. Another response which I’ve invoked on this blog and Rajiv has also mentioned in a recent post focuses on the pervasive principal-agent relationship in the financial economy. However, I am going to focus on a third and a more broadly applicable rationale which utilises a “rationality” that incorporates Knightian uncertainty as the basis for the FIH. 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.

Cognitive Rigidity as a Rational Response to Uncertainty

Rajiv touches on the crux of the issue when he notes: “Selection of strategies necessarily implies selection of people, since individuals are not infinitely flexible with respect to the range of behavior that they can exhibit.” But is achieving infinite flexibility a worthwhile aim? The evidence suggests that it is not. In the face of true uncertainty, infinite flexibility is not only unrealistic due to finite cognitive resources but it is also counterproductive and may deliver results that are significantly inferior to a partially “rigid” framework. V.S. Ramachandran explains this brilliantly: “At any given moment in out waking lives, our brains are flooded with a bewildering variety of sensory inputs, all of which have to be incorporated into a coherent perspective based on what stored memories already tell us is true about ourselves and the world. In order to act, the brain must have some way of selecting from this superabundance of detail and ordering it into a consistent ‘belief system’, a story that makes sense of the available evidence. When something doesn’t quite fit the script, however, you very rarely tear up the entire story and start from scratch. What you do, instead, is to deny or confabulate in order to make the information fit the big picture. Far from being maladaptive, such everyday defense mechanisms keep the brain from being hounded into directionless indecision by the ‘combinational explosion’ of possible stories that might be written from the material available to the senses.”

This rigidity is far from being maladaptive and appears to be irrational only when measured against a utopian definition of rational choice. Behavioural Economics also frequently commits the same error – As Brian Loasby notes: “It is common to find apparently irrational behaviour attributed to ‘framing effects’, as if ‘framing’ were a remediable distortion. But any action must be taken within a framework.” This notion of true rationality being less than completely flexible is not a new one – Ramachandran’s work provides the neurological bases for the notion of ‘rigidity as a rational response to uncertainty’. I have already discussed Ronald Heiner’s framework in a previous post which bears a striking resemblance to Ramachandran’s thesis:

“Think of an omniscient agent with literally no uncertainty in identifying the most preferred action under any conceivable condition, regardless of the complexity of the environment which he encounters. Intuitively, such an agent would benefit from maximum flexibility to use all potential information or to adjust to all environmental conditions, no matter how rare or subtle those conditions might be. But what if there is uncertainty because agents are unable to decipher all of the complexity of the environment? Will allowing complete flexibility still benefit the agents?

I believe the general answer to this question is negative: that when genuine uncertainty exists, allowing greater flexibility to react to more information or administer a more complex repertoire of actions will not necessarily enhance an agent’s performance.”

Brian Loasby has an excellent account of ‘rationality under uncertainty’ and its evolutionary implications in this excellent book which traces hints of this idea running through the work of Adam Smith, Alfred Marshall, George Kelly’s ‘Personal Construct Theory’ and Hayek’s ‘Sensory Order’. But perhaps the clearest exposition of the idea was provided by Kenneth Boulding in his description of subjective human knowledge as an ‘Image’. Most external information either conforms so closely to the image that it is ignored or it adds to the image in a well-defined manner. But occasionally, we receive information that is at odds with our image. Boulding recognised that such change is usually abrupt and explained it in the following manner: “The sudden and dramatic nature of these reorganizations is perhaps a result of the fact that our image is in itself resistant to change. When it receives messages which conflict with it, its first impulse is to reject them as in some sense untrue….As we continue to receive messages which contradict our image, however, we begin to have doubts, and then one day we receive a message which overthrows our previous image and we revise it completely.” He also recognises that this resistance is not “irrational” but merely a logical response to uncertainty in an “imperfect” market. “The buyer or seller in an imperfect market drives on a mountain highway where he cannot see more than a few feet around each curve; he drives it, moreover, in a dense fog. There is little wonder, therefore, that he tends not to drive it at all but to stay where he is. The well-known stability or stickiness of prices in imperfect markets may have much more to do with the uncertain nature of the image involved than with any ideal of maximizing behavior.”

Loasby describes the key principles of this framework as follows: “The first principle is that all action is decided in the space of representations. These representations include, for example, neural networks formed in the brain by processes which are outside our conscious control…None are direct copies of reality; all truncate complexity and suppress uncertainty……The second principle of this inquiry is that viable processes must operate within viable boundaries; in human affairs these boundaries limit our attention and our procedures to what is manageable without, we hope, being disastrously misleading – though no guarantees are available……The third principle is that these frameworks are useless unless they persist, even when they do not fit very well. Hahn’s definition of equilibrium as a situation in which the messages received by agents do not cause them to change the theories that they hold or the policies that they pursue offers a useful framework for the analysis both of individual behaviour and of the co-ordination of economic activity across a variety of circumstances precisely because it is not to be expected that theories and policies will be readily changed just because some evidence does not appear readily compatible with them.” (For a more detailed account, read Chapter 3 ‘Cognition and Institutions’ of the aforementioned book or his papers here and here.)

The above principles are similar to Ronald Heiner’s assertion that actions chosen under true uncertainty must satisfy a ‘reliability condition’. It also accounts for the existence of the stability-resilience trade-off. In Loasby’s words: “If behaviour is a selected adaptation and not a specific application of a general logic of choice, then the introduction of substantial novelty – a change not of weather but of climate – is liable to be severely disruptive, as Schumpeter also insisted. In biological systems it can lead to the extinction of species, sometimes on a very large scale.” Extended periods of stability narrow the scope of events that fit the script and correspondingly broaden the scope of events that appear to be anomalous and novel. When the inevitable anomalous event comes along, we either adapt too slowly or in extreme cases, not at all.

In arguing the case for the moral hazard explanation of the financial crisis, I have frequently utilised evolutionary metaphors. This approach is not without controversy and this post is a partial justification as well as an explication of the conditions under which such an approach is valid. In particular, the simple story of selective forces maximising the moral hazard subsidy that I have outlined is dependent upon the specific circumstances and facts of our current financial system.

The “Natural Selection” Analogy

One point of dispute is whether selective forces are relevant in economic systems. The argument against selection usually invokes the possibility of firms or investors surviving for long periods of time despite losses i.e. bankruptcy is not strong enough as a selective force. My arguments rely not on firm survival as the selective force but the principal-agent relationship between investors and asset managers, between shareholders and CEOs etc. Selection kicks in much before the point of bankruptcy in the modern economy. In this respect, it is relevant to note the increased prevalence of shareholder activism in the last 25 years which has strengthened this argument. Moreover, the natural selection argument only serves as a more robust justification for the moral hazard story that does not depend upon explicit agent intentionality but is nevertheless strengthened by it.

The “Optimisation” Analogy

The argument that selective forces lead to optimisation is of course an old argument, most famously put by Milton Friedman and Armen Alchian. However, evolutionary economic processes only lead to optimisation if some key assumptions are satisfied. A brief summary of the key conditions under which an evolutionary process equates to neoclassical outcomes can be found on pages 26-27 of this paper by Nelson and Winter. Below is a partial analysis of these conditions with some examples relevant to the current crisis.

Diversity

Genetic diversity is the raw material upon which Darwinian natural selection operates. Similarly, to achieve anything close to an “optimal” outcome, the strategies available to be chosen by economic agents must be sufficiently diverse. The “natural selection” explanation of the moral hazard problem which I had elaborated upon in my previous post, therefore depends upon the toolset of banks’ strategies being sufficiently varied. The toolset available to banks to exploit the moral hazard subsidy is primarily determined by two factors: technology/innovation and regulation. The development of new financial products via securitisation, tranching and most importantly synthetic issuances with a CDS rather than a bond as an underlying which I discussed here, has significantly expanded this toolset.

Stability

The story of one optimal strategy outcompeting all others is also dependent on environmental conditions being stable. Quoting from Nelson and Winter: “If the analysis concerns a hypothetical static economy, where the underlying economic problem is standing still, it is reasonable to ask whether the dynamics of an evolutionary selection process can solve it in the long run. But if the economy is undergoing continuing exogenous change, and particularly if it is changing in unanticipated ways, then there really is no “long run” in a substantive sense. Rather, the selection process is always in a transient phase, groping toward its temporary target. In that case, we should expect to find firm behavior always maladapted to its current environment and in characteristic ways—for example, out of date because of learning and adjustment lags, or “unstable” because of ongoing experimentation and trial-and-error learning.”

This follows logically from the ‘Law of Competitive Exclusion‘. In an environment free of disturbances, diversity of competing strategies must reduce dramatically as the optimal strategy will outcompete all others. In fact, disturbances are a key reason why competitive exclusion is rarely observed in ecosystems. When Evelyn Hutchinson examined the ‘Paradox of the Plankton’, one of the explanations he offered was the “permanent failure to achieve equilibrium” . Indeed, one of the most accepted explanations of the paradox is the ‘Intermediate Disturbance Hypothesis’ which concludes that ecosystem diversity may be low when the environment is free of disturbances.

Stability here is defined as “stability with respect to the criteria of selection”. In the principal-agent selective process, the analogous criteria to Darwinian “fitness” is profitability. Nelson and Winter’s objection is absolutely relevant when the strategy that maximises profitability is a moving target and there is significant uncertainty regarding the exact contours of this strategy. On the other hand, the kind of strategies that maximise profitability in a bank have not changed for a while, in no small part because of the size of the moral hazard free lunch available. A CEO who wants to maximise Return on Equity for his shareholders would maximise balance sheet leverage, as I explained in my first post. The stability of the parameters of the strategy that would maximise the moral hazard subsidy and accordingly profitability, ensures that this strategy outcompetes all others.

Moral Hazard and Agent Intentionality

A common objection to the moral hazard explanation of the financial crisis is the following: Bankers did not explicitly factor in the possibility of being bailed out. In fact, they genuinely believed that their firms could not possibly collapse under any circumstances. For example, Megan McArdle says: “I went to business school with these people, and talked to them when they were at the banks, and the operating assumption was not that they could always get the government to bail them out if something went wrong.  The operating assumption was that they had gotten a whole lot smarter, and would not require a bailout.” And Jeffrey Friedman has this to say about the actions of Ralph Cioffi and Matthew Tannin, the managers of the Bear Stearns fund whose collapse was the canary in the coal mine for the crisis: “These are not the words, nor were Tannin and Cioffi’s actions the behavior, of people who had deliberately taken what they knew to be excessive risks. If Tannin and Cioffi were guilty of anything, it was the mistake of believing the triple-A ratings.”

This objection errs in assuming that the moral hazard problem requires an explicit intention on the part of economic agents to take on more risk and maximise the free lunch available courtesy of the taxpayer. The essential idea which I outlined at the end of this post is as follows: The current regime of explicit and implicit bank creditor protection and regulatory capital requirements means that a highly levered balance sheet invested in “safe” assets with severely negatively skewed payoffs is the optimal strategy to maximise the moral hazard free lunch. Reaching this optimum does not require explicit intentionality on the part of economic actors. The same may be achieved via a Hayekian spontaneous order of agents reacting to local incentives or even more generally through “natural selection”-like mechanisms.

Let us analyse the “natural selection” argument a little further. If we assume that there is a sufficient diversity of balance-sheet strategies being followed by various bank CEOs, those CEOs who follow the above-mentioned strategy of high leverage and assets with severely negatively skewed payoffs will be “selected” by their shareholders over other competing CEOs. As I have explained in more detail in this post, the cheap leverage afforded by the creditor guarantee means that this strategy can be levered up to achieve extremely high rates of return. Even better, the assets will most likely not suffer any loss in the extended stable period before a financial crisis. The principal, in this case the bank shareholder, will most likely mistake the returns to be genuine alpha rather than the severe blowup risk trade it truly represents. The same analysis applies to all levels of the principal-agent relationship in banks where an asymmetric information problem exists.

Self-Deception and Natural Selection

But this argument still leaves one empirical question unanswered – given that such a free lunch is on offer, why don’t we see more examples of active and intentional exploitation of the moral hazard subsidy? In other words, why do most bankers seem to be true believers like Tannin and Cioffi. To answer this question, we need to take the natural selection analogy a little further. In the evolutionary race between true believers and knowing deceivers, who wins? The work of Robert Trivers on the evolutionary biology of self-deception tells us that the true believer has a significant advantage in this contest.

Trivers’ work is well summarised by Ramachandran: “According to Trivers, there are many occasions when a person needs to deceive someone else. Unfortunately, it is difficult to do this convincingly since one usually gives the lie away through subtle cues, such as facial expressions and tone of voice. Trivers proposed, therefore, that maybe the best way to lie to others is to first lie to yourself. Self-deception, according to Trivers, may have evolved specifically for this purpose, i.e. you lie to yourself in order to enable you to more effectively deceive others.” Or as Conor Oberst put it more succinctly here: “I am the first one I deceive. If I can make myself believe, the rest is easy.” Trivers’ work is not as relevant for the true believers as it is for the knowing deceivers. It shows that active deception is an extremely hard task to pull off especially when attempted in competition with a true believer who is operating with the same strategy as the deceiver.

Between a CEO who is consciously trying to maximise the free lunch and a CEO who genuinely believes that a highly levered balance sheet of “safe” assets is the best strategy, who is likely to be more convincing to his shareholders and regulator? Bob Trivers’ work shows that it is the latter. Bankers who drink their own Kool-Aid are more likely to convince their bosses, shareholders or regulators that there is nothing to worry about. Given a sufficiently strong selective mechanism such as the principal-agent relationship, it is inevitable that such bankers would end up being the norm rather than the exception. The real deviation from the moral hazard explanation would be if it were any other way!

There is another question which although not necessary for the above analysis to hold is still intriguing: How and why do people transform into true believers? Of course we can assume a purely selective environment where a small population of true believers merely outcompete the rest. But we can do better. There is ample evidence from many fields of study that we tend to cling onto our beliefs even in the face of contradictory pieces of information. Only after the anomalous information crosses a significant threshold do we revise our beliefs. For a neurological explanation of this phenomenon, the aforementioned paper by V.S. Ramachandran analyses how and why patients with right hemisphere strokes vehemently deny their paralysis with the aid of numerous self-deceiving defence mechanisms.

Jeffrey Friedman’s analysis of how Cioffi and Tannin clung to their beliefs in the face of mounting evidence to the contrary until the “threshold” was cleared and they finally threw in the towel is a perfect example of this phenomenon. In Ramachandran’s words, “At any given moment in our waking lives, our brains are flooded with a bewildering variety of sensory inputs, all of which have to be incorporated into a coherent perspective based on what stored memories already tell us is true about ourselves and the world. In order to act, the brain must have some way of selecting from this superabundance of detail and ordering it into a consistent ‘belief system’, a story that makes sense of the available evidence. When something doesn’t quite fit the script, however, you very rarely tear up the entire story and start from scratch. What you do, instead, is to deny or confabulate in order to make the information fit the big picture. Far from being maladaptive, such everyday defense mechanisms keep the brain from being hounded into directionless indecision by the ‘combinational explosion’ of possible stories that might be written from the material available to the senses.” However, once a threshold is passed, the brain finds a way to revise the model completely. Ramachandran’s analysis also provides a neurological explanation for Thomas Kuhn‘s phases of science where the “normal” period is overturned once anomalies accumulate beyond a threshold. It also provides further backing for the thesis that we follow simple rules and heuristics in the face of significant uncertainty which I discussed here.

Fix The System, Don’t Blame the Individuals

The “selection” argument provides the rationale for how the the extraction of the moral hazard subsidy can be maximised despite the lack of any active deception on the part of economic agents. Therefore, as I have asserted before, we need to fix the system rather than blaming the individuals. This does not mean that we should not pursue those guilty of fraud. But merely pursuing instances of fraud without fixing the incentive system in place will get us nowhere.

This note examines the implications of adaptation by economic agents under Knightian uncertainty for the resilience of the macroeconomic system. It expands on themes I touched upon here and here. To summarise the key conclusions,

• Under Knightian uncertainty, homo economicus is an irrelevant construct. The “optimal” course of action is one that restricts the choice of actions available and depends on a small set of simple rules and heuristics.
• The choice of actions is restricted to those that are applicable in reasonably likely or recurrent situations. Actions applicable to rare situations are ignored. Therefore, it is entirely rational to take on severely negatively skewed bets.
• By the same logic, economic agents find it harder to adapt to severe macroeconomic shocks as compared to mild shocks. This is the rationale for Axel Leijonhufvud’s “Corridor Hypothesis”.
• Minsky’s Financial Instability Hypothesis states that prolonged periods of stability reduce the width of the “corridor” until the point where a macroeconomic crisis is inevitable.
• The only assumptions needed to draw the above conclusions are the existence of uncertainty and sufficient adaptive/selective forces operating upon economic agents.
• Minksy believed that this loss of resilience in the macroeconomic system is endogenous and inevitable. Although such a loss of resilience can arise endogenously, the evidence suggests that a significant proportion of the blame for the current crisis can be attributed to the stabilising policies favoured during the Great Moderation.
• Buzz Holling’s work on ecosystem resilience has highlighted the peril of stabilising complex adaptive systems and how increased stability reduces system resilience.

Uncertainty and Negatively Skewed Payoffs

In a previous note, I explained how the existence of Knightian uncertainty leads to a perceived preference for severely negatively skewed payoffs. Ronald Heiner explains exactly how this occurs in his seminal paper on decision making under uncertainty.

Heiner argues that in the presence of uncertainty, the “optimal” course of action is one that restricts the choice of actions available and depends on a small set of simple rules and heuristics. In his words,

” Think of an omniscient agent with literally no uncertainty in identifying the most preferred action under any conceivable condition, regardless of the complexity of the environment which he encounters. Intuitively, such an agent would benefit from maximum flexibility to use all potential information or to adjust to all environmental conditions, no matter how rare or subtle those conditions might be. But what if there is uncertainty because agents are unable to decipher all of the complexity of the environment? Will allowing complete flexibility still benefit the agents?

I believe the general answer to this question is negative: that when genuine uncertainty exists, allowing greater flexibility to react to more information or administer a more complex repertoire of actions will not necessarily enhance an agent’s performance. “

In Heiner’s framework, actions chosen must satisfy a “Reliability Condition” which he summarises as: ” do so if the actual reliability in selecting the action exceeds the minimum required reliability necessary to improve performance. ” This required reliability cannot be achieved in the tails of the distribution and economic agents therefore ignore actions that are appropriate only in such situations. This explains our reluctance to insure against rare disasters which Heiner notes:

” Rare events are precisely those which are remote to a person’s normal experience, so that uncertainty in detecting which rare disasters to insure against increases as p(probability of disaster) approaches zero. Such greater uncertainty will reduce the reliability of insurance decisions as disasters become increasingly remote to a person’s normal experience.”

” At some point as p approaches zero, the Reliability Condition will be violated. This implies people will switch from typically buying to typically ignoring insurance conditions, which is just the pattern documented in Kunreuther’s 1978 study.”

Note the similarity between Heiner’s analysis of tail risks under uncertainty and Kahneman and Tversky’s distinction between “possible” and “impossible” events. The reliability problem is also connected to the difficulty of ascertaining the properties of tail events through a statistical analysis of historical data.

In an uncertainty-driven framework, it may be more appropriate to refer to this pattern as a reluctance to insure against tail risks rather than a preference for “blowup risks”. This distinction is also relevant in the moral hazard debate where the actions are often characterised better as a neglect of insurance of tail risks than an explicit taking on of such risks.

“Impossible“ Events and Axel Leijonhufvud’s “Corridor Hypothesis”

Heiner also extends this analysis of the reluctance to insure against “impossible” events to provide the rationale for Axel Leijonhufvud’s “Corridor Hypothesis” of macroeconomic shocks and recessions. In his words:

“Now suppose, analogous to the insurance case, that there are different types of shocks. some more severe than others; where larger shocks are possible but less and less likely to happen. In addition, the reliability of detecting when and how to prepare for large shocks decreases as their determinants and repercussions are more remote to agents’ normal experience.

In a similar manner to that discussed for the insurance case, we can derive that the economy’s structure will evolve so as to prepare for and react quickly to small shocks. However, outside of a certain zone or “corridor” around its long-run growth path, it will only very sluggishly react to sufficiently large, infrequent shocks.”

Minsky’s Financial Instability Hypothesis and Leijonhufvud’s Corridor

Minsky’s Financial Instability Hypothesis (FIH) asserts that stability breeds instability i.e. stability reduces the width of the corridor to the point where even a small shock is enough to push the system outside it. Leijonhufvud acknowledged Minsky’s insight that the width of the corridor was variable and depended upon the recency of past disturbances. In his own words: “Our theory implies a variable width of the corridor. Transactors who have once suffered through a displacement of unanticipated magnitude (on the order of the Great Depression, say) will be encouraged to maintain larger buffers thereafter-until the memory dims…”

The assertion that stability breeds instability is well established in ecology, especially in Buzz Holling’s work as I discussed here. Heiner’s framework explains Minsky’s assertion as the logical consequence of agent adaptation under uncertainty. But the same can also be explained via “natural selection”-like mechanisms as well. The most relevant is the principal-agent relationship. Principals that “select” agents under asymmetric information can effectively mimic the effect of natural selection in ecosystems.

Minsky also argues that sooner or later, a capitalist economy will move outside this corridor due to entirely endogenous reasons. This is a more controversial assertion and can only be evaluated through a careful analysis of the empirical evidence. The assertion that an economy can move outside the corridor due to endogenous factors is difficult to reject. All it takes is a chance prolonged period of stability. However, this does not imply that the economy must move outside the corridor, which requires us to prove that prolonged periods of stability are the norm rather than the exception in a capitalist economy.

Minsky’s Financial Instability Hypothesis and C.S. Holling’s conception of Resilience and Stability

Minsky’s idea that stability breeds instability is an important theme in the field of ecology. Buzz Holling however defined the problem as loss of resilience rather than instability. Resilience and stability are dramatically different concepts and Holling explained the difference in his seminal paper on the topic as follows:

“Resilience determines the persistence of relationships within a system and is a measure of the ability of these systems to absorb changes of state variables, driving variables, and parameters, and still persist. In this definition resilience is the property of the system and persistence or probability of extinction is the result. Stability, on the other hand, is the ability of a system to return to an equilibrium state after a temporary disturbance. The more rapidly it returns, and with the least fluctuation, the more stable it is. In this definition stability is the property of the system and the degree of fluctuation around specific states the result.”

The relevant insight in Holling’s work is that resilience and stability as goals for an ecosystem are frequently at odds with each other. In many ecosystems, “the very fact of low stability seems to produce high resilience“. Conversely, “the goal of producing a maximum sustained yield may result in a more stable system of reduced resilience”. Minsky’s hypothesis is thus better described as “stability breeds loss of resilience”, not “stability breeds instability”.

The Pathology of Macroeconomic Stabilisation

The “Pathology of Natural Resource Management” is described by Holling and Meffe as follows:

“when the range of natural variation in a system is reduced, the system loses resilience.That is, a system in which natural levels of variation have been reduced through command-and-control activities will be less resilient than an unaltered system when subsequently faced with external perturbations.”

Similarly, the dominant macroeconomic policy paradigm explicitly aims to stabilise the macroeconomy. In particular, monetary policy during the Great Moderation was used as a blunt instrument to put out all but the most minor macroeconomic fire. Stabilising policies of this nature can and do cause the same kind of loss of resilience that Minsky describes. Indeed, as I mentioned in my previous note, agent adaptation to stabilising monetary and fiscal policies can be viewed as a more profound kind of moral hazard. Economic agents may take on severely negatively skewed bets not even as an adaptation to uncertainty but merely as a rational response to stabilising macroeconomic policies.