### General Information

Course Code | E_EOR2_ME1 |
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Credits | 6 EC |

Period | P1+2 |

Course Level | 200 |

Language of Tuition | Dutch |

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:

This course is offered in Dutch. Some of the descriptions may therefore only be available in Dutch.

### 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 can be used ineconomic decision making in complex economic 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

financial crisis of 2007, and its aftermath, made clear that the classic

models of economic decision making developed in the second half of the

twentieth century are not up to this task. Also, these models ignored

the classic anomalies (some dated early 1950s) and fundamental critique

raised by fact-driven behavioral economics for too long. Since the

financial crisis, (mathematical) economics is in transition, and for

good reasons. This transition is reflected in this course and requires

more academic reflection from participants than they are used in other

EOR courses.

This course deals with individuals, companies, governments, NGOs that

(need/want to) take economic decisions. Each decision maker is embedded

by an

economic context, e.g. you deciding how much effort to put in a team

assignment. 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 (how do

groups or societies reach decisions) and interdependent decision making

(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 possible

alternatives (possible choices) and utility / objective functions. In

this course we introduce these concepts and investigate what

mathematical structure needs to be imposed to move from preference

relations to utility functions. From a descriptive perspective, this

course addresses whether the mathematical structure is evidence-based.

From a normative perspective, how to obtain preferences and how to

compute what is best

according to these preferences. This is facilitated by constructive

mathematical proofs that can transformed into algorithms (and would lend

itself for programming, which is outside the scope of this course).

Classic economic theories about market demand of consumers, or the

market supply of a product and market demand for inputs by price-taking

firms are derived from objective functions. Noisy decision making, as

introduced by Duncan Luce and popular in A/B testing in Data Analytics,

will be introduced. Preferences for risky decisions are developed and

expected utility theory derived. The famous Allais-paradox experiment

that empirically rejects this theory is discussed, and Prospect theory,

which can explain the paradox, will be discussed.

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? Impossible. 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.

Interdependent 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 teammates. Or, whether you win the

item in an auction depends upon your own bid and the others’ bids.

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

becomes crucial in the mathematical analysis. Although this part can be

used for normative theory (f.e. all driving on the same side of the road

reduces accidents, or to develop good antitrust policy to destabilize

cartels), the focus of this part of the course is mainly descriptive

because of the need for evidence-based theories.

We focus on Nash equilibrium, the empirical need to refine Nash

equilibrium and two of such refinements: k-level reasoning and (agent)

quantal response equilibrium. The latter is a descriptive theory, Nash

equilibrium is in trouble while quantal response equilibrium deals

better with experimental data and is easier to bring to data.

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. You will be

introduced to the fascinating world of interdependent decision making

with private information. Because analyzing such games by hand is rather

hard, you will solve such games numerically in Gambit. (Freeware Gambit

is an open-source software tool programmed in Python that computes Nash

equilibrium and quantal response equilibrium.) Interpretation of the

computed solution and its economic implications will be addressed.

Gambit will be part of an assignment that counts as part of the final

grade.

This part will also focus on the economic literature during the 1980s

and 1990s that were so influential that many mathematical economists

became Nobel laureates in Economics. Classic theories about Cournot

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

prices, sustainable cooperation in repeated games, antitrust policy to

destabilize cartels are part of the 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, ot 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.