Topics change from year to year. Most recent topics include: optimal fiscal and monetary policy; optimal capital taxation; time inconsistency and incentive incompatibility of optimal policies; redistribution and political economics; heterogeneous agents and incomplete markets; Real Business Cycle models and new-keynesian models; endogenous growth; aggregate fluctuations and propagation mechanisms; recursive methods and robust control in macro. 14.462 is the second semester of the second-year Ph.D. macroeconomics sequence. The course is intended to introduce the students, not only to particular areas of current research, but also to some very useful analytical tools. It covers a selection of topics that varies from year to year. Recent topics include: Growth and Fluctuations; Heterogeneity and Incomplete Markets; Optimal Fiscal Policy; Time Inconsistency; Reputation; Coordination Games and Macroeconomi; Complementarities; Information.

The Art of the Probable" addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world. Our course therefore aims to broaden students’ appreciation for and understanding of how literature interacts with--both reflecting upon and contributing to--the scientific understanding of the world. We are just as centrally committed to encouraging students to regard imaginative literature as a unique contribution to knowledge in its own right, and to see literary works of art as objects that demand and richly repay close critical analysis. It is our hope that the course will serve students well if they elect to pursue further work in Literature or other discipline in SHASS, and also enrich or complement their understanding of probability and statistics in other scientific and engineering subjects they elect to take.

This course addresses the challenges of defining a relationship between exposure to environmental chemicals and human disease. Course topics include epidemiological approaches to understanding disease causation; biostatistical methods; evaluation of human exposure to chemicals, and their internal distribution, metabolism, reactions with cellular components, and biological effects; and qualitative and quantitative health risk assessment methods used in the U.S. as bases for regulatory decision-making. Throughout the term, students consider case studies of local and national interest.

" In establishing the Engineering Systems Division, MIT has embarked on a bold experiment – bringing together diverse areas of expertise into what is designed to be a new field of study. In many respects, the full scale and scope of Engineering Systems as a field is still emerging. This seminar is simultaneously designed to codify what we presently know and to give direction for future development."

" This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations."

Choice of material has implications throughout the life-cycle of a product, influencing many aspects of economic and environmental performance. This course will provide a survey of methods for evaluating those implications. Lectures will cover topics in material choice concepts, fundamentals of engineering economics, manufacturing economics modeling methods, and life-cycle environmental evaluation.

This course emphasizes three methodologies - reliability and probabilistic risk assessment (RPRA), decision analysis (DA), and cost-benefit analysis (CBA). In this class, the issues of interest are: the risks associated with large engineering projects such as nuclear power reactors, the International Space Station, and critical infrastructures; the development of new products; the design of processes and operations with environmental externalities; and infrastructure renewal projects.

Engineering School-Wide Elective Subject. Description given at end of this chapter in SWE section.

This Farmers' Agribusiness training course has been developed to help both farmers and farmer organisations. Its intention is to provide access to provide access to additional skills and knowledge that will allow farmers to move from a 'farm' to a 'firm'. This lesson continues on from lesson 3 and should be seen as a continuation. You will need to review lesson 3 and the discussion on Economic versus Accounting Costs &Relevant Cost Analysis. In lesson 4, however, we will focus on Revenues & Measurement of Profitability and Risk &Uncertainty.

Principles of supervisory control and telerobotics. Different levels of automation are discussed, as well as the allocation of roles and authority between humans and machines. Human-vehicle interface design in highly automated systems. Decision aiding. Tradeoffs between human control and human monitoring. Automated alerting systems and human intervention in automatic operation. Enhanced human interface technologies such as virtual presence. Performance, optimization, and social implications of the human-automation system. Examples from aerospace, ground, and undersea vehicles, robotics, and industrial systems. Human Supervisory Control of Automated Systems discusses elements of the interactions between humans and machines. These elements include: assignment of roles and authority; tradeoffs between human control and human monitoring; and human intervention in automatic processes. Further topics comprise: performance, optimization and social implications of the system; enhanced human interfaces; decision aiding; and automated alterting systems. Topics refer to applications in aerospace, industrial and transportation systems.

This team-taught subject is for doctoral students working on emerging technologies at the interface of technology, policy and societal issues. It integrates concepts of research strategy and design from a variety of disciplines. The class addresses problem identification and formulation of research topics, the role of qualitative and quantitative research methods, and the use of various data collection techniques. Coursework focuses on students' thesis proposals, faculty-student study panels, critical evaluation of research design, and ethical issues in conducting research and gathering data.

Provides ways to conceptualize and analyze manufacturing systems and supply chains in terms of material flow, information flow, capacities, and flow times. Fundamental building blocks: Inventory and Queuing Models, Forecasting and Uncertainty, Optimization, Process Analysis, Linear Systems and System Dynamics. Factory Planning: Flow Planning, Bottleneck Characterization, Buffer and Batch-Size Tactics, Seasonal Planning, Dynamics and Learning for Various Process Flow Topologies and for Various Market Contexts.

" This half-semester course provides an introduction to microeconomic theory designed to meet the needs of students in the economics Ph.D. program. Some parts of the course are designed to teach material that all graduate students should know. Others are used to introduce methodologies. Topics include consumer and producer theory, markets and competition, general equilibrium, and tools of comparative statics and their application to price theory. Some topics of recent interest may also be covered."

Decision-making under uncertainty, information economics, incentive and contract theory.

Oxford mathematician Peter Donnelly reveals the common mistakes humans make in interpreting statistics -- and the devastating impact these errors can have on the outcome of criminal trials. Peter Donnelly is an expert in probability theory who applies statistical methods to genetic data -- spurring advances in disease treatment and insight on our evolution. He's also an expert on DNA analysis, and an advocate for sensible statistical analysis in the courtroom. A quiz, thought provoking question, and links for further study are provided to create a lesson around the 11-minute video. Educators may use the platform to easily "Flip" or create their own lesson for use with their students of any age or level.

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information technology, and graduate (16.413) students.

" This introductory course teaches the fundamentals of microeconomics. Topics include consumer theory, producer theory, the behavior of firms, market equilibrium, monopoly, and the role of the government in the economy. 14.01 is a Humanities, Arts, and Social Sciences (HASS) elective and is offered both terms. "

Modeling, quantification, and analysis of uncertainty. Formulation and solution in sample space. Random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference. Interpretations, applications, and lecture demonstrations.

Interpretations of the concept of probability. Basic probability rules; random variables and distribution functions; functions of random variables. Applications to quality control and the reliability assessment of mechanical/electrical components, as well as simple structures and redundant systems. Elements of statistics. Bayesian methods in engineering. Methods for reliability and risk assessment of complex systems, (event-tree and fault-tree analysis, common-cause failures, human reliability models). Uncertainty propagation in complex systems (Monte Carlo methods, Latin Hypercube Sampling). Introduction to Markov models. Examples and applications from nuclear and chemical-process plants, waste repositories, and mechanical systems. Open to qualified undergraduates.