The Cloud and Collaboration
Paper written as a contribution to the Ars Electronica symposium on Cloud Intelligence. Here is the presentation page.
Let's take as a starting point the discussion of 'cloud intelligence' on the conference website:
In the cloud of connections, we each become social neurons, mimicking the biological human brain but on a giant scale. This collective knowledge is far beyond anything a single search engine could index and archive. Intelligence is spreading everywhere, every minute, and cloud computing can draw new links across new ideas. (80+1, 2008)
This idea of the connected world as a global brain is not new, nor surprising. It seems clear that we can identify something like social intelligence in the community, and the analogy between humans and neurons is compelling.
Peter Russell's The Global Brain explicitly makes the connection.
We have already noted that there are, very approximately, the same number of nerve cells in a human brain as there are human minds on the planet. And there are also some interesting similarities between the way the human brain grows and the way in which humanity is evolving. (Russell, 2008)
According to Russell, the brain develops in two phases. First, there is a massive explosion in the number of neurons. And second, isolated neural cells begin making connections with each other. A similar pattern, he argues, is observed in society.
Tom Stonier writes,
In principle, this process does not differ from the evolution of primitive nervous systems into advanced mammalian brains... each node, rather than being a neuron, is a person comprising trillions of neurons ... coupled ... to their personal computers... We are now dealing with the very top end of the known spectrum of intelligence. (Hofkirchner, 2005)
As we read and hear more about the growing internet and the emerging cloud, we are also hearing more about the way in which we, as connected members of the cloud, work together. The conference website also addresses this point.
We think together but remain independent in our identity. If we could foster co-thinking to reach consensus about new solutions, we may be able to find a new direction for the future. Hope can emerge from new collaborative models based on a new paradigm; science and art will act gracefully to match human nature, and to shape the future of humanity. (80+1, 2008)
This is a common refrain. It expresses the idea that the cloud enables us to work together, to collaborate, to forge a new consensus. The cloud, in other words, reinforces the ways with which we have attempted hitherto to organize ourselves. The divisiveness, the factionalism, the disputes and conflicts that have blocked our efforts in the past, we are told, can be effectively overcome using the new technology.
Dimitar Tchurovsky's Google knol titled the 'Global Virtual Brain and Mind Project' is a good example of this. (Tchurovsky, 2009) He cites the conflicts of interests, media manipulations, bribery and the influence industry as barriers to a genuine global consensus. The response is a "worldwide social network of self-selected people resembling human brain and mind, who will collaborate in attempt to solve social problems."
The associating of collaboration and global consciousness is natural, as collaboration is central to our concept of community, and the global mind can be seen as an extension of community. We see much the same language as that used to describe the global mind, for example, "people inspired to create healthy communities cross pollinate ideas, connect & exchange stories that harness our collective wisdom." (McCarthy) These examples are typical; they could be multiplied almost indefinitely.
What is collaboration, though? Is it something that neurons in a human brain actually do? Can we describe the organization of our mind in the same terms we currently use to describe the organization of society?
The characteristics identified by the National Network for Collaboration (The National Network for Collaboration, 1995) are typical:
* Accomplish shared vision and impact benchmarks
* Build interdependent system to address issues and opportunities
* Consensus used in shared decision making
* Roles, time and evaluation formalized
* Links are formal and written in work assignments
* Leadership high, trust level high, productivity high
* Ideas and decisions equally shared
* Highly developed communication
Collaboration, on this model, can be contrasted with looser forms of association such as networking, alliance-formation or cooperation.
What distinguishes collaboration from these other forms of organization is a commonality of understanding or purpose. This theme permeates writing on the subject. Schrage calls collaboration "an act of shared creation and/or shared discovery" (Schrage, 1990, p. 6) Senge talks about the creation of a shared vision. (Senge, 1994)
In learning communities, as well, we see commonality or shared vision as central to the creation of a learning community. The idea that learning is social in nature has been a recurring theme in education, from Dewey to Brown & Duguid. Learning communities, write Kilpatrick, Barrett & Jones, "are operationalised through collaboration, cooperation, and/or partnerships. The shared goals are achieved through working together and potentially building or creating new knowledge." (Kilpatrick, Barrett, & Jones, 2003)
Or as Brown & Duguid write,
reciprocity is strong. People are able to affect one another and the group as a whole directly. Changes can propagate easily. Coordination is tight. Ideas and knowledge may be distributed across the group, not held individually. These groups allow for highly productive and creative work to develop collaboratively. (Brown & Duguid, 2000, p. 143)
Or, as they write, forging a single group around a shared task, overlapping knowledge, blurred boundaries and a common working identity. (Brown & Duguid, 2000, p. 127)
Do neurons collaborate like this? Though there may be a sense to be made of vocabulary such as a 'common identity' and 'shared task' for a collection of neurons, it seems highly artificial, based on a certain perspective of their activities as a whole, and most significantly, of limited utility is describing the mechanisms that neurons employ to form a mind.
If we push the language a bit, we can see how awkward this characterization becomes. Does it make sense to say of two neurons that they have a "shared understanding"? Neurons are not the sort of things that can even have an understanding. Do neurons unite behind a 'common vision'? Do they 'reach a consensus' and 'share in decision-making'? Does one neuron 'trust' another neuron? The language begins to stretch credibility.
Equally, the forms and mechanisms of social organization, as we understand them in contemporary society, are completely alien to the functioning of neurons. There is no 'lead neuron' who articulates a vision for all to share. Neurons don't employ a mission statement, strategies or mechanisms in order to complete organizational tasks. Neurons are not client focused, results driven or process oriented. Neurons are not managed and there is no sense to be made of them belonging to a community in anything like a normal usage of the term.
What characterizes collaborative forms of organization is, in one sense or another, sameness in the people. Sometimes this sameness is a mental property - a sameness of vision, understanding or belief. Otherwise, this sameness may be of some aptitude or capacity - a shared vocabulary, shared skill set, shared comprehension. In other forms of community, a more basic sameness is required: sameness of residency, of nationality, of language, or of religion.
By the same token, collaborative forms of organization are directed toward mental content. Communication consists of a transfer of information, with some process undertaken to ensure sameness of content in the receiver as was found in the sender. It is a model of learning and communication as diffusion. There are clear roles of knowledge production and knowledge reception. In a collaboration all members work on the same content (even if each has only a partial view of that content). There is a semantic consistency in their work.
Space precludes a detailed analysis of this phenomenon, however, it can be seen in a wide variety of models of learning and communication, from Moore's theory of transactional distance, to the concepts of knowledge translation or knowledge mobilization, to the power law model of online community, to "core knowledge" advocacy, to Vygotsky's concept of the zone of proximal development (of the latter, Cheyne and Tarulli write, "all of this is organized around the issue of control which, through ontogenesis, becomes transformed from that of an external agent over a subordinate to one of an internal agent over self and ultimately to a principle over an instance"). (Cheyne & Tarulli, 1999)
This 'sameness of entity' thesis (as we may call it) may be distinguished from an alternative representation in which diversity among entities is expected and accepted. Such an alternative model can seem quite radical. Insofar as entities are diverse, so therefore also are their mental contents, which means that when one person says "Paris is the capital of France" he or she means something different from what another person means when uttering the same sentence.
Such approaches to communication have their grounding in "incommensurability" or "indeterminacy" theses of meaning; we see these reflected in Kuhn's theory of paradigm change and Quine's discussion of radical translation respectively. As Quine says, it's not simply that we can't say that two utterances have the same meaning, it's that there might not even be an objective meaning to be right about. (Quine, 1960, p. 73) What underlies communication, what makes community possible, in such cases is not sameness of entity or shared meaning, but rather, our entering into a system of interaction with each other, into what Wittgenstein calls a "language game", the result of a negotiation calls and responses, where thinking is an activity, similar to, as he says in the Blue Book, a movement of the hand, the presumption of meaning being an ungrounded inference, a projection, or as Quine says, an "analytic hypothesis." (Wittgenstein, 1991, p. 16)
When we are not concerned with sameness of entity, when we are not concerned with shared meaning, when we are not concerned with diffusion of content, then the mechanisms for community look very different. They do not resemble collaboration, as we have described it above, but rather, what we may style here as cooperation. For the purposes of the current discussion, 'cooperation' may be thought of as the sharing by entities of a common system of communication or infrastructure. Community, then, would be defined by the interactions or connections among those entities, and the process of the global brain described in terms of those interactions.
From the perspective of a human brain, there is a very good reason why we would want the structure of neural interaction to proceed in this way. If the creation of a neural community - a mind - depended on neurons achieving a commonality of meaning, then the mind as a whole would never be capable of entertaining more meaning than a single neuron. From the perspective of a mind, meaning is not something that is passed from one neuron to the next, but rather, something that emerges from the interaction of neurons. Whether or not one neuron means the same thing as another is completely irrelevant from the point of view of the mind.
The foundation of community understood as arising from the sharing of a common system of communication is not collaboration but is rather, as suggested above, cooperation. A cooperation, then, is formed through the creation or formation of links or connections among its entities, a negotiation of communications among them. A cooperation among entities implies a separation or distinctness of interests between them; we see game-theoretic models of cooperation, for example, in such scenarios as the prisoner's dilemma, where individual interests create the possibility of conflict or betrayal. (Mayberry, Harsanyi, Scarf, & Selten, 1992) It is a mechanism similar to what we see in market economics; there is no presumption of shared objectives or goals, only a negotiation of a means of interaction.
There is no clear statement as to the exact mechanisms through which neurons connect with each other (though the biological and chemical processes are reasonably well understood (LeDoux, 2002)) but for the purposes of this paper four major models of association can be described, each of which does appear to comprise at least a part of the overall process.
First of all, simple association, also known as Hebbian association, occurs when two neurons are activated at the same time and are not activated at the same time. If the patterns of activation and inactivation are sufficiently similar, Hebb postulated in 1949, a connection between those neurons is more likely to be created. (Hebb, 2002) In human communities, Hebbian mechanisms can be seen in the connections that form between people who have similar interests; what creates the connection is not the interest itself, but rather, the fact that such people tend to read the same resources, comment on the same websites, and appear at the same events.
A second associative mechanism might be called 'association by proximity'. Neural cells may connect simply by virtue of being located in the same region of the brain. We see this, for example, in the clustering that takes place in the visual cortex, where contiguity of retinal cells is reflected in links among neurons at deeper layers. Rumelhart and McClelland describe how contiguous neurons may form inhibitory 'pools' of neurons, where the activation of one neuron actually inhibits the activation of the next. (Rumelhart & McClelland, 1986, p. 28) In communities, contiguity is often the basis for association: neighbours get to know each other, and colleagues take part in social activities.
A third associative mechanism, competitive theory, is similar to the 'trial and error' pattern of learning familiar in pedagogical theory. In connectionism (the study of computational neural networks) this is described as 'back propagation'. Associative networks are formed and then fed input, from which, via their connections, they produce an output. This output is then corrected, and a signal is sent back through the network, on the basis of which the connections between neurons are modified. (Rumelhart & McClelland, 1986, p. 328) The complex interactions that characterize dating, negotiations, and other iterative communications may be reflective of back propagation.
And a fourth associative mechanism, harmony theory, is based on the idea that systems of connected entities 'settle' into a state of least potential energy. This mechanism, referred to in connectionist circles as the 'Boltzmann Machine', employs the principles of thermodynamics to describe a settling function. In a Boltzmann machine, networks are repeatedly stimulated by increasing the probability that one neuron will be connected with another, then 'annealed' by gradually lowering this probability. (Rumelhart & McClelland, 1986, p. 282) This is like the periodic staging of 'mixers' in which the frequency of interaction is greatly increased, followed by periods of time during which more close connections may be negotiated.
These four mechanisms (and there may be more) are distinct from the mechanisms we described above, under the heading of 'collaboration', in that there is no presumption of management or authority, no privileged nodes, and no hierarchy. The idea is that each entity is autonomous - a model most popularized in Marvin Minsky's The Society of Mind, where "each mind is made of many smaller processes" he calls "agents". (Minsky, 1985, p. 17) A (human) society of agents also sounds more like what we would want to describe as constituting a global mind, a society in which each individual is autonomous, performing his or her individual (and unique) function, forming an intelligence through interaction with the rest of society, rather than by conforming with it.
We hear, sometimes, the emerging structure of the web described as a 'new socialism'. (Kelly, 2009) But there is a tendency to represent this new socialism as an economic theory, in terms of the creation and consumption of content. "They have already constructed a vast online repository of culture, knowledge, and tools. And we are just at the beginning of what's to come." (Oso, 2009) There is, it seems, a desire to represent this as a collaboration or type of collectivism, "Wikipedia, Flickr, and Twitter the 'vanguard of a cultural movement', an emerging 'global collectivist society.'" And concordantly, there seems to be an inclination to weight people according to the value of their contribution (and by extension, even, to value people by the number of their connections). The content-based 'new socialism' is the same as the authority-based power-law driven old capitalism.
In reality, the 'new socialism' that ought to be understood as emerging on the internet is not one dogged by the tired stereotypes that seem to characterize American descriptions of the term (Lawrence Lessig, for example, defining 'socialism as coercion' (Lessig, 2009)) A more modern version of socialism may be found in the forms of 'democratic socialism' current around the world, forms of socialism as a form of personal empowerment, equality of opportunity, and association and interaction.
The concept echos what Illich talks about as 'conviviality'.
Illich's 'tools for conviviality' are appropriate and congenial alternatives to tools of domination, as convivial tools promote learning, sociality, community, 'autonomous and creative intercourse among persons, and the intercourse of persons with their environment' (Illich, 1973, p. 27). These criteria, he felt, could guide reconstruction of education to serve the needs of varied communities, to promote democracy and social justice, and to redefine learning and work to promote creativity, community, and an ecological balance between people and the earth. (Kahn & Kellner, 2007)
If we are to think of the internet as a global mind, then the interpretation of the community created by such a network as characterized by cooperation, rather than collaboration, then we need to reframe some of the discussion regarding the attributes of that network, and reform our understanding of the processes and the technologies most appropriate for the creation of such a network.
Instead of attempting to identify thought-leaders, for example, and instead of attempting to identify and understand the content created on the web, the various activities of participants in the network are acts of interaction and communication. The semantics, the meaning, of interactions are not deducible from their contents; indeed, their contents are, from the larger perspective, irrelevant. Rather, we should treat them as contentless 'words' or 'signals' in a complex communication taking place among the entities. A web video created by a skateboarder: that's a word. A lolcat created in photoshop: that's a word. This article: that's a word.
We communicate with each other with these words, and the important things are, first that we communicate, not the particular nature or content of our communication, and second, what we as a species do as a consequence of that communication, not in the sense of having common ideas or doctrines or philosophies, but rather in terms of global expressions or behaviours. In such a network there are no special, privileged, nodes; being a consumer is as important as being a creator, and indeed (as has often been noted) the roles of creator and consumer become indistinguishable, and more important, so to do the roles of master and servant.
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