«Summary The globalised economy has become more complex (connectivity, interdependence, and speed), de-local- ized, with increasing concentration ...»
Catastrophic Shocks Through Complex Socio-Economic Systems:
A Pandemic Perspective
The globalised economy has become more complex (connectivity, interdependence, and speed), de-local-
ized, 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 so -
cio-economic systems using some simple models. It discusses the links between initial pandemic absentee- ism and supply-chain contagion, and the evolution and rate of shock propagation. It discusses systemic col - lapse 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 hur - ricane 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 globalised economy. The interest in global shocks and how they manifest themselves has grown in recent years (WEF 2012, 2013; Helbing 2013,; Buldyrev et. al.
First it useful to acknowledge that the hazards referred to in the opening scenarios are increasingly likely. Po- tentially new pandemic strains are being encouraged by increasing human pressure on the biosphere, while mass global air transport could aid rapid global transmission. Ecological constraints, presently pre-eminent amongst them are food and oil flows and increasingly the effects of climate change are growing. Stresses in the credit backing of our financial and monetary systems are arguably increasing, with the additional vulner - ability that such systems are the primary vector through which major ecological constraints in energy and food would be expressed (Korowicz 2011).
One of the primary issues for this paper are, given any signif icant hazard, how does the impact spread through the globalised 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 globalised economy is an example of a complex adaptive system that dynamically links people, goods, factories, services, institutions and commodities across the globe. Such systems can be represented by a ‘state’ that is not in equilibrium, but defines a set of ordered characteristics that exist within a range of devi ations from a mean and persist for a period of time. For example, 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 an 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.
• Efficiency: increasing competition and global trade arbitrage driving down inventories; and globalised economies of scale.
• Concentration: The emergence of ‘hubs’ within the globalised economy- a small number of very highly connected nodes whose function (or loss of function) have a disproportionate role in the operation of the globalised 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 country’s economic output is smeared over the whole globalised economy. The corollary is that if there is a major failure of the systems integration in the globalised economy, a localised community may have extreme difficulties meet ing 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 en able 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 inter ruptions 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 localised 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 re silience 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 McLuhan’s 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-local ised 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.
After a very brief review of other researchers work (section 2), some real life examples of partial systems failure are reviewed (section 3). This allows us to make make some estimates of shock spreading rates. In sec tion 4 the links between pandemic absenteeism and supply-chain contagion is discussed and related to soci etal complexity. In section 5 we look at how contagion spreads, the rate, and the relationship to complexity.
In section 6 we look at some of the multi-system interactions. In 7, we look at why after a major collapse, the pre-shock socio-economic state may not be recoverable. Finally there is a short conclusion. This paper aims to broadly outline how very simple models can shed light on catastrophic shocks in complex socio-economic systems. A signif icantly more detailed discussion on several issues may be found here,(Korowicz 2012).
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 2 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.
There are two general perspectives to studying such impacts. The first focusses on the impact on a specif ic industry or service, often with a view to Business Continuity Planning (BCP). Unsurprisingly, the question of how a health service would manage a pandemic when its own operation is compromised is of recurrent interest (Bartlett and Hayden 2005; Itzwerth et al. 2006). Or for example the effect of worker absenteeism on the movement of freight in a coupled US port-rail system (Jones et al. 2008). This analysis is important for local preparations however it suffers from having to isolate the system under consideration from the environment to avoid the analysis becoming too open and complex.
The alternative track is to use macroeconomic modeling to look at the impact on an economy as a whole.
This type of modeling might be useful for low impact pandemics where the economy remains in its historical range of conditions, for example the impact of the 2003 SARS outbreak ( Knapp et al. 2004; Keogh-Brown and Smith 2008).
However when considering major pandemics (McKibbin and Sidorenko 2006; Keogh-Brown et al. 2010) it is highly questionable if such conventional macroeconomic modeling works, or would be very mis-leading. This is firstly because such models are built out of, and parameterized within the context of long run macroeconomic stability. A major pandemic could be highly de-stabilising, causing, as we shall see, cascading systemic disruption and failure.
Secondly, such models are blind to the issue of rising complexity and the speed of processes, which we ar gue here are essential for understanding major shocks. Finally, they have little to say about the dynamics of the impact, how it spreads through time and cascading failure. This is of most interest to actual risk manage ment.
3. Vulnerability Revealed One way to understand and even parameterize the structure and behavior of complex socio-economic systems is to empirically 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 (Public Safety and Emergency Preparedness Canada 2005; McKinnon 2006; Peck 2006). The petrol stations reliance on Just-In-Time re-supply meant the impact was rapid. Within 2 days of the blockade starting approximately half of the UK’s petrol stations had run out of fuel and supplies to industry and utilities had begun to be severely affected. The initial impact was on transport - people couldn’t get to work and businesses could not be re-supplied. This then began to have a systemic impact.
The protest finished after 5 days at which point: supermarkets had begun to empty of stock, large parts of the manufacturing sector were about to shut down, hospitals had begun to offer ‘emergency only’ care; automatic cash machines could not be re-supplied and the postal service was severely affected. There was panic buying at supermarkets and petrol stations. It was estimated that after the first day an average 10% of na tional 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.
In the end this was a point hazard with systemic impacts, so once government became aware of the systemic risks they forced the truckers’ hands and they desisted. Still the event concentrated minds. The UK was within days of a severe food security crisis and widespread socio-economic breakdown.
Lest one think this is an issue for only the most complex societies -a week-long truckers strike in September 2012 in South Africa again saw emptying petrol station and ATM machines within a week of the disruption.
And hospitals reliant on burning coal for power had to fall back on reserve stocks (Boesler 2012).