Radio Regulation Revisited: Coase, the FCC, and the Public Interest

It is now more than forty years since Ronald Coase’s seminal article on the Federal Communications Commission first appeared in the pages of the Journal of Law and Economics.1 The article remains important for a number of reasons, not least of which is that it offered his first articulation of the Coase Theorem.2 Of even greater importance for our purposes, the article literally redefined the terms of debate over American broadcast regulation, in both historical and contemporary treatments of the subject. Focusing particularly on the development of radio regulation, Coase rejected the prevailing notion that the establishment of the Federal Communications Commission (FCC) served the public interest. Rather, he concluded that its creation had been a mistake, the product of faulty economic reasoning. The complex regulatory apparatus developed under the Federal Radio Act of 1927 and recodified in the Federal Communications Act of 1934 was built on the flawed assumption that scarce resources—in this case the radio spectrum—had to be allocated by government fiat. A more efficient solution, Coase maintained, would have been to allocate the spectrum like any other scarce resource, on the basis of well-defined property rights and a free market guided by the price mechanism. Indeed, this is why he suggested that the spectrum ought to be cut up and sold at auction rather than regulated by the federal government.

Universal criterion for the breakup of invariant tori in dissipative systems

The transition from quasiperiodicity to chaos is studied in a two-dimensional dissipative map with the inverse golden mean rotation number. On the basis of a decimation scheme, it is argued that the (minimal) slope of the critical iterated circle map is proportional to the effective Jacobian determinant. Approaching the zero-Jacobian-determinant limit, the factor of proportion becomes a universal constant. Numerical investigation on the dissipative standard map suggests that this universal number could become observable in experiments. The decimation technique introduced in this paper is readily applicable also to the discrete quasiperiodic Schrodinger equation.Comment: 13 page

A framework for experimental-data-driven assessment of Magnetized Liner Inertial Fusion stagnation image metrics

A variety of spherical crystal x-ray imager (SCXI) diagnostics have been developed and fielded on Magnetized Liner Inertial Fusion (MagLIF) experiments at the Sandia National Laboratories Z-facility. These different imaging modalities provide detailed insight into different physical phenomena such as mix of liner material into the hot fuel, cold liner emission, or reduce impact of liner opacity. However, several practical considerations ranging from the lack of a consistent spatial fiducial for registration to different point-spread-functions and tuning crystals or using filters to highlight specific spectral regions make it difficult to develop broadly applicable metrics to compare experiments across our stagnation image database without making significant unverified assumptions. We leverage experimental data for a model-free assessment of sensitivities to instrumentation-based features for any specified image metric. In particular, we utilize a database of historical and recent MagLIF data including $N_{\text{scans}} = 139$ image plate scans gathered across $N_{\text{exp}} = 67$ different experiments to assess the impact of a variety of features in the experimental observations arising from uncertainties in registration as well as discrepancies in signal-to-noise ratio and instrument resolution. We choose a wavelet-based image metric known as the Mallat Scattering Transform for the study and highlight how alternate metric choices could also be studied. In particular, we demonstrate a capability to understand and mitigate the impact of signal-to-noise, image registration, and resolution difference between images. This is achieved by utilizing multiple scans of the same image plate, sampling random translations and rotations, and applying instrument specific point-spread-functions found by ray tracing to high-resolution datasets, augmenting our data in an effectively model-free fashion.Comment: 17 pages, 14 figure