201 - 210 of 236 results for: MS

Topics in financial statistics with focus on current research: Time-series modeling, volatility modeling, high-frequency statistics, large-dimensional factor modeling and estimation of continuous-time processes. Prerequisites: 220, 226 or STATS 200, 221 or STATS 217, 245A, or equivalents.

Network representations for reasoning under uncertainty: influence diagrams, belief networks, and Markov networks. Structuring and assessment of decision problems under uncertainty. Learning from evidence. Conditional independence and requisite information. Node reductions. Belief propagation and revision. Simulation. Linear-quadratic-Gaussian decision models and Kalman filters. Dynamic processes. Bayesian meta-analysis. Limited Enrollment. Prerequisite: probability such as MS&E 220. Recommended: 152 or 252.

The rapid deployment of LLMs and autonomous agents may reshape how markets are structured and how participants will interact and what frictions will arise. These technologies raise questions about incentives, information, and institutional design. Examples include entry-level labor markets, transportation, healthcare, etc. This PhD-level course surveys recent theoretical and applied research at the intersection of economics, OR, and CS that may be affected by AI. The course further examines how AI-based capabilities challenge and enrich traditional approaches to market design. In what ways does the presence of autonomous agents alter strategic behavior? How should markets be designed when AI systems act as complements or substitutes for human participants? How to achieve alignment in marketplaces with AI agents? How can AI algorithms be incorporated into the design of marketplaces? The course encourages students to begin research projects in this emerging field. The course assumes basic knowledge in game theory and market design.

Survey of recent research on market design and resource allocation with a focus on under-explored domains in non-profit settings. Will start with classic results in allocation, matching and social choice, and discuss them in the context of relevant objectives such as social welfare and equity. Will then draw on techniques from operations research and economics to explore the design of resource allocation platforms in emerging applications including housing, humanitarian logistics, volunteer coordination, food allocation, conservation and sustainability, and informal markets in the developing world. Prerequisite: consent of instructor; background material will be covered throughout the course as necessary. May be repeated for credit.

This course will cover focused exploration of contemporary readings and classics as relevant in strategy, innovation and entrepreneurship such as platforms, ecosystems, institutional logics, and strategic "games" in nascent markets. The course will include both content and methods discussions, including theory-building from multiple cases. PhD students only. Prerequisite: Consent of instructor.

Doctoral research seminar, limited to Ph.D. students. Current research on innovation strategy. Topics: scientific discovery, innovation search, organizational learning, evolutionary approaches, and incremental and radical change. Topics change yearly. Recommended: course in statistics or research methods.

Classic and current research on entrepreneurship. In this class, we will focus on questions of how entrepreneurship may exacerbate or alleviate inequalities in society across race/ethnicity, gender and class. How do institutional environments shape who engages in entrepreneurship and how successful they become? We will read literature from economics, sociology and strategy/management that has theoretically and empirically examined the phenomenon of entrepreneurship. Limited enrollment, restricted to PhD students. Prerequisites: SOC 363 or equivalent, and permission of instructor.

Classic and current research on business and corporate strategy. Limited enrollment, restricted to PhD students. Course may be repeated for credit.

Applied introduction to good empirical research and causal inference for social scientists and others analyzing social data. Designed to provide an introduction to some of the most commonly used quantitative techniques for causal inference in social data including: survey design and inference, regression and propensity score matching, instrumental variables, differences-in-differences, regression discontinuity designs, standard errors, and the analysis of big data. Applications: organizations, entrepreneurship, public policy, innovation, economics, online education, visual representations, communication, critique and design of figures, graphs. Does not explicitly cover social network structure or machine learning as these topics are well-covered elsewhere. Students work in groups and individually to design and carry out a small research project based on the use of analytics, large data sets, or other digital innovations related to business or other organizations. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects. Course prioritizes a thorough substantively grounded understanding of assumptions over mathematical proofs and derivations. Aimed at PhD students, but open by permission to Master's students and to students in other Stanford programs with relevant coursework or experience in analytics and statistics.