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Natural and Social Sciences

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Earth Sciences
Date: 
Wednesday, October 15, 2014
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About This Course

People depend on nature to sustain and fulfill human life, yet the values of nature are typically ignored in decisions. Mapping and modeling ecosystem services can help highlight the diverse benefits provided to people by nature (what and where) and explore how those benefits might change under different management options--thus bringing information about nature’s values into decisions in practical ways. With these approaches, we can improve the state of biodiversity and human well-being by motivating greater and more cost-effective investments in both.

This course introduces the Natural Capital Project’s (NatCap’s) approach to using ecosystem service information to inform decisions. It uses specific examples to illustrate how the approach has worked in each case and highlights key methods and tools used in implementation.

Split into four modules, NC101 first introduces the concepts of natural capital and ecosystem services, the stocks and flows of vital benefits flowing from nature to people. The second module describes InVEST, NatCap’s software tool for mapping, modeling, and valuing ecosystem services. In addition, it provides guidance on project scoping and on matching approaches and tools to a project’s goals, decision context, timeline, capacity, and quality of data available. Modules 3-4 offer an overview of the skills needed to use InVEST models, including recommendations for how to effectively summarize and communicate model outputs to stakeholders and other audiences.

Intended Audience

This course is intended for those interested in how natural capital approaches can inform decisions taken by governments, multi-lateral development institutions, the private and finance sectors, and non-governmental organizations. It can be a resource for individuals interested in simply learning about these concepts or for those interested in using the NatCap’s approaches and tools in research or to influence decisions. This course can also serve as a primer for those individuals planning to attend one of our in-person training workshops in the future.

Prerequisites

There are no prerequisites for this course. However, we recommend that you download InVEST and GIS software (either QGIS or ArcGIS) if you intend to follow the technical examples or complete the optional assessments contained in modules 3 and 4.

Course Staff

Gregg Verutes

Geographer - Lead Instructor

Gregg Verutes leads NatCap's training program which hosts both introductory and technical workshops throughout the world. His current focus is developing innovative techniques that utilize maps, games, and problem-based exercises to teach students, scientists and practitioners about valuing nature. Gregg also serves as a GIS specialist for the marine team working on coastal zone management and spatial planning in Belize, Vietnam and the Americas. He worked previously for National Geographic as a GIS instructor and a visiting scientist with the World Wildlife Fund's Conservation Science Program. Gregg received his M.S. from San Diego State University and his B.S. in Policy Analysis and Management from Cornell University. 

Adrian Vogl

Senior Scientist

Adrian Vogl is leading the application of InVEST models for watershed services, and developing decision support models for spatial planning, permitting new infrastructure projects and mitigation, and targeting investments in watershed conservation. Adrian co-led development of the RIOS tool, in partnership with The Nature Conservancy and the Latin American Water Funds Platform. In addition, Adrian is leading efforts to link the InVEST economic valuation approach with outputs from other hydrologic models. Before joining the Natural Capital Project, Adrian worked in central Texas developing land-use planning decision support tools that incorporate freshwater and groundwater ecosystem services, land development, and conservation planning. Adrian received her Ph.D. in Aquatic Resources from Texas State University-San Marcos, and her B.A. from the University of Arizona in Cultural Anthropology.

Henry Borrebach

Training Coordinator

Henry Borrebach is on the Natural Capital Project's training team, overseeing online education and the annual Natural Capital Symposium, as well as coordinating NatCap trainings around the globe. Henry has extensive experience in applied pedagogy and international education, and he is passionate about making the science behind conservation accessible to the public. He is currently working with the team to develop online training courses that make NatCap's approach and tools available to a wider audience. Henry holds a B.F.A. from Carnegie Mellon University and an M.F.A. from Florida International University. Before joining the project, he co-founded the O, Miami international poetry festival. 

Frequently Asked Questions

Do I need to complete all the modules in the course?

While the lessons contained in each of the four modules are intended to stand alone, we strongly encourage all participants to begin by reading through the Course Roadmap. This section explains how the course is organized and provides important background information about the two case study examples included throughout. To launch the Course Roadmap, click the "Start here" button on the top-left panel of the Courseware.

Do you offer a Statement of Accomplishment for completing the course?

The course is structured to provide two levels of accomplishment. Students completing only Modules 1 and 2 will be provided with a Statement of Accomplishment for Intro to Ecosystem Services. Students who complete Modules 1 through 4 (including the 2 assessments) will receive a Statement of Accomplishment in Ecosystem Services and Applications.

Do I need to buy a textbook?

This course is completely free. Links to download all the necessary course materials and tools are provided within each unit.

How long should it take to complete this course?

The course is divided into four modules. It should take approximately one hour to finish each module and about four hours to complete the entire course.

What is the best way to ask questions or provide feedback?

Click on the "Discussion" tab to link to our online user forum. This forum is monitored daily by our software engineers and scientists.

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Date: 
Sunday, September 21, 2014 to Sunday, November 23, 2014
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Social networks pervade our social and economic lives.   They play a central role in the transmission of information about job opportunities and are critical to the trade of many goods and services. They are important in determining which products we buy, which languages we speak, how we vote, as well as whether or not we decide to become criminals, how much education we obtain, and our likelihood of succeeding professionally.   The countless ways in which network structures affect our well-being make it critical to understand how social network structures impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do.  This course provides an overview and synthesis of research on social and economic networks, drawing on studies by sociologists, economists, computer scientists, physicists, and mathematicians.
 
The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks.   Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids.   We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences.

Course Syllabus

  • Week 1: Introduction, Empirical Background and Definitions
Examples of Social Networks and their Impact, Definitions, Measures and Properties: Degrees, Diameters, Small Worlds, Weak and Strong Ties, Degree Distributions

  • Week 2: Background, Definitions, and Measures Continued
Homophily, Dynamics,  Centrality Measures: Degree, Betweenness, Closeness, Eigenvector, and Katz-Bonacich. Erdos and Renyi Random Networks: Thresholds and Phase Transitions,

  • Week 3: Random Networks 
Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation

  • Week 4:   Strategic Network Formation 
Game Theoretic Modeling of Network Formation, The Connections Model, The Conflict between Incentives and Efficiency, Dynamics, Directed Networks, Hybrid Models of Choice and Chance
 
  • Week 5:  Diffusion on Networks. 
Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data

  • Week 6:  Learning on Networks. 
Bayesian Learning on Networks, The DeGroot Model of Learning on a Network, Convergence of Beliefs, The Wisdom of Crowds, How Influence depends on Network Position.

  • Week 7: Games on Networks. 
Network Games, Peer Influences:  Strategic Complements and Substitutes, the Relation between Network Structure and Behavior, A Linear Quadratic Game, Repeated Interactions and Network Structures.

Recommended Background

The course has some basic prerequisites in mathematics and statistics.  For example, it will be assumed that students are comfortable with basic concepts from linear algebra (e.g., matrix multiplication), probability theory (e.g., probability distributions, expected values, Bayes' rule), and statistics (e.g., hypothesis testing), and some light calculus (e.g., differentiation and integration).  Beyond those concepts, the course will be self-contained.

Suggested Readings

The course is self-contained, so that all the definitions and concepts you need to solve the problem sets and final are contained in the video lectures.  Much of the material for the course is covered in a text: Matthew O. Jackson  Social and Economic Networks, Princeton University Press (Here are Princeton University Pressand Amazon pages for the book).  The text is optional and not required for the course.  Additional background readings, including research articles and several surveys on some of the topics covered in the course can be found on my web page.

Course Format

The course will run for eight weeks.  Each week there will be video lectures available, as well as a standalone problem set and some occasional data exercises, and there will be a final exam at the end of the course for those who wish to earn a course certificate.  

 

 

FAQ: 

Will I get a Statement of Accomplishment after completing this class?

Yes. Students who successfully complete the class (above 70 percent correct on the problem sets and final exam) will receive a Statement of Accomplishment signed by the instructor - and those earning above 90 percent credit on the problem sets and final will earn one with distinction.


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Date: 
Tuesday, June 24, 2014 to Tuesday, September 2, 2014
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THE COURSE

This course is designed as an eight-week introduction to the study of economics. Participants will be exposed to the economic way of thinking and learn about the functioning of a modern market economy. The early part of the course focuses on microeconomic analysis including the behavior of consumers and firms. We analyze markets for goods and services and policy choices that affect these markets. The later part of the course moves on to macroeconomic concepts such as national production, employment, inflation and interest rates. We explore models that determine long-run growth and short-term fluctuations in national economies. We then discuss the role of government regulation, monetary policy, and fiscal policy.

COURSE OUTLINE

Part 1

  • The Basic Core
  • Getting Started
  • Observing and Explaining the Economy
  • The Supply and Demand Model
  • Using the Supply and Demand Model

Part 2

  • The Competitive Equilibrium Model
  • Deriving Demand
  • Deriving Supply
  • Market Equilibrium and Efficiency
  • Firms and Industries Changing Over Time
  • Cost and Changes at Firms Over Time
  • The Rise and Fall of Industries

Part 3

  • Deviations from Competition
  • Monopoly and Market Power
  • Between Monopoly and Competition
  • Antitrust Policy and Regulation

Part 4

  • Labor Markets
  • The Labor Supply and Demand Model
  • Labor Model Cont. – Min. Wage and Discrimination
  • Key Economic Policy Issues
  • Taxes, Transfers and Income Distribution
  • Public Goods and Externalities
  • Government Failure and Success

Part 5

  • Financial and Capital Markets
  • Markets for Physical Capital
  • Financial Markets: Risk and Return
  • Macro Facts and Measures
  • Getting Started with Macroeconomic Ideas
  • Measuring Production, Income and Spending of Nations

Part 6

  • Long Run Macro
  • Determining Consumption, Investment and Govt. Shares
  • Employment and Unemployment
  • Productivity, Econ. Growth and Determining Factors
  • A Look at Money, Inflation and the Fed

Part 7

  • Short Run Macro
  • Introduction to Economic Fluctuations
  • Economic Fluctuations Model
  • Using the ADIA Model

Part 8

  • Macro Policy Issues
  • Intro to Macroeconomic Policy
  • Fiscal Policy
  • Monetary Policy
  • Monetary Policy Analysis
  • International Economic Issues
  • Gains from Trade
  • International Trade Policy – Tariffs and Quotas

FAQ

Do I need to buy a textbook?

No. All required course materials will be provided through the online platform. The textbook Principles of Economics, Seventh Edition, by John B. Taylor and Akila Weerapana, may be used as a study resource, but is not required. Used books, earlier editions, rentals, or e-books versions of this book are options to keep the cost down.

Will I receive Stanford credit for this course?

No. For those interested in a for-credit, Stanford Summer Session course covering similar material, see Econ 1V.

COURSE STAFF

John B. Taylor

John B. Taylor is the George P. Shultz Senior Fellow in Economics at the Hoover Institution and the Mary and Robert Raymond Professor of Economics at Stanford University. He was previously the director of the Stanford Institute for Economic Policy Research and was founding director of Stanford's Introductory Economics Center. He has a long and distinguished record of public service. Among other roles, he served as a member of the President’s Council of Economic Advisors from 1989 to 1991 and as Under Secretary of the Treasury for International Affairs from 2001 to 2005.

Ryan Triolo (Head TA)

Ryan is a Master's student in Public Policy and International Policy Studies, and has 2 years experience as a Teaching Assistant for Introductory Economics at Stanford.

Nick Pataki

Nick is a Master's student in Public Policy and International Policy Studies, and has 2 years experience as a Teaching Assistant for Introductory Economics at Stanford.

Constantine Yannelis

Constantine is a PhD student in Economics, and has one year experience as a Teaching Assistant for Introductory Economics at Stanford.

Jessie Li

Jessie is a PhD student in Economics, and has one year experience as a Teaching Assistant for Introductory Economics at Stanford


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Date: 
Tuesday, June 24, 2014 to Monday, September 1, 2014
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This course aims to provide a firm grounding in the foundations of probability and statistics. Specific topics include:

1. Describing data (types of data, data visualization, descriptive statistics)
2. Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values)
3. Specific statistical tests (ttest, ANOVA, linear correlation, non-parametric tests, relative risks, Chi-square test, exact tests, linear regression, logistic regression, survival analysis; how to choose the right statistical test)

The course focuses on real examples from the medical literature and popular press. Each week starts with "teasers," such as: Should I be worried about lead in lipstick? Should I play the lottery when the jackpot reaches half-a-billion dollars? Does eating red meat increase my risk of being in a traffic accident? We will work our way back from the news coverage to the original study and then to the underlying data. In the process, students will learn how to read, interpret, and critically evaluate the statistics in medical studies.

The course also prepares students to be able to analyze their own data, guiding them on how to choose the correct statistical test and how to avoid common statistical pitfalls. Optional modules cover advanced math topics and basic data analysis in R.

COURSE SYLLABUS

Week 1 - Descriptive statistics and looking at data
Week 2 - Review of study designs; measures of disease risk and association
Week 3 - Probability, Bayes' Rule, Diagnostic Testing
Week 4 - Probability distributions
Week 5 - Statistical inference (confidence intervals and hypothesis testing)
Week 6 - P-value pitfalls; types I and type II error; statistical power; overview of statistical tests
Week 7 - Tests for comparing groups (unadjusted); introduction to survival analysis
Week 8 - Regression analysis; linear correlation and regression
Week 9 - Logistic regression and Cox regression

PREREQUISITES

There are no prerequisites for this course.

Students will need to be familiar with a few basic math tools: summation sign, factorial, natural log, exponential, and the equation of a line; a brief tutorial is available on the course website for students who need a refresher on these topics.

 

FAQ: 

Can I get CME credit for this course?

This free version of the course does not offer CME credits, but there is a fee-based CME version available as well. Go to the Stanford online CME course page for more information. You are welcome to take this free version of the course before the CME course, but note that you will still need to create an account on the CME site, pay the registration fee, and complete the CME Pre-test, Post-test, Evaluation Survey, and Activity Completion Attestation statement in order to receive your credits.


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Welcome to the world’s first online course for deforestation and forest degradation mapping. This course is designed to equip government, academic and non-commerical, non-government organizations with the knowledge needed to monitor forests using the Carnegie Landsat Analysis System-lite or CLASlite. The course provides the scientific basis for each module in CLASlite, along with other information to make forest monitoring easy using Earth observing satellite data.

Background information available from the Carnegie Institute for Science at http://claslite.carnegiescience.edu/en/

To take the course, please begin by completing the Pre-Course Survey.

PREREQUISITES

We only provide CLASlite training and licensing to non-profit, non-commercial organizations. To successfully complete the course, you should have a basic familiarity with geospatial data and Windows PCs.

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Date: 
Monday, February 3, 2014
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NOTE: For the Winter 2014 session, the course website will go live at 10:00 AM US-PST on Saturday February 1, two days before the course begins, so you have time to familiarize yourself with the website structure, watch some short introductory videos, and look at some preliminary material.

The goal of the course is to help you develop a valuable mental ability – a powerful way of thinking that our ancestors have developed over three thousand years.
 
Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking.
 
The course is offered in two versions. The eight-week-long Basic Course is designed for people who want to develop or improve mathematics-based, analytic thinking for professional or general life purposes. The ten-week-long Extended Course is aimed primarily at first-year students at college or university who are thinking of majoring in mathematics or a mathematically-dependent subject, or high school seniors who have such a college career in mind. The final two weeks are more intensive and require more mathematical background than the Basic Course. There is no need to make a formal election between the two. Simply skip or drop out of the final two weeks if you decide you want to complete only the Basic Course.

Subtitles for all video lectures available in: Portuguese (provided by The Lemann Foundation), English
 

Course Syllabus

Instructor’s welcome and introduction
 1.  Introductory material
 2.  Analysis of language – the logical combinators
 3.  Analysis of language – implication
 4.  Analysis of language – equivalence
 5.  Analysis of language – quantifiers
 6.  Working with quantifiers
 7.  Proofs
 8.  Proofs involving quantifiers
 9.  Elements of number theory
10.  Beginning real analysis

Recommended Background

High school mathematics. Specific requirements are familiarity with elementary symbolic algebra, the concept of a number system (in particular, the characteristics of, and distinctions between, the natural numbers, the integers, the rational numbers, and the real numbers), and some elementary set theory (including inequalities and intervals of the real line). Students whose familiarity with these topics is somewhat rusty typically find that with a little extra effort they can pick up what is required along the way. The only heavy use of these topics is in the (optional) final two weeks of the Extended Course.

A good way to assess if your basic school background is adequate (even if currently rusty) is to glance at the topics in the book Adding It Up: Helping Children Learn Mathematics (free download), published by the US National Academies Press in 2001. Though aimed at K-8 mathematics teachers and teacher educators, it provides an excellent coverage of what constitutes a good basic mathematics education for life in the Twenty-First Century (which was the National Academies' aim in producing it).

Suggested Readings

There is one reading assignment at the start, providing some motivational background.
 
There is a supplemental reading unit describing elementary set theory for students who are not familiar with the material.
 
There is a course textbook, Introduction to Mathematical Thinking, by Keith Devlin, available at low cost (under $10) from Amazon, in hard copy and Kindle versions, but it is not required in order to complete the course.

For general background on mathematics and its role in the modern world, take a look at the five week survey course on mathematics ("Mathematics: Making the Invisible Visible") Devlin gave at Stanford in fall 2012, available for free download from iTunes University (Stanford), and on YouTube (1234,5), particularly the first halves of lectures 1 and 4.

Course Format

The Basic Course lasts for eight weeks, comprising ten lectures, each with a problem-based work assignment (ungraded, designed for group work), a weekly Problem Set (machine graded), and weekly tutorials in which the instructor will go over some of the assignment and Problem Set questions from the previous week. 

The Extended Course consists of the Basic Course followed by a more intense two weeks exercise called Test Flight. Whereas the focus in the Basic Course is the development of mathematically-based thinking skills for everyday life, the focus in Test Flight is on applying those skills to mathematics itself. 

 

FAQ: 

FAQ

  • Will I get a certificate after completing this class?

    The course does not carry Stanford credit. If you complete the Basic Course with more than a minimal aggregate mark, you will get a Statement of Accomplishment. If you go on to complete the Extended Course with more than a minimal mark, you will receive a Statement of Accomplishment with Distinction.

  • What are the assignments for this class?

    At the end of each lecture, you will be given an assignment (as a downloadable PDF file, released at the same time as the lecture) that is intended to guide understanding of what you have learned. Worked solutions to problems from the assignments will be described the following week in a video tutorial session given by the instructor.

    Using the worked solutions as guidance, together with input from other students, you will self-grade your assignment work for correctness. The assignments are for understanding and development, not for grade points. You are strongly encouraged to discuss your work with others before, during, and after the self-grading process. These assignments (and the self-grading) are the real heart of the course. The only way to learn how to think mathematically is to keep trying to do so, comparing your performance to that of an expert and discussing the issues with fellow students.

  • Is there a final exam for this course?

    No. The Test Flight exercise in the final two weeks of the Extended Course is built around a Problem Set similar to those used throughout the course, and your submission will be peer evaluated by other students, but the focus is on the process of evaluation itself, with the goal of developing the ability to judge mathematical arguments presented by others. Whilst not an exam, Test Flight is an intense and challenging capstone experience, and is designed to prepare students for further study of university level mathematics.

  • How is this course graded?

    In the Basic Course, grades are awarded for the weekly Problem Sets, which are machine graded. The aggregate grade is provided in the cover note to the Statement of Accomplishment, with an explanation of its significance within the class. In the Extended Course, additional grades are awarded for a series of proof evaluation exercises and for the Test Flight Problem Set (peer evaluated). The aggregate grade is provided in the cover note to the Statement of Accomplishment with Distinction, with an explanation of its significance within the class.

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Date: 
Tuesday, January 21, 2014
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This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website.

Instructor(s): 
Trevor Hastie
Rob Tibshirani
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Date: 
Monday, January 6, 2014
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About the Course

Social networks pervade our social and economic lives.   They play a central role in the transmission of information about job opportunities and are critical to the trade of many goods and services. They are important in determining which products we buy, which languages we speak, how we vote, as well as whether or not we decide to become criminals, how much education we obtain, and our likelihood of succeeding professionally.   The countless ways in which network structures affect our well-being make it critical to understand how social network structures impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do.  This course provides an overview and synthesis of research on social and economic networks, drawing on studies by sociologists, economists, computer scientists, physicists, and mathematicians.
 
The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks.   Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids.   We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences.

Course Syllabus

  • Week 1: Introduction, Empirical Background and Definitions
Examples of Social Networks and their Impact, Definitions, Measures and Properties: Degrees, Diameters, Small Worlds, Weak and Strong Ties, Degree Distributions

  • Week 2: Background, Definitions, and Measures Continued
Homophily, Dynamics,  Centrality Measures: Degree, Betweenness, Closeness, Eigenvector, and Katz-Bonacich. Erdos and Renyi Random Networks: Thresholds and Phase Transitions,

  • Week 3: Random Networks 
Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation

  • Week 4:   Strategic Network Formation 
Game Theoretic Modeling of Network Formation, The Connections Model, The Conflict between Incentives and Efficiency, Dynamics, Directed Networks, Hybrid Models of Choice and Chance
 
  • Week 5:  Diffusion on Networks. 
Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data

  • Week 6:  Learning on Networks. 
Bayesian Learning on Networks, The DeGroot Model of Learning on a Network, Convergence of Beliefs, The Wisdom of Crowds, How Influence depends on Network Position.

  • Week 7: Games on Networks. 
Network Games, Peer Influences:  Strategic Complements and Substitutes, the Relation between Network Structure and Behavior, A Linear Quadratic Game, Repeated Interactions and Network Structures.

Recommended Background

The course has some basic prerequisites in mathematics and statistics.  For example, it will be assumed that students are comfortable with basic concepts from linear algebra (e.g., matrix multiplication), probability theory (e.g., probability distributions, expected values, Bayes' rule), and statistics (e.g., hypothesis testing), and some light calculus (e.g., differentiation and integration).  Beyond those concepts, the course will be self-contained.

Suggested Readings

The course is self-contained, so that all the definitions and concepts you need to solve the problem sets and final are contained in the video lectures.  Much of the material for the course is covered in a text: Matthew O. Jackson  Social and Economic Networks, Princeton University Press (Here are Princeton University Pressand Amazon  pages for the book).  The text is optional and not required for the course.  Additional background readings, including research articles and several surveys on some of the topics covered in the course can be found on my web page.

Course Format

The course will run for seven weeks, plus two for the final exam.  Each week there will be video lectures available, as well as a standalone problem set and some occasional data exercises, and there will be a final exam at the end of the course for those who wish to earn a course certificate.  

FAQ

Will I get a Statement of Accomplishment after completing this class?

Yes. Students who successfully complete the class (above 70 percent correct on the problem sets and final exam) will receive a Statement of Accomplishment signed by the instructor - and those earning above 90 percent credit on the problem sets and final will earn one with distinction.

 

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Date: 
Tuesday, September 24, 2013 to Saturday, November 23, 2013
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ABOUT THIS COURSE

This course aims to teach quantum mechanics to anyone with a reasonable college-level understanding of physical science or engineering. Quantum mechanics was once mostly of interest to physicists, chemists and other basic scientists. Now the concepts and techniques of quantum mechanics are essential in many areas of engineering and science such as materials science, nanotechnology, electronic devices, and photonics. This course is a substantial introduction to quantum mechanics and how to use it. It is specifically designed to be accessible not only to physicists but also to students and technical professionals over a wide range of science and engineering backgrounds.

COURSE SYLLABUS

Introduction to quantum mechanics

How quantum mechanics is important in the everyday world, the bizarre aspects and continuing evolution of quantum mechanics, and how we need it for engineering much of modern technology.

Schroedinger’s wave equation

Getting to Schroedinger’s wave equation. Key ideas in using quantum mechanical waves — probability densities, linearity. The "two slit" experiment and its paradoxes.

Getting "quantum" behavior

The "particle in a box", eigenvalues and eigenfunctions. Mathematics of quantum mechanical waves.

Quantum mechanics of systems that change in time

Time variation by superposition of wave functions. The harmonic oscillator. Movement in quantum mechanics — wave packets, group velocity and particle current.

Measurement in quantum mechanics

Operators in quantum mechanics — the quantum-mechanical Hamiltonian. Measurement and its paradoxes — the Stern-Gerlach experiment.

Writing down quantum mechanics simply

A simple general way of looking at the mathematics of quantum mechanics — functions, operators, matrices and Dirac notation. Operators and measurable quantities. The uncertainty principle.

The hydrogen atom

Angular momentum in quantum mechanics — atomic orbitals. Quantum mechanics with more than one particle. Solving for the the hydrogen atom. Nature of the states of atoms.

How to solve real problems

Approximation methods in quantum mechanics.

PREREQUISITES

The course is approximately at the level of a first quantum mechanics class in physics at a third-year college level or above, but it is specifically designed to be suitable and useful also for those from other science and engineering disciplines.

The course emphasizes conceptual understanding rather than a heavily mathematical approach, but some amount of mathematics is essential for understanding and using quantum mechanics. The course presumes a mathematics background that includes basic algebra and trigonometry, functions, vectors, matrices, complex numbers, ordinary differential and integral calculus, and ordinary and partial differential equations.

In physics, students should understand elementary classical mechanics (Newton’s Laws) and basic ideas in electricity and magnetism at a level typical of first-year college physics. (The course explicitly does not require knowledge of more advanced concepts in classical mechanics, such as Hamiltonian or Lagrangian approaches, or in electromagnetism, such as Maxwell’s equations.) Some introductory exposure to modern physics, such as the ideas of electrons, photons, and atoms, is helpful but not required.

The course will include “refresher” resources for the required mathematics and physics background.

FAQ: 

Do I need to buy a textbook?

You do not need to buy a textbook; the course is self-contained. My book “Quantum Mechanics for Scientists and Engineers” (Cambridge, 2008) is an optional additional resource for the course. It follows essentially the same syllabus, has additional problems and exercises, allows you to go into greater depth on some ideas, and also contains many additional topics for further study.

How much of a time commitment will this course be?

You should expect this course to require 7 – 10 hours of work per week.

Does this course carry any kind of Stanford University credit?

No.

Will I get a Statement of Accomplishment?

Yes, students who score at least 70% will pass the course and receive a certificate. Students who score at least 90% will receive a certificate with distinction.


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Date: 
Monday, May 27, 2013
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Our 4-week advanced course considers how to design interactions between agents in order to achieve good social outcomes. The course -- which is free and open to the public -- considers three main topics: social choice theory (i.e., collective decision making), mechanism design, and auctions. More specifically, in the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems. We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms. The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory.


This course is a follow-up to a more basic course in which we provided the foundations to game theory, covering topics such as representing games and strategies, the extensive form, Bayesian games, repeated and stochastic games, and more. Although to a substantial extent our new course stands alone, some of the previous material -- e.g., Bayesian games, Nash equilibrium, and dominant strategies -- is needed for this more advanced course, whether picked up through our previous course or elsewhere.


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