Mecanismos y Teoría de Juegos en Redes

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Créditos
6
Tipos
Obligatoria de especialidad (Redes de Computadores y Sistemas Distribuidos)
Requisitos
Esta asignatura no tiene requisitos, pero tiene capacidades previas
Departamento
AC
Mail
The goal of this course is giving the student a background in the methodologies in the advanced design of mechanisms using non-lineal convex optimization and game theory. The program will cover from basic concepts related to convexity, convex optimization problems, Game Theory, Nash Equilibria, to applications of these methodologies to networking such as resource allocation, back-pressure models, power-control models compressive sensing, game theory in wireless networks, pricing models in networks, game theory in routing problems or incentives in P2P systems.

Horas semanales

Teoría
4
Problemas
0
Laboratorio
0
Aprendizaje dirigido
0
Aprendizaje autónomo
7

Objetivos

  1. Capacity to formulate a convex optimization problem
    Competencias relacionadas: CTR6, CEE2.3,
  2. Capacity to apply convex optimization to networking problems.
    Competencias relacionadas: CTR6, CEE2.1, CEE2.2, CEE2.3,
  3. Capacity to understand what game theory is and how a game is solved.
    Competencias relacionadas: CTR6, CEE2.3,
  4. Capacity to apply game theory to networking problems
    Competencias relacionadas: CTR6, CEE2.3,

Contenidos

  1. Convex Optimization basics
    Convex sets, convex functions, convex optimization problems (COP) and duality (Lagrange dual function, KKT optimality conditions), methods for solving COP's (General Descent Methods, Interior Point Methods)
  2. Convex Optimization Applications to networking
    Exxamples on Resource allocation in networks, back-pressure, Power control, Publish-subscribe in DTN, Compressive Sensing.
  3. Game Theory basics
    Strategic and Extensive Forms of a Game, Non cooperative Games (Nash pure and mixed equilibria, correlated equilibria), Cooperative Games (core of a game, Shapley values, Nash Arbitration scheme), cost-sharing (Braess Paradox, Price of Anarchy and Stability), Auctions.
  4. Game Theory Applications to Networking
    Wireless Networking games, Energy-Efficient Power Control games, pricing, P2P games

Actividades

Actividad Acto evaluativo


Convex Optimization basics


Objetivos: 1
Contenidos:
Teoría
10h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Convex Optimization Applications to Networking


Objetivos: 2
Contenidos:
Teoría
10h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Game Theory Basics


Objetivos: 3
Contenidos:
Teoría
10h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Game Theory Applications to Networking


Objetivos: 4
Teoría
10h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Evaluations

Evaluations: exam and presentation from students

Teoría
5h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Studying materials and project's ralization



Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Metodología docente

During the initial sessions of each topic, the main results will be explained in the blackboard. the student will solve some exercises to prove their skills in the topic. Finally, there will be some sessions devoted to discuss in the classroom models taken from research papers that apply the related topics.

Método de evaluación

The evaluation is based on different activities

- Short projects and presentations in which the student has to deliver and defend the obtained results (P)
- A final exam (FE)

Each of the activities will be evaluated (0=
The final mark for the course (FM) will be:

FM= 0.60xP+0.4xFE

Where P=(1/N) x Sum (Pi) with i=1,...N

with Pi the projects and oral presentation mark. There will be a minimum of 2 practical projects and 1 oral presentation.

Bibliografía

Básica:

Web links

Capacidades previas

None.