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“There is an entire genealogy to be written from the point of view of the challenge posed by insect coordination, by “swarm intelligence.” Again and again, poetic, philosophical, and biological studies ask the same question: how does this “intelligent,” global organization emerge from a myriad of local, “dumb” interactions?” — Alex Galloway and Eugene Thacker, The Exploit.
[…] The interest in swarms was intimately connected to the research on emergence and “superorganisms” that arose during the early years of the twentieth century, especially in the 1920s. Even though the author of the notion of superorganisms was the now somewhat discredited writer Herbert Spencer,63 who introduced it in 1898, the idea was fed into contemporary discourse surrounding swarms and emergence through myrmecologist William Morton Wheeler. In 1911 Wheeler had published his classic article “The Ant Colony as an Organism” (in Journal of Morphology), and similar interests continued to be expressed in his subsequent writings. His ideas became well known in the 1990s in discussions concerning artificial life and holistic swarm-like organization. For writers such as Kevin Kelly, mentioned earlier in this chapter, Wheeler’s ideas regarding superorganisms stood as the inspiration for the hype surrounding emergent behavior.64 Yet the actual context of his paper was a lecture given at the Marine Biological Laboratory at Woods Hole in 1910.65 As Charlotte Sleigh points out, Wheeler saw himself as continuing the work of holistic philosophers, and later, in the 1910s and 1920s, found affinities with Bergson’s philosophy of temporality as well.66 In 1926, when emergence had already been discussed in terms of, for example, emergent evolution, evolutionary naturalism, creative synthesis, organicism, and emergent vitalism, Wheeler noted that this phenomenon seemed to challenge the basic dualisms of determinism versus freedom, mechanism versus vitalism, and the many versus the one.67 An animal phenomenon thus presented a crisis for the fundamental philosophical concepts that did not seem to apply to such a transversal mode of organization, or agencement to use the term that Wheeler coined. It was a challenge to philosophy and simultaneously to the physical, chemical, psychological, and social sciences, a phenomenon that seemed to cut through these seemingly disconnected spheres of reality.
In addition to Wheeler, one of the key writers on emergence – again also for Kelly in his Out of Control 68 – was C. Lloyd Morgan, whose Emergent Evolution (1927) proposed to see evolution in terms of emergent “relatedness”. Drawing on Bergson and Whitehead, Morgan rejected a mechanistic dissecting view that the interactions of entities “whether physical or mental” always resulted only in “mixings” that could be seen beforehand. Instead he proposed that the continuity of the mechanistic relations were supplemented with sudden changes at times. At times reminiscent of Lucretius’s view that there is a basic force, clinamen, that is the active differentiating principle of the world, Morgan focused on how qualitative changes in direction could affect the compositions and aggregates. He was interested in the question of the new and how novelty is possible. In his curious modernization of Spinoza, Morgan argued for the primacy of relations – or “relatedness,” to be accurate.69 Instead of speaking of agencies or activities, which implied a self-enclosed view of interactions, in Emergent Evolution Morgan propagated in a way an ethological view of the world. Entities and organisms are characterized by relatedness, the tendency to relate to their environment and, for example, other organisms. So actually, what emerge are relations:
“If it be asked: What is it that you claim to be emergent? the brief reply is: Some new kind of relation. Revert to the atom, the molecule, the thing (e.g. a crystal), the organism, the person. At each ascending step there is a new entity in virtue of some new kind of relation, or set of relations, within it, or, as I phrase it, intrinsic to it. Each exhibits also new ways of acting on, and reacting to, other entities. There are new kinds of extrinsic relatedness“.70
The evolutionary levels of mind, life, and matter are in this scheme intimately related, with the lower levels continuously affording the emergence of so-called higher functions, like those of humans. Different levels of relatedness might not have any understanding of the relations that define other levels of existence, but still these other levels with their relations affect the other levels. Morgan tried, nonetheless, to steer clear of the idealistic notions of humanism that promoted the human mind as representing a superior stage in emergence. His stance was much closer to a certain monism in which mind and matter are continuously in some kind of intimate correspondence whereby even the simplest expressions of life participate in a wider field of relatedness. In Emergent Evolution Morgan described relations as completely concrete. He emphasized that the issue is not only about relations in terms but as much about terms in relation, with concrete situations, or events, stemming from their relations.71 In a way, other views on emergence put similar emphasis on the priority of relations, expressing a kind of radical empiricism in the vein of William James. Drawing on E. G. Spaulding’s 1918 study The New Rationalism, Wheeler noted the unpredictable potentials in connectionism: a connected whole is more than (or at least nor reducible to) its constituent parts, implying the impossibility to find causal determination of aggregates. Whereas existing sciences might be able to recognize and track down certain relationships that they have normalized or standardized, the relations might still produce properties that are beyond those of the initial conditions – and thus also demand a vector of analysis that parts from existing theories – dealing with properties that open up only in relation to themselves (as a “law unto themselves”). 72 Instead, a more complicated mode of development was at hand, in which aggregates, or agencements, simultaneously involved various levels of reality. This also implied that aggregates, emergent orders, have no one direction but are constituted of relations that extend in various directions:
“We must also remember that most authors artificially isolate the emergent whole and fail to emphasize the fact that its parts have important relations not only with one another but also with the environment and that these external relations may contribute effectively towards producing both the whole and its novelty“.73 […]
in (passage from), Jussi Parikka, “Insect Media: An Archaeology of Animals and Technology“, Chapter II – Genesis of Form: Insect Architecture and Swarms, (section) Emergence and Relatedness: A Radical Empiricism – take one, pp. 51-53, University of Minnesota Press, Minneapolis, 2011.
Fig. – A cartoon by Nitrozac and Snaggy (The Joy of Tech, link) July 2011 [Geek Culture]. Click to enlarge.
“I was never interested in Facebook or MySpace because they feel like malls to me. Twitter actually feels like the street. You can bump into anybody on Twitter.” — Science-fiction novelist William Gibson – New York (October 11, 2010)
“There is almost certainly an evolutionary drive toward increasing complexity in the face of entropy. That’s practically a definition of life. Technology is so powerful and attractive to us because it holds the promise of greater complexity and greater connectedness. Atoms to molecules to cells to organelles to organisms. What’s next? No one knows for sure, but it sure ain’t Facebook.” — American media theorist Douglas Rushkoff, writer, columnist, early cyberpunk culture adopter, and advocacy of open source solutions to social problems.
Precursors of social networks in the late 1800s include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (gemeinschaft) or impersonal, formal, and instrumental social links (gesellschaft) [in Linton Freeman, “The Development of Social Network Analysis“, Vancouver, Empirical Press, 2004 (here is a valuable must-read review on it)]. Durkheim gave a non-individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. He distinguished between a traditional society – “mechanical solidarity” – which prevails if individual differences are minimized, and the modern society – “organic solidarity” – that develops out of cooperation between differentiated individuals with independent roles. Then, Georg Simmel (1908-1971), writing at the turn of the twentieth century, was the first scholar to think directly in social network terms. His essays pointed to the nature of network size on interaction and to the likelihood of interaction in ramified, loosely-knit networks rather than groups.
Nowadays, however, the paraphernalia of increasing intelligent tools (network metrics) are widely available, mainly to the exponential role of the science of complex networks. As stated by Matthias Scholz (Network Science.org / Webpage) (…) Network science has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings, popularized by Albert-László Barabási and his team, is that real networks behave very distinct from traditional assumptions of network theory. Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modelled by random graphs. However, modern network research revealed that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). This power-law characteristics, termed scale-free by Barabási, can be found in many complex real networks from biological (natural) to social man-made networks (…).
Book cover – Linton Freeman, “The Development of Social Network Analysis“, Vancouver, Empirical Press, 2004 (and a valuable review on it).
While embedding themselves on social-networking, people do tend to forget this, of course, but here are 2 or 3 things you should know about Social Networks before stupefying registering yourself on FarmVille (actually, this is a limited list of some of the actual metrics usually employed on current network analysis provided with a short description – So, … do really ponder yourself where you are – on the street or in the mall!):
Betweenness (link) The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node’s neighbours, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links. | Bridge (link) An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph. | Centrality (link) This measure gives a rough indication of the social power of a node based on how well they “connect” the network. “Betweenness”, “Closeness”, and “Degree” are all measures of centrality. | Centralization (link) The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses. | Closeness (link) The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. The shortest path may also be known as the “geodesic distance”. | Clustering coefficient (link) A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater ‘cliquishness’. | Cohesion (link) The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted. | …
Fig. – Hue (from red=0 to blue=max) shows the node betweenness. (link)
… Degree (link) The count of the number of ties to other actors in the network. See also degree (graph theory). | (Individual-level) Density (link) The degree a respondent’s ties know one another/ proportion of ties among an individual’s nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks). | Flow betweenness centrality (link) The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node). | Eigenvector centrality (link) A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question. | Local Bridge (link) An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle. | Path Length (link) The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes. | Prestige (link) In a directed graph prestige is the term used to describe a node’s centrality. “Degree Prestige”, “Proximity Prestige”, and “Status Prestige” are all measures of Prestige. See also degree (graph theory). | Radiality (link) Degree an individual’s network reaches out into the network and provides novel information and influence. | Reach (link) The degree any member of a network can reach other members of the network. | Structural cohesion (link) The minimum number of members who, if removed from a group, would disconnect the group. | Structural equivalence (link) Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent. | Structural hole (link) Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.
Of course, some of these metrics are redundant over each other, and in fact there is some intelligence on having this on-purpose redundancy. But I would say that Path Length and Cliqueness, Eigenvector centrality, Betweenness, Clustering coefficient, Degree and last but not least Flow (btw, here‘s my own poetic lateral view of it), would be the most important of them all, even if, these all depends on what you are, on what you see, on what you feel, and mostly on what you somehow rather expect from it as a whole experience over time. So, finally, let me just add that all these metrics will not avoid people from writing/sharing experiences like: “Son las 7 y 30 y estoy cagando” followed by “Son las 5 y 31 y ya cagado“. Unfortunately, what follows is precisely a video parody on Facebook not far from his own reality. As usual, you are always free to choose from the geriatric-like shelter malls or the open-air streets…
Video – “Journalism in the age of data” is a 50-minute documentary by Geoff McGhee on information visualization, data as medium, and its use in journalism. Produced during a 2009-2010 John S. Knight Journalism Fellowship at Stanford University.
“Pensar por imagens é talvez o único processo eficaz de que a inteligência dispõe para perscrutar os altos problemas da filosofia, da
ciência e da arte” ~ Manuel Teixeira-Gomes, Escritor nascido em Portimão. 7º Presidente da Rép. Portuguesa, in Carnaval Literário, 1938.
Journalists are coping with the rising information flood by borrowing data visualization techniques from computer scientists, researchers and artists. Some newsrooms are already beginning to retool their staffs and systems to prepare for a future in which data becomes a medium. But how do we communicate with data, how can traditional narratives be fused with sophisticated, interactive information displays? For more, watch the full version with annotations and links at datajournalism.stanford.edu.
Figure – A swarm cognitive map (pheromone spatial distribution map) in 3D, at a specific time t. The artificial ant colony was evolved within 2 digital grey images based on the following work. The real physical “thing” can be seen here.
[] Vitorino Ramos, The MC2 Project [Machines of Collective Conscience]: A possible walk, up to Life-like Complexity and Behaviour, from bottom, basic and simple bio-inspired heuristics – a walk, up into the morphogenesis of information, UTOPIA Biennial Art Exposition, Cascais, Portugal, July 12-22, 2001.
Synergy (from the Greek word synergos), broadly defined, refers to combined or co-operative effects produced by two or more elements (parts or individuals). The definition is often associated with the holistic conviction quote that “the whole is greater than the sum of its parts” (Aristotle, in Metaphysics), or the whole cannot exceed the sum of the energies invested in each of its parts (e.g. first law of thermodynamics) even if it is more accurate to say that the functional effects produced by wholes are different from what the parts can produce alone. Synergy is a ubiquitous phenomena in nature and human societies alike. One well know example is provided by the emergence of self-organization in social insects, via direct (mandibular, antennation, chemical or visual contact, etc) or indirect interactions. The latter types are more subtle and defined as stigmergy to explain task coordination and regulation in the context of nest reconstruction in Macrotermes termites. An example, could be provided by two individuals, who interact indirectly when one of them modifies the environment and the other responds to the new environment at a later time. In other words, stigmergy could be defined as a particular case of environmental or spatial synergy. Synergy can be viewed as the “quantity” with respect to which the whole differs from the mere aggregate. Typically these systems form a structure, configuration, or pattern of physical, biological, sociological, or psychological phenomena, so integrated as to constitute a functional unit with properties not derivable from its parts in summation (i.e. non-linear) – Gestalt in one word (the English word more similar is perhaps system, configuration or whole). The system is purely holistic, and their properties are intrinsically emergent and auto-catalytic.
A typical example could be found in some social insect societies, namely in ant colonies. Coordination and regulation of building activities on these societies do not depend on the workers themselves but are mainly achieved by the nest structure: a stimulating configuration triggers the response of a termite worker, transforming the configuration into another configuration that may trigger in turn another (possibly different) action performed by the same termite or any other worker in the colony. Recruitment of social insects for particular tasks is another case of stigmergy. Self-organized trail laying by individual ants is a way of modifying the environment to communicate with nest mates that follow such trails. It appears that task performance by some workers decreases the need for more task performance: for instance, nest cleaning by some workers reduces the need for nest cleaning. Therefore, nest mates communicate to other nest mates by modifying the environment (cleaning the nest), and nest mates respond to the modified environment (by not engaging in nest cleaning).
Swarms of social insects construct trails and networks of regular traffic via a process of pheromone (a chemical substance) laying and following. These patterns constitute what is known in brain science as a cognitive map. The main differences lies in the fact that insects write their spatial memories in the environment, while the mammalian cognitive map lies inside the brain, further justified by many researchers via a direct comparison with the neural processes associated with the construction of cognitive maps in the hippocampus.
But by far more crucial to the present project, is how ants form piles of items such as dead bodies (corpses), larvae, or grains of sand. There again, stigmergy is at work: ants deposit items at initially random locations. When other ants perceive deposited items, they are stimulated to deposit items next to them, being this type of cemetery clustering organization and brood sorting a type of self-organization and adaptive behaviour, being the final pattern of object sptial distribution a reflection of what the colony feels and thinks about that objects, as if they were another organism (a meta- global organism).
As forecasted by Wilson [E.O. Wilson. The Insect Societies, Belknam Press, Cambridge, 1971], our understanding of individual insect behaviour together with the sophistication with which we will able to analyse their collective interaction would advance to the point were we would one day posses a detailed, even quantitative, understanding of how individual “probability matrices” (their tendencies, feelings and inner thoughts) would lead to mass action at the level of the colony (society), that is a truly “stochastic theory of mass behaviour” where the reconstruction of mass behaviours is possible from the behaviours of single colony members, and mainly from the analysis of relationships found at the basic level of interactions.
The idea behind the MC2 Machine is simple to transpose for the first time, the mammalian cognitive map, to a environmental (spatial) one, allowing the recognition of what happens when a group of individuals (humans) try to organize different abstract concepts (words) in one habitat (via internet). Even if each of them is working alone in a particular sub-space of that “concept” habitat, simply rearranging notions at their own will, mapping “Sameness” into “Neighborness“, not recognizing the whole process occurring simultaneously on their society, a global collective-conscience emerges. Clusters of abstract notions emerge, exposing groups of similarity among the different concepts. The MC2 machine is then like a mirror of what happens inside the brain of multiple individuals trying to impose their own conscience onto the group.
Through a Internet site reflecting the “words habitat”, the users (humans) choose, gather and reorganize some types of words and concepts. The overall movements of these word-objects are then mapped into a public space. Along this process, two shifts emerge: the virtual becomes the reality, and the personal subjective and disperse beliefs become onto a social and politically significant element. That is, perception and action only by themselves can evolve adaptive and flexible problem-solving mechanisms, or emerge communication among many parts. The whole and their behaviours (i.e., the next layer in complexity – our social significant element) emerges from the relationship of many parts, even if these later are acting strictly within and according to any sub-level of basic and simple strategies, ad-infinitum repeated.
The MC2 machine will reveal then what happens in many real world situations; cooperation among individuals, altruism, egoism, radicalism, and also the resistance to that radicalism, memory of that society on some extreme positions on time, but the inevitable disappearance of that positions, to give rise to the convergence to the group majority thought (Common-sense?), eliminating good or bad relations found so far, among in our case, words and abstract notions. Even though the machine composed of many human-parts will “work” within this restrict context, she will reveal how some relationships among notions in our society (ideas) are only possible to be found, when and only when simple ones are found first (the minimum layer of complexity), neglecting possible big steps of a minority group of visionary individuals. Is there (in our society) any need for a critical mass of knowledge, in order to achieve other layers of complexity? Roughly, she will reveal for instance how democracies can evolve and die on time, as many things in our impermanent world.
Figure – My first Swarm Painting SP0016 (Jan. 2002). This was done attaching the following algorithm into a robotic drawing arm. In order to do it however, pheromone distribution by the overall ant colony were carefully coded into different kinds of colors and several robotic pencils (check “The MC2 Project [Machines of Collective Conscience]“, 2001, and “On the Implicit and on the Artificial“, 2002). On the same year when the computational model appeared (2000) the concept was already extended into photography (check original paper) – using the pheromone distribution as photograms (“Einstein to Map” in the original article along with works like “Kafka to Red Ants” as well as subsequent newspaper articles). Meanwhile, in 2003, I was invited to give an invited talk over these at the 1st Art & Science Symposium in Bilbao (below). Even if I was already aware of Jeffrey Ventrella outstanding work as well as Ezequiel Di Paolo, it was there where we first met physically.
[] Vitorino Ramos, Self-Organizing the Abstract: Canvas as a Swarm Habitat for Collective Memory, Perception and Cooperative Distributed Creativity, in 1st Art & Science Symposium – Models to Know Reality, J. Rekalde, R. Ibáñez and Á. Simó (Eds.), pp. 59, Facultad de Bellas Artes EHU/UPV, Universidad del País Vasco, 11-12 Dec., Bilbao, Spain, 2003.
Many animals can produce very complex intricate architectures that fulfil numerous functional and adaptive requirements (protection from predators, thermal regulation, substrate of social life and reproductive activities, etc). Among them, social insects are capable of generating amazingly complex functional patterns in space and time, although they have limited individual abilities and their behaviour exhibits some degree of randomness. Among all activities by social insects, nest building, cemetery organization and collective sorting, is undoubtedly the most spectacular, as it demonstrates the greatest difference between individual and collective levels. Trying to answer how insects in a colony coordinate their behaviour in order to build these highly complex architectures, scientists assumed a first hypothesis, anthropomorphism, i.e., individual insects were assumed to possess a representation of the global structure to be produced and to make decisions on the basis of that representation. Nest complexity would then result from the complexity of the insect’s behaviour. Insect societies, however, are organized in a way that departs radically from the anthropomorphic model in which there is a direct causal relationship between nest complexity and behavioural complexity. Recent works suggests that a social insect colony is a decentralized system composed of cooperative, autonomous units that are distributed in the environment, exhibit simple probabilistic stimulus-response behaviour, and have only access to local information. According to these studies at least two low-level mechanisms play a role in the building activities of social insects: Self-organization and discrete Stigmergy, being the latter a kind of indirect and environmental synergy. Based on past and present stigmergic models, and on the underlying scientific research on Artificial Ant Systems and Swarm Intelligence, while being systems capable of emerging a form of collective intelligence, perception and Artificial Life, done by Vitorino Ramos, and on further experiences in collaboration with the plastic artist Leonel Moura, we will show results facing the possibility of considering as “art”, as well, the resulting visual expression of these systems. Past experiences under the designation of “Swarm Paintings” conducted in 2001, not only confirmed the possibility of realizing an artificial art (thus non-human), as introduced into the process the questioning of creative migration, specifically from the computer monitors to the canvas via a robotic harm. In more recent self-organized based research we seek to develop and profound the initial ideas by using a swarm of autonomous robots (ARTsBOT project 2002-03), that “live” avoiding the purpose of being merely a simple perpetrator of order streams coming from an external computer, but instead, that actually co-evolve within the canvas space, acting (that is, laying ink) according to simple inner threshold stimulus response functions, reacting simultaneously to the chromatic stimulus present in the canvas environment done by the passage of their team-mates, as well as by the distributed feedback, affecting their future collective behaviour. In parallel, and in what respects to certain types of collective systems, we seek to confirm, in a physically embedded way, that the emergence of order (even as a concept) seems to be found at a lower level of complexity, based on simple and basic interchange of information, and on the local dynamic of parts, who, by self-organizing mechanisms tend to form an lived whole, innovative and adapting, allowing for emergent open-ended creative and distributed production.
Chemoton § Vitorino Ramos' research notebook 
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