# Course Guide for BSc and MSc Students

## Who is this guide for?

The information on this page is intended for BSc and MSc students at UZH who are generally interested in topics related to "Computation and Economics." Students who are thinking about writing their BSc or MSc thesis in our group often ask us "which courses would you recommend I take." To answer this question, this website provides a list of courses that would be an ideal prepartion for a BSc or MSc thesis, or for doing a PhD in the area of Computation and Economics.

## Courses offered by the Computation and Economcis Research Group

Every year, we offer the at least one lecture and one seminar, which are both open to BSc and MSc students. However, the seminar is only open to students who have successfully completed the lecture before or who have obtained similar knowledge elsewhere. The same applies to independent studies and Master Projects:

## General Comments on this Course Guide

The research done at the intersection of Economics and Computer Science is generally very interdisciplinary. Thus, students benefit from a good background in mathematics, theoretical computer science, and economics. While microeconomics (and in particular game theory) is the foundation for most of our work, different research projects require differnt skill sets. For the theoretical work, you need a very good background in mathematics (including proofs). To analyze data from an economics experiment, you need a good background in econometrics. Depending on the domain, a good background in optimization methods (linear and integer programming) is needed, or an advanced course in algorithms design would be beneficial. On this page, we have listed foundational as well as advanced courses covering all of theses areas. Obiously, each student must choose for himself/herself which course is most appropriate given his/her goals. If you are still in doubt after reading this website, feel free to contact Prof. Sven Seuken for additional advice!

## Foundational Courses Recommended for BSc Students

For students who want to write their BSc thesis on a topic related to Economics and Computation, we highly recommend that they take all of the following foudational courses:

Course Title | ECTS | What will you learn? Who should take it? | Offered? |
---|---|---|---|

Microeconomics I | 9 | Foundations of microeconomics, including producer theory, consumer theory, markets, externel effects, etc. This course will teach you the basics of economic thinking. Thus, recommended for all students. | Fall |

Introduction to Game Theory | 6 | A first course in Game Theory, including dynamic games, repeated games, signaling games, evolutionary game theory, bargaining, etc. This course will teach you the most important conepts of strategic interactions, going beyond what we cover in the first two weeks of Economics and Computation. | Fall |

Formal Methods II | 6 | This course will teach you the basics of theoretical computer science, including Turing machines and computability, complexity theory and NP-completeness, and the design of algorithms for hard problems. Many problems in Economics and Computation are NP-complete (e.g., winner determination in combinatorial auctions) and it is important to understand what that means. | Fall |

Introductory Econometrics | 6 | This course is an extension of the statistics course and covers the basics of statistical data analysis, inlcluding regression analysis, hypothesis tests, etc. Recommended if you are intersted in running experiments and analyzing data. | Fall |

Mathematics III | 6 | A natural extension of Mathematics I+II. However, this course will take a more formal approach and also cover proofs of mathematical propositions. A good foundation of mathematics is important for many aspects of Economics and Computation. Thus, highly recommended for all students. | Fall |

## Recommended Courses Recommended for BSc Students

The following courses either cover advanced topics (building on some of the courses in the previous section), or they cover an area that is useful to know for research in Economics and Computation, but not necessarily foundational for most of our work. Before taking any of those courses, it is recommended that you take most (or all) of the foundational courses listed above. If in doubt, feel free to contact Prof. Seuken regarding advice.

Course Title | ECTS | What will you learn? Who should take it? | Offered? |
---|---|---|---|

Microeconomics II | 4.5 | This is a continuation of microeconomics I, with some coverage of game theory. Recommended mainly for students who want to get a more formal background in microeconomic theory. | Fall |

Linear Programming | 6 | This course will teach you the theory and applications of Linear Programming, an important technique for solving many computational mechanism design problems. A natural (more theoretical) complement to the "Introduction to Operations Research" course. | Fall |

Numerics | 3 | This course will teach you efficient algorithms for implementing a number of mathematical methods for solving practical problems. Recommended for students interested in the details of how to implement mathematical methods via programming. | Spring |

## Foundational Courses Recommended for MSc Students

The following list of MSc courses will provide you with good foundations in economic theory, game theory, econometrics, optimization methods, and theoretical computer science. Additionally, it will give you an opportunity to refresh some of your math skills. If you have a broad interest in Economics and Computation, then all of these courses are recommened, and will give you a very good foundation in all relevant areas.

Course Title | ECTS | What will you learn? Who should take it? | Offered? |
---|---|---|---|

Advanced Microeconomics I | 6 | Introduction to Game Theory on the Master's level (no prior game theory course required). Recommened for all students, in particular if you have not taken a game theory class in your BSc program. | Fall |

Mathematics for Economists | 6 | The goal of this course is to introduce students to mathematical concepts fundamental to much of economics. Recommended for students interested in formal/mathematical aspects of Economics and Computation, in particular if you did not take a lot of math courses in your BSc program. | Fall |

Empirical Methods | 6 | This course will teach you the basics of statistical data analysis (econometrics), starting with linear regression analysis, and then moving on towards more advanced methods (no prior course in econometrics required). Recommended for all students interested in running experiments and analyzing data. | Fall |

Advanced Algorithms: Design and Analysis | 6 | This course covers the advanced design and analysis of algorithms (e.g., Greedy Algorithms, Matchings, Network Flows, Dynamic Programming, (Integer) Linear Programming, Approximation Algorithms, Randomized Algorithms, and Online Algorithms). Recommended for students with a particular interest in algorithms design. Offered for the first time in Spring 2014! | Spring |

Avanced Microeconomics II | 6 | The core topics of the lecture are consumer theory (under certainty and uncertainty), production theory, as well as general equilibrium theory. An advanced version of microeconomics I. Recommended in particular for all students who did not take microeconomics I in their BSc program. | Spring |

Optimization Methods | 6 | Introduction to the basics of linear and non-linear programming. These techniques are important for solving a variety of computational mechanism design problems (e.g., winner determination in combinatorial auctions). Recommended for all students, in particular those who did not have a course covering linear programming in their BSc program. | Spring |

Complexity Theory (ETH) | ? | This course gives an introduction to modern complexity theory. It introduces basic complexity classes (such as L, P, BPP, PH, PSPACE, IP, EXP), and studies the known relationship to uniform complexity. Recommended for students with a particular interested in complexity theory. If you did not have a course covering complexity theory in your BSc program (e.g., covering NP-completeness, etc.), then expect to put extra work into this course. | Spring |

## Other (Non-foundational or Advanced) Courses Recommended for MSc Students

The following courses either cover advanced topics (building on some of the courses in the previous section), or they cover an area that is useful to know for research in Economics and Computation, but not necessarily foundational for most of the work done in Economics and Computation. Before taking any of those courses, it is recommended that you take most (or all) of the foundational courses listed above. Obviously, students should take those courses most related to the topic on which they intend to write their MSc thesis. If in doubt, feel free to contact Prof. Seuken regarding advice.

Course Title | ECTS | What will you learn? Who should take it? | Offered? |
---|---|---|---|

Practical Artificial Intelligence | 6 | This class covers the foundational theories (mostly) from the field of (classical) artificial intelligence that have made it possible to evolve to more «intelligent» applications. It will cover areas such as knowledge representation and reasoning (increasingly important through the semantic web effort of the w3c), learning, problem solving, planning, and reasoning under uncertainty. Recommended for students interested in AI, or more generally, in "intelligent systems." | Spring |

Auction Theory and Mechanism Design | 6 | This course will cover auction theory and mechanism design in more detail than possible in the lecture "Economics and Computation." Recommended for students with a particular interest in mechanism design. | irregular |

Social and Economic Foundations of Information Systems | 3 | The goal of this course is to explore the social science and economic foundations of computer-use and information systems in general and to scrutinize these foundations to gain a deeper understanding of the phenomenon. Recommended for all students with a particular interest in information systems. | Fall |

Experimental Economics | 6 | This course will teach you empirical and theoretical findings that have been discovered using economics experiments. Furthermore, it will teach you how to design and execute your own experiments, and how to analyze the resulting data. Recommended for students interested in running experiments (e.g., evaluating a particular market design). | Spring |

Social Choice Theory | 6 | This course provides an in-depth introduction to social choice theory, including the investigation of several social choice mechanisms, such as plurality voting, Borda count, or the Copeland method. Furhtermore, it covers Arrow’s impossibility theorem, the manipulability of voting systems, etc. Thus, it goes much further than what we typically cover in one lecture in the "Economics and Computation" course. Recommended for students interested in computational social choice or mechanism design without money (including matching). | Fall |

Business Network Analysis & Applications | 5 | This course will cover topics including network theory, social network analysis, network visualizations, business intelligence, relational data mining, recommender systems, financial and marketing networks. Particularly recommended for students interested in analzying real-world networks data. | Fall |

Human Computer Interaction | 3 | Recommended for all students interested in topics at the intersection of market design and user interface design. | Fall |

Methods for Human-Centered Computing | 3 | This course introduces key HCI and interdisciplinary theory, and advanced methods for data collection, design, evaluation, and analysis. Areas of focus will include field study methods, study design, technology deployment, and qualitative data analysis. Recommended for students interested in running user studies and analyzing qualitative data gathered from such user studies. | Spring |

Econometrics for Research Students Part I | 4.5 | The course covers at an advanced level the most important areas of econometrics as needed for research students (classical linear model, generalized least squares, nonlinear regression, instrumental variables regression, generalized method of moments, discrete and limited dependent variables, specification testing). Only recommended for students who are interested in research that will involve running lots of experiments and analyzing data. | Fall |

Econometrics for Research Students Part II | 4.5 | This course builds on the estimation theory developed in "Econometrics for Research Students, part I" and studies a number of specific models that are frequently used by practitioners when simple linear regression is inappropriate. Only recommended for students with a very specific interest in experimental economics and econometrics. | Spring |

Microeconomics for Research Students Part I | 9 | This is a PhD-level course on microeconomic theory, covering consumer choice and general equilibrium theory. Only recommended for students who have already taken Advanced Microeconomics II and who have a particular interest in economic theory. | Fall |

Microeconomics for Research Students Part II | 4.5 | Second part of obligatory doctoral course in microeconomic theory; topics include game theory and information economics. Recommended for students who have already taken Advanced Microeconomics I (game theory) and who want an even more rigorous background in game theory. | Spring |

Microeconomic Theory of the Firm | 6 | The lecture covers selected topics in microeconomic theory; the focus is on theoretical modelling and methods of microeconomic theory. Recommended for students interested in advanced topics related to game theory. | Spring |

Principles of Economic Decision Making | 3 | This course teaches theoretical and empirical foundations of economic decision making under risk and over time. Only recommended to students with a particular interest in those special topics. | Spring |

Machine Learning (ETH) | ? | This course is an introduction to Machine learning, i.e., to analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. Recommended for students with an interest in AI (in particular uncertainty and learning). | Spring |

Advanced Statistics | 6 | This course covers aspects of probability theory and statistical methods at a higher level than previous courses in the bachelor. These aspects will be crucial knowledge for those students who wish to continue with a PhD in economics afterwards. | Fall |

## Disclaimer

The information provided on this page is only an inofficial guide, and is not meant to discourage students from taking any particular course at UZH. On the contrary, we encourage all students to get informed about the full spectrum of all offered courses to be able to make an optimal decision which courses to take. Furthmore, we provide no guarantees regarding specific courses taking place in specific semesters. When planning your course schedule, you should make sure that a) the courses are indeed offered in the semester you want to take them, b) that the content of the course is what you expect it to be, c) that you satisfy the pre-conditions for taking the courses, and d) that (at least some of) the ECTS points will count towards your degree (pay attention when taking courses in other departments or at ETH that have content which partially overlaps with courses you have already taken). Students who are still unsure which courses to take after reading this website are encouraged to contact Prof. Sven Seuken for further advice.