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# Strategic Decision Models (4705), 2012/13

### AUTHOR:

Ana Meca Martínez

Dept. Statistics, Mathematics and Computer Science

Area Statistics and Operation Research

This subject has been teached in
Degree in Statistical Sciences and Techniques (P97), year 2012-2013

The essence of game theory is not a set of results but rather a process – the way in which an argument is constructed, how a puzzle about human behavior is solved. To learn game theory means learning the logical argument that produces a solution, a conclusion, a resolution of a mistery. Therefore the primary objective of this course is to teach how to analyze situations of strategic omteraction between agents. Of course in doing so the students will become familiar with the terminology and basic definitions of game theory as well as solution concepts employed in game theory to predict what the outcome of a specific game will be.

#### Materials Theory Practice Solved practice Exercise Solved Exercises Proyectos Case studies Exams Self-assessment Seminar Presentation

#### Type of documents pdf ppt avi swf xls html

## Contenidos del curso

### Description

All the materials here included are used by students during the year 2012/13 in the subject 4705   called Strategic Decision Models.

In particular, they match to : Theory Practice Solved practice Exercise Solved Exercises Proyectos Case studies Exams Self-assessment Seminar Presentation

 Subject name Description Departamento Área Grado Centro Number CT CP CTOT STRATEGIC DECISION MODELS 4705 3 3 6 Type Elective Semester First Course 5 The essence of game theory is not a set of results but rather a process – the way in which an argument is constructed, how a puzzle about human behavior is solved. To learn game theory means learning the logical argument that produces a solution, a conclusion, a resolution of a mistery. Therefore the primary objective of this course is to teach how to analyze situations of strategic omteraction between agents. Of course in doing so the students will become familiar with the terminology and basic definitions of game theory as well as solution concepts employed in game theory to predict what outcome of a specific game will be STATISTICS, MATHEMATICS AND COMPUTER STATISTICS ANS OPERATIONS RESEARCH DEGREE IN STATISTICAL SCIENCIES AND TECHNIQUES (P97) FACULTY OF EXPERIMENTAL SCIENCIES

### Learning objectives

The essence of game theory is not a set of results but rather a process – the way in which an argument is constructed, how a puzzle about human behavior is solved.   To learn game theory means learning the logical argument that produces a solution, a conclusion, a resolution of a mystery. Therefore the primary objective of this course is to teach how to analyze situations of strategic interaction between agents. Of course in doing so the students will become familiar with the terminology and basic definitions of game theory as well as solution concepts employed in game theory to predict what the outcome of a specific game will be.

### Contents

The global contents of this subject are the following:

• 1. Introduction
1.1. Strategic decision models: game theory. Individual decision situations versus interactive decision situations.
1.2. The history of game theory
1.3. Modelling a trategic decision model. Basic teminology and examples.
1.4. Extensive form models.
1.5. Normal form models.
1.6. Coalitional form models.
Practical sessions:
1.1. Read and comment the paper «Génesis y evolución de la teoría de juegos. Sus orígenes en España». Published in BEIO in 2006.

• 2. Non cooperative models
2.1. Static games with complete information: Nash equilibrium (1950) and Pareto efficiency. Examples.
2.2. Dynamic games with complete imformation: perfect and imperfect information, subgame perfect Nash equilibrium (Selten, 1965). Prisioner Dilema.Examples.
Practical sessions:
2.1. Application I (Static games): Cournot Oligopoly (1838).
2.2. Application II (static games): Bertrand Duopoly(1878).
2.3. Application III (dynamic games): Stackelber leadership model (1934).

• 3. Cooperative models
3.1. Cooperative games (von Neumann and Morgenstern, 1974): with/without transferible utility (TU/NTU). Different kind of TU games.Examples.
3.2. Cost/Benefit allocations: the imputation set. Examples.
3.3. Set solutions: the core (Gillies, 1953). Bondareva-Shapley conditions for the non-emptiness of the core. Examples.
3.4. The core for concave/convex games. Examples.
3.5. Point solutions: proportional allocations (Moulín, 1988), the Shapley value (Shapley, 1953), the nucleolus (Schmeidler, 1969). Examples. Proportional allocations versus the Shapley value and the nucleoulus.
Practical sessions:
3.1. Application I: the glove games (Aumann, 1987). The core.
3.2. Application II: linear producgtion games (Owen, 1975). Core allocations.
3.3. Application III: the game of producer firms. The core, the Shapley value, and the nucleoulus.
3.4. Application IV: the airport game (Littlechild and Owen, 1973). The core, the Shapley value, and the nucleoulus.

• 4. Coordination in logistic and supply chains
4.1. Cooperation and competition in inventory games (Meca et al., 2003,2004).
4.2. Horizontal cooperation: inventory games with temporary discounts (Meca et al., 2007), production and inventory games (Guardiola et al., 2008, 2009).
4.3. Vertical cooperation: relationships among a single supplier and multiple retailers (Guardiola et al., 2007).
Practical sessions:
4.1. Read and comment the paper «Supply chain collaboration». Publicado en el libro «Supply Chain, Theory and Applications». Edited by Kordic, I-Tech Education and Publishing, Vienna 2008.
4.2. Read and comment the paper «Cooperative Game Theory and Inventory Management». Published in Eurpean Journal of Operational Research, 2010

### Teaching method

• Theoretical and practical clases, project-based group works, and seminars.

### Evaluation system

The learning outcomes/objectives will be assessed by presenting students with certain questions/puzzles during the first week of the semester (first quiz or first homework). Similar questions/puzzles will be asked again at the end of the semester (last quiz or last homework). A comparison of the answers to these two set of questions – or better, the process by which the students come to an answer and the arguments put forward by the students will allow an assessment of how well the learning objectives have been achieved.

To encourage to the student to follow the learning process successfully, a continuous evaluation system will be used. Durinig this process, all activivities related to theoretical and practical clases, project-based group works, and seminars will be taken into account before giving the final assessment. It will be 80% of the total score.

In addition, a practical work will be developed and presented   to the audience (teacher and other studentes). It will be 20% of the total score.

To pass the course each student will be required   to be atended, at least, to 80% of lessons.

### Instructor(s)

 Nombre E-mail ANA MECA MARTÍNEZ *protected email*

Theory