This work by the UCLA Mathematicians is representative of the prevalent ethic among scientists. The highest value to which the scientist aspires is the advancement of her/his science.
The ethic
The thread proceeds as follows: The advancement of knowledge is the supreme good-->my field is about the advancement of knowledge in the area to which i have committed myself and which brings me joy in its pursuit-->I need resources to advance knowledge in my field--> resources are available from the military-industrial complex, the anti-terrorist complex, the police and others in kind-->to have access to those resources i must work on problems that they perceive as advancing their goal-->the pleasure of pursuing my discipline is the opiate that insulates me from concerns of the use and consequences of my work.
Its history
This ethic is not peculiar to Capitalism. although its consequences and its prevalence has never been as widespread. It is a product of class societies. Archimedes, considered the greatest mathematician of antiquity used his talents to produce several war machines. Galileo did the same, as did Poincare. These great minds needed access to resources as trivial as the free time to think, experiment, and write and as expansive as a laboratory and assistants. They are representative of how scientists and mathematicians have been compelled to relate to the class that in each social formation posessed the wealth and determined values.
A reflective comment
Not all components of the ruling class always subscribe to this dominant ethic. Neither can one deny that not all work funded by the dominant class is malevolent. Most importantly one must not assume that all scientists, engineers and mathematicians that objectively subscribe to this ethic subjectively subscribe to it. There are those, who intoxicated by the joys of scientific work, are clueless; and there are those who despise the class system itself but are compelled, if they are to pursue their science, to work within the system. So also is it true that the daily participation as a wage earner in society is objectively to be a part of the necessary strcture that maintains the society and thus the hegemony of its ruling class.
A global conclusion
It is essential for the future of our species (at least) that class society be abolished. The class aspect of society that must be done away with is that which allows dominance of one minority of society over the rest of society and which perpetuates the accumulation of wealth by that minority. Because not all humans are the same, this is not a call for the levelling of all; it is for the opportunity of self-realization for all within a framework of equality as human beings, peace, and cooperation.
Our role
The structure of capitalism makes the emergence of an alternative society within it much more difficult than in previous class societies. Also its ability to integrate essentially everyone from all classes into its mechanism makes the necessary massive change in consciousness for it to be sent into the proverbial dustbin of history all the more difficult relative to previous historical social transformations. Among scientists, engineers, and mathematicians, guilt tripping will transform the consciousness of but a few and probably alienate even more. In the classroom socially conscious instructors will also be able to transform the consciousness of but a few. Exemplary behavior also influences some few, although without either giving up one's discipline or being independently self sufficient, it is difficult to pursue one's discipline and be exemplary. What then is to be done among the technological-scientific community, a component of the salary compensated part of the working class, to effect its change in consciousness? That is, in my judgment, the primary role this list, as representative of the socially conscious technological-scientific proletariat, must assume.
Our task
The value in our communicating among ourselves includes the possibility of discovering how we can best reach out to the virtually 100% of the Science community not on the list. To do that we must first recognize that the categories good and evil do not apply to human beings (perhaps in rare instances). What is good for and what is destructive of our species are more appropriate categories. We also have to recognize that scientists who find that the only way they can get research funded is by doing shit like this post of Sam's are also victims--victims of a system that provides few if any opportunities to pursue their discipline and further develop their talents outside of funding from the ruling structure. In fact, it is that structure that is the enemy. Although i have some ideas on how we can broaden our reach, i'm sure we'll learn more, if i leave that question open.

herb

On 11/2/2011 10:17 AM, S E Anderson wrote:
[log in to unmask]" type="cite">

Fighting Violent Gang Crime With Math

Math in the service of "fighting crime." Under capitalism, it is not about taking millions of dollars to try to solve poverty that breeds low level street crime "analyzed" here. No. It's about how can the capitalists make greater profits and create new markets via math and computer technology.- SEA

ScienceDaily (Oct. 31, 2011) UCLA mathematicians working with the Los Angeles Police Department to analyze crime patterns have designed a mathematical algorithm to identify street gangs involved in unsolved violent crimes. Their research is based on patterns of known criminal activity between gangs, and represents the first scholarly study of gang violence of its kind.

The research appears October 31 on the website of the peer-reviewed mathematical journal Inverse Problems and will be published in a future print edition.

In developing their algorithm, the mathematicians analyzed more than 1,000 gang crimes and suspected gang crimes, about half of them unsolved, that occurred over a 10-year period in an East Los Angeles police district known as Hollenbeck, a small area in which there are some 30 gangs and nearly 70 gang rivalries.

To test the algorithm, the researchers created a set of simulated data that closely mimicked the crime patterns of the Hollenbeck gang network. They then dropped some of the key information out -- at times the victim, the perpetrator or both -- and tested how well the algorithm could calculate the missing information.

"If police believe a crime might have been committed by one of seven or eight rival gangs, our method would look at recent historical events in the area and compute probabilities as to which of these gangs are most likely to have committed crime," said the study's senior author, Andrea Bertozzi, a professor of mathematics and director of applied mathematics at UCLA.

About 80 percent of the time, the mathematicians could narrow it down to three gang rivalries that were most likely involved in a crime.

"Our algorithm placed the correct gang rivalry within the top three most likely rivalries 80 percent of the time, which is significantly better than chance," said Martin Short, a UCLA adjunct assistant professor of mathematics and co-author of the study. "That narrows it down quite a bit, and that is when we don't know anything about the crime victim or perpetrator."

The mathematicians also found that the correct gang was ranked No. 1 -- rather than just among the top three -- 50 percent of the time, compared with just 17 percent by chance.

Police can investigate further when the gangs are narrowed down.

"We can do even better," Bertozzi said. "This is the first paper that takes this new approach. We can only improve on that 80 percent by developing more sophisticated methods.

"Our algorithm exploits gang activity patterns to produce the best probability of which gang, or which three gangs, may have been responsible for the crimes," she said.

Bertozzi and her colleagues have been working with the LAPD on a variety of classes of crime. The implications of the research go beyond fighting gangs and beyond fighting crime.

"The algorithm we devised could apply to a much broader class of problems that involve activity on social networks," Bertozzi said. "You have events -- they could be crimes or something else -- that occur in a time series and a known network. There is activity between nodes, in this case a gang attacking another gang. With some of these activities, you know exactly who was involved and with others, you do not. The challenge is how to make the best educated judgment as to who was involved in the unknown activities. We believe there are a number of social networks that have this same kind of pattern."

Identifying hackers would be an example; helping businesses target advertising to consumers who would be most interested in their products and services in a way that would protect privacy would be another.

"An advertiser may not care who individual people are but just how they behave," Bertozzi said. "Advertisers could target consumers by knowing their shopping behavior without knowing their identities."

The lead author of the study is Alexey Stomakhin, a UCLA doctoral student in applied mathematics who worked for a year to design the algorithm that can fill in the missing information.

The new research is federally funded by the National Science Foundation, the U.S. Army Research Office's mathematics division, the U.S. Office of Naval Research, and the U.S. Air Force Office of Scientific Research.

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Story Source:

The above story is reprinted from materials provided by University of California - Los Angeles. The original article was written by Stuart Wolpert.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.


Journal Reference:

  1. Alexey Stomakhin, Martin B Short, Andrea L Bertozzi. Reconstruction of missing data in social networks based on temporal patterns of interactions. Inverse Problems, 2011; 27 (11): 115013 DOI: 10.1088/0266-5611/27/11/115013