ESTRAC26 - ECONOMICS OF STRATEGIC COMPETITION

courses
ID:
ESTRAC26
Duration (hours):
36
CFU:
6
SSD:
Economia applicata
Located in:
PESCARA
Url:
DIGITAL MARKETING/CORSO GENERICO Year: 1
Year:
2026
Course Catalogue:
https://unich.coursecatalogue.cineca.it/af/2026?co...

Overview

Date/time interval

Primo Quadrimestre (16/09/2026 - 20/12/2026)

Syllabus

Course Objectives

Knowledge and understanding: at the end of the course students know the main models of market structure and strategic firm behaviour, and understand how data, platforms and algorithms reshape competitive dynamics.
Applying knowledge and understanding: students can analyse the competitive structure of a real industry (including digital industries relevant to marketing), assess pricing and differentiation strategies, and interpret the effects of mergers, vertical integration and exclusionary practices.
Making judgements: students can critically evaluate firms' competitive strategies and competition-authority interventions, distinguishing pro-competitive from anti-competitive effects.
Communication skills: students can present an industry analysis using rigorous economic language, both in writing and orally.
Learning skills: students acquire a conceptual framework enabling them to independently follow the evolution of digital markets and of the literature on platform and AI economics.

Course Prerequisites

Basic knowledge of microeconomics (supply and demand, elasticity, profit maximisation) and descriptive statistics. No programming experience is required. There are no formal prerequisite courses.

Teaching Methods

Lectures with interactive sessions; in-class labs with browser-based market simulations (e.g. repeated pricing game, platform adoption dynamics with network effects); discussion of real business-strategy and antitrust cases; group project with final presentation. The course includes the guided and critical use of generative AI tools to support industry analysis, with emphasis on verifying sources and outputs.

Assessment Methods

Attending students: (a) final written exam (70% of the grade), 90 minutes, consisting of open questions on the theoretical models and an applied mini-case requiring the analysis of a real market by combining classic tools and digital dynamics; (b) group project (30% of the grade): analysis of the competitive structure of a chosen industry, with a compulsory section on the impact of digital technologies and AI, presented in class during the final lectures. Optional lab assignments earn a bonus of up to 2 points on the final grade.
Non-attending students: final written exam (100% of the grade) on the whole syllabus, with an additional question on the supplementary readings. Assessment criteria (correctness of the economic analysis, rigour in the use of models, quality of argumentation) are identical for all students.

Texts

Main textbook: D.W. Carlton, J.M. Perloff, “Modern Industrial Organization”, Pearson (chapters 1–14).
Lecture notes provided by the instructor for each class, linking classic theory to the digital economy.
Supplementary readings (available on the e-learning platform), including: A. Goldfarb, C. Tucker, “Digital Economics”, Journal of Economic Literature (2019); E. Calvano et al., “Artificial Intelligence, Algorithmic Pricing, and Collusion”, American Economic Review (2020); J. Crémer et al., “Competition Policy for the Digital Era”, European Commission (2019); J.-C. Rochet, J. Tirole, “Two-Sided Markets: A Progress Report”, RAND Journal of Economics (2006).

Contents

The course provides the tools of industrial organization to analyse strategic competition among firms, with systematic reference to digital markets and artificial intelligence. Main topics: market structures (perfect competition, monopoly, oligopoly, monopolistic competition) and entry barriers, including digital ones (data, network effects, switching costs); game theory and oligopoly models (Cournot, Bertrand, Stackelberg); collusion, cartels and algorithmic collusion; platforms and two-sided markets; product differentiation; price discrimination and data-driven personalised pricing; advanced pricing strategies (two-part tariffs, bundling, freemium, dynamic pricing); concentration, mergers and acquisitions; vertical integration, self-preferencing and the structure of the AI value chain; information asymmetries, digital advertising and the regulation of digital markets (antitrust, DMA, AI Act).

Course Language

Italian. Slides and part of the study materials (scientific articles and institutional reports) are in English.

More information

All materials (slides, lecture notes, readings, lab worksheets) are made available on the University e-learning platform. Attendance is not compulsory but strongly recommended, particularly for lab activities and the group project. The use of AI tools in assessed work is permitted only if declared and documented according to the guidelines provided in class. Office hours are published on the instructor's webpage.

Degrees

Degrees

DIGITAL MARKETING 
Master’s Degree
2 years
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