resilience, not stability

Archive for February, 2012

The Control Revolution And Its Discontents

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One of the key narratives on this blog is how the Great Moderation and the neo-liberal era has signified the death of truly disruptive innovation in much of the economy. When macroeconomic policy stabilises the macroeconomic system, every economic actor is incentivised to take on more macroeconomic systemic risks and shed idiosyncratic, microeconomic risks. Those that figured out this reality early on and/or had privileged access to the programs used to implement this macroeconomic stability, such as banks and financialised corporates, were the big winners – a process that is largely responsible for the rise in inequality during this period. In such an environment the pace of disruptive product innovation slows but the pace of low-risk process innovation aimed at cost-reduction and improving efficiency flourishes. therefore we get the worst of all worlds – the Great Stagnation combined with widespread technological unemployment.

This narrative naturally begs the question: when was the last time we had a truly disruptive Schumpeterian era of creative destruction. In a previous post looking at the evolution of the post-WW2 developed economic world, I argued that the so-called Golden Age was anything but Schumpeterian – As Alexander Field has argued, much of the economic growth till the 70s was built on the basis of disruptive innovation that occurred in the 1930s. So we may not have been truly Schumpeterian for at least 70 years. But what about the period from at least the mid 19th century till the Great Depression? Even a cursory reading of economic history gives us pause for thought – after all wasn’t a significant part of this period supposed to be the Gilded Age of cartels and monopolies which sounds anything but disruptive.

I am now of the opinion that we have never really had any long periods of constant disruptive innovation – this is not a sign of failure but simply a reality of how complex adaptive systems across domains manage the tension between efficiency,robustness, evolvability and diversity. What we have had is a subverted control revolution where repeated attempts to achieve and hold onto an efficient equilibrium fail. Creative destruction occurs despite our best efforts to stamp it out. In a sense, disruption is an outsider to the essence of the industrial and post-industrial period of the last two centuries, the overriding philosophy of which is automation and algorithmisation aimed at efficiency and control. And much of our current troubles are a function of the fact that we have almost perfected the control project.

The operative word and the source of our problems is “almost”. Too many people look at the transition from the Industrial Revolution to the Algorithmic Revolution as a sea-change in perspective. But in reality, the current wave of reducing everything to a combination of “data & algorithm” and tackling every problem with more data and better algorithms is the logical end-point of the control revolution that started in the 19th century. The difference between Ford and Zara is overrated – Ford was simply the first step in a long process that focused on systematising each element of the industrial process (production,distribution,consumption) but also crucially putting in place a feedback loop between each element. In some sense, Zara simply follows a much more complex and malleable algorithm than Ford did but this algorithm is still one that is fundamentally equilibriating (not disruptive) and focused on introducing order and legibility into a fundamentally opaque environment via a process that reduces human involvement and discretion by replacing intuitive judgements with rules and algorithms. Exploratory/disruptive innovation on the other hand is a disequilibriating force that is created by entrepreneurs and functions outside this feedback/control loop. Both processes are important – the longer period of the gradual shedding of diversity and homogenisation in the name of efficiency as well as the periodic “collapse” that shakes up the system and puts it eventually on the path to a new equilibrium.

Of course, control has been a aim of western civilisation for a lot longer but it was only in the 19th century that the tools of control were good enough for this desire to be implemented in any meaningful sense. And even more crucially, as James Beniger has argued, it was only in the last 150 years that the need for large-scale control arose. And now the tools and technologies in our hands to control and stabilise the economy are more powerful than they’ve ever been, likely too powerful.

If we had perfect information and everything could be algorithmised right now i.e. if the control revolution had been perfected, then the problem disappears. Indeed it is arguable that the need for disruption in the innovation process no longer exists. If we get to a world where radical uncertainty has been eliminated, then the problem of systemic fragility is moot and irrelevant. It is easy to rebut the stabilisation and control project by claiming that we cannot achieve this perfect world.

But even if the techno-utopian project can achieve all that it claims it can, the path matters. We need to make it there in one piece. The current “algorithmic revolution” is best viewed as a continuation of the process through which human beings went from being tool-users to minders and managers of automated systems. The current transition is simply one where the many of these algorithmic and automated systems can essentially run themselves with human beings simply performing the role of supervisors who only need to intervene in extraordinary circumstances. Therefore, it would seem logical that the same process of increased productivity that has occurred during the modern era of automation will continue during the creation of the “vast,automatic and invisible” ‘second economy’. However there are many signs that this may not be the case. What has made things better till now and has been genuine “progress” may make things worse in higher doses and the process of deterioration can be quite dramatic.

The Uncanny Valley on the Path towards “Perfection”

In 1970, Masahiro Mori coined the term ‘uncanny valley’ to denote the phenomenon that “as robots appear more humanlike, our sense of their familiarity increases until we come to a valley”. When robots are almost but not quite human-like, they invoke a feeling of revulsion rather than empathy. As Karl McDorman notes, “Mori cautioned robot designers not to make the second peak their goal — that is, total human likeness — but rather the first peak of humanoid appearance to avoid the risk of their robots falling into the uncanny valley.”

A similar valley exists in the path of increased automation and algorithmisation. Much of the discussion in this section of the post builds upon concepts I explored via a detailed case study in a previous post titled ‘People Make Poor Monitors for Computers’.

The 21st century version of the control project i.e. the algorithmic project consists of two components:
1. More Data – ‘Big Data’.
2. Better and more comprehensive Algorithm.

The process goes hand in hand therefore with increased complexity and crucially, poorer and less intuitive feedback for the human operator. This results in increased fragility and a system prone to catastrophic breakdowns. The typical solution chosen is either further algorithmisation i.e. an improved algorithm and more data and if necessary increased slack and redundancy. This solution exacerbates the problem of feedback and temporarily pushes the catastrophic scenario further out to the tail but it does not eliminate it. Behavioural adaptation by human agents to the slack and the “better” algorithm can make a catastrophic event as likely as it was before but with a higher magnitude. But what is even more disturbing is that this cycle of increasing fragility can occur even without any such adaptation. This is the essence of the fallacy of the ‘defence in depth’ philosophy that lies at the core of most fault-tolerant algorithmic designs that I discussed in my earlier postthe increased “safety” of the automated system allows the build up of human errors without any feedback available from deteriorating system performance.

A thumb rule to get around this problem is to use slack only in those domains where failure is catastrophic and to prioritise feedback when failure is not critical and cannot kill you. But in an uncertain environment, this rule is very difficult to manage. How do you really know that a particular disturbance will not kill you? Moreover the loop of automation -> complexity -> redundancy endogenously turns a non-catastrophic event into one with catastrophic consequences.

This is a trajectory which is almost impossible to reverse once it has gone beyond a certain threshold without undergoing an interim collapse. The easy short-term fix is always to make a patch to the algorithm, get more data and build in some slack if needed. An orderly rollback is almost impossible due to the deskilling of the human workforce and risk of collapse due to other components in the system having adapted to new reality. Even simply reverting to the old more tool-like system makes things a lot worse because the human operators are no longer experts at using those tools – the process of algorithmisation has deskilled the human operator. Moreover, the endogenous nature of this buildup of complexity eventually makes the system fundamentally illegible to the human operator – a phenomenon that is ironic given that the fundamental aim of the control revolution is to increase legibility.

The Sweet Spot Before the Uncanny Valley: Near-Optimal Yet Resilient

Although it is easy to imagine the characteristics of an inefficient and dramatically sub-optimal system that is robust, complex adaptive systems operate at a near-optimal efficiency that is also resilient. Efficiency is not only important due to the obvious reality that resources are scarce but also because slack at the individual and corporate level is a significant cause of unemployment. Such near-optimal robustness in both natural and economic systems is not achieved with simplistically diverse agent compositions or with significant redundancies or slack at agent level.

Diversity and redundancy carry a cost in terms of reduced efficiency. Precisely due to this reason, real-world economic systems appear to exhibit nowhere near the diversity that would seem to ensure system resilience. Rick Bookstaber noted recently, that capitalist competition if anything seems to lead to a reduction in diversity. As Youngme Moon’s excellent book ‘Different’ lays out, competition in most markets seems to result in less diversity, not more. We may have a choice of 100 brands of toothpaste but most of us would struggle to meaningfully differentiate between them.

Similarly, almost all biological and ecological complex adaptive systems are a lot less diverse and contain less pure redundancy than conventional wisdom would expect. Resilient biological systems tend to preserve degeneracy rather than simple redundancy and resilient ecological systems tend to contain weak links rather than naive ‘law of large numbers’ diversity. The key to achieving resilience with near-optimal configurations is to tackle disturbances and generate novelty/innovation with an an emergent systemic response that reconfigures the system rather than simply a localised response. Degeneracy and weak links are key to such a configuration. The equivalent in economic systems is a constant threat of new firm entry.

The viewpoint which emphasises weak links and degeneracy also implies that it is not the keystone species and the large firms that determine resilience but the presence of smaller players ready to reorganise and pick up the slack when an unexpected event occurs. Such a focus is further complicated by the fact that in a stable environment, the system may become less and less resilient with no visible consequences – weak links may be eliminated, barriers to entry may progressively increase etc with no damage done to system performance in the stable equilibrium phase. Yet this loss of resilience can prove fatal when the environment changes and can leave the system unable to generate novelty/disruptive innovation. This highlights the folly of statements such as ‘what’s good for GM is good for America’. We need to focus not just on the keystone species, but on the fringes of the ecosystem.


The Business Cycle in the Uncanny Valley – Deterioration of the Median as well as the Tail

Many commentators have pointed out that the process of automation has coincided with a deskilling of the human workforce. For example, below is a simplified version of the relation between mechanisation and skill required by the human operator that James Bright documented in 1958 (via Harry Braverman’s ‘Labor and Monopoly Capital’). But till now, it has been largely true that although human performance has suffered, the performance of the system has gotten vastly better. If the problem was just a drop in human performance while the system got better, our problem is less acute.


But what is at stake is a deterioration in system performance – it is not only a matter of being exposed to more catastrophic setbacks. Eventually mean/median system performance deteriorates as more and more pure slack and redundancy needs to be built in at all levels to make up for the irreversibly fragile nature of the system. The business cycle is an oscillation between efficient fragility and robust inefficiency. Over the course of successive cycles, both poles of this oscillation get worse which leads to median/mean system performance falling rapidly at the same time that the tails deteriorate due to the increased illegibility of the automated system to the human operator.


The Visible Hand and the Invisible Foot, Not the Invisible Hand

The conventional economic view of the economy is one of a primarily market-based equilibrium punctuated by occasional shocks. Even the long arc of innovation is viewed as a sort of benign discovery of novelty without any disruptive consequences. The radical disequilibrium view (which I have been guilty of espousing in the past) is one of constant micro-fragility and creative destruction. However, the history of economic evolution in the modern era has been quite different – neither market-based equilibrium nor constant disequilibrium, but a series of off-market attempts to stabilise relations outside the sphere of the market combined with occasional phase transitions that bring about dramatic change. The presence of rents is a constant and the control revolution has for the most part succeeded in preserving the rents of incumbents, barring the occasional spectacular failure. It is these occasional “failures” that have given us results that in some respect resemble those that would have been created by a market over the long run.

As Bruce Wilder puts it (sourced from 1, 2, 3 and mashed up together by me):

The main problem with the standard analysis of the “market economy”, as well as many variants, is that we do not live in a “market economy”. Except for financial markets and a few related commodity markets, markets are rare beasts in the modern economy. The actual economy is dominated by formal, hierarchical, administrative organization and transactions are governed by incomplete contracts, explicit and implied. “Markets” are, at best, metaphors…..
Over half of the American labor force works for organizations employing over 100 people. Actual markets in the American economy are extremely rare and unusual beasts. An economics of markets ought to be regarded as generally useful as a biology of cephalopods, amid the living world of bones and shells. But, somehow the idealized, metaphoric market is substituted as an analytic mask, laid across a vast variety of economic relations and relationships, obscuring every important feature of what actually is…..
The elaborate theory of market price gives us an abstract ideal of allocative efficiency, in the absence of any firm or household behaving strategically (aka perfect competition). In real life, allocative efficiency is far less important than achieving technical efficiency, and, of course, everyone behaves strategically.
In a world of genuine uncertainty and limitations to knowledge, incentives in the distribution of income are tied directly to the distribution of risk. Economic rents are pervasive, but potentially beneficial, in that they provide a means of stable structure, around which investments can be made and production processes managed to achieve technical efficiency.
In the imaginary world of complete information of Econ 101, where markets are the dominant form of economic organizations, and allocative efficiency is the focus of attention, firms are able to maximize their profits, because they know what “maximum” means. They are unconstrained by anything.
In the actual, uncertain world, with limited information and knowledge, only constrained maximization is possible. All firms, instead of being profit-maximizers (not possible in a world of uncertainty), are rent-seekers, responding to instituted constraints: the institutional rules of the game, so to speak. Economic rents are what they have to lose in this game, and protecting those rents, orients their behavior within the institutional constraints…..
In most of our economic interactions, price is not a variable optimally digesting information and resolving conflict, it is a strategic instrument, held fixed as part of a scheme of administrative control and information discovery……The actual, decentralized “market” economy is not coordinated primarily by market prices—it is coordinated by rules. The dominant relationships among actors is not one of market exchange at price, but of contract: implicit or explicit, incomplete and contingent.

James Beniger’s work is the definitive document on how the essence of the ‘control revolution’ has been an attempt to take economic activity out of the sphere of direct influence of the market. But that is not all – the long process of algorithmisation over the last 150 years has also, wherever possible, replaced implicit rules/contracts and principal-agent relationships with explicit processes and rules. Beniger also notes that after a certain point, the increasing complexity of the system is an endogenous phenomenon i.e. further iterations are aimed at controlling the control process itself. As I have illustrated above, after a certain threshold, the increasing complexity, fragility and deterioration in performance becomes a self-fulfilling positive feedback process.

Although our current system bears very little resemblance to the market economy of the textbook, there was a brief period during the transition from the traditional economy to the control economy during the early part of the 19th century when this was the case. 26% of all imports into the United States in 1827 sold in an auction. But the displacement of traditional controls (familial ties) with the invisible hand of market controls was merely a transitional phase, soon to be displaced by the visible hand of the control revolution.

The Soviet Project, Western Capitalism and High Modernity

Communism and Capitalism are both pillars of the high-modernist control project. The signature of modernity is not markets, but technocratic control projects. Capitalism has simply done it in a manner that is more easily and more regularly subverted. It is the occasional failure of the control revolution that is the source of the capitalist economy’s long-run success. Conversely, the failure of the Soviet Project was due to its too successful adherence and implementation of the high-modernist ideal. The significance of the threat from crony capitalism is a function of the fact that by forming a coalition and partnership of the corporate and state control projects, it enables the implementation of the control revolution to be that much more effective.

The Hayekian argument of dispersed knowledge and its importance in seeking equilibrium is not as important as it seems in explaining why the Soviet project failed. As Joseph Berliner has illustrated, the Soviet economy did not fail to reach local equilibria. Where it failed so spectacularly was in extracting itself out of these equilibria. The dispersed knowledge argument is open to the riposte that better implementation of the control revolution will eventually overcome these problems – indeed much of the current techno-utopian version of the control revolution is based on this assumption. It is a weak argument for free enterprise, a much stronger argument for which is the need to maintain a system that retains the ability to reinvent itself and find a new, hitherto unknown trajectory via the destruction of the incumbents combined with the emergence of the new. Where the Soviet experiment failed is that it eliminated the possibility of failure, that Berliner called the ‘invisible foot’. The success of the free enterprise system has been built not upon the positive incentive of the invisible hand but the negative incentive of the invisible foot to counter the visible hand of the control revolution. It is this threat and occasional realisation of failure and disorder that is the key to maintaining system resilience and evolvability.




  • Borrowing from Beniger, control here simply means “purposive influence towards a predetermined goal”. Similarly, equilibrium in this context is best defined as a state in which economic agents are not forced to change their routines, theories and policies.
  • On the uncanny valley, I wrote a similar post on why perfect memory does not lead to perfect human intelligence. Even if a computer benefits from more data and better memory, we may not. And the evidence suggests that the deterioration in human performance is steepest in the zone close to “perfection”.
  • An argument similar to my assertion on the misconception of a free enterprise economy as a market economy can be made about the nature of democracy. Rather than as a vehicle that enables the regular expression of the political will of the electorate, democracy may be more accurately thought of as the ability to effect a dramatic change when the incumbent system of plutocratic/technocratic rule diverges too much from popular opinion. As always, stability and prevention of disturbances can cause the eventual collapse to be more catastrophic than it needs to be.
  • Although James Beniger’s ‘Control Revolution’ is the definitive reference, Antoine Bousquet’s book ‘The Scientific Way of Warfare’ on the similar revolution in military warfare is equally good. Bousquet’s book highlights the fact that the military is often the pioneer of the key projects of the control revolution and it also highlights just how similar the latest phase of this evolution is to early phases – the common desire for control combined with its constant subversion by reality. Most commentators assume that the threat to the project is external – by constantly evolving guerrilla warfare for example. But the analysis of the uncanny valley suggests that an equally great threat is endogenous – of increasing complexity and illegibility of the control project itself. Bousquet also explains how the control revolution is a child of the modern era and the culmination of the philosophy of the Enlightenment.
  • Much of the “innovation” of the control revolution was not technological but institutional – limited liability, macroeconomic stabilisation via central banks etc.
  • For more on the role of degeneracy in biological systems and how it enables near-optimal resilience, this paper by James Whitacre and Axel Bender is excellent.
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Written by Ashwin Parameswaran

February 21st, 2012 at 5:38 pm

Private Equity and the Greenspan Put

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Mitt Romney’s campaign for the Republican nomination for the US Presidential election has triggered a debate as to the role of private equity (PE) in the economy. The critical of the private equity industry tend to focus on their perceived tendency to layoff employees and increase leverage. Regarding layoffs, there is very little evidence that PE firms are worse than the rest of the corporate sector. However, this does not imply that their role is entirely positive. But it does imply that the excesses of PE mirror the excesses of the larger economy during the neoliberal era. This is obvious when the role of leverage is examined. As Mike Konczal notes, “something did change during the 1980s, and LBO was part of this overall shift.” The road that started with LBOs in the 1980s ended with the rash of dividend recapitalisations between 2003–2007, a phenomenon that has even resurfaced post the crisis.

It is easy to find proximate causes for this dynamic and commentators on both sides of the political spectrum attribute much of the above to the neo-liberal revolution – the doctrine of shareholder value maximisation, high-powered managerial incentives, a drive towards increased efficiency etc. The acceleration of this process in the last decade usually gets explained away as the inevitable consequence of a financial bubble with irrationally exuberant banks making unwise loans to fuel the leverage binge. But these narratives miss the obvious elephant in the room – the role of monetary policy and in particular the dominant monetary policy doctrine underpinning the ‘Great Moderation’ which focused on shoring up financial asset prices as the primary channel of monetary stimulus, otherwise known as the ‘Greenspan Put’. All the above proximate causes were the direct and inevitable result of economic actors seeking to align themselves to the central banks’ focus on asset price stabilisation.

As I elaborated upon in an earlier post:

creating any source of stability in a capitalist economy incentivises economic agents to realign themselves to exploit that source of security and thereby reduce risk. Similar to how banks’ adaptation to the intervention strategies preferred by central banks by taking on more “macro” risks, macro-stabilisation incentivises real economy firms to shed idiosyncratic micro-risks and take on financial risks instead. Suppressing nominal volatility encourages economic agents to shed real risks and take on nominal risks. In the presence of the Greenspan/Bernanke put, a strategy focused on “macro” asset price risks and leverage outcompetes strategies focused on “risky” innovation. Just as banks that exploit the guarantees offered by central banks outcompete those that don’t, real economy firms that realign themselves to become more bank-like outcompete those that choose not to…….When central bankers are focused on preventing significant pullbacks in equity prices (the Greenspan/Bernanke put), then real-economy firms are incentivised to take on more systematic risk and reduce their idiosyncratic risk exposure.

The focus on cost reduction and layoffs is also a result of this increased market-sensitivity combined with the macro-stabilisation commitment encourages low-risk process innovation and discourages uncertain and exploratory product innovation. The excesses of some forms of private equity are often instances in which they apply the maximum possible leverage to extract the rents available via the Greenspan Put. Dividend recaps are one such instance.

James Kwak summarises the case of Simmons Bedding Company:

In 2003, for example, THL bought Simmons (the mattress company) for $327 million in cash and $745 million in debt. In 2004, Simmons (now run by THL) issued more debt and paid a $137 million dividend to THL; in 2007, it issued yet more debt and paid a $238 million dividend to THL. Simmons filed for bankruptcy in 2009.

The obvious question here is why banks and financial institutions would lend so much money and allow firms to lever up so dramatically. Kwak lays the blame on the financial bubble, principal-agent problems, bankers bonus structures etc. TED counters that lenders do in fact typically make informed decisions and also correctly points out that the rest of corporate America is not immune to such leveraged mishaps either. Both explanations ignore the fact that this sort of severely tail-risk heavy loan is exactly the payoff which maximises the banks‘ and their employees’ own moral hazard rent extraction. In an earlier post, I identified that many hedge fund strategies are an indirect beneficiary of moral hazard rents – the same argument also applies to some private equity strategies.

But as I have noted on many occasions, the moral hazard problem from tail-risk hungry TBTF financial institutions is simply the tip of the iceberg. It was not only the banks with access to cheap leverage that were heavily invested in “safe” assets, but also asset managers, money market mutual funds and even ordinary investors. The Greenspan/Bernanke Put incentivises a large proportion of real and financial actors in the economy into taking on more and more tail risk with the expectation that the Fed will avoid any outcomes where these risks will be realised.

Too many commentators fail to recognise that so much of what has made the neo-liberal era a thinly disguised corporate welfare state can be traced to the impact of a supposedly “neutral” macroeconomic policy instrument that in reality has grossly regressive consequences. To expect corporate America to not take advantage of the free lunch offered to it by the Fed is akin to dangling a piece of meat in front of a tiger and expecting it not to bite your hand off.

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Written by Ashwin Parameswaran

February 1st, 2012 at 5:56 pm