Master 2 / MScT 2A

Syllabus-Firms’ Strategies and Markets (9 classes-3hours each)

Claire Chambolle 

This course aims at equipping students to understand complex strategies that firms apply to maximize their profits. Emphasis is given to pricing, advertising and innovation strategies of firms in a static and/or a dynamic perspectives. The range of strategies analyzed is wide because firms operate in diverse market organizations (various competitive environment, direct sales vs indirect sales to consumers) and demand conditions (uncertainty, consumers’ heterogeneity, imperfect information of consumers about prices and products).

The methodology used is Microeconomic Theory and Industrial Organization. Each class will contain a case study 0.5 hours, 1,5 hours of theory and 1hour of exercises.

Traffic and Transportation systems in urban contexts

M. Benoit Vedel (DG Aimsun), M. Xavier Boulet ( chargé d'études Aimsun) 

The level of traffic in cities has increased significantly over the last decades. Such level of trafic has a considerable impact on the quality of life for all citizens and economic growth.

 Balancing the demand for increasing personal mobility and economic growth, with the need to respect the environment is a real challenge. The link between transportation networks and economics is manifold both at the macro and micro level. Value flows on the transport network are capitalized into land values, economic recessions stall the mobility of people and goods, higher-income households travel more frequently, and so on.

This course analyzes Artificial Intelligence through the lens of microeconomics, industrial organization, and applied econometrics, following Joshua Gans' The Economics of Artificial Intelligence. Core topics include:

Infrastructure & Inputs: Data centers, environmental resources, data as a non-rival good, privacy, and intellectual property.

Competition & Regulation: Network effects, loss-leading, open-source dynamics, and the EU AI Act.

Economic Impacts: Labor market displacement/augmentation, firm productivity, algorithmic bias, consumer search, and the economics of generative art.

A core component of the course is the empirical replication and extension of an existing economics paper on AI. Students will utilize the LLM and computational tools taught in the TD sessions to process data and run econometric analyses. Potential data sources include the AIOE Database, employment surveys, AI-simulated datasets for algorithmic bias, or web-scraped data from AI platforms.

Grading is based entirely on group work (groups of 5):

• 50% — Literature Presentations: Graded on analytical comprehension, presentation materials, discussion engagement, and a 1-page (A4) synthesis.

• 50% — Empirical Replication Project: Graded on the originality of the empirical variation, correct implementation of LLM/econometric tools, and critical assessment of the original paper's methodology. The final session (Session 6) is dedicated to presenting these projects.