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Computational Economics

Computational Economics
type: Vorlesung (V)
semester: WS 16/17
time: 2016-10-18
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)


2016-10-25
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2016-11-08
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2016-11-15
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2016-11-22
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2016-11-29
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2016-12-06
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2016-12-13
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2016-12-20
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2017-01-10
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2017-01-17
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2017-01-24
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2017-01-31
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)

2017-02-07
09:45 - 11:15 wöchentlich
05.20 1C-03 05.20 Kaiserstraße 89-93 (Allianz-Gebäude)


lecturer: Dr.Rer.Nat. Pradyumn Kumar Shukla
sws: 2
lv-no.: <a target="lvn" href="https://campus.studium.kit.edu/events/8ujatO3j102aftl-ylOPiQ">2590458</a>
Notes

Die Vorlesung wird vom Institut AIFB angeboten. Daher ist eine Einrechnung der Leistung NUR in der Informatik möglich, d. h.die Vorlesung wird nicht im Market Engineering Modul anrechenbar sein.

Bibliography
  • R. Axelrod: "Advancing the art of simulation in social sciences". R. Conte u.a., Simulating Social Phenomena, Springer, S. 21-40, 1997.
  • R. Axtel: "Why agents? On the varied motivations for agent computing in the social sciences". CSED Working Paper No. 17, The Brookings Institution, 2000.
  • K. Judd: "Numerical Methods in Economics". MIT Press, 1998, Kapitel 6-7.
  • A. M. Law and W. D. Kelton: "Simulation Modeling and Analysis", McGraw-Hill, 2000.
  • R. Sargent: "Simulation model verification and validation". Winter Simulation Conference, 1991.
  • L. Tesfation: "Notes on Learning", Technical Report, 2004.
  • L. Tesfatsion: "Agent-based computational economics". ISU Technical Report, 2003.

Elective literature:

  • Amman, H., Kendrick, D., Rust, J.: "Handbook of Computational Economics". Volume 1, Elsevier North-Holland, 1996.
  • Tesfatsion, L., Judd, K.L.: "Handbook of Computational Economics". Volume 2: Agent-Based Computational Economics, Elsevier North-Holland, 2006.
  • Marimon, R., Scott, A.: "Computational Methods for the Study of Dynamic Economies". Oxford University Press, 1999.
  • Gilbert, N., Troitzsch, K.: "Simulation for the Social Scientist". Open University Press, 1999.
Content of teaching

Examining complex economic problems with classic analytical methods usually requires making numerous simplifying assumptions, for example that agents behave rationally or homogeneously. Recently, widespread availability of computing power gave rise to a new field in economic research that allows the modeling of heterogeneity and forms of bounded rationality: Computational Economics. Within this new discipline, computer based simulation models are used for analyzing complex economic systems. In short, an artificial world is created which captures all relevant aspects of the problem under consideration. Given all exogenous and endogenous factors, the modelled economy evolves over time and different scenarios can be analyzed. Thus, the model can serve as a virtual testbed for hypothesis verification and falsification.

Aim

The student

  • understands the methods of Computational Economics and applies them on practical issues,
  • evaluates agent models considering bounded rational behaviour and learning algorithms,
  • analyses agent models based on mathematical basics,
  • knows the benefits and disadvantages of the different models and how to use them,
  • examines and argues the results of a simulation with adequate statistical methods,
  • is able to support the chosen solutions with arguments and can explain them.
Exam description

The assessment consists of a written exam (60 min) (according to §4(2), 1 of the examination regulation). By successful completion of the exercises (according to §4(2), 3 of the examination regulation) a bonus can be obtained. If the grade of the written exam is at least 4.0 and at most 1.3, the bonus will improve it by one grade level (i.e. by 0.3 or 0.4). The bonus only applies to the first and second exam of the semester in which it was obtained.