David Korowicz. 2013. Catastrophic Shocks through Complex Socio-Economic Systems.

The globalized economy has become more complex (connectivity, interdependence, and speed), delocalized, with increasing concentration within critical systems. This has made us all more vulnerable to systemic shocks. This paper provides an overview of the effect of a major pandemic on the operation of complex socio-economic systems using some simple models. It discusses the links between initial pandemic absenteeism and supply-chain contagion, and the evolution and rate of shock propagation. It discusses systemic collapse and the difficulties of re-booting socio-economic systems.

1. A New Age of Risk

Consider the following scenarios:

• A highly contagious pandemic outbreak in South-East Asia (of comparable or greater human impact than the 1918 influenza outbreak) .
• A disorderly break-up of the Eurozone and global financial system implosion.
• A “perfect storm” during a time of major global financial instability – there are terrorist attacks on North African oil installations (partially driven by social unrest arising from record food prices) & a category 5 hurricane hits a major population/ industrial/ oil producing regions of the US east coast.

These are all examples of potential global shocks, that is hazards that could drive fast and severe cascading impacts mediated through global systems. Global systems include telecommunications networks; financial and banking networks; trade networks; and critical infrastructure networks. These systems are themselves highly interdependent and together form part of the globalized economy.

One of the primary issues for this paper are, given any significant hazard, how does the impact spread through the globalized economy and in what way are we vulnerable to the failure of interconnected systems. To answer this we need to understand how complex societies are connected and how they have changed over time. The globalized economy is an example of a complex adaptive system that dynamically links people, goods, factories, services, institutions and commodities across the globe.

The state is characterized by exponential growth in Gross World Product of about 3.5% per annum over nearly 200 years within a range of several percentage points. This had correlated with emergent and self-organizing growth in socio-economic complexity which is reflected in the growth of the:

• Number of interacting parts (nodes): This includes exponential population growth; the 50,000+ different items available in Wal-Mart; the 6 billion+ digitally connected devices; the number of cars, factories, power plants, mines and so on.
• Number of linkages (edges): This includes the 3 billion passengers traveling between 4000 airports on over 50 million flights each year; the 60,000 cargo ships moving between 5000 ports with about a million ship movements a year; the average number of media channels (internet sites, TV channels, twitter feeds) per person times the population; and the billions of daily financial transactions.
• Levels of interdependence between nodes: The growing number of inputs necessary to make a good, service, livelihood, infrastructural output or the function of society as a whole.
• The speed of processes (or time compression) :This includes the increasing speed of financial transactions; transportation; digital signaling; and Just-In-Time logistics. If we consider the globalized economy as a form of singular organism, we can understand this process as an increasing metabolic rate.
• Efficiency: increasing competition and global trade arbitrage driving down inventories; and globalized economies of scale.
• Concentration: The emergence of ʻhubsʼ within the globalized economy- a small number of very highly connected nodes whose function (or loss of function) have a disproportionate role in the operation of the globalized economy . For example, banks are not connected at random to other banks, rather a very small number of large banks are highly connected with lots of other banks, who have few connections to each other. These arrangements are sometimes known as scale-free networks. We can also see concentration in critical infrastructure, and trade networks.
• De-localization: The conditions of personal welfare; business or service output; or countryʼs economic output is smeared over the whole globalized economy. The corollary is that if there is a major failure of the systems integration in the globalized economy, a localized community may have extreme difficulties meeting its basic needs.

Economic and complexity growth have in many ways reduced risk. Localized agricultural failure once risked famine in isolated subsistence communities, but now such risk is spread globally. It has made critical infrastructure such as sewage treatment and clean water available and affordable. Global financial markets enable an array of risks, from home insurance and pensions to default risk and export credit insurance, to be dispersed and potential volatility reduced. Indeed, what is remarkable is just how reliable our complex society is given the number of time sensitive inter-connections.

Another way of saying all this is that our society is very resilient, within certain bounds, to a huge range interruptions in the flow of goods and services. Within those bounds our society is self-stabilizing. For example supply-chain shocks from the Japanese tsunami in 2011, the eruption of the Icelandic Eyjafjallajokull volcano in 2010 or the UK fuel blockades in 2000 all had severe localized effects in addition to shutting down some factories across the world as supply-chains were interrupted. However the impacts did not spread and amplify, and normal functioning of the local economy quickly resumed.

But we know from many complex systems in nature and society that a system can rapidly shift from one state to another as a threshold is crossed (Scheffer 2009). One way a state shift can occur is when a shock drives the system out of its stability bounds. The form of those stability bounds can increase or decrease resilience to shocks depending upon whether the system is already stressed prior to the shock.

The commonalities of global integration mean that diverse hazards may lead to common shock consequences. The systems that transmit shocks are also the systems we depend upon for our welfare and the operation of businesses, institutions and society, so to borrow Marshal McLuhanʼs phrase, the medium is the message. One of the primary consequences of a generic shock is an interruption in the flow of goods and services in the economy. This has diverse and profound implications – including food security crises, business shut-downs, critical infrastructure risks and social crises. This can in turn quickly destroy forward looking confidence in an economy with major consequences for financial and monetary stability which depend ultimately on the collateral of real economic production. More generally it can entail multi-network and de-localized cascading failure leading to a collapse in societal complexity.

Previously the dynamics of such a scenario was studied when the initial shock was caused by a systemic banking collapse and monetary shock. This coupled the exchange of goods and services causing financial system supply-chain cross contagion and a re-enforcing cascade of de-localizing multi-system risk (Korowicz 2012). In this paper a similar methodology is used to look at the socio-economic implications of a major pandemic.

2. Socio-economic Impact of a Major Pandemic

We are interested in the socio-economic implications of a major influenza pandemic whose initial impact would be direct absenteeism from illness and death, and absenteeism for family and prophylactic reasons. The pandemic wave (we will only consider one) lasts 10-15 weeks. We assume this causes an absenteeism rate of 20% or 40% over the peak period of 2-4 weeks, and a rate above 20% for 4-8 weeks when the peak is 40%. This represents our initial impact. Our question is then what happens next.

Some key personnel that might not show up for work are in health care, shipping / train / truck drivers, (and I’ve read elsewhere that the electric grid might fail if key workers don’t show up because they’re afraid of catching something at work, and that would bring ALL systems down).

how a health service would manage a pandemic when its own operation is compromised

3. Vulnerability Revealed

One way to understand complex socio-economic systems is to study occasions when there has been some systemic failure. In September 2000 truckers in the United Kingdom, angry at rising diesel duties, blockaded refineries and fuel distribution outlets. Consequences:

a)      The petrol stations reliance on Just-In-Time re-supply meant the impact was rapid. Within 2 days about half of the petrol stations had run out of fuel and supplies to industry and utilities had begun to be severely affected.

b)     People couldn’t get to work and businesses could not be re-supplied.

c)      Supermarkets had begun to run out of food

d)     Large parts of the manufacturing sector were about to shut down

e)      Hospitals began to offer emergency only care

f)       Automatic cash machines could not be re-supplied

g)      The postal service was severely affected.

h)      There was panic buying at supermarkets and petrol stations.

i)        It was estimated that after the first day an average 10% of national output was lost. Surprisingly, at the height of the disruption, commercial truck traffic on the UK road network was only 10-12% below average values. There were clear indications that had the fuel blockades gone on just a few days longer large parts of UK manufacturing including the automotive, defense and steel industries would have had to shut down.

Failure of production or supply from one area can shut down factories on the other side of the world within days of the initial interruption as was seen in the 2010 Icelandic volcano eruption in 2010 and the 2011 Japanese tsunami and Thai flooding.

A report from the think-tank Chatham House on the impacts of the Icelandic volcano and subsequent interviews with businesses about its impact and their preparedness came to the general conclusion: “One week seems to be the maximum tolerance of a Just-In-Time economy”…..before major shut-downs in business and industries would occur, and things would not just return to normal afterwards. … many businesses said that had the disruption continued just a few days longer, it would have taken at least a month for companies to recover” And a quote from a desk study on the impact of a one week long absence of (just) trucks in the UK economy, things would not just return to normal (McKinnon 2006): “..After a week, the country would be plunged into a deep social and economic crisis. It would take several weeks for most production and distribution systems to recover”

The studies do not consider what would happen if the primary disruption were to continue for many weeks.

4. Interdependence, Liebigʼs law, and Cascading

One of the defining features of rising complexity is growing interdependence. Now, the output of a person, service provider, factory, piece of critical infrastructure, etc., depends upon ever more inputs, be they tools, intermediate products, consumables, specialist skills and knowledge or collective societal infrastructures. And those outputs in turn become further inputs through the dispersed networks of the globalized economy.

Some of the least substitutable critical inputs are labeled hubs. Hubs are things like electricity, fuel, water, and financial system functionality – things generally referred to as critical infrastructure. They are societal services and functions upon which all society depends.

A simple but important principle, Liebig ʼs Law of the Minimum, says that the production is constrained by the scarcest critical input. So even if you have ample supplies of all but one critical input, your production fails. That is, production fails on the weakest link.

This explains why the most exposed businesses to supply-chain failure are the most complex businesses. First they have some of the most inputs (making a car can mean assembling up to 15,000 components). Second, they have more inputs are very complex and specialized, and so cannot be easily substituted. Alternative production lines might not be available or take months to re-engineer or specialist skills may be in limited supply. Thus, auto and electronics manufacturers were some of the most affected by the Icelandic volcano, the Japanese tsunami and the Thai flooding in 2011. What Liebig ʼs law shows is that you do not need to lose everything to stop a business, service or function or society – just the right bit. This helps to explain why a loss of only 10-12% of commercial vehicles had such a big impact during the fuel blockades in the U.K. As our economies have become more complex we have been adding more inputs into our lives, goods and services, and the functioning of our societies. More of these are critical with low substitutability.

Let us now apply Liebig’s law to pandemic absenteeism. The people affected by a pandemic are part of the supply of inputs to any systems function. There may be many people contributing to one output of a business, service or function. We assume that most employees are either unnecessary for the period of the pandemic, can telecommute, or are easily substituted. But there is a smaller number of sub-functional roles occupied most likely by those with specialist skills who are critical with low substitutability. If any one of them is unavailable, the sub-functional role fails and with that, the output of the whole organization/ function.

With the loss of this output good or service (especially if it is critical with low substitutability) other businesses and services may be affected potentially causing cascading affects through complex socioeconomic networks as a whole.

5. Time and Cascading Failure

There is always a level of absenteeism and a percentage of goods and services that can’t be delivered for whatever reason. The reason you don’t have supply-chain contagion spreading with every problem is that complex societies are efficient at finding alternative suppliers, and some inventories are carried to help when there is a hiatus. Also, most factories don’t produce very critical things or there is lots of substitutability. One won’t miss a brand of toothpaste in the supermarket when there are 20 brands available.

To initiate a cascading failure:

1)      It has to be large scale, i.e. from a major hub failure or large enough absenteeism.

2)      The function needs to be central, like the electric grid, financial system, or pandemic that keeps people from going to work. All of these are critically connected to other parts of a socio-economic network. Thus the effects of a pandemic or hub failure in a weakly connected country, Mali say, would be unlikely to spread supply-chain failure widely. Thus we can conclude that there might be point above which supply-chain contagion takes off, and below which the society is still operational and recovery can occur. This point depends upon the initial pandemic absenteeism rate and the societies complexity at the epicenter of the pandemic.

A simple model of supply-chain failure can be based upon the idea that the more supply-chains are disrupted or infected, the greater the chance that further supply-chains will be infected

6. External Cross-Network Contagion

Imagine a pandemic outbreak occurs in South-East Asia. The main vectors through which a shock could propagate outside the region are pandemic contagion, financial system contagion, and supply-chain contagion.

We would expect the shock to spread at different rates (banking shock could travel faster than supply-chain contagion because the operational speed of the financial system is greater than the inventory turn-over time).

Some countries’ role in trade is far more important to the globalized economy than others. The more important the initially impacted region is, the greater is the likelihood of spreading supply-chain contagion globally. Kali measured countries’ influence on global trade, not only by trade volumes, but the influence a country has on the global trading system. They used an Importance Index to rank their influence. For example, they find that Thailand, which was at the center of the 1997-1998 Asian financial crisis ranked 22nd in terms of global trade share, but 11th on their level of importance. In another study, Garas used an epidemic model to look at the potential any country had to spread a crisis. One of their data sets is based upon international trade in 2007. It uses a measure of centrality to identify countries with the power to spread a crisis via their level of trade integration. Like the previous paper, the centrality in the network does not necessarily correspond to those countries with the highest trade volumes. There are 12 inner core countries, which are listed in no particular order are: China, Russia, Japan, Spain, UK, Netherlands, Italy, Germany, Belgium-Luxembourg, USA, and France.

Hidalgo used international trade data to look at two things – the diversity of products a country produces, and the exclusivity of what they produce. An exclusive product is something made by few other countries. Most countries in the world are non- diversified and make standard products. The most complex countries are diversified and make more exclusive products. More exclusive products have less substitutability.

Financial system contagion outside the initially impacted region could be through banking networks, the bond market, the shadow banking system, currency volatility and confidence. Again the structure of financial networks and the centrality of the region with respect to financial assets and liabilities would determine the extent of any shock.

More broadly, if an economy was shattered, and its forward looking viability looked both precarious and uncertain one would expect a collapse in the value of a country’s currency. Rather than helping exports (which would be very little because the economy’s productive capacity had collapsed), it would hinder imports of emergency supplies and make debt in external currencies much more difficult to service. The economic damage and reduced economic prospects may then cause tightened credit conditions, spiraling bond yields and systemic bank failure.

There are also issues that are most pertinent for more complex societies. We imagine that after a pandemic wave people are again available for work. But people cannot however become productive immediately because other inputs are also needed. But those inputs are stalled because they rely upon other inputs and so on. More broadly we may define Recursion failure as: “the inability of a complex economy to easily resume production and trade after a significant collapse because in a complex and interdependent economy, production and trade must resume in order for production and trade to resume”.

Further, even if a government wanted to rebuild, it may be too complex to orchestrate resumption from the top down. This is because the economy has evolved by self-organization. Nobody ever put its elements together in the first place. And even if it could be done, the systems of command, control and supply that might do it would be the very systems that had been undermined. Over time entropy would become an issue as engines rust, reagents become contaminated and expected maintenance and repairs left undone. This would all add to the cost and inputs needed for resumption.

The longer a socio-economic system spends in the critical regime, the more likely it is to undergo a complete systemic collapse and loss of basic function. In addition, the longer it spends in this state, the more difficult it may be to ever return to its pre-pandemic state. This is a complex society’s equivalent of a heart attack. When a person has a heart attack, there is a brief period during which CPR can revive the person. But beyond a certain point when there has been cascading failure in co-dependent life support systems, the person cannot be revived. This means that the socioeconomic system could be changed irretrievably and the job of society and government would be to both manage the crisis and plot a fundamentally different path.

To make the systems we depend upon more resilient ideally we would want more redundancy within critical systems and weaker coupling between them.

Localization and de-complexification of basic needs (food, water, waste etc) would provide some societal resilience if systems resilience was lost. We would have more buffering at all levels, that is, larger inventories throughout society. All this is the very opposite of the direction of economic forces.

The reason we have such tight inventories, tight coupling, and concentration in critical infrastructure is they bring efficiency and competitive advantage. But when something goes wrong, this makes recovery harder. For example, during super-storm Sandy, fuel shortages were exacerbated by low inventories that were the direct result of cost cutting arising from the financial crisis.

We are locked into socio-economic processes that are at an increasingly complex that make us ever more vulnerable. Increasing vulnerability coupled with increasing hazard mean that the risk of a major socio-economic collapse is rising.

Because a permanent state shift could occur, planning needs to consider how to deal with non-reversion to pre-shock conditions.