Agent Based Models in Economics and Finance (VWL-FinEc-ABM / VWLfeABM-02a) (030166)- Dozent/in
- Prof. Dr. Thomas Lux
- Angaben
- Vorlesung, 2 SWS, benoteter Schein, ECTS-Studium
Unterrichtssprache Englisch
Zeit und Ort: Mi 8:00 - 10:00, WSP1 - R.506
vom 9.4.2023 bis zum 9.7.2023
1. Prüfungstermin (Klausur am Ende der Vorlesungszeit eines Semesters): 12.7.2023, 10:00 - 12:00 Uhr, Raum CAP2 - Hörsaal A
2. Prüfungstermin (Klausur zu Beginn der Vorlesungszeit des Folgesemesters): 11.10.2023, 10:00 - 12:00 Uhr, Raum CAP2 - Hörsaal A
- Voraussetzungen / Organisatorisches
- Module Code: VWL-FinEc-ABM / VWLfeABM-02a
Module Number: 3050700 / 3050701
Exam Number: 3050710
Exam type according to FPO: Module Exam (Modulprüfung)
Specific exam type in summer term 2022: written exam
Further information:: Given in OLAT
The number of ECTS Credits as well as the admission to the examination for this module is determined by the information regarding this module in the FPO (Examination Regulations) of your program (possibly only in the Appendix of the German version). If this module is not explicitly listed in your FPO, please check at the beginning of the semester about admission options. Typically, admission to the examination of this module is not possible in this case. An overview for all programs which can choose modules of the Institute of Economics can also be found here: Nebenfach Volkswirtschaftslehre Handbuch für Exportmodule (Minor in Economics - Handbook of Export Modules). You can also check in advance in QIS whether you can find this module listed there in the overview for exam registration, with the exam number mentioned in univis. Registration to the exam is only possible during the registration period. If you still have questions after reviewing these documents, please contact your student advisor (Studienfachberater).
DE: Die angegebenen Prüfungszeiten beinhalten die Ausgabe und das Einsammeln der Prüfungsunterlagen. Die tatsächliche Prüfungszeit beträgt eine Stunde.
ENG: Stated exam periods include distribution and collection of exam materials. Actual test duration is one hour.
- Inhalt
- Please vistit the homepage of our chair for further information and news.
The course will provide a rigorous, but accessible and self-contained introduction to recent stochastic models of interacting autonomous agents with a particular emphasis on financial applications and their empirical motivation. While many traditional classroom models use a representative agent approach in order to derive choice-theoretic micro-foundations for the market behaviour of firms or households, an increasing number of recent contributions in economics and finance have tried to overcome the limitation of the representative agent approach. In these models, markets are distributed dynamical systems, whose macroscopic properties are derived from their microscopic structure in a non-trivial way. The analytical apparatus that has been developed to derive macroscopic approximations for mean values and higher moments of the aggregate dynamics of distributed systems of agents will be sketched, and we will illustrate how simple ABMs can be rigorously estimated on the base of such analytical solutions. The lecture concludes with an introduction into methods from artificial intelligence and machine learning that have been used to model the active adaptation of economic agents to changing environments.
1. Social Interactions and Opinion Formation of Agents
1.1. Interaction and Emergence of Macroscopic Order: An Example of Involuntary Racial Segregation
1.2. Strategic Choice and Historical Path Dependence
1.3 Discrete Choice with Social Interaction
2. Stochastic Models of Interacting Agents: Structure and Quantitative Modeling Concepts
2.1. The Master Equation Formalism: Stationary Solutions and Transient Behavior
2.2. Dynamics of Means and Higher Moments
2.3. Heterogeneous Beliefs and Asset Price Dynamics
2.4. Artificial Markets with Herding and Strategy Choice
2.5. Estimation of Agent-Based Models
3. Agents with Artificial Intelligence
3.1. Genetic Algorithms: Principles and Economic Applications
3.2. Genetic Algorithms as Explanations of Human Behavior
- Empfohlene Literatur
- Nice introductory examples can be found in:
- Schelling, T., Micromotives and Macrobehavior, New York, 2006
- Arthur, B., Competing Technologies, Increasing Returns and Lock-in by Historical Events, Economic Journal, 99, 106-131, 1989
Excellent introductions to stochastic modeling principles for systems of interacting agents and more interesting applications can be found in:
- Weidlich, W. and G. Haag, Concepts and Models of a Quantitative Sociology: The Dynamics of Interacting Populations, Springer: Berlin 1983
- Weidlich, W. , Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences. Amsterdam , Harwood Academic, 2000.
- Aoki, M., Modeling Aggregate Behavior and Fluctuations in Economics: Stochastic Views of Interacting Agents. Cambridge: University Press 2002
- Aoki, M. and H. Yoshikawa, Reconstructing Macroeconomics: A Perspective from Statistical Physics and Combinatorial Stochastic Processes. Cambridge University Press, 2007.
Part 2 of the lecture draws on material from the following papers:
- Lux, T., Rational Forecasts or Social Opinion Dynamics: Identification of Interaction Effects in a Business Climate Index, in: Journal of Economic Behavior and Organization 72, 2009, 638 655
- Lux, T. , Stochastic Behavioral Asset Pricing Models and the Stylized Facts, chapter 3 in T. Hens and K. Schenk-Hoppé, eds., Handbook of Financial Markets: Dynamics and Evolution. Amsterdam, 2009, 161 215 (North-Holland)
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 20
- Zugeordnete Lehrveranstaltungen
- UE: Excercise: Agent Based Models in Economics and Finance
-
Dozent/in: Prof. Dr. Thomas Lux
|
|
