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A wealth of empirical evidence shows that people display opposite behaviors when deciding whether to rely on an algorithm, even if it is inexpensive to do so and using the algorithm should enhance their own performance. This paper develops a formal theory to explain some of these conflicting facts and submit new testable predictions. Drawing from decision analysis, I invoke two key notions: the 'value of information' and the 'value of control'. The value of information matters to users of algorithms like recommender systems and prediction machines, which essentially provide information. I find that ambiguity aversion or a subjective cost of employing an algorithm will tend to decrease the value of algorithmic information, while repeated exposure to an algorithm might not always increase this value. The value of control matters to users who may delegate decision making to an algorithm. I model how, under partial delegation, imperfect understanding of what the algorithm actually does (so the algorithm is in fact a black box) can cause algorithm aversion. Some possible remedies are formulated and discussed. • This paper initiates a formal decision-theoretic approach to make sense of the empirical evidence concerning people's attitudes towards algorithms. • This approach exploits two fundamental notions: the value of information and the value of control. • Ambiguity aversion will tend to decrease the value of algorithmic information; repeated exposure to algorithms may not increase it. • A first model of 'black box' algorithms is developed to analyze the value of keeping versus delegating control. [ABSTRACT FROM AUTHOR]

In this paper, a high-precision two-dimensional (2D) direction of arrival (DOA) estimation algorithm by three parallel uniform linear arrays (ULAs) is proposed. Both the internal spacing of each sub-array and the spacing between two adjacent sub-arrays can exceed the half-wavelength of incident signal. An extended propagator method (PM) algorithm is introduced to obtain a multiple extended propagator matrix by processing the covariance matrix of received signals. Then, by using the extended propagator matrix, unambiguous and high-precision 2D DOA estimation can be obtained without extra pairing process. For the proposed array construction, mutual coupling effect can be ignored as long as the element interval is large enough. The extended PM algorithm based on the proposed array has far higher precision than the existing similar algorithms, which can be proved by some simulation experiments. [ABSTRACT FROM AUTHOR]