Analysis of Competition and Competitiveness

Basic knowledge of microeconomics and quantitative methods.

The classes will be in-person with a theoretical explanation of the subjects and Exercises application.

The students working groups will be supervised during classes along the trimester.

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Understand market structures and their implications for economic efficiency.

1. Identify different market structures, their characteristics, and associated efficiency.

2. Analyze price formation and business strategies in different markets.

Use tools to understand price formation processes and evaluate business strategies in different contexts.

3. Assess the impact of market power on consumers and social welfare.

Measure and interpret the effects of market power; understand its social cost and evaluate regulatory policies.

4. Apply basic concepts of Artificial Intelligence to market analysis and economic decisions.

Interpret predictions from machine learning-based models.

5. Develop a critical perspective on the ethical and regulatory challenges in the application of AI.

Reflect on the ethical implications of using AI.

I – Perfect Competition

1.1 When is a market highly competitive?

1.2 Profit maximization

1.3 Marginal Revenue, Marginal Cost and Profit Maximization

1.4 Choosing output in the short run – the competitive firm’s short-run supply curve

1.5 The short-run market supply curve

1.6 The efficiency of a competitive market

II- Monopoly

2.1 Average Revenue and Marginal Revenue

2.2 The monopolist’s output decision

2.3 Profit maximization

2.4 Measuring monopoly power

2.5 Sources of monopoly power and natural monopoly

2.6 The social costs of monopoly power

2.7 The price discrimination

III – Monopolistic Competition Oligopoly

3.1 The characteristics of monopolistic competition

3.2 Equilibrium in the short run and the long run 

3.3 Monopolistic competition and economic efficiency 

V -Artificial Intelligence in Economics

4.1 AI and market analysis

4.2 Machine learning: price and demand forecasting

4.3 Personalization and price discrimination via AI

4.4 Ethics and regulation in the use of AI

Two possible methods:

        1) Final Exam (100% of final mark)

        2) Distributed evaluation which comprises 3 components:

                   (i) Two multiple-choice tests (15% of final mark)

                   (ii) A written group assignment, the progress of which will be discussed along the trimester (45% of final mark)

                   (iii) Final exam (45% of final mark)

     Final mark = simple mean of the 2 tests (15%) + Work group (45%) + Final Exam (40%)

     The final exam component (iii) must be greater than or equal to 7,5.

     In either methodology, the final mark must be greater than or equal to  9,5.

NAO