### General Information

Course Code | E_EOR2_ME1 |
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Credits | 6 EC |

Period | P1+2 |

Course Level | 200 |

Language of Tuition | English |

Faculty | School of Business and Economics |

Course Coordinator | dr. H.E.D. Houba |

Examiner | dr. H.E.D. Houba |

Teaching Staff |
dr. H.E.D. Houba prof. dr. J.R. van den Brink |

### Practical Information

You need to register for this course yourself

Teaching Methods | Study Group, Lecture |
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Target audiences

This course is also available as:

### Course Objective

- Acquaint participants with classic mathematical models of economicdecision making developed in the second half of the twentieth century,

the fundamental critique of fact-driven behavioral economics (classic

anomalies) and a sketch of economic models of the future.

- The focus is on three topics: individual decision making, collective

decision making (voting in groups or societies) and interdependent

decision making (or game theory).

- Participants understand the purpose and the mathematical properties of

each model. Participants are able to execute several strategies to

calculate simple models by hand, embed such strategies in algorithms

(pseudo-code for software) and being able to use freeware Gambit.

- Participants are confronted with the important difference between

descriptive theory, aimed at explaining and predicting reality, and

normative theory, what intervention should ideally be done.

- Economic modeling of reality, embedding economic models in software

and bringing economic models to the data will also be addressed.

To summarize, participants will learn, understand and reflect on

important economic models, their implementation in algorithms, and

experiments.

### Course Content

Society asks for evidence-driven economic theories that are ready foruse in economic decision making situations. This requires on the one

hand descriptive theory that explains and predicts economic reality, and

on the other hand normative theory that guides the decision maker what

economic intervention should ideally be done. The rise of Behavioral

Economics and the financial crisis of 2007 made clear that classical

economic models were not up to this task. Since then, (mathematical)

economics is in transition, which is reflected in this course. It also

requires more academic reflection from participants than they are used

in other EOR courses.

This course deals with modeling individuals, companies, governments,

NGOs that take economic decisions in an economic context, e.g. Apple

deciding its pricing strategy taking into account its competitors. The

interaction of decision makers and their economic surrounding is at the

heart of this course. We distinguish three major topics: individual

decision making, collective decision making (f.e. how do groups or

societies reach decisions) and strategic decision making (f.e. how to

bid in an auction anticipating others’ bids).

Individual decision making (period 1)

In order to evaluate whether a decision is a good decision, economists

developed the notion of preference relations that rank available

alternatives and maximization of objective functions, called utility

functions. From a normative perspective, you deal with calculating the

utility maximizing alternative, which is facilitated by studying

constructive mathematical proofs that can be transformed into pseudo

code for software (programming is outside the scope of this course).

From a descriptive perspective, you learn whether the mathematical

structure imposed is evidence-based.

Classical economic theories include market demand of price-taking

consumers, the market supply of output and market demand for inputs by

price-taking firms, and discrete choice (popular in A/B testing in Data

Analytics). Decision making under risk discusses expected utility

theory, the Allais’ paradox and Prospect theory.

Collective decision making (period 1)

Individual decision makers often participate in groups or teams, and

live in a society. Is it mathematically possible to derive group

preferences from individual preferences? No, what then? This part of the

course is merely normative in analyzing classic ranking methods and

voting procedures that are observed in reality. These methods and

procedures will be compared with each other. One criterion employed is

Pareto efficiency.

Strategic decision making (period 2)

In many, if not all, economic situations what eventually happens depends

upon decisions made by more than one individual of individuals. Whether

your team assignment is evaluated with a high grade depends upon your

own effort and that of your other team members. Or, whether you win the

item in an auction depends upon your own bid strategy and that of

others. In many economic situations some individual are better informed

than others, which is called private information. For example, in Poker

you know the cards you are holding while the others do not.

Predicting what others will do, how they predict what you will do, etc.

is crucial in the mathematical analysis of strategic decision making. In

this course, classical game theoretic concepts of Nash equilibrium and

backward induction, as well as recent evidence-based concepts such as

k-level reasoning and (agent) quantal response equilibrium, will be

discussed. Because calculations for medium-sized games by hand is rather

hard, you will learn to solve such games numerically in Gambit.

(Freeware Gambit is an open-source software tool programmed in Python.)

Interpretation of computed solutions and its economic interpretations

will be addressed. Gambit will be part of an assignment that counts as

part of the final grade.

Game theory is widely applied in industrial organization (the modeling

of market competition). Some influential models such as Cournot

competition in quantities (e.g. OPEC cartel), Bertrand competition in

prices, Hoteling’s model of spatial competition are part of this course.

### Teaching Methods

Classes. One lecture and one practical per week. Active participation iskey.

Participants may be partitioned to groups for the practical.

Participants of the practical PREPARE BEFORE coming to class and are

expected TO PRESENT their answers before the Canvas in class and

discuss where problems in solving questions arose.

### Method of Assessment

One team assignment based upon Gambit in period 2 – team assessmentPartial exams in October (covering period 1) and December (covering

period 2) – individual assessment

An exam in March (covering period 1 and 2) – individual assessment

Individual Assignment (presenting before class) – individual assessment

### Literature

A syllabus that contains exercises and that is supplemented by somevideos from Massive Open Online Courses (MOOCs). All compulsory

literature and links will be provided through Canvas.

### Target Audience

This course is an obligatory second-year course in the bachelorEconometrics and Operations Research. Exchange students and other

students from other bachelors, such as

Economics, are welcome but should be motivated to follow a course with a

lot of mathematics. Preferably, you have a sufficient mathematical

background and can reason logically. For more information, or in doubt,

contact the course coordinator.

### Recommended background knowledge

Knowledge of elementary mathematics and elementary probability theory.This includes differentiation, the Lagrange method, expectation, Bayes

Rule.

For EOR students this translates in knowledge from Analysis I and II,

Linear Algebra and Probability Theory, and to a much lesser extent

Finance, Statistics and Programming.