Topics in Digital Media – Fall 09

Note: The readings this week were hard for me to grasp, so I apologize if I didn’t ‘get’ some of the things in the articles. This subject is totally new to me and I still feel like I don’t really ‘get’ it… I look forward to talking about this in class!

Networks: A Talk by Anna Nagurney

This talk is a great introduction to networks and network theory, the different kinds of networks and the application of network theory. At the end, the speaker talks about a new paradigm for the study of networks – Supernetworks.

Background of Networks:

• Pervasive and essential for functioning of societies and economies.
• Networks exist all around us: business, science, social systems, technology, and education and provide the infrastructure for communication, production and transportation.

Examples of Physical Networks:

• Transportation is a good (and big) example of a networks – transportation allows for face-to-face interaction, access to consumer products and food, etc. transportation networks include highway, railroad, and waterways (for freight). Transportation networks work over roads, rails, water, and air.
• Communication Networks – allowing for communication and connection with our communities and international communities. These networks have also changed our lives – personal and professional. Communication took place based on available resources – from smoke signals and pigeons to computers and phones. The earliest forms of the application of network theory is from Roman times dealing with congestion – constraints on what time chariots can come into the city!
• A type of network that isn’t studied as much (maybe not any more, in the face of the Global Warming and oil shortage issues?) are Energy networks. Ms. Nagurney talks about the blackout of 2003 and the problems that caused.

Scientific Study of Networks: The study of networks dates back about 50 years.

Network Components:

• Nodes: origin, destination, or stopping points in a network. Examples are homes (transportation), distribution points (manufacturing/logistics), satellites (communication), pumping stations (energy).
• Links or arcs: the directional connections between nodes. Examples are roads (transportation), shipment (manufacturing/logistics), fiber-optic cables (communication), pipelines (energy)
• Flows: the components that are carried – these are different depending on the network being looked at. Examples are automobiles (transportation), finished goods (manufacturing/logistics), video (communication), oil (energy).
• These are physical networks, but the power of networks as a scientific methodology is also the power to abstract non-physical networks.

Visualizations of Networks:

Listening to this made me think of the many data visualizations that are used today in almost every field. And then it made me think that a lot of these visualizations are also visualizations of networks! This is a really cool site for data visualizations. Can you find a few that are ‘networks’?

• These are useful because human beings understand visuals easily. Visualizations also allow for showing different dimensions of networks like not just the nodes and the links, but also the amounts of the flows.
• The aim of the study of networks is to make the flow between nodes as efficient as possible. This is achieved by looking at how to model networks as ‘mathematical entities’, how to study the models qualitatively (looking at the impact of adding a bridge or removing a road, etc.), and how to design algorithms to solve resulting models – which essentially means how to make the flow efficient.
• Examples: shortest path problem (moving message, etc. between nodes), maximum flow problem (how much flow can you put in a link), minimum cost flow problem (flow patter that minimizes the total cost of moving along a link).
• The shortest path problem is often in transportation and communication. Other examples include building evacuation models and DNA sequencing.
• The maximum flow problem is applied to machine scheduling and network reliability testing. Example: capacity of stairwells during an evacuation. The minimum cost of flow problem is applied to cash management, vehicle fleet management, etc.
• Network problems can also be applied to other problems with network structure – abstract networks in which nodes are not necessarily physical locations. Looking at these problem from a network theory perspective can make it easier to understand the problems and hence arrive at solutions for the problems.
• Earliest example of network study was in finance in a book called Tableau Economique by Quesnay where he depicted the circular flow of financial funds in an economy.

Advantages of ‘Scientific Network’ formalism:

• Today’s problems invlove flow – material flow in logistics, military, human flow (migration), capital (finance) and these are flows over space and time, so looking at these from the network theory angle is useful.
• Applying network theory allows us to represent these problems and flows in a visual way.
• We’ll be able to see the underlying similarities and differences in the network structure
• Network algorithms can be developed and applied
• Provides a unifying methodology for different domains.

“One of the primary purposes of scholarly and scientific investigation is to structure the world around us and to discover patterns that cut across boundaries and, hence, help to unify diverse applications. Network theory provides us with a powerful methodology to establish connections with different disciplines and to break down boundaries.”

• Many different disciplines are successfully applying network theory: economics and finance, management science and operations research, applied mathematics, public policy, biology, engineering, computer science, etc.

Characteristics of Networks Today:

• Large scale – an example of this is Chicago’s Regional Network or AT&T’s domestic network – these each have crazy amounts of nodes and links and flows, which also increases the number of origin-destination pairs. This makes it very challenging in terms of displaying or even mapping the number of flows and links and nodes. Earlier, congestion wasn’t a big problem, but today it’s much much bigger. Congestion causes loss in productivity – and this is bound to increase based on the expected increase in the number of automobiles over the next 20 years. Another problem with congestion is the change in links – China had congestion due to bicycles, but now it’s cars.
• User behavior changes = ‘paradoxical phenomena’. This can occur in system-optimized vs user-optimized networks. This means that in a larger picture, the choices an individual makes (user-optimization) may not be beneficial to society (system-optimization) as a whole. So, system-optimization is more efficient, but it is still affected by users. There are ways to ‘trick’ people into doing particular things (using toll prices, etc.) in order to increase efficiency, but ultimately, users make their own choices.
  • Braess’ Paradox – this was a little difficult for me to grasp, but, as I understand it: in an existing network with a given origin/destination and a set of paths, there is a particular flow, and this is the equilibrium path with a fixed cost (time, etc.). Creating a new path will not reduce the cost, but, on the contrary, it can increase the cost! Examples of this are:
    • Stuttgart – they created a new road downtown to ostensible reduce congestion, but it ended up increasing travel time, so they closed the road.
    • NYC – they closed 42nd street on Earth Day and people were scared that this would disrupt traffic, but actually noone complained and it turned out to be better – reverse Braess’ Paradox!
  • This paradox has also been discovered on the Internet by a group from Columbia and Israel.
  • Moral of the story:” …you have to take the behavior of users into consideration when you model and solve network problems.”
• Another characteristic is that multiple networks can interact with each other
• There are network policies which can affect social, political, economic, and security-related issues.

A New Paradigm – Supernetworks:

• Ms. Nagurney proposes looking at the paradigm of Supernetworks. This is essentially a combination of two or more networks that are dependent on each other or interact with each other. These can be multilayered (like supply-chain) or multitiered (financial networks). Looking at multiple networks will involve multiple criteria, and so will also include the study of multicriteria decision-making.
• Tools that Ms. Nagurney has been using for the study of supernetworks include network theory, optimization theory, game theory, variational inequality theory, projected dynamical systems theory, and network visualization tools. (Looking at something visually can help to make sense of the network.)
• In her words: “We are interested not only in addressing topological issues in terms of connectivity but in predicting the various flows on the networks whether physical or abstract subject to human decision-making under the associated constraints, be they budget, time, security, risk, and/or cost-related.”

Applications of Supernetworks:

• Telecommuting/commuting Decision-making: looking at the trade-offs between using transportation networks vs telecommuting networks. In Sweden and other european nations, people can drive to ‘work centers’ or work from home, but only a few days a week. These are different options that have different effects on transportation and telecommunication networks.
• Teleshopping/Shopping Decision-Making.
• Supply Chain Networks with Electronic Commerce: you have different decision-makers at different nodes and you want to capture the behavior of these individuals and look at the competition between these individuals, but also the cooperation, because this supernetwork will not work without cooperation.
• Financial Networks with Electronic Transactions – especially international financial networks with electronic transactions.
• Reverse Supply Chains with E-Cycling: what happens to our computers or mobile phones after we discard them?
• Knowledge Networks (Innovation networks also. NSF approached them to use network research to look at knowledge-intensive dynamic systems. How do you produce knowledge?)
• Energy Networks/Power Grids

Supernetworks visualizations include the network visualizations of all the networks involved, and, to my eye, look way more complicated, but will supposedly make the workings and analysis of these networks easier for those studying them. These visualizations also help when comparing different networks.

An important factor that supernetworks take into consideration is the relationship levels of the people involved – the social network. These decision-making relationships affect costs, and higher relationship levels reduce transaction costs, reduce risk, and add value. So basically, the supernetworks will also have a social network element to look at how the flows of the network are affected by the relationships and relationship levels in that network.

Ms. Nagurney and her colleagues also look at dynamic networks because these models are not static. They track the trajectories – prices as they evolve over time, product transactions, financial flows and relationship levels.

And, to pass some time, here’s a cool and addictive network theory-based game.

The Principle of Notworking – Concepts in Critical Internet Culture (pp 3-11), Dr. Geert Lovink

In this article, Dr. Lovink talks about his theoretical work dealing with Network Cultures while at the Hogeschool van Amsterdam, focussing on the changing culture of the Internet.

He starts by talking about George Yudice’s statement that “we have moved from the attitude of suspicion towards culture, and what the danger of its ‘inherent fall’, towards a so-called ‘productive view’.” (p3) He defines culture as “an active and, potentially, innovative sector with the capacity to mobilize forces.” (p3), an example best seen in Internet culture.

The author talks about the mistakes of attaching monetary ‘value’ to Internet culture or trying to measure it by ‘pageviews’ (citing the failed dotcom models that followed this viewpoint as an example) and that we should look at it instead as the ‘currency of diversity’ (p4). Culture was being used as a commodity but it is not something that can be or should be controlled by any one entity. The author talks about culture as something to be nurtured and allowed to grow organically in its own space. As an example, he talks about ‘creative industries’ and how they exist today but if they wanted to succeed, they would have to consider their own sustainability outside the realms of the ‘new economy’ and nurture their own ‘culture’… or drop the pretense and just be part of something bigger that controls them.

The author talks about a shift in Internet culture (keep in mind this is 2005), the indicators of which he sees are:

• From the predominantly English-speaking ‘Western’ Internet, Japanese and Chinese intranets and cultural movements are on the rise.
• Opposite to the view that the Internet serves to connect all humans, the author bemoans the downside of the inevitable rise of apps like wikis and blogs and P2P apps that will forever change the face of new media.
• The decline of the dominance of the IT industry in the West. This was the main ‘culture’ but with outsourcing and a shift in gender roles, there is a chance of ‘culture mingling’ (p6).
• The rise in multimedia-related courses in educational institutions. The author notes that the education sector has been slow to adopt this network culture because it is entrenched in differences between the disciplines and because of Microsoft! Another reason for slow adoption of this network culture is apparently the lack of use of open-source software. Lovink says that despite playing a major role in Internet culture, academia has fallen behind and is making feeble attempts at catching up. There needs to be attention paid to the study of mobile devices and user studies with relation to application development.

The author calls for a change in the approach to Internet research saying that current methods of ethnography and other user-centered models do not distinguish between the micro and the macro level of users (p6). This approach tends to overlook larger movements within the Internet culture. He calls for a new network theory specifically for new media – for a ‘new media language’ (p7). Lovink talks about the obvious network system underlying the Internet network – something that would be surprising for scientists, but should not be for Internet scholars.

Hardt and Negri (2004) use the term ‘multitude’ when talking about social networks. They recognize networks in every aspect of our lives, from military to personal and they talk about networks emerging as a way in which people understand the world around them and their role in it. (Refer back to Nagurney’s talk about how network theory exists in everything!)

Lovink claims that the nature of a network is that it looks inward and, as Hardt and Negri note “creativity, communication, and self-organized cooperation are its primary values”. He believes that these characteristics are what can prevent terrorist groups and other forms of uprisings from within networks – “Networks undermine, but not entirely eliminate, authority and make decision-making next to impossible.” (p8). Because of a network’s ability to ‘prevent a lot of events from happening’, he presents ‘networks’ as a new strategy that we should consider in order to prevent the repetition of tragic historical events.

The way in which networks are now growing and the roles that browsers, operating systems, and search engines play in dictating the form of these networks, has made Lovink call for the new media community at large to step up and help deconstruct this network and “mediate the outcomes to a multitude of audiences.” namely, the rest of us (p9).

At the same time, Lovink talks about Alexader Galloway’s ‘protocol’ theory – “Protocol is based on two contradicting machines: ‘one machine radically distributes control into autonomous locales, the other machine focuses control into rigidly defined hierarchies.’” Following this theory, networks may be a new approach to replace old forms of power, but they will also create another form of power – one that gives the illusion of freedom, but will “install themselves into everyday life as ideal machines for control.” (p9).

The author goes on to talk about some of the incidental or not-so-incidental drawbacks of networks:

• Spam, viruses, and identity theft that exist because of the original network architecture of the Internet.
• ‘Noise’ – non-essential communication.

He talks about the theory of protocols as being ‘retro-grade’ as it describes of how networks used to be and also because it did not foresee the clash between the ‘open source’ movement and the “neo-conservative takeover after 9/11″ (p10).

Another area of research that Lovink in interested in is how networks deal with those who go against the network or “the frustrated” as he calls them. To him, networks are not the best medium for religious or propaganda purposes – at least not better than traditional broadcast media. He talks about Hoffer’s ‘true believer’ and how frustration may cause true believer traits to manifest, but this was not a good thing for “online dialogues” (p10). The ‘frustrated’ are not accepted in the network and are then forced to the fringe and Lovink wonders what happens to these individuals – he doubts that they form networks themselves!

Finally, Lovink talks about the nature of interaction within a network and the thing that is the network’s biggest challenge – no tolerance of “outside points of view”. He goes on to say that networks are not good examples of natural human behavior and that they are “complex techno-social environments that defy simplistic reductions” (p11) and that there is not yet a network that has been able to bring down an existing power structure.

Possibly Relevant Posts:

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• networking a la Galloway and Thacker… (5) | sava
• Do community based social networks improve visibility of resources and civic engagement? (1) | Gabriel Mugar