- 🏢(🎲🌲)-🧍‍♀️(🎲🕸️) - make a story of how my understanding evolved from [[Jun20_econometrics.pdf]], [[24_emtom]], [[24_1439_inf_dec]], [[24_1436_app_econometric]] My understanding of approaches to measure and discover metrics in decision-making evolved significantly by synthesizing insights from various sources. Starting with the foundational econometric concepts in [[Jun20_econometrics.pdf]], I gained a structured basis for interpreting empirical data, particularly in areas such as regression analysis, instrumental variables, and causal inference. For example, the use of instrumental variables helped in mitigating endogeneity issues, which was crucial in understanding complex relationships in empirical settings. Building on this, the exploration of empirical Technology and Operations Management (TOM) in [[24_emtom]] provided practical examples of applying these concepts, such as the study of productivity gaps in worker performance (e.g., Tucker et al.'s research on public performance feedback) and the empirical examination of data-driven decision-making by Simchi-Levi. Additionally, examples like the work by Van Mieghem on the Three Rs of Operations Management—Research, Relevance, and Rewards—highlighted the importance of connecting empirical research with real-world outcomes. The examination of digital exhaust by Terwiesch illustrated the evolving nature of empirical TOM research in the context of modern data sources. Other examples included Fisher's study on strengthening the empirical base of operations management, which provided foundational insights into empirical challenges, and Song et al.'s work on productivity and validation of best practices, offering a clear view of how empirical studies can drive operational improvements. These examples illustrated the challenges in operationalizing empirical methodologies in real-world settings, such as managing variability and ensuring robustness in conclusions. - [x] [[24_emtom]] lec3~12 (@2024-12-31) Furthermore, the insights from [[25_TEPEI_empirical]] enriched my perspective on the entrepreneurial context, including examples like Decker et al.'s studies on job creation and the economic dynamism brought by high-growth firms. The discussions around the rationale for entrepreneurial firms, as well as the structure of arrangements between entrepreneurs and investors, highlighted how empirical approaches can reveal insights into broader economic and strategic behaviors. Specific examples included Davydova et al.'s work on the unicorn puzzle, which delved into the dynamics behind high-growth startup valuations, and Azoulay et al.'s exploration of age and high-growth entrepreneurship, providing nuanced views on the factors affecting entrepreneurial success. Additionally, Bernstein et al.'s study on how economic downturns affect talent flows to startups underscored the relationship between macroeconomic factors and entrepreneurial talent acquisition. Moving further, [[24_1439_inf_dec]] introduced advanced methods for large-scale inference, such as false discovery rates, multiple testing, and empirical Bayes. Specific examples include the estimation of economic opportunity measures across over 70,000 census tracts, which required a shift from traditional hypothesis testing to scalable, data-driven methods that address issues like multiple comparisons. The course also covered approaches like simultaneous confidence bands to manage uncertainties across multiple parameters in large datasets. Additionally, the use of empirical Bayes decision rules provided a way to make optimal decisions under uncertainty, which was particularly valuable in large-scale applications such as estimating the value of tracking data for thousands of advertisers on Meta's platforms. Other notable examples included experimental estimates for the value of tracking data across various settings, which demonstrated how empirical Bayes techniques could be calibrated to improve decision-making accuracy across numerous scenarios. - [x] [[24_1439_inf_dec]] (@2024-12-31)