Comparison of requirements of the liquid network to the mathematical requirements of Social Networks
Steven Johnson (2010), the popular author of “Where Good Ideas Come From- The Natural History of Innovation”, wrote that innovation is structure based. A cornerstone of Johnson’s (2010) book is this idea of an informal, liquid network in which ideas collide. Johnson (2010) visualized the liquid network as the Hogarth’s painting “Humours of an Election”. Ideas generate from informal meetings not that company annual retreat in Florida ac-cording to Johnson (2010). The closer the people’s ideas are in space and time, the better the chance for the ideas to collide with a few new (idea) bonds formed along the way. Johnson’s use of network would better be labeled as idea management rather than being complexity driven. But for policy to be able to account for the long-term implications of idea management, ethical deliberation drives the complexity of organization. The included table compares the offerings of the liquid network of Johnson (2010) to the social networks for idea management. Collision unfortunately lacks the formality and accountability afforded by the structural relationships of social networks. Broadening idea generation beyond a “bump” illustrates the immensity of the effect of the social and political reach of policy.
People connect within a social space. According to Alba and Kadushin (1976), the measure of proximity is concerned with pairs of individuals and how “distant” they are. Traditionally, proximity is all what diffusion of social commodities, often defined by information sharing, as a flow (Alba and Kadushin 1976). In a liquid network of Johnson’s conceptualization, ideas bump into each other due to the act of discourse as an event. First, there must be connections to other people and the environment supports the development of ties. Johnson concurs. There are the things colliding in a network which in Johnson’s (2010) case is the idea. Discourse turns that “private solid state to a (public) liquid network” (Johnson 2010). In what Johnson (2010) called the state of adapting exaptation, people can informally engage due to the “bump factor” of these chance meetings (Johnson 2010). To Johnson (2010), innovations linger in a “slow hunch” countering the eureka moment. The power of leverage points values the ability to find a small change that can make a huge impact. This is not the same as the epiphany. Systems that undulate slowly can be most frustrating to the urgency placed on having policies work.
The liquid network is mildly bound enough to be a network but not enough to strangle innovation with rigidness. Ideas are innovative because of the people that offer them with their skills and prestige. Some ideas are drowned out or flatly ignored. Connecting the ideas as a structure is often descriptive. Which idea (or person with the idea) was most connected? Which idea structurally demonstrated power in the network of ideas? Then this whole idea of playing with private ideas within a public space by basing it on the structure of the irrational agents is the more interesting, and fruitful question to pose.
Johnson (2010) proposed that platforms, or orchestrated spaces, may foster innovation. Within these platforms, liquid networks which do not restrict movement within an organization and their innovation ideas may be better supported to thrive. The ideas take on a fluid state without outside restriction. Does the event become more consequential than the interaction supported by the event? Networks are systems in which the nodes (the things that are connected) are connected by ties. When one is interested in reasons for why people affiliate over an idea, two mode networks could be employed (Borgatti and Everett 1997). The people would be agents (set 1 mode) while the IDEAS serve as a “series of events” (set 2 mode) that they are sharing. In a two-mode network, both the idea and agent indicates the elements of the network. The idea becomes an element to be weighted with the agents and chance meetings. So there is a tie be-tween the agent and the event. The tie formation could be due to “bump factor”:
1) to accept the chance meeting and
2) engage in the innovating chance meeting.
Both nodes engage in it since Johnson (2010) is basing adapting exaptation on engagement not its possibility. But a concern with approaching such two-mode networks, which mode takes the primary position? For instance, Johnson (2010) does not divorce the person from the idea and but for his paradigm the idea is element of focus. The idea is the primary node. The idea comes from the person (secondary node). The argument would also be made that if innovation is viewed as a dynamic, social experiment, the person as the primary mode may say more about why that idea even came to be in the first place. While the platform may be nature be useful in revisiting existing resources such as ideas in a liquid network, the problem often uncovered in policy is the lack of new ideas and continued reliance on existing ideas that may lead to attribution errors. To Johnson (2010), the ideas are used as bonding agents. Two-mode networks deal with the nature of affiliation. The idea is no longer just assumed but is now used as a means to investigate the structure in which ideas are embedded within the ties connecting the author of the idea (see Smith and Christakis 2008). The approach of Johnson (2010) had ideas brought “together” by its messengers as one frame. I do not believe that Johnson (2010) is marginalizing the person behind the idea of the liquid network. By stressing the network, the structure at two mode levels appears to capture the reality of how innovation in a liquid network more likely occurs.
To Johnson (2010) removing boundaries to the liquid network leads to chaos. This may be wise as any chaotic system does not return back to original state before the spiraling began. This chaos could hamper innovation by changing the new starting point and disrupting the momentum started in creating the innovative (see Feigenbaum 1983). As Johnson (2010) was intending avoiding chaos for a malleable liquid state, there is no history of events connected with a state of chaos. When things are chaotic, the past matters far less, only the energies running amok are important. What significance might this hold for developing innovation? Unlike chaotic systems, complexity is tied to its history of events upon which the system was built (see Buchanan 2000).
What is applicable to policymakers under liquid networks, according to Johnson, is less promising. In offering the example of Howard Dean’s Presidential Campaign “supernova”, Johnson (2010) admitted that “political leadership involves some elements (decision making and oratory) that aren’t best outsourced to a liquid network.” So how do policymakers innovate and think creatively under political stressors? Using Wasserman and Faust (1994) taxonomy, two-mode networks have three possible purposes under Johnson’s (2010) liquid networks:
(1) The affiliation is the tie of the person/ideas (node) to the informal chance meetings (event).
(2) The “bump factor” (if it supports interaction) may make interaction, therefore innovation, more likely.
(3) In order to framing the adapting exaptation realistically, the measurement of homophily is required.
The two mode network is difficult to interpret, let alone analyze. But how ever said that policymaking was easy? People tend to clique around policy issues, perhaps based on the process of idea generation or even bunch out due to personal access due to co-location. Two modes capture this bunching up as cliques that overlap, perhaps by party affiliation or side of a legislative issue. But the point of this methods tale is not the complicated analysis but that that coming together to work policy out that on the surface can be deceivingly simplistic. This is a great example of how the mental model of a liquid network does not apply neatly to policymaking but still has a place in the discussion. Unlike the informal liquid network, the innovation required by policy must translate into proper strictures under governance. The chaos of a liquid network must be operationally linked to the structural nature of complexity. But this fact does not mean that networks, whether liquid or mathematically understood, are not useful in discerning policy. While the informal ideas that are eventually brought to the policy table may utilize adapting exaptation, the policymaking process strips away the lacquer of casualness.
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Borgatti S, Everett M (1997) Network analysis of 2-mode data. Soc Network 19: 243-269
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Feigenbaum M J (1983) Universal behavior in nonlinear systems. Physica 7: 16-39
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Smith K, Christakis N (2008). Social Networks and Health. Annual Review of Sociology 34: 405-429