Actor Networks, Rare Events and Antifragility

In a recent blog I discuss some aspects of antifragility as suggested by Nassim Taleb’s recent book on Antifragility. Thinking a bit more about the nature of fragile and antifragile networks of relations could be of use in planning for rare events and their impacts. A well-aligned and well co-ordinated network of actors with a dense set of relations defining and binding their netowrk tightly may mean that the network is deeply embedded but this may be a disaster when a rare event hits. As I mentioned before, an event can illuminate the structure and relations in a network. A rare event, a major disruption, puts the spotlight on the fragility (or otherwise) of the web of relations. A well-aligned and co-ordinated network may function excellently for specific actants under ‘normal’ conditions, but in a rare, extreme event these relations may not be able to function. A dense network of relations may be too dense under these extreme conditions. The failure of one relation or the disappearance of one actant may produce a domino effect and trigger the unravelling of the whole web. A dense and highly focused actor network may be fragile to such disruption. A less dense and less well-aligned actor network may be at a disadvantage under ‘normal’ conditions but may have the flexibility to form new relations in disruptive events due to this weaker alignment and co-ordination of relations. Similarly, an actant with the flexibility to activate a different set of relations from the actor network it is usually associated with may be more able to survive and thrive in an disruptive event than a more specialist and network dependent actant or even a whole network.
If correct, then the above suggests that the density (and strength) of relations that define an actor network as well as the specialisation of actants will affect the fragility and antifragility of this network to rare events. Where an actor network has dormant relations, ones that are either unnoticed or unused during ‘normal’ periods, then there is a chance that the actor network could survive by activating these relations in times of crisis. The actor network that emerges, however, would be different from the one that entered the crisis. The dormant relations would now be known to the actants and be active rather than passive. The current banking crisis could be viewed in this light. When the crisis hit the usual sources of safety in the network failed. It was only when the dormant relationship between finance and the state was explicitly activated to prevent those ‘too big to fail’ from failing that some degree of stability was felt by the financial sector (OK oversimplifying like mad but you get the idea). But now that dormant relation is clear and present, everyone knows about it and the new financial network is being constructed with that relation in clear focus and all the issues of moral hazard and tax-payer bail-out that it brings.

There is an assumption in the above, however, that all rare events are the same. This is not necessarily the case as a recent paper by Lampel, Shamsie and Shapira (2009) in Organization Science (you need an account to access the journal). The paper ‘Experiencing the improbable: Rare event and organizational learning’ is a brief summary of the ideas in the special issue of papers on rare events and organizational learning. Importantly, they provide a four-fold classification of the types of learning that rare events produce in organizations based on the potential relevance of the event and the potential impact as in the table below.

                                                                       Potential Impact


Potential Relevance                                 High                          Low

High                                               Transformative              Reinterpretative

Low                                               Focusing                       Transitory


Table: Types of learning associated with rare events
Leaving aside the detail of the table (the subject of future blog!), the idea that a rare event has different affects depending upon the nature of the organization it impacts upon can be translated to actor networks as well. A rare event that is high on both criteria will have the potential to transform the nature of the network. In this case the points about relation density, dormant relations and actant characteristics are highly relevant. These are the rare events that can expose antifragility. A rare event with high potential relevance for a network but low potential impact (such as near-misses) can act as a means of forces reinterpretation of the current web of relations. The impetus to act on reinterpretation will, however, be determined by the interests of the actants and the ease with which the relations that define the network can be altered. Effort is required to overcome resistant to change in the absent of an event that causes transformation. If handled appropriately though this type of rare event could enable the actor network to alter and so improves its robustness or even atnifragiltiy to rare events without having to go through the pain of a transformative event. Drawing the lessons from such events and finding the will amongst key actants is however a major barrier as it is likely that no-one organziatino can affect such leanrign on its own - a sector-wide or even government-led inititative maybe required. A rare event that has high relevance but low potential impact for a network can, similarly, focuses attention on specific issues and problems within the network. Once again, however, change will depend upon who defines these problems and the willingness or ability of actants to alter the relations that define the network.

ANT and Antifragility in ‘No Man’s Land’ Oklahoma

A recent paper by Rebecca Sheehan and Jacqueline Vadjunec  (Oklahoma State University) in Social and Cultural Geography (Volume 13, December 2012, pages 915-936 you will need an account to access the journal online) on communities in Oklahoma’s ‘No Man’s Land’ is a very good demonstration of how actor network theory can be used to analyse how communities are constructed and, importantly, how they behave under stress. Sheehan and Vadjunec note how residents work together on tasks such as branding in the spring, collecting necessities in towns that could be 30-150 miles away and travelling to hospital when a ranching or farming accident happens. This neighbourly behaviour and the relations it is based on underlies what they describe as a robust actor network of relations.

I was wondering if you could go further than this and suggest that the actor network is actually antifragile? The authors point out two examples that may back up this idea that the actor network actually gains strength from adversity. Medical expenses for individuals in the community were often covered by fundraisers or anonymous donations that were also made to cover funeral expenses. Likewise, these adverse events produced responses of kindness that ranged from phone calls of sympathy and understanding to practical help of meals and contributions to ranch work. In one case the death of a farmer at harvest time resulted in the unplanned, spontaneous reaction of several farmers turning up with their combines within 36 hours of his death to help the widow to collect the harvest.

Adverse, or what seem to be adverse events, activate relations in the actor network that produce behaviour that help individuals and seem to strengthen the sense of community and the actor network as a whole. It is only by the enactment of these relations in times of adversity however that this strengthening can occur.

If this argument is accepted then a whole battery of other issues arise that only the detailed analysis of actor networks in particular locations can answer. These actor networks need to be studied before during and after adverse events to analyse which relations are activated, how and if there is any pattern to these relations. Events are the only means by which relations can be identified and their role in strengthening the actor network understood. Similarly, it is through such detailed analysis that we can begin to map out the limits to such antifragile behaviour. The strengthening behaviour in this case seems to be an organic outgrowth from the underlying relations that define and bind the community. Eroding these relations will erode the ability of the community to define itself and to strengthen itself in the face of adverse events. Understanding the type of adverse events such actor networks can cope with, absorb the impacts of and gain strength from is also an important aspect that requires further research. Communities may be antifragile in the face of certain adverse events but be extremely fragile should the nature of the adverse event change. In the case of this community, if the adverse event is a general failure of all harvests then the capacity to respond and help other members of the network dissipates. If the encroachment of ‘new’ people into the area happens then this again may weaken the underlying relations that aid community definition, eroding the capacity to activate relations in crisis events and so gain strength from the community-based respond to a crisis event. Starting to map the contours of what an antifragile actor network looks like and the limits of antifragile behaviour could be an interesting area of research.

Antifragility and actor networks

Nassim Taleb’s recent book on Antifragility may have some implications for understanding actor networks. Taleb suggests a triad of system types: fragile, robust or resilient and antifragile. Fragile systems are ones that collapse under stressful events, robust or resilient systems remain relatively neutral in the face of stressful events, whilst antifragile systems positively respond, strengthening under stressful events. Within hazards analysis such a distinction might be very helpful in separating out communities that are vulnerable to hazards, those that hold their own and those that thrive in adversity.

 

Actor network theory, as outlined in an earlier blog, is a very useful method for mapping actants (human and non-human), their relationships and how these relationships operate in changing contexts.  Leaving aside the complicated and, sometimes competing, definitions and deep conceptual issues of this approach, there is much in the simple drawing of nodes and relations that could help in identifying the basis of antifragile behaviour as illustrated in the simple network below.

 

Actants in the network may try to align and co-ordinate the network of relations to produce the outcome that they desire. Supermarkets put pressure on farmers to produce vegetables for them, controlling the prices asked for vegetables, the transport available for vegetables and even finances by tying farmers into specific contracts. In other words, the supermarkets are key actants who have extended their co-ordination and alignment of the network in such a manner as to virtually control how it operates. But is this network fragile or not?

 

Questions of fragility and antifragility can be answered only when the network is stressed, only when an event causes disruption. The nature of such events will vary with the nature of the network; event characteristics that cause network disruption will always be context dependent. This means that it may not be possible beforehand to predict the fragility or otherwise of a network. It is only when under stress that parts of that network may buckle or may develop novel means of relieving or even using the stress to strengthen the network.  Likewise, events can be propagated through the network in a variety of ways, so although one seemingly similar event may point to a stress point or relationships in the network, once that stressed node is 'fixed', the next event may pick out and illuminate another, different stress point. 

 

Antifragile behaviour can result if an actant can exploit the stress within the network to ensure that their vision or goals for the network are increasingly likely after the disruptive event. This realignment or co-ordination of the network could result from taking over the function of other nodes or exploiting a relationship that enables an actant to more deeply embed the relationships it needs to achieve its ends as in the figure. A particular bad year for crops due to drought, for example, could provide an opportunity for farmers who invested in irrigation methods to dictate prices to major suppliers or to cheaply buy up the land of farmers who did not invest in irrigation. The relationships and nodes existed before the disruptive event, but the multiple impacts (or the multiple manners in which the event plays out in the network) open up a range of opportunities for antifragile actants. It is important to note that antifragility is only definable in relation to the disruptive event (or the multiple manifestations of that event). Similarly, antifragility is only noticed if actants exploit the disruption to improve or enhance their own position and power (expressed through alignment and co-ordination of the network).

 

It is through actions within the network of relationships that antifragility and fragility is expressed. It may be possible to begin to identify some general properties of actants and relationships that may enable antifragility, but it is only through the expression of these properties by actant’s actions during and after a disruptive event that such properties will be identified as important. Future blogs will begin to characterise such network based properties by exploring the response of actants to specific disruptive events.

 

Hazards: The Complexity Approach

I was at a conference on water and risk at the start of the year. A very distinguished professor had delivered an extremely interesting talk on a key concept he had taken decades to get across to policy makers involved in development. As the presentations went on he became increasingly concerned and agitated that researchers should realise that they had to get their ideas across to policy makers who were not well versed in either the details of academic debate or the intricate nature of conceptual frameworks. He questioned a final year postgraduate about what the major new concept in hazard and risk was. Without hesitation the postgraduate replied ‘complexity’. The distinguished professor paused, audibly drew in a long breath and said ‘God help us!’

Complexity is, as the name implies, neither the easiest concept to get across nor the easiest one to illustrate. Part of this fogginess is because the concept is still evolving within hazard analysis. The fixed and clear definitions of what it is and how to use it are still in their infancy and still subject to intense academic debate (although a useful discussion of the concept is given in Smith and Petley, Environmental Hazards, 2009, Routledge). Different researchers from different field converge on a particular disaster and each applies their own view and meaning of complexity to the analysis of that disaster. So what follows is a partial interpretation of complexity thinking and hazards but one that I hope will nonetheless provide a flavour of how a new concept is starting to mesh and enhance hazards analysis.

Complexity theory is a borrowed, as most geographical concepts are, this time from physics and mathematics where it evolved from a detailed equation based theory into something that even geographers could begin to understand. The central idea is that a system of components operating together produces some output. This may not sound that dramatic but it is the type of output that is a little unexpected. Traditionally, it has tended to be assumed that you can understand something better if you start to pull it apart and study each component one by one, individually. Once you have a detailed knowledge of the components then you have a detailed knowledge of the system. Simplify the system to understand it. This is a highly reductionist view of reality and how you go about studying it. To understand a car you dismantle it and study each component in great detail then put the parts back together and you understand the car. Even with my limited mechanical knowledge, I can see this will not work! Complexity is a brake on this view of simplifying reality to study it.

Complexity recognises that real systems are complicate and intricate networks of components acting together in a variety of ways. Simply studying one component or even a small group, does nothing to help us understand how the system really works. It is the interactions, the relations, which drive the system that produce the emergent behaviour that we observe and try to study. In complexity theory the bits of the system, the actual components are still vital as without them the system would not exist, but to understand the system, to grasp how it works, it is the interactions, the relations and their changes that it is vital to understand. From these interactions there does tend to emerge some predictable forms of overall system behaviour. Sometimes, however, change the relations and the output can alter in unexpected and unpredictable ways. In this view of hazards, hazards and disasters occur not necessarily because of one factor but through a combination of and the complex interactions of a number of factors.

My earlier blog on the BP oil spill and the Swiss cheese model of hazards could be seen as an illustration of complexity in action. In this model, it is the interaction between specific ‘holes’ that results in the incident occurring, without this interaction there would be no incident, no explosion, no oil spill. This model is only one by which hazards can be understood however. My earlier blog on ash cloud again focuses on interactions, this time amongst a group of actants to start to form an understanding of how the system evolved and how the hazard itself became defined.

The compelxity approach is outliend albeit briefly in Petley adn Smith's book below.