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to improve R&D efficiency, and the influence of investors and other external actors on entrepreneurial outcomes. Our research also examines decision-making under uncertainty, including the use of Bayesian
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used to approximate these nonlinear terms and accelerate solving such problems, at the cost of some optimality guarantees. The trade-offs between speed and optimality could be investigated as well
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to highlight the strength and boundaries of the proposed methods. Identifying the critical assumptions needed to draw inferences from empirical results. Writing computer code to analyse experimental or secondary
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experimental data to test hypotheses or measure phenomena, in online, lab and /or field settings. Identifying the critical assumptions needed to draw inferences from empirical results. Writing computer code to
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of the proposed methods. Identifying the critical assumptions needed to draw inferences from empirical results. Writing computer code to analyse experimental or secondary data according the best practices and tools
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for prospective projects, rather than as a fixed or exhaustive list of topics. The following provides an approximate timeline, which may vary depending on methodological approach, data collection, and other factors