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Price-based irrigation water-conservation policies are often designed as fixed per unit fees. In groundwater commons, however, this approach presupposes that irrigators assign the same value to each unit of water withdrawn, irrespective of the scarcity levels they individually face. This ignores spatial interdependencies in groundwater commons. In this paper, I examine the effect this possible tax structure misspecification has in measuring the performance of such Pigouvian taxes. I model the price of irrigation water as a non-constant marginal cost function dependent on the constant per unit fee and a variable cost-metric measure of scarcity, namely depth-to-water. Using a difference-in-difference econometric framework with irrigation data from San Luis Valley, results show that irrigators' response to the constant marginal fee significantly depends on the scarcity levels individual irrigators face. More importantly, the results suggest that models that overlook the spatial element of scarcity would overestimate irrigators' response to such pumping fee—which can misguide policy decisions. [ABSTRACT FROM AUTHOR]

Multiprocessor systems offer numerous configurations in terms of a different number of cores and frequency levels that may become optimal with respect to energy, performance, or other metrics. On FPGAs, a convenient solution for designing and building a multiprocessor system is the use of soft-core processors. The soft-core processor configuration and frequency are customizable and configurable at design time and according to the FPGA capacity, the number of cores and its configuration can be changed. In this research, different workloads have been studied and results shown that the amount of speedup would be different for each workload due to their behavior in an MPSoC fashion and it was revealed that in a multiprocessor system based on soft-core on an FPGA platform, increasing the number of processors and their operating frequency will not always improve the system energy-delay product (EDP). Hence, identifying an optimal configuration with respect to a given metric such as EDP is a complex process due to a large number of workloads and configurations. To achieve this, we use the power consumption and execution time information to identify the optimal configuration for different workloads with respect to EDP. We also perform an extensive workload characterization using performance counters available on the target platform. Using these performance counters, a vast amount of characterization data for each workload was collected. Then, we used this characterization data to choose the optimal configuration for each workload. This paper proposes a characterization method for parallel workloads that can be used to determine the optimal core and frequency configuration of an FPGA-based homogenous soft-core multiprocessor system with respect to EDP as a function of the workload (It is a datatype. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).). [ABSTRACT FROM AUTHOR]

This paper aims to study lifetime ruin minimization problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expected to be multi-dimensional. New mathematical challenges arise as the standard dimension reduction cannot be applied, and the convexity of the value function and Isaacs condition may not hold in our probability minimization problem with drift uncertainty. We propose to employ the stochastic Perron's method to characterize the value function as the unique viscosity solution to the associated Hamilton–Jacobi–Bellman (HJB) equation without resorting to the proof of dynamic programming principle. The required comparison principle is also established in our setting to close the loop of stochastic Perron's method. [ABSTRACT FROM AUTHOR]