COMPUTATIONAL ECONOMICS

The PhD Program in Economics offers advanced training in economics, econometrics and finance and aims to provide highly selected students with the ability to conduct original research, enabling them to pursue a career in academia or in economic and financial organizations. This course aims at strenghtening quantitative and computational skills exploring comprehensive modelling issues, agent-based models and their computational application.

a) Knowledge and understanding:
- know terminology and concepts related to modelling issues;
- know terminology and ideas to describe agent-based models;
- know terminology and main features of NetLogo programming.

b) Applying knowledge and understanding:
- ability to setup a simple model and its main goals;
- ability to formally define an agent-based model fitting the given purpose;
- ability to use, re-use or develop some computational draft of the model using NetLogo.

c) Making judgements:
- understand and critically assess the merits and problems of any modelling effort;
- make sense of the structure and internal consitency of an agent-based models, suggesting, if appropriate, revision and further invetigations;
- write reports/papers on models and results, positioning the content in the literature and assessing the novelty and importance of the outcome.

Students should be familiar with the material taught in their first year. Knowledge of a programming language would be helpful (however, NetLogo is typically quite different from standard tools and, hence, don’t despair if you have no coding experience!).

Tentatively, the following topics/material will be covered:
- well-known ABM models, including Schelling segregation model, traffic models, the Santa Fe Artificial Stock Market, El Farol Bar Problem (and minority games), the Public Investment Game (PIG), Sugarscape.
- programming in Netlogo. Working versions of most the previously listed models will be analyzed and studied, many models described in the textbook by Railsback and Grimm will be coded from scratch and modified/generalized.
- ideas taken from complexity theory and genetic algorithms (e.g., related to variation, interaction and selection).

[RG] Railsback and Grimm, “A Course in Individual-based and Agent-based Modeling”, Princeton University Press, 2012.
[A] Ashlock, “Evolutionary Computation for Modeling and Optimization”, Springer, 2006.
[AC] Axelrod and Cohen, “Harnessing Complexity”, Basic Books, 2000.
[N] Netlogo website and documentation, see http://ccl.northwestern.edu/netlogo/ The files will be downloaded to your PC when you install Netlogo.

Grading may be based on:
- three presentations in Pechakucha style, see http://www.pechakucha.org/ to be done in couples (week 1, 2 and 5): 30%
- one individual working paper describing an agent-based model and related NetLogo code (expected at the end of week 4): 30%
- final written or oral or practical exam: 30%
- active participation in class and online discussions: 10%

Lectures, practical PC sessions, presentations.

The course will present well-known and insightful agent-based models taken from economics and social sciences, as well as fundamental ideas and computational methods to develop your own models. The student will also become familiar with NetLogo, an object-oriented programming language, coding platform and state-of-the-art tool in computational economics (download the code at http://ccl.northwestern.edu/netlogo/ ). The student will reach the level needed to select his/her modelling idea, implement an agent-based model in NetLogo, verify and document the code using the ODD protocol and improve the presentation skills needed to demonstrate the functioning of the computational model and its scientific use.

written and oral

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development