Good Bye, Newton!


In an era of complexity, quantum thinking is needed – particularly in central banking

With complexity on the rise, central banking needs a fresh framework.

Every day, 1) simple systems’ become complex: for example, a village becomes part of a city; and 2)complex systems’ become more complex: for instance, financial markets embrace machine learning. Often, an increase in complexity leads to unintended consequences. After the 2008-crisis, unconventional easing by systemic central banks stabilized the global economy, but brought about new economic challenges and higher geopolitical risks. When ‘navigating new seas with old boats’, shipwreck is hardly avoidable: if poorly handled, ‘complex systems’ are unforgiving. Monetary policy needs an upgrade; non-linear thinking can help.

To better understand the world, we need to draw lessons from physics.

Last century, ‘quantum physics’ overtook ‘classical physics’. Developed by Newton, ‘classical physics’ was based on deterministic concepts: 1) linearity: a change in input brings about a proportional change in output; 2) cause-effect: an event is the direct, sequential result of another; and 3) a central authority is in charge of administration and control. Conversely, ‘quantum physics’ is based on probabilistic concepts: 1) non-linearity: a change in input brings about a non-proportional, apparently random change in output; 2) quantum jumps: transitions are often abrupt and can lead to systemic, transformational leaps; and 3) network relations: existence requires interaction; for example: in absence of a sequence of events, ‘time’ does not exist; without objects, there is no ‘space’.

What is a ‘complex system’?

Any system - e.g.: air transport, the internet - is constituted by a hub-and-spoke network of: 1) components; and 2) connections. A system becomes complex when: a) it lacks both an orderly pattern and a central authority; and b) connections matter more than components, because they are: i) non-linear; ii) structured with frequent feedback loops; and hence iii) capable of transforming the whole system. Intuitive examples are the global economy and telecommunications.

In ‘complex systems’, change is fast and unpredictable.

By nature, ‘complex systems’ grow or decay at exponential rates. As suggested by the butterfly-effect in chaos theory, a negligible change in an ‘original state’ can lead to a radically different ‘resulting state’. In other words, a minor event might have large, unpredictable effects - depending on its: a) position in network (i.e.: initial conditions); b) degree of connectivity (i.e.: ease of interaction); and c) size of the impact. [1]

To predict what’s coming, linear thinking doesn’t help.

The future is not a straight-line-projection of the past. Yet, most people - even scientists - think in linear, cause-effect patterns. The real world works differently: events follow discontinuous random walks, not predictable trends. At best, we can only assess the ‘likelihood of forthcoming events’. [2] A rule-based approach is often misleading; to forecast future developments, we need probabilistic - rather than deterministic – thinking.

Unintended consequences matter: the example of monetary policy.

Until 2008, monetary policy was fundamentally a “one goal, one tool” exercise: achieving price stability (the goal) by fine-tuning short-term interest rates (the tool). After the 2008-crisis, central banks created and injected a massive amount of liquidity into the global economy, adding complexity to an already ‘complex system’. The monetary expansion: a) successfully stabilized one part of the system (i.e.: the economy); but b) did not bring about ‘system stabilization’ - as tensions moved elsewhere (i.e.: into geopolitics).

  • Unconventional easing achieved stabilization … By atypically extending their mandate almost into fiscal policy, [3] central banks achieved two remarkable goals, as they: 1) avoided multiple recessions, if not a multi-year depression; and 2) stabilized potential growth.
  • … but suffered from unintended consequences, contributing to the rise of inequality … By printing money, central banks buoyed financial markets. While Wall Street got richer, Main Street remained stagnant, exacerbating disparities and feelings of unfairness. [4]
  • … populism and geopolitical tensions ... Populism ensued, all over the world. [5] Once in power, populists started spending. Higher deficits and debt brought about the need of either: a) more central bank printing; or b) re-thinking geopolitical relations; for instance, most governments turned to China: i) asking for patronage and money; or ii) imposing restraint.  
  • … and invalidated portfolio investment theory. Since the 2008-crisis, high liquidity and non-linear network connections have created asset class co-movements and volatility; to reduce risk, portfolio diversification doesn’t work as well as before.

What to do? “Complex problems” require “complex problem-solving”.

‘Managing complexity’ seems easier than it is. From the sofa - as seen on TV - F1-driving looks simple. It isn’t. Over the next few years, complexity will radically increase, driven by hyper-connectivity, artificial intelligence [6] and social changes. [7] We need to learn how to manage ‘non-linear feedback loops’, ‘quantum jumps’, and the unpredictable consequences any event can bring about.

1. A theory of ‘complex systems’, inspired by ‘quantum thinking’. As a bike needs acceleration to stabilize, ‘managing complexity’ requires shouldering higher levels of fragility. In order to move from an unstable equilibrium to temporary stability, a ‘complex system’ might require higher, and not lower, risks. Over time, both randomness and fragility increase. ‘Quantum theory’ can help anticipate likely developments.

2. New, complex tools are needed. Going forward, connections - rather than components - will be ‘game changers’. To understand and manage the quantum evolution of ‘complex systems’, new and more sophisticated instruments need to be developed. The F1 steering wheel isn’t even (shaped like) a wheel, full of buttons and switches.

3. Skilled, intelligent managers in positions of power. As showed in the 2008-crisis, in absence of intelligent experts, crashes are likely. Going forward, a degree of contrarian thinking and imagination is needed to solve complex problems. Highly-skilled individuals, able to take suitable risks, need to be put in power - and properly rewarded.

Central banks manage complex systems, without an explicit mandate.

To avoid further imbalances in the global economy, central bankers must better understand the ramifications of their actions, especially when applied to ‘complex systems’. Implicit mandates are dangerous and need to be avoided. In such context, the appointment of Christine Lagarde as ECB head is welcome news. According to ‘Newtonian orthodoxy’, she is neither an economist nor a central banker - i.e., a wrong choice. On the contrary, precisely because not tied down by old paradigms, she might intelligently surf the complexity wave.

More ‘quantum thinking’, less Newton.

Enlightened, quantum policymaking has an important role to play. Yet, ‘classical physics’ still shapes our way of learning. Cause-effect, linear thinking should be abandoned. [8] To better understand our complex world, we must say “Bye” to Newton.

1. A drastic ‘change in dimension’ can entail a ‘change in nature’. Urban growth is a valid example: with 20.6 million inhabitants, Beijing is the world’s largest capital, while Valletta - with 6,500 - is the smallest EU capital. Beijing is not just bigger than Valletta; because of its size, it is different, and faces challenges unknown in Malta’s capital.

2. On June 28, 1914, one gunshot caused two world wars: when the Archduke Franz Ferdinand—heir to the Austro-Hungarian Empire— was killed in Sarajevo by the Serbian nationalist Gavrilo Princip, nobody foresaw what would happen over the following 80 years.

3. Progressively, central banks took on more responsibilities: 1) injected liquidity to support inflation and increase employment; 2) made use of unconventional tools, such as large-scale asset purchases of government bonds with newly printed money (quantitative easing, QE); 3) provided low interest rate funding [in the EZ, (targeted) long term refinancing operations, (T)LTROs]; and to strengthen financial stability, while avoiding higher interest rates that would threaten growth: 4) enhanced bank supervision making use of “macro-prudential policies”, through: i) reserve requirements (cash that commercial banks must keep on deposit at the central bank); or ii) quantitative limits on bank lending,  to control credit growth and capital flows - mostly in emerging markets (EMs) - and deflate stock or property bubbles.

4. In a world of rising complexity, technocratic management ends up mattering more than politics, which gets the back seat and becomes a mere spectator. For decades, the international élite seemed able to manage global affairs with ease (while easily getting wealthy). Yet, when income inequality rises, technocracy is perceived as despicably rewarding.

5. Economic complexity increased the political demand for simplicity. As a reaction against a myopic élite, fed up citizens want to “return to a simpler past, with a bigger role for politics”. Populists rode the wave, promising a restoration of post-WWII economic conditions.

6. Robotics, machine learning, augmented and virtual reality, digital manufacturing, adoption of frontier technologies.

7. Over time, because of demographics and aging, behaviors, norms and values will change.

8. In other fields, this concept is not new: in the late 1960s, poststructuralist thinkers such as Derrida, Foucault, and Deleuze - together with Guattari, highlighted the importance of a shift from ‘logic and order’ to a ‘more complex way of thinking and analyzing’. See Deleuze and Guattari, 1980. A Thousand Plateaus. Trans. Brian Massumi. London and New York: Continuum, 2004. Vol. 2 of Capitalism and Schizophrenia. 2 vols. 1972-1980. Trans. of Mille Plateaux. Paris: Les Editions de Minuit.

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