Bruce Clarke, Mark B. N. Hansen (eds.): Emergence and Embodiment: New Essays on Second-Order Systems Theory (2009)

“Emerging in the 1940s, the first cybernetics—the study of communication and control systems—was mainstreamed under the names artificial intelligence and computer science and taken up by the social sciences, the humanities, and the creative arts. In Emergence and Embodiment, Bruce Clarke and Mark B. N. Hansen focus on cybernetic developments that stem from the second-order turn in the 1970s, when the cyberneticist Heinz von Foerster catalyzed new thinking about the cognitive implications of self-referential systems. The crucial shift he inspired was from first-order cybernetics’ attention to homeostasis as a mode of autonomous self-regulation in mechanical and informatic systems, to second-order concepts of self-organization and autopoiesis in embodied and metabiotic systems. The collection opens with an interview with von Foerster and then traces the lines of neocybernetic thought that have followed from his work.

In response to the apparent dissolution of boundaries at work in the contemporary technosciences of emergence, neocybernetics observes that cognitive systems are operationally bounded, semi-autonomous entities coupled with their environments and other systems. Second-order systems theory stresses the recursive complexities of observation, mediation, and communication. Focused on the neocybernetic contributions of von Foerster, Francisco Varela, and Niklas Luhmann, this collection advances theoretical debates about the cultural, philosophical, and literary uses of their ideas. In addition to the interview with von Foerster, Emergence and Embodiment includes essays by Varela and Luhmann. It engages with Humberto Maturana’s and Varela’s creation of the concept of autopoiesis, Varela’s later work on neurophenomenology, and Luhmann’s adaptations of autopoiesis to social systems theory. Taken together, these essays illuminate the shared commitments uniting the broader discourse of neocybernetics.”

Contributors. Linda Brigham, Bruce Clarke, Mark B. N. Hansen, Edgar Landgraf, Ira Livingston, Niklas Luhmann, Hans-Georg Moeller, John Protevi, Michael Schiltz, Evan Thompson, Francisco J. Varela, Cary Wolfe.

Publisher Duke University Press, 2009
Science and Cultural Theory series
ISBN 0822391384, 9780822391388
xiii+296 pages

Reviews: Ramesh Mishra (MetaPsychology, 2010), Ana Teixeira Pinto (Int’l Studies in the Philosophy of Science, 2011), Jon Goodbun (Radical Philosophy, 2011), Peter Cariani (Constructivist Foundations, 2010).

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Florian Hoenig: Computational Aesthetics and Visual Preference: An Experimental Approach (2006)

Computational Aesthetics is a term which has been frequently used by scientists interested in quantitative approaches towards the concept of aesthetics during the last century. A review of both past and recent works attempting to quantify aesthetic preference of various stimuli is given, which shows that aesthetics was described as some function of complexity and order in many theories.

Since most measures were hardly relating to knowledge of human perception, complexity is reinterpreted in the context of a currently accepted model of visual perception and a hypothesis is formulated which states that human visual preference is not independent of complexity (cell excitation) at the very first stage of visual processing.

An estimate representative for cell activity in early visual processing is presented: Multivariate Gabor Filter Responses. Additionally, image properties such as aspect ratio, resolution and JPEG compressibility are used to sanity-check any correlations.

The estimate calculated, compared against human preference ratings of photographs, shows statistically significant but low correlations. However, the machine learning experiments performed, fail to predict any better than one would by taking the mean value of the data.

Even though these results only loosely relate to aesthetic perception, it’s motivating further research and closer inspection of image features and their relation to perceptual properties and visual (aesthetic) preference.

Thesis
Computer Science, JKU Linz, Austria
Supervisor Josef Scharinger
118 pages

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Hausmann, Hidalgo et al.: The Atlas of Economic Complexity: Mapping Paths to Prosperity (2011)

The Atlas of Economic Complexity: Mapping Paths to Prosperity measures the diversity of productive knowledge of 128 countries and demonstrates remarkable predictive value in forecasting how fast countries will grow. Its authors argue that it is 10 times more accurate at predicting growth over a decade than the World Economic Forum’s Global Competitiveness Index. The framework is used to project growth to 2020.

China (1), India (2) and Thailand (3) top the rankings for per capita growth potential followed by Belarus (4), Moldova (5), Zimbabwe (6), Ukraine (7), Bosnia and Herzegovina (8), Panama (9), and Mexico (10). For these countries, the current level of productive knowledge is unusually high for their level of income which should allow them to catch up faster than other nations. Seven Eastern European countries rank in the top 20 in terms of expected growth in income per capita while only two Latin American countries (Panama and Mexico) are in that group.

The Atlas identifies eight Sub-Saharan African countries among the Top Ten for expected GDP growth: Uganda (1), Kenya (2), Tanzania (3), Zimbabwe (4), Madagascar (5), Senegal (6), Malawi (7), and Zambia (10). The other Top Ten nations are India (8) and Guatemala (9). Unfortunately, Sub-Saharan African countries also dominate the bottom 10 countries in terms of expected growth per capita.

Meanwhile, several Eastern European countries rank surprisingly high in their Economic Complexity, which is a gauge to measure their productive knowledge. The Atlas ranks the Czech Republic eighth and Slovenia tenth while Hungary and the Slovak Republic appear in the top 20. Other Top Ten ranked countries in economic complexity include Japan (1), Germany (2), Switzerland (3), Sweden (4), Austria (5), Finland (6), Singapore (7), and the United Kingdom (9).

The United States, at position 13, is not listed among the Top Ten ranking for economic complexity and is ranked 85th for expected GDP growth.

“A country’s competitiveness is driven by the amount of productive knowledge that its people and organizations hold and it is expressed in the variety and complexity of the products it is able to successfully export. Productive knowledge does a remarkable job at explaining why countries are rich or poor and why some catch up and others do not,” says Ricardo Hausmann, report co-author and director of CID.

“In the short run, countries with natural resource wealth can be rich without much productive knowledge and get access to the world’s knowledge through imports. In the long run, however, wells run dry and mines get depleted, and income sooner or later will reflect the productive knowledge of the economy,” says César Hidalgo, report co-author and director of the Macro Connections group at the MIT Media Lab. (from press release)

Authors: Ricardo Hausmann, Cesar A. Hidalgo, Sebastian Bustos, Michele Coscia, Sarah Chung, Juan Jimenez, Alexander Simoes, Muhammed A. Yildirim.
Published in October 2011
A collaboration between Center for International Development at Harvard University and Macro Connections group at the MIT Media Lab.
ISBN 0615546625, 9780615546629
362 pages
Licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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