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.

Haddon Matrix and ‘Black Swans’

The Haddon Matrix is an extremely useful way to express the factors associated with a hazardous event and the changes that need to be affected in the host, the equipment and the environment (both social and physical). I have covered the Haddon Matrix in a previous post, in fact to date the most popular post on this blog. I am not denigrating the Haddon Matrix and its usefulness but recent publications Nassim Nicolas Taleb such a The Black Swan: The Impact of the Highly Improbable (2007, second edition 2010) highlight the potential of unexpected, rare events in systems. Taleb does not believe that effort such be wasted trying to predict these rare events but rather than robust systems should be devised to avoid the negative impacts of these events. So does the Haddon Matrix help to prevent hazards or accidents when a Black Swan strikes?

The Haddon Matrix tends to focus on specific events and their immediate impact. The ‘classic’ example often seen on the Web is a car accident where there is a clearly defined agent or host, a clearly defined piece of equipment and a fuzzy but often clearly defined environment at least in the mind of the person who constructs the matrix. The matrix is focused on a particular event usually one that is well known to the person constructing the matrix. The event is singular and derived from thinking about common scenarios of ‘what ifs’. Importantly, the event is divorced and isolated from its complex context. The event is treated as an individual example of an oft-repeated set, as an individual example of a particular kind of hazard or accident. This means that the contours of the event are relatively well know, the limited impact and the limited range of changes that need to be made to the host or equipment clearly demarcated. The event is somewhat simplified by removing it from its context.

Rare events can also be considered within the Haddon Matrix and planned for but events that have never happened or are not within the experience of the constructor of the matrix can not be considered. A series of events could be dealt with by interlinking matrices or even by using Reason’s Swiss cheese model of accidents but each matrix or cheese slice will deal only with a single event not the interconnected system as a whole not the complex and potentially unique relations that these rarities activate within the whole system of which the event identified is only a part. In this case, however, the accident or hazard itself is actually a chain or web of events operating in unison under the influence of the rare event. The exact connections in the system will give the rare event its character. Given the rarity of the event can you be sure that when it happens again the system will be connected, or rather interconnected, in exactly the same manner and so will the precautions that you take have to be exactly the same? As the complexity of the system behind the hazard or accident you are dealing with increases then the possibility that impacts will occur via different connections or pathways is likely to increase. A static Haddon Matrix may not be able to cope with such dynamism that a Black Swan generates within a system.

Black Swan events may also imply that there are two classes of hazards or accidents that need to be considered. The first is the hazard that is known about, one for which have occurred and reoccurred again and again with sufficient regularity that their characteristics can be well defined and clearly defined steps taken to prevent their escalation. The second class of hazards or accidents are those that occur so rarely that each instant is a novel and unusual case with its own set of peculiar characteristics. These events are so infrequent that no reasonable plans can be made to prevent them. It is only after they have happened that we can understand why they happened, what aspects of the system were compromised and then take steps to ensure that the same pathways to failure do not happen again, although the next Black Swan event may be so different as to circumvent our efforts.

If the Black Swan, almost by definition, falls outside the experience of the matrix constructor then is the matrix of any use in these cases? Black Swans may not be predictable but that should not stop attempts to build a robust system to manage impacts. A densely connected system is likely to transmit impacts rapidly from one part to another, maybe along channels or by connections that can be predicted as weak links or pinch points.  Ensuring that there are ‘firebreaks’ in the system, potential break-points in its connectivity, could help prevent a systemic failure even if the exact nature of the rare event is unclear and unpredictable.