Supply Chains Need Protection from Covid-19

Covid-19 coronavirus is bound to have significant impact on global supply chains as it impacts all sectors of the economy. See graphs from the Spectator for illustration. It is also somewhat unprecedented as it in many area both supply and demand are impacted. Both our ability to consume and produce has in many areas been significantly impacted by many countries adopting long periods of lock-down. Covid-19 is therefore a systemic shock to the economy, and a systemic shock to supply chains.

Systemic Impact on Supply Chains

The danger with supply chains is that Covid-19 significantly disrupts what are now frequently called supply chain networks. Where the term network recognises the complex inbound and outbound interdependencies between organisations. Supply chain networks are difficult to fully map and understand. Couple to this a move in the economy post WWII towards lean supply chains. Where there are low levels of redundancy in the supply chain, and resources (in all senses of the word) are delivered just in time. Disruption in one part of the network can quickly spread (cascade) to other areas. This could cause the network to fail in unpredictable ways. Where the ability of an organisation to produce something being halted by it being unable to acquire an essential input. Materials, parts, service, resources in general.

Supply Chain Deadlock

The danger with a large scale systemic shocks is that they sufficiently disrupt something like a supply chains to the point where it can no longer function. In the case of supply chains and Covid-19, one possibility is that they become deadlocked. The term deadlock is perhaps most frequently used to describe a situation where a series of interdependent processes all stop because they are all waiting for each other to do something. Process A is waiting for process B, B is waiting for process C, and C is waiting for process A. A situation that computer programmers have long had to deal with an mitigate.

Resource deadlock between two processes that are waiting for each other.
Resource Deadlock

This situation of deadlock could arise in lean supply chains. The operations of one organisation may be halted due to it being unable to acquire some essential resource for its operations. The halting of this organisation’s operations could have effects elsewhere in the supply chain. The difficulty is that this might not be a simple, easily mapped, supply chain. Similar to that shown above. Instead it might be a complex network of interdependent relationships, and therefore understanding how to release the deadlock might be extremely difficult.

International Coordination

If Covid-19 causes deadlock, releasing this deadlock in global supply chains will require international coordination. For example, it might require organisations to temporarily produce resources that they normally wouldn’t. Or resources might have to be moved between organisations. Both to allow processes in other organisations to get underway. There will need to be some way to both map resource requirements, and move them. This will need to be done internationally. Perhaps through the production of an international register of needs are resources.

Why The UK Covid-19 Strategy is High Risk

Thursday (12th March 2020) afternoon’s post COBRA press conference with the Prime Minister Johnson provided an interesting update on the UK’s strategy to delay the spread of the Covid-19 coronavirus. It was interesting because it diverts significantly with what many other countries are doing to limit coronavirus spread, but in my opinion is also a very high risk strategy.

Competing Strategies

The strategy seems to be largely based on getting people to remain at home if they think there is even a relatively small chance that they have contracted the virus (UK Gov Stay at Home Guidance). Thus, if you believe you might be showing symptoms of illness, isolate yourself for the period of 7 days. To facilitate this we are to ask our “employer, friends and family to help you get the things you need to stay at home”, sleep alone when at home, wash our hands and stay away from vulnerable people. Only if symptoms persist beyond 7 days or worsen are we to seek the advice of NHS 111.

Essentially this is a request from the Government that we act to remove ourselves from the circulating population at the first sign of illness. Doing this will ‘flatten the curve’, reducing the peak number of cases at any one time, and also push that peak into the summer months. At which point the NHS will be better able to cope. Sounds great if it works, but will it?

This strategy is in rather stark contrast to a number of other countries that have imposed highly disruptive restrictions to people’s movement and behaviour. Such as Norway (and Ireland) that advises against gatherings and human contact (NIPH), and has essentially closed itself off (Visit Norway). Italy, currently the worst affected country in Europe, has also imposed tight restrictions even on things like movement and shopping (BBC News). 

Its Complex

As a complex systems theorist this difference in intervention strategies is interesting. Complexity theory takes a view of the world as a system of interacting parts. It is also a relational worldview, which means it is not only the parts (such as people, buildings, objects general) that are important. The relationships between the parts, how they interact, is also important to how a system behaves. It is a systemic view.

The UK strategy seems to have at its centre the idea that a relatively small intervention in our behaviour, our relationship with each other and the world around us, will produce a significant effect on the spread of the virus. Self isolating at the first sign of illness, hand washing and so on. These changes are intended to flatten the curve of infection, lowering its peak, and move it to summer. This small intervention might be followed later by something more like what we have seen in Norway and Italy. The problem is, will such a small intervention limit the spread of pandemic flu?

Three Assumptions

There are at least three significant assumptions underpinning this strategy. On which its success depends.

One, people are willing and able to do it. They are assuming that people will do what they ask, and can do what they ask. There could be many reasons why people do not follow the advice. They might think that their symptoms have nothing to do with Covid-19, or don’t immediately recognise them as illness. Financially taking time off work might be impossible. Not all households have enough space at home to self isolate, including ‘sleeping alone’. Lack of compliance with the changes to behaviour will mean the virus spreads more quickly and they fail in their attempt to flatten the curve and shift the peak to the summer.

Two, they will know when to switch strategy. Central to the UK strategy is an ability to know if or when there is a need to switch from this modest intervention to a more draconian one. As at some point it is likely that more restrictions will be needed. When you do that is a trade off between wanting to shift that peak to the summer, and also limiting the spread. Also in the press conference was a decision to stop testing outside of hospital. By doing this they lose good data of how the virus is spreading in the population, making it harder to know when to change strategy. Perhaps making it harder to know when the system is approaching a tipping point into uncontrolled spreading.

There are hints that some of that data and analysis might be sought from big tech companes, with Buzzfeed reporting that Dominic Cummings will chair a “Tech CEO Round Table”. With the suggestion being that they have data that could help understand and combat the flow of coronavirus. This again is risky, as a source of data to understand viral spread social media is unproven, and perhaps this isn’t the time to be testing unproven methodologies. It does seem to betray some of the thinking within No. 10. That all problems can be manipluated through data and analytics, mediated via social media companies.

Three, changing behaviours will have the effect they predict. This assumption is more difficult to analyse and impacts any strategy. When we make changes to a complex system, in this case our day to day modes of interacting, working, and living. We cannot be sure that the system will react as we predict. It could quite easily do something unexpected, and that unexpected behaviour will interact with how the virus spreads. What that will do is currently anyone’s guess, but will soon be our lived experience. However it is an area of scientific study that we should focus more attention on. What are the systemic impacts of large scale interventions in societies? The types of interventions that will be needed to fight things like pandemics and climate change.

Is It a Good Strategy?

The logic of the strategy of countries like Norway is more simple that of the UK. There the idea is to be very restrictive and try to slow it down straight away. One would assume that they have factored in that it will not be fully complied with, some non-compliance with the restrictions will cause spreading of the virus. They might even be counting on that to spread it slowly to allow people to become immune without their systems being overwhelmed. This a shock to the system that they hope will flatten the curve.

The UK on the other hand is betting on compliance with their strategy, they need people to comply for it to spread slowly, flatten the curve, and move the peak. They are also assuming that they can spot the time when they need to move to Norway strategy should they need to. Both of these are more risky, in the sense that you are reliant on getting more things right. If people don’t comply the virus will spread rapidly through the population, and if they miss the time to change strategy then again it could get out of control.

Of course, if it works it will turn out to have been the right thing to do! However it is definitely very high risk, and the costs if it fails will be significant.

Complexity and the collapse of the financial system – from the Archive

Originally Posted on January 14, 2015 by Institute of Hazard, Risk and Resilience

by Philip Garnett and Brett Cherry

Finance influences everything, from the growth of businesses and employment to capital and even public services. As the world becomes increasingly subject to the all-encompassing influence of financialisation, it is confronted with problems that require new perspectives from studies in complex systems.

The recent global financial crisis revealed that some of the world’s brightest economists were unable to foresee the failure of the financial market they grew to admire, which became so complex that even they couldn’t understand it. What was needed at the time and at present are studies in complex systems that can help us understand the vulnerabilities of the UK financial sector, and the global financial market.

Complexity science examines the underlying nature of how systems evolve over time. ‘If the whole matters more than the parts’, as Aristotle once declared long ago, then examining the financial system using approaches from complexity science may help financial regulators and society come to grips with the ‘unknown unknowns’ of finance.

How starlings flock or swarm together in flight is an example of emergence.
How starlings flock or swarm together in flight is an example of emergence.

Ideas from complexity science useful to understanding finance include emergence. Emergence refers to something that was created from the interactions between the constituent parts of a system or multiple systems. The UK financial sector consists of interlinked banks and other financial companies. From their behaviours and interactions with each other and other systems, such as the economy, emerges the financial system. This makes the financial system similar to examples of ecosystems, such as plant or animal communities, which are also complex, interdependent and vulnerable to systemic failure.

If a financial system is complex how do you define or attribute the causes of events that lead to a crisis? The globalised banking system has radically changed finance which has led to new challenges in financial regulation. Questions from a complexity science perspective on the financial sector include (1) how do you govern a system that is too complex to understand? and (2) how can you ensure that your regulations will have the intended effect?

One potential solution is to simply make the financial system less complex. Give it a set of simple rules and its actions should be more or less predictable, but actually it doesn’t matter how simple the rules are, a system can still behave in a complex way. A good example of this comes from a computer simulation known as ‘Conway’s game of life’ that while is governed by a simple set of rules, will still have emergent properties that are difficult to predict.

In Conway’s game of life cellular automata are governed by a short list of simple rules:

For a space that is ‘populated’:

  • Each cell with one or no neighbours dies, as if by loneliness.
  • Each cell with four or more neighbours dies, as if by overpopulation.
  • Each cell with two or three neighbours survives.

For a space that is ’empty’ or ‘unpopulated’:

  • Each cell with three neighbours becomes populated.

Guided by these simple rules, complex movements and patterns emerge from the interactions between different cells.

Bank networks

Finance is complex mainly because it operates through networks of banks. There are numerous interactions that take place across these bank networks in the UK financial sector. The changes in one part of the network by regulators could produce unwanted emergent effects in another. This is because it’s incredibly difficult to predict how interventions into a system will evolve over time.
In the UK, while it could be argued that complexity helped make London a global financial centre, namely through globalisation and relaxing controls on competition, it has brought with it risks that were mostly unknown prior to the 2007-08 banking crisis. The crisis revealed how a complex banking system is in some ways more vulnerable to collapse because of the levels of connectivity between banks.

The more interconnected a bank is the more likely it could be exposed to systemic failure. If its failure is not prevented it potentially brings down other banks with it and severely hampers the financial economy. This was the case with Lehman Brothers in the US, which was in turn connected to the failure of Northern Rock in the UK through sub-prime mortgage lending. The crisis also revealed that it is even more difficult to identify the causes of failure because in a complex system what causes it to fail has no immediately apparent causal link. It is only after the event that people are able to justify what happened.

The rise and fall of the British bank population.
The rise and fall of the British bank population.

Supersize me

In the 19th century banking changed drastically in a short period of time. In 1826 the implementation of joint stock banking made merging easy to do because it lifted the restrictions placed on the size of banks. Over a 15 year period (1888-1902) 29 per cent of the total banks in Britain disappeared, and the remaining banks became much bigger as a result. 270 bank mergers took place between 1870 and 1921 alone. Large banks ate up smaller banks, which made bank cartels a historic reality by the 1920s, one that has continued to this day.

Mergers caught on quickly with other banks who copied their behaviour. In some cases banks may have been forced to copy their competitors to look like they were competing. To buck the merger trend could have been damaging for a bank. They had to copy in order to survive by merging together. It was either grow with the herd or die. Banks had created a positive feedback loop. The creation of banks promoted the creation of even more banks. A positive feedback is self-reinforcing; it creates more of the behaviour that started it.

Eventually bank mergers had to be brought to a halt because government feared that the population of banks would drop too low. Legislation influences the environment of banks and in turn affects their behaviour. Joint stock banking in 1826 allowed unrestricted mergers so banks could grow as big as they like. It also made bankers no longer liable if their bank failed. For banks in Britain, consolidation and the end of liabilities were evolutionary responses to a changing financial environment.

Bank evolution

Not only were banks allowed to increase enormously in size, but they could engage in kinds of risk taking behaviour that were unheard of in the past. However, eventually partnerships and limited liability, which made partners liable if their bank failed, had to be done away with at some point. If they had not there would have likely been thousands of banks in place, making the system completely unworkable. The UK financial system had to evolve in response to government interventions that changed the rules.

But how do legislators know how banks would evolve in response to their interventions? The answer is ‘they don’t’, not exactly, as in the case of the 2007-08 banking crisis, banks can behave in rather unpredictable ways, and before government regulation catches on to what they’re up to, it’s too late. While legislative intervention influences the financial environment, what banks do in response changes the nature of the financial system, pushing it into a new state entirely. A casualty of the financial crisis in the UK was Northern Rock.

The run on Northern Rock bank branches in 2007 is an example of a positive feedback.
The run on Northern Rock bank branches in 2007 is an example of a positive feedback.

When Northern Rock became insolvent, meaning it could no longer meet its liabilities, news of this spread rapidly and erupted into a public panic. People began queuing outside of the bank to withdraw their money inciting more people to do the same creating another positive feedback. However, Northern Rock was merely a symptom of systemic instability in the UK financial sector. This means the UK financial system was already unstable before Northern Rock went bust.

Forecasting failure

Financial history has some important lessons for how the UK banking system has responded to its changing financial environment over time. But history alone is not enough for understanding what the financial system may be in store for in the future. The UK financial sector today is even more complex than in the past. Globalisation continues to make it vastly more interconnected and complicated. If a highly connected bank in one part of the world fails it could cause others to go down with it, regardless of what country they are in.

Finance is a global enterprise and it is likely to stay that way, especially as other financial sectors beyond North America and Europe become more well-established. Similar to coming to grips with other complex global crises such as climate change, complex modelling approaches can help us better understand financial crises, not by simply modelling the potential risks the financial system is exposed to but also bank networks.

If you built a model of the financial sector it could run constantly to look for rare events or signs of systemic failure. Take real time information gathered from financial monitoring, plug it into the model and see what you get. Any information from banks could potentially be fed into a series of slightly different models that could be run simultaneously.

While no model would be a perfect replica of the financial system, modelling could reveal so-called ‘black swan events’ that otherwise would go unnoticed. However, even if you were able to predict the next financial crisis, there is still the problem of adapting to it, something that regulators tend to do on an ad-hoc basis. When the financial system crashes government implements policies that patch the holes that started the crisis, but what they don’t do is look for where new holes could open up. How can you adapt to something that you’ve never experienced before?

Adapting to the unknown

Imagine a financial system that is complex-adaptive: self-regulating and aware of what is happening. If banks were aware of the complex interconnections they share with other banks or financial companies it could lead to a more stable system, potentially discouraging the kinds of overly risky behaviours that underpinned the last banking crisis. If a system was more self-regulatory it would need to be responsible for its own stability, and regulators would have to continuously monitor and reflect upon the behaviours of banks to respond more quickly.
Simulating bank networks could help test assumptions about the behaviours governing the financial system. This would require real data to determine the accuracy of the simulation but once calibrated could say something about how banks may behave in the future, or address external influences on the financial system, as was the case with merger legislation. However, a model is still only as good as the data that are available. A complex-adaptive financial system would potentially be a more transparent structure than what is in place today that recognises how the success of banks is linked to their competitors, and if they were wiped out they would fail also.

It is already the case that decisions within the UK financial sector tend to be far more effective in influencing the financial system than external government regulation alone. For legislation to be effective it needs to investigate the potential consequences of certain policies by examining the long-term evolution of the financial system, which can be accomplished through modelling and simulation.

Where data is incomplete in financial history complex modelling and simulation can be used to refine historical understanding and provide similar insights. Models that enhance understanding of the past, and how we arrived at the present, can be run to make forecasts of possible futures, but to achieve this involves first answering what kind of financial system is desirable for the future? In a complex system the decisions we make now could have important impacts on the future of finance that need to be given attention today.

Total Systemic Failure?

My new paper, “Total Systemic Failure?”, is out (this link should get you a free copy for a limited time). I wanted to write something that was more a big picture look at how the world is working. Or perhaps how it isn’t working. I think there is a problem that the world is stuck arguing about whether climate change exists when it is a least possible that multiple global-systems are failing. I also wanted to write something that is very clearly about complexity theory and systems analysis.

Systemic Failure

So what is systemic failure? The paper goes through how complex systems theory describes how the world works. It then moves onto the idea that if a system is put under enough pressure, and this starts to affect the relationships between the parts of the system, then that system could collapse. This might either be a change in the nature of the system, so that the global system behaviour changing significantly. Or it might be that the system collapses completely.

We don’t have particularly good methods for understanding if a system is likely to collapse. How close it is, or if it is in the process of failing? We don’t know what we need to know. If we did then we might be better able to understand which systems are likely to fail, and perhaps what we might do to change that.

Total Systemic Failure

So what is different about total systemic failure? Here we are asking the question of if a number of systems start to fail, will this result in all systems failing? Systems are connected, and the degree to which they are connected and the significance of these connections is difficult to understand. Therefore, could the failure of one system precipitate the failure of another system? If this happens could it cause a cascade of failures? We do not understand how individual systems failure, we are even further away from understanding how a system-of-systems might fail.

What Can We Do?

We need to work out what state we are in, perhaps there are things we can do. Perhaps there is an opportunity to build a connected, distributed, sensor network that can provide data on global systems? It will not be easy to build this network, or analyse the data. Artificial intelligence could help, perhaps we can build AIs that can help develop the sensor network and also analyse the results. A global distributed sensor network. The AI could learn about what data is needed and what interventions could be made.

We need to start thinking more about the big picture. Otherwise we are going to find ourselves in a real mess with little hope of getting ourselves out of it.