Though the existence of swarm intelligence and the emergence of complex intelligent systems is supported through research at many scales, certain questions arise when thinking about implementation at the scale of our cities. Does a knowledge of the existence of emergence as a social phenomenon play into our societal structure? If so, who are the actors in implementing this knowledge? How is it implemented? How do we overcome the paradox of implementing strategies which will strengthen the phenomenologics of swarm intelligence within our societal structure while curbing the development of a top-down state where knowledge of the systems overall operations are reserved only for those who are in the know?

There is a veritable line in the sand between the opposing approaches of top-down organization and bottom-up hive intelligence, but either strategy in isolation does not produce tangible results. Effectively, the bottom is too dumb to produce due to the simplicity of its operations relative to scale of the system. The bottom essentially is composed of components which perform, in the Darwinian sense, simple yes/no computations to accumulate local knowledge that is fed into the database of the system. The system, on the other hand, is far too complex for the derivation of universally applicable rules, and its collective conscience too impatient to wait for the bottom to self-organize. Thus, we see an shift in scale not only in terms of the breadth of the collective knowledge, but also in the understanding of time. The growth of the system results in deeper and deeper clustering of information and the intensive organization of form, differing fundamentally to the invention and application of derived form. We can then begin to redefine the notion of formal design or organization to coincide not with the behavior of the system, but to the behavior of the agent of the system and its local interactions.

The methodology of behavioral design is discussed by Kokkugia in ‘Behavioral Matter’, as it relates to swarm urbanism and the shift from the notion of a ‘master plan’ to that of a ‘master-algorithm’ at the urban scale. Through this re-appropriation, we can see the beginnings of a response to the posited question of implementation, and that the fundamental hierarchies of urbanism such as scalar time and intensity of agents acting in the system are platforms for the development of this logic. For the Melbourne Docklands, Kokkugia proposed urban structures by firstly using the self-organizing design agents of the system to reform the matter in the creation of circulatory and infrastructural networks, and by secondly programming urban elements and topologies with embedded behavioral traits. In this experiment, all elements in the system are conceived as agents which conceptualize and form complex environments through behavioral operations.

The embedding of behavioral design methodologies within urbanism offers a step forward in bridging the gap between top-down intervention and the slow-burning power  of the hive mind. It also manifests the ‘editor’ within the agents themselves, based on an extrapolation of their behavior rather than an derivation from the overall composition of the system. As Kevin Kelly identifies in ‘The Bottom is Not Enough’, editors are a means by which to maximize the efficiency of the collective through moderate top-down intervention, while not hindering its operations. Although he is talking about the world of digital publication, the same basic principles apply at the urban scale as they do to omnipresent information systems such as Wikipedia. Though they operate in principal through bottom-up formation, the need for expertise on a particular subject requires a the existence of an ‘expert’ which can provide a clustered wealth of knowledge. Clustering which occurs in Wikipedia can then be identified as an agent-based algorithm which is embedded from the top-down into the system itself, taking cues from the behavior of the source rather than the overall behavior.

Thompson himself identifies that most websites possess more collective wisdom than any given city, but lack the ability to effectively cluster and process the received information in the same way. As the breadth of digital knowledge continues to widen, the process of editing through embedded behavioral algorithms becomes for complex (Kelly imagines in one day to include controlled edits, peer review, verification and authentication certificates to name a few). If the Wikipedia of 2056 will better represent the idea of a complete encyclopedia due to the ever-increasing complexity of its ‘designed’ editing process, perhaps the same level of complexity embedded into our urban environments can similarly streamline our world.

references:

Kokkugia. “Behavioral Matter”. Swarm Intelligence: Architecture of Multi-Agent Systems. Ed. Neil Leach and Roland Snooks.

Kelly, Kevin. “The Bottom is Not Enough”. Swarm Intelligence: Architecture of Multi-Agent Systems. Ed. Neil Leach and Roland Snooks.

Thompson, Steven. Emergence: the connected lives of ants, brains, cities and software. New York: Scribner; 1 edition (Sept. 10 2002)

Thompson, Steven. Only Connect. thegaurdian. 15 October 2001. Accessed 20 November 2013. http://www.theguardian.com/books/2001/oct/15/society

Summary.
This reading describes the behavior of ants, living cells, and people, and how their individual (inter)actions follow and provoke a global system behavior. Global behavior, local interaction.
There was a study which revealed a completely new way of understanding ant colonies and their behavior. The normal approach was to study several ant colonies, but instead they focused on one single colony over time. They discovered that colonies display a sort of an aging behavior, as a whole organism, through steps such as infancy, adolescence and matureness. Taking in count that colonies can live for 15 years and ants for 1 year, the question is how such simple organisms as ants can achieve a global result that far exceeds their live time and awareness.
It is assumed that ants wouldn´t be able to have an understanding of the colony as a whole or plan its development. So how is it that they know how to act individually, so that the colony survives? Local feedback, decentralized planning. This might be explained by noticing that ants have sort of tasks assigned. They just know they have to bring nutrients to the colony and defend their territory. Then their behavior depends on the interaction between the ants, sharing information about where is food, what nutrients they need, threatening and menaces, etc. The global behavior comes from this interaction between more simple activities of the basic units.
Code: nutrients=information
Similar behavior can be seen in the cell interaction of living organisms. The global actions begin with the interaction between the cells, and the transfer of nutrients (information) from one cell to the other through their membranes. In the embryo development, we can notice how cells define whether they will be heart cells, bone cells, blood cells, etc., based on the interaction and the information of the surrounding neighbor cells. DNA acts as a manual that indicates the cell how to become bone, heart, blood, etc., depending on the needs of their environment. As well as the ants, the global coordination depends on the exchange of information and a pre-established frame code of behavior for the basic units.
City simulation
Sims City is a good example of how a set of rules and units of interaction can evolved into apparently more complex systems. The video game depends on the exchange of information between the units of the city that, when enough, display complex global reactions depending on each unit´s feedback to/from the whole system. This is achieved by a logarithm and mathematics that act like an invisible operator that make thing happen.
Street view
Nature itself appears to have a code for doing things and solving problems, that also governs human behavior. We see behaviors in a galactic scale reflected in the atomic scale. Cities become with time into a huge organism of interactions. Humans of course we are more complex units than ants, cells, or computer bits. But the concept is the same. In this way city sidewalks are the spaces on which more of these interactions can take place. You have images, smells, sounds and exchange of information that keeps our brains active, and healthy. Nobody likes traffic jams, the interaction between people from one car to the other is poor compared to our capacity of information processing and interaction. Basically: it is boring and we don´t enjoy it.
Each space configuration that evolved in the cities like restaurants, plazas, parks, stadiums, offer a different code of interaction that we follow and under which things happen between people. This also happens with the recent virtual spaces which will be more and more important in the future. Complex systems offer the opportunity to fulfill our needs and even create new ones. When the interactions are encouraged, intelligence emerges from the exchange of information, so we can fulfill necessities more efficiently, enjoying the complexity.
The understanding of unit interaction is getting more complex through technology, so we can expect more complex and interactive technology in the future. It is just natural.

Personal Research Interest.
Information is the basis for interaction. Interaction defines intelligence. Emergent intelligence comes from the ability to read information and related it in different ways to obtain different results and more information.

Swarm intelligence is repeated throughout nature. We see can it in neurons, ants, shoals and bird flocks. Swarm intelligent systems show similar properties:

-Group of individuals.

-Relatively homogeneous individuals.

-Simple behavior of individuals, which act in response to local information from environment and other individuals.

-Overall behavior by the convention of the individuals.

The information is the element that promotes the action and interaction of the swarms. Information can be transmitted in different ways and channels like chemicals, physical interaction, movement…

Light for example, give us information in the way of color. But our intelligence is not limited to tell as that an apple is red. It tell us that it is ready to be eaten, and that it is going to be sweet. We get an idea of its flavor even before biting it. We already know its smell. The ability to record experiences takes intelligence one step further. New research is being done on how color and sound can relate to transfer information.

Can sound, music and color, be used as medium for information transfer, and what kind of intelligence can emerge from that?

By: Sebastian Alvarado Grugiel. Arquitecto.