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This article tests capture theory by analyzing voting behavior on U.S. Regional Fishery Management Councils. Some seats on the councils are reserved for state and federal agency representatives; others, for political appointees. The political appointees primarily represent special interests (specifically, commercial and recreational fishing interests); a smaller number of appointees represent public interests. We use logistic regression to model the vote of state and federal agency representatives on the councils as a function of the votes of commercial interests, recreational interests, and public interests. We find evidence that some state agencies are captured by special interests from their states, but not systematic evidence across all states. We find that state agency representatives voted with commercial interests from their own state in five of the sixteen states in our sample; with recreational interests in three states; and with both special interests in two states. These ten states support the capture hypothesis; the other six states do not. We find no evidence that federal agencies were captured on the councils. We conclude that the gubernatorial‐driven appointment process leads to capture at the state level by promoting voting blocs among state agency representatives and special interests from those states. Federal agency representatives, by contrast, are better able to maintain their distance from state‐level politics on the councils, and thereby enhance their ability to vote independently on fishery management measures.

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System-on-Chip (SoC) Field Programmable Gate Arrays (FPGAs) are ideal for real-time signal processing due to their low, deterministic latency and high performance. To showcase the utility of our open FPGA computational platform for real-time audio signal processing and computational modeling, several applications have been implemented. We have ported the openMHA hearing aid software [1] to our platform to show that pre-existing audio processing software can be implemented in SoC FPGAs by making external audio interfaces show up as a sound card. To highlight the ability to perform real-time computational modeling on our performance platform, we are implementing a real-time version of Laurel Carney’s auditory-nerve model [2] running in its own custom accelerator in the FPGA fabric. To illustrate the ability to develop DSP algorithms in MathWork’s Simulink and then implement them in the FPGA fabric we have taken several algorithms from Issa Panahi’s group [3] to show both frame-based processing (noise reduction) and sample-based processing (dynamic range compression). Finally, we show that the platform can be used to visualize audio signals using a real-time spectrogram where FFTs are computed in the FPGA fabric. [1] www.openmha.org. [2] JASA 126, 2390–2412. [3] www.utdallas.edu/ssprl/hearing-aid-project/.