Inclusive Exclusivity

The Inclusive Exclusivity of the “Dynamique d’Enfer”

by Dmitry Paranyushkin, Berlin, November 2010 Marten Spangberg writes about cultural networks being much more exclusive than digital communication networks: “In dance networks operate strictly on strategic levels, without any concern for structural or tactical openness or deployment. Networks in the cultural sector are absolutely closed and are all about membership. You have to make yourself worthy of being part, you will have to go through a test, and you have to invest a fair amount on energy in lobby and travel-costs. [...] Networks in cultural businesses operate due a mode or production known as “dynamique d’enfer” or dynamics of hell. [...] You are responsible for the maintenance of hierarchies, the preservation of an aristocratic society that operates like a flock of vampires, like apologetic blood suckers that obediently confess there compulsive lust. “ [1] Rem Koolhaas also writes about the “dynamique d’enfer” existing in cultural networks, pointing out their innovative potential: “I asked the director, a brilliant developer with whom we worked closely, why he never said no [...] when we came with all our insane proposals [...]

He said his strategy to succeed into the twenty-first century was to create within a limited territory what he called a dynamique d’enfer – a dynamic from hell, which is so relentlessly complex that all the partners are involved in it like prisoners chained to each other so that nobody would be able to escape. [...] What was exciting here was that we introduced buildings on a scale that Europe had almost never seen, therefore we could experiment with completely new typologies.” [2] So, on one side – exclusivity, hierarchies, vampires, prisons, and even the hell itself. On the other side – innovative potential, strength, and success.

Let’s exorcise the demons.

This is a network of 44 DanceWeb 2010 participants, with 440 links in between.

This is a network of the 73 most active contemporary dance practitioners in Europe with 633 links.

In order to create the both graphs, the data was imported from Facebook using Netwizz software [3] and then visualized in Gephi [4]. We assume here that Facebook more or less mirrors the actual network of acquaintances and collaborators within a professional field from which the data was extracted. The nodes which have more connections are shown bigger on the chart.

It is visible that most of the nodes have more or less similar number of connections. Some have more, but mainly everyone is the same. Also, every node is chained to many other nodes, the network is closed onto itself, making it quite hard to enter, leave, or rewire. The “dynamique d’enfer� in action.

It is striking that these two graphs look very similar to a computer-generated random network, where every node has the same probability of being connected to any other node (p = 0.4) and where the number of connections each node has is approximately the same (following Poisson distribution). [5]

Random network, wiring probability p = 0.4

Moreover, the key parameters of the two networks above and the random one are almost the same: Parameter

Random Network

DanceWeb Network

European Dance Scene

Average path between the nodes

1.6

1.5

1.9

Longest path between the nodes

2

2

4

1.1

1.9

1.2

0.41

0.7

0.54

0.4

0.46

0.24

Degree power law distribution Clustering coefficient Probability of two nodes linked Analysis performed using Gephi software

That means that the networks functioning on the basis of “dynamique d’enfer� are in fact very close to random networks in their structure. The path between any two nodes is quite short, all the nodes are quite well integrated in the network, and the degree power law distribution is relatively low (meaning that the power or the number of connections is equally distributed across the network).

If cultural networks resemble random networks in their structure and properties, what are the implications?

Most real-life networks are not random, they are scale-free: digital communication networks, friends, scenes, etc. [6] That means that a few, but significant number of hubs accumulate most of the connections while the other nodes get the status of the periphery. The nodes form communities around hubs, the network operates on the basis of differences between the participants.

An example of a network of friends that is scale-free (imported from Facebook)

Scale-free networks are more open, inclusive, and readily accept outsiders. Once the node is integrated, it can become a hub by forming links with the other outsiders, while connecting to the major hubs to ensure its connectivity to the rest of the network. It is then very expensive for hubs to withdraw, because they invested so much in getting to the position of power and are unwilling to give it up. The nodes on the periphery are consumed in negotiating their differences and getting closer to the hubs. The “dynamique d’enfer” in scale-free networks is based on this self-perpetuating power struggle, but it also produces stability. It’s harder for them to act as a unified whole towards the other networks on the outside, and they become exclusive on the meta-level where different networks connect. Therefore, while scale-free networks produce meaning internally, they have little effect in changing the current state of things in general.

Random networks are more exclusive in terms of accepting the outsiders (because there is a limited number of connections a node can maintain), but also much more inclusive when it comes to the interactions between the nodes that are already part of the network. The nodes in random networks connect, exchange, and cooperate in an almost promiscuous manner compared to the nodes in scale-free networks. This enables random networks to act as a unified whole and produce change on a larger scale, beyond their own structure. The nodes are not consumed with negotiating the internal differences, but serve to fulfill the function of the whole network. At the same time, they also lack self-criticality produced by the difference inherent in scale-free networks.

Random networks are not as stable as scale-free networks. Research has shown that random networks are more susceptible to random node removal than scale-free networks. [7]. As the probability of any two random nodes to be connected (or disorder, in other words) increases in the network, the epidemic threshold decreases [8], making them more open to information propagation. That means that highly connected random networks will much more readily accept and follow a certain ideology as a unified whole than scale-free networks, where the existence of communities ensures that there will always be several competing forces in play.

It has also been shown that random networks tend to shift towards high amplitude oscillations when the number of connections increases (when probability of two nodes connected p > 0.5), which makes the behavior of such networks quite unstable and unpredictable [9].

A random network with probability of two nodes linked p = 0.9

Therefore, while random networks have innovative potential, they are not really sustainable in long-term and also can even be dangerous in the way that an uncontrolled unified mass can be.

In Hanna Arendt’s words: “Totalitarian movements are possible wherever there are masses who for one reason or another have acquired the appetite for political organization.” [10] In our terms if a random networks reaches a point where most of the nodes have many connections (p > 0.5), effaces most the differences, and establishes a certain internal hierarchical structure, it becomes very easy to pour any ideology into that structure and be more or less confident that it would readily be taken on by the network for further dissemination.

What would be a way to avoid the formation of the “mass”, introduce the stability inherent in scale-free networks, and at the same time – retain the innovative potential that random networks have?

One possible strategy can come from any individual within the network. A shot into the unknown, refusal to obey, a fanatical gesture breaking through the web and going for the winning throw into the random void. Such strategy is exemplified by the reactionary movements, which base their ideology on denying whatever it is that is dominant. Leaving the network in order to evolve. Such gesture can only be expected from those at the periphery, who don’t have much to lose. If enough nodes defect from a random network it will collapse, because its connectivity will be severely affected. So the problem here is that this strategy might work for an individual, but is unlikely to have an effect on the whole group. In addition, it does not utilize the innovative potential that random networks have.

Another strategy is to put a time limit to the existence of a random network. When the network reaches the point of high connectivity and is starting to produce change as a unified whole, it should be shut down, so that the individuals inside are forced to start making connections on the outside of the network. For example, temporary projects, residencies, project groups function in this way. The problem here is that the potential of the network is only utilized for a short period of time.

In order for a random network to continue existing without slipping into uncontrolled oscillation, it should constantly maintain a certain level of connectivity. This level is reached when the probability p of any two random nodes to be connected ensures the formation of the network motifs that allow information to spread easily (p > 1 / N^2/3 where N is the number of nodes) [11], but that at the same time doesn’t allow uncontrolled oscillation and a possible slip of the network into an uncontrolled mass (p < 0.5). For instance, in the example above with the network of dance practitioners in Europe that would be p > 1 / 72^(2/3) ~ 0.058 and p < 0.5. So if the total number of possible connections is 73 * 72 / 2 = 2628, then as long as there are more than 152 but less than 1314 connections in this network it will keep its innovative potential and prevent itself from becoming a “mass” and losing its specificity.

How could this range of connectivity be maintained?

First, the network as a whole should be always on the lookout for the other networks (based on the same “dynamique d’enfer” principle) and the individual nodes should be willing to forge the connections. The inclusion should happen as globally as possible to avoid the formation of hubs, but at the same time there should be a degree of hesitation or exclusivity in order to avoid the formation of a “mass”. This will allow these networks to combine their innovative potentials without compromising the equality and ease of communication within each network.

Second, the network should avoid being too hermetic. If it’s too locked on itself then at some point it will reach a gridlock, become a “mass”, lose its specificity. It should be always on the lookout for the new elements from the outside and make new connections with them, integrating them into the network. In order to ensure that they don’t bring with them a dominating power structure these elements can be taken from the “foam”: a free floating mass of nodes or small communities, which do not belong to certain groups or organizations, but are ready to join (even temporarily) to a structure whose purposes they share. At the same time as this inclusive movement is happening it should be combined with the exclusive movement on the part of those nodes inside the network who are willing to “travel” towards other formations.

Otherwise, this constant inclusion will simply maintain the existing hierarchies and increase the size of the network, which may be not sustainable in long term. The inclusive movement should happen on the periphery, while the exclusion should happen from the center towards the periphery to allow for the constant renewal and sustainability.

In a way, this latter strategy is related to the previous proposition to form temporary random networks, because there will be a certain time limit on how long a node can be part of the main component. Rotating boards in cultural organizations are a good example.

Thus, we can also redefine the “dynamique d’enfer” as something that is more akin to limitcycle behavior exhibited by what is called “dissipative structures”, which settle down into periodic patterns and produce non-equilibrium stability [12]. The resulting network functions on the basis of constant interplay between inclusivity and exclusivity. The clusters on the periphery are inclusive towards the “foam”, forming communities based on random distribution of power. Constant influx of newcomers will ensure the maintenance of differences within the community that allow for individuation and makes the community exclusive. The nodes that cannot identify with a community will make random shots into the unknown, becoming a foam, only to be integrated at some later point of time again. The communities, as soon as they reach a point of a unified whole, will align together to foster new connections and the same process will happen on a meta scale, forming super-networks comprised of the already existing ones.

Inclusive exclusivity of “dynamique d’enfer” in action.

“Decisive here is the idea of an inessential commonality, a solidarity that in no way concerns an essence. Taking-place, the communication of singularities in the attribute of extension, does not unite them in essence, but scatters them in existence.” [13] Something like Belousov-Zhabotinsky reaction: http://www.youtube.com/watch? v=3JAqrRnKFHo

Resources [1] Marten Spangberg, “Black Box Life” (Spangbergianism, 2010) [2] Rem Koolhaas, “Beyond Delirious” (lecture at University of Toronto School of Architecture, 1993) [3] Netvizz - apps.facebook.com/netvizz/ [4] Gephi - www.gephi.org [5] Erdos and Renyi, On the Evolution of Random Graphs (1960) [6] Newman, Barabasi, Watts, “The Structure and Dynamics of Networks” (Princeton University Press, 2006) [7] Albert, Jeong, Barabasi, “Error and Attack Tolerance of Complex Networks” (Letters to Nature 406, 2000) [8] Pastor-Satorras, Vespignani, “Epidemic Spread in Scale-Free Networks” (Physical Review Letters 86-14, 2001)

[9] Kuperman, Abramson, “Small World Effect in an Epidemiological Model” (Physical Review Letters, 86-13, 2001) [10] Hanna Arendt, “The Origins of Totalitarianism” (1951) [11] Milo et al, Network Motifs: Simple Building Blocks of Complex Networks (Science, 298, 2002) [12] Ilya Prigogine, "From Being to Becoming" (WH Freeman, 1980) [13] Giorgio Agamben, “The Coming Community” (University of Minnesota Press, 1993)

Dmitry Paranyushkin is an artist, curator, and media entrepreneur working primarily with live performance, image, and text. He is interested in dysfunctional interfaces, networks, nonequilibrium stability, Belousov-Zhabotinsky reaction, and having more than two choices but less than four. Dmitry was born in Moscow in 1981 and currently is based between Berlin, France, and England. He can be contacted via his website www.deemeetree.com or by e-mail d@deemeetree.com