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The syntax of economic theory closely resembles the syntax of physics. But physics deals with what seems to be the objective world. In contrast, the essence of economics is subjectivity and moral choice. What can you reasonably expect from treating economics as a branch of science? This talk compares the nature and efficacy of models in both physics and finance, and tries to reach a sane answer to the question above.

Sunyaev-Zel'dovich Effect (SZE) experiments such as the South Pole Telescope (SPT) are currently surveying a large area of sky searching for clusters via their imprint on the CMB. In order to use the resulting cluster catalogues for cosmology, it is necessary to know the mass- and redshift-dependent cluster selection function. I will describe ongoing work to understand and characterize the current SPT cluster yield, using synthetic SZ sky maps constructed from cosmological simulations and noise models calibrated against SPT data.

The APEX Sunyaev Zel'dovich experiment will be described and its performance since first light in 2006 summarized. Recent results will be presented together with plans for future observations/analysis.

An unexpected economic crisis provides an excellent opportunity to better understand the state of Economic theory as a science. While there appears to have been a broad systemic failure within the community of professional economists to predict the current collapse, it must be noted that there have been scattered successes which appear striking and demand our attention. The goal of this conference is to bring together economists, biologists, mathematicians, physicists, programmers, and financial professionals to explore the opportunities for bringing economic theory into closer contact with the more traditional sciences as the basis for ongoing work, partnership, and collaboration.

Extracting compact sources from maps contaminated with noise and unwanted astrophysical signals is a well-studied problem. In anticipation of the now-current generation of large-scale SZ surveys, many authors arrived at the conclusion that a simple multi-scale spatial/spectral filter would be the optimal way to find galaxy clusters in data from these surveys. I will briefly present the basics of the spatial/spectral optimal filter and then show in some detail how this has been implemented in one real-world case, namely in data from the South Pole Telescope (SPT) survey.