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.

Electric Cars: A Matter of Managing Spatial Scales

The UK government has recently announced that it will fund up to 75% of the costs of installing charging points for electric vehicles in garages and driveways. (http://www.bbc.co.uk/news/uk-politics-21503532). The estimated cost of installing a power point capable of charging two cars is about £10,000 with local authorities expected to contribute £2,500 towards this cost. The report says that the government estimates that it will cost between £1,000 and £1,500 to install charging points for drivers with off-street parking for power points in their garages and driveways. Rapid chargers will cost about £45,000 each. The government believes that 75% of costs is an appropriate level of incentive for individual drivers and local authorities to invest in such technology. 

This may seem like a great idea for improving everyone’s environmental quality but, as a Commons Transport Select Committee has already asked, is this the best way to use government funds. The incentive only works if people buy electric cars, can afford the additional costs of installing such power points and only if the burden on the electricity generating system allows recharging (a big surge of power demand in domestic supply overnight might not be a good idea).  Maybe installing power points maybe an answer but it is not convincing that the right question is being asked.

The question that should be asked is why aren’t people buying electric cars? Ron Adner used the EV (electric vehicle) as an example in his recent book ‘The Wide Lens’ as an example of having to undertake ecosystem style thinking about innovations and their economic development or acceptance. Reducing the issues of EVs to one key component, Adner argues that the need to buy an expensive, cumbersome, lengthy to recharge and soon obsolete battery is an important impediment to purchase. Adner uses the example of Better Place to illustrate how rethinking the ecosystem can result in a novel solution to the battery issue. Better Place envisages a system where the battery is replaced when it runs low on power through a network of battery replacement stations. The operation is a quick change over of a discharged fro a charged battery with the car driver having as much ownership over the battery as a driver does over the petrol in a petrol station (http://www.betterplace.com/). This system transfers ownership of the battery from the driver to the battery exchange company. Better Place can now deal with, problems of obsolete batteries and charging requirements in bulk with all the benefits that brings.

The scheme was launched in Israel and Denmark that the firm believed would be ideal test sites as their relatively small size meant that a network of battery replacement stations could be established at relatively low initial investment costs. Unfortunately, extension of this novel way of thinking about EVs has not been a success in the US or Australia and the company has had to move out of these countries (http://wheels.blogs.nytimes.com/2013/02/06/better-place-proponent-of-e-v-battery-swapping-pulls-out-of-u-s-and-australia/). This does not necessarily mean that the idea is wrong just that all parts of the ecosystem need to be in place before successful acceptance can be achieved. The business model relies upon all potential actors in the network or ecosystem agreeing to run the battery replacement system as each actor benefits from participation in the network. Central to this set of relationships is the involvement of major car manufacturers who sign up to making electric cars compatible with the robotic battery replacement stations. Only Renualt had agreed to this. Without this key set of actors in place, the network or ecosystem had no chance of success.

The relative success of the battery changing strategy in Israel and Denmark and its failure in the US and Australia highlight the need to think about the ecosystem approach advocated by Adner and the importance of scale issues within it. Establishing a network of battery-changing stations requires investment but without this network the concept and practice of battery-changing would not catch on. The practice is only advantageous if there is a demand and supply of electric cars that in turn depends upon the ‘solution’ of the battery issue. Use of an electric car is a very personal issue with the decision to buy or not located in the individual and their specific context. Just this simplified description crosses and defines a range of scales all of which need to be aligned to enable the network or ecosystem to work. If none of these actors across the scales in this simple network can see an advantage to themselves in taking the plunge into electric cars then there is no way the system will even develop.

The micro-scale of the individual needs to be explored and barriers to adoption of the electric car clearly stated and translated into ecosystem or network terms. Likewise, the scale of the individual firm operating a battery-changing centre needs to be understood and linked to the other actors so it is to their advantage to adopt the new technology. Car manufacturers operate at a global, macro scale but with supposed sensitivity to local contexts and are driven by economic needs at these scales. Add the complexity provided by the competitive, established network of petrol stations, cars and owners, all forming an aggressive ecosystem at all the same scales into which the electric car network is trying to meddle and you have an idea of the complex cross-scale issues that need to be addressed. Maybe the government should spend funds on trying to resolve how to manage these multiple spatial scales of actors and networks to produce an economically viable, self-sustaining ecosystem for the electric car rather than putting the responsibility on the individual car owners to respond in the way the government wish to a few incentives at a single scale.

 

 

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.

 

ACTANTS AND ASH CLOUDS

The risks raised by the ash cloud that swamped Europe in April/May 2010 could be thought of in terms of a set of actants (things, people, institutions anything entity really that has the ability to act upon and be acted upon by other entities). Relations between these actants are not fixed but change as the interactions between the actants change. Some relations and actants are harder to change, more entrenched, than others but all are capable of change even if this change is more painful to some than to others.

Figure 1 illustrates the main actants involved in the ash cloud story. The actants are presented as simple boxes but this hides a great deal of differentiation within each box. All airlines, for example, are not the same and or, initially anyway, were they response to the ash clod. Some airlines complained bitterly after a few days of grounding, others took the air in uninstrumented flights to ‘prove’ the safety of the airspace. Likewise, the government is likely to have had different factions pushing for grounding and for letting flights take place. All the actants relations end up focusing on airspace, the theatre in which the drama is played out.


Figure 1 Main actants in ash cloud drama

Importantly, none of the boxes is isolated; many of the boxes are intricately interlinked. Some of the links are relatively straight-forward. The Met Office and CAA, for example, are linked in a very formal manner. The CAA have set criteria for dust concentrations deemed safe. The Met Office provided that information based on computer modelling and data from instrumented flights. The Met Office may also provide the CAA with information on hazardous weather conditions but again the link is formal and highly structured. The link between the met Office and government is more of an economic link, the government paying for an impartial service, whilst the CAA has a regulatory link to the government in setting the legal parameters of responsibility for the airlines. Links need not be singular in nature. The airlines pay tax to the government (economic link), but also lobby on environmental issues and apply pressure when they interests are threatened.

The whole network trundles along, changing and developing as the actants interact, each trying to make the whole network function for their benefit. Each actant has a role in the network. The Met Office has a ‘scientific’ role of monitoring, the CAA a regulatory role, the airlines an economic role. This does not mean to say that each actant will not press into service different aspects of their character in pursuit of their goals, in their attempts to align the network and how it operate to their benefit. The Met Office tries to monitor the ash concentrations, measure and characterize the ash partilces and transmit this information effectively to all actants. The resultant grounding of flights, based on the CAA interpretation this information, meant the network wasn’t functioning in a manner that matched the desires of the airlines. The airlines tried to usurp the role of the Met Office by undertaking their own ‘tests’, flying unistrumented planes into the ash cloud and then transmitted this information through the network and beyond. The airlines tried to take on a role where they collected and transmitted information about the ash to parts of the network where that information could be understood in a way that benefited them. The general public could understand a plane going through an ash cloud and coming out the other side – could they understand complicated mathematically models that predicted ash concentrations? The airlines played to the general public, part of a wider network, to influence the government and CAA, part of the immediate network focused on the UK airspace.

Expanding the network out, it is relatively easy to include other actants (Figure 2). The CAA insisted that they were setting limits based on advice from VAAC and engine manufacturers. It didn’t take long before the economic relation between engine manufacturers and airlines resulted in the release of new information from the engine manufacturers as to the limits of operation in ash. Similarly, the wheel network could be expanded out to include the general public. There is however a danger with this type of analysis. You must always be aware that drawing a box around group doesn’t mean that that group is real or that that group is static. Entities evolve and are differentiated. Airlines are not all the same nor they necessarily behave in the same way to each hazard that they encounter. Likewise, the general public will not necessarily act as a mindless mass if given certain information. What this type of analysis does do is to help to clarify what entities are involved, how they are related and how they use these relationships to try to align the whole network to their benefit.


Figure 2 Expanding the network: VAAC and engine manufacturers