Vital Systems Security

Hi everyone,

Below you can find an interesting talk that is on the application of network techniques on complex, dynamic and stochastic processes such as the spread of diseases within population. We had first seen network analysis in the OEP’s engagement with pipeline optimization problems. Then if I am not wrong this sort of thinking came up again with Lyle’s engagement with syndromic surveillance, and we had discussed both in New York and Berkeley how these techniques might have lead to a reproblematization and reproblematization of ‘population’ as an ontological being under a new and novel form different from its 19th century understanding (as we know from Foucault’s and his followers’ work on this period).

Perhaps one of the epistemic shifts that become possible with the application of techniques such as network analysis and monte carlo simulations is the ability to intervene in very calculated and precise ways as the abstract below suggests. Fuzzy and macro processes that have been the object of intervention in the 19th century and most of the 20th century are now unconcealed in novel ways with these techniques which allow the experts to examine the object in exact and precise ways by visually re-constructing the object of intervention in its exact detail. To my knowledge there is almost no work that pays attention to this aspect, and furthermore to me this seems to be one of the crucial points of the vital systems argument.

 

 

Date: Tue, 14 Oct 2008 09:23:56 -0400 (EDT)

From: Rocco A. Servedio <ras2105@columbia.edu>

To: theoryread@lists.cs.columbia.edu

Subject: Theory reading group: David Kempe talk Mon Oct 20, 2:30pm

 

Please join us for a theory seminar on Monday, October 20 at 2:30pm in the

CS Conference Room.  David Kempe of USC will speak about “Optimization

Problems in Social Networks”; details below.

 

Please send me email if you would like to meet with David during his

visit.

 

Hope to see you at the talk,

 

Rocco

 

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Title:  Optimization Problems in Social Networks

 

Abstract: A social network - the graph of relationships and interactions

within a group of individuals - plays a fundamental role as a medium for

the spread of information, ideas, influence, or diseases among its

members. An idea or innovation will appear, and it can either die out

quickly or make significant inroads into the population. Similarly, an

infectious disease may either affect a large share of the population, or

be confined to a small fraction.

 

The collective behavior of individuals and the spread of diseases in a

social network have a long history of study in sociology and epidemiology.

In this talk, we will investigate graph-theoretic optimization problems

relating to the spread of information or diseases. Specifically, we will

focus on two types of questions: influence maximization, wherein we seek

to identify influential individuals to start a cascade of an innovation to

maximize the expected number of eventual adopters; and infection

minimization, wherein we seek to remove nodes so as to keep a given

infected component small.

 

We will present constant factor and bicriteria algorithms for versions of

these problems, and also touch on many open problems and issues regarding

competition among multiple innovators.

 

(This talk represents joint work with Jon Kleinberg, Eva Tardos, Elliot

Anshelevich, Shishir Bharathi, Ara Hayrapetyan, Martin Pal, Mahyar Salek,

and Zoya Svitkina.)

Here is an interesting piece by a French theoretical physicist on how one might want to do security when it comes to the economic catastrophes. His starting point is no surprise: that neo-classical model of the markets do not make sense b/c they are not realistic. That is not how markets work…

Well, probably many of us would agree with him, but what makes this point not interesting is precisely “we” would also include the economists he blames. This is probably an important point to make, though not it is not an interesting one as far as positive knowledge goes: I am not sure if economists think in terms of classical Walrasian neo-classical market models of general equilibrium of supply and demand. It is true that economists will teach this in their undergraduate courses and will speak in these terms to the public or the politicians. Also probably policy advisors, technocrats, experts, and politicians find this language helpful because we are all familiar with it. But I do not think this is how they operate. I do not think they wake up and look at their screens and see if the market is in equilibrium or not. First of all, general equilibrium models left their place to partial equilibrium models. Second, with rational choice theory and other decision making models economists turned to new analytical techniques to analyze the economy since the 1950s. So, to me it seems that the language of market equilibrium is a heuristic discursive device for certain actors who are responsible for informing the public on the economy. This is however is not an unimportant task as we are right now seeing there is a huge gap between what actually these experts do and how they talk about what they do. One might argue that this is why the bailout failed, precisely because it was not a bailout but a rescue attempt to save the economy conceptualized as a vital system. It would also be interesting to think about how as the Fed was trying to manage the crisis what they called systemic risk and therefore rescued, and what they called moral hazard and how they let it sank. There seems to be a high level of imprecision and non-correspondence between the language and terminology of the Fed and what they do…

Second, if we actually come back to his interesting argument, we see that what he is proposing is not so far from the kinds of things we saw at OEP. His basic point is that we cannot rely  on these ideal typical models and rather we should ground our regulatory practices on a realistic model of science instead of a formal one. Given that he is a physicist it is not surprising that his solution is “simulate the market” as opposed to model it.  The funny thing with this proposition is that the two are not contradictory practices, but rather are mutually complementary. This is one of the interesting things with the experts who are in the business of simulation. Often times because they get their models from other experts already black-boxed, they forget that simulation rests on a diffuse network that includes both the theoreticians and applied scientists. Without models, i.e. a formalist scientific practice, simulations would not make any sense; it would simply be guess work. (We might want to think if there is a similar process in the case of catastrophe enactments and exercises that Andy, Stephen and others on this blog have been thinking about. In other words, how do we or rather the experts themselves know that we are basing our simulations on right kinds of scenarios? Are scenarios types of models in correspondence to the computer simulations?) 

Finally, the article gives us interesting clues on how simulation is used to govern the economy in comparison to the early attempts we saw at the OEP. He gives 3 examples:

1) An agent model being developed by the Yale economist John Geanakoplos, along with two physicists, Doyne Farmer and Stephan Thurner, looks at how the level of credit in a market can influence its overall stability.

2) Professor Westerhoff and colleagues have used agent models to build realistic markets on which they impose taxes of various kinds to see what happens.

3) Charles Macal and colleagues at Argonne National Laboratory in Illinois and aimed at providing a realistic simulation of the interacting entities in that state’s electricity market, as well as the electrical power grid. 

These can be interesting experiments to follow to see where practices of governing might go that are linked to the types of problems we would like to tackle on VSS…