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Lecture

Agency Theory: Incentives and Contracts

In course

MGT-301: Foundations in financial economics

The aim of this course is to expose EPFL bachelor students to some of the main areas in financial economics. The course will be organized around six themes. Students will obtain both practical insights through real-world examples and understand how one can ...

Description

This lecture covers the concepts of incentive compatibility and individual rationality in agency theory, emphasizing the importance of aligning the interests of principals and agents through optimal contracts. It discusses the optimization problems faced by both parties, the need for incentives to induce desired actions, and the calculation of utility and optimal actions. The lecture explores how optimal contracts are designed to maximize the principal's payoff while ensuring the agent's rational behavior. It also delves into benchmark scenarios, first-best outcomes, and the agency cost incurred due to the principal-agent relationship.

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Instructors (3)

Julien Hugonnier

Pierre Collin-Dufresne

Rüdiger Fahlenbrach

RUEDIGER FAHLENBRACH is Full Professor at Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Formerly on the faculty of the Fisher College of Business of the Ohio State University (USA), he holds a Ph.D. in Finance from the University of Pennsylvania (Wharton). He holds a senior research chair from the Swiss Finance Institute, and is research member at the European Corporate Governance Institute (ECGI). He has research interests in empirical corporate finance, in particular corporate governance and entrepreneurship. Ruediger Fahlenbrach has published in the leading academic journals in finance, including the Journal of Finance, the Journal of Financial Economics, the Review of Financial Studies and the Journal of Financial and Quantitative Analysis. Ruediger currently serves a three year term as elected director of the European Finance Association (2018-2020). He is Associate Editor of the Review of Finance, and former associate editor of the Review of Financial Studies and Financial Management. His research has been reported in many large-circulation newspapers such as The New York Times, The Wall Street Journal, The Economist, Le Temps, NZZ, Handelsblatt, Forbes Magazine, USA Today, and Fortune Magazine.

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Related concepts (32)

Principal–agent problem

The principal–agent problem refers to the conflict in interests and priorities that arises when one person or entity (the "agent") takes actions on behalf of another person or entity (the "principal"). The problem worsens when there is a greater discrepancy of interests and information between the principal and agent, as well as when the principal lacks the means to punish the agent. The deviation from the principal's interest by the agent is called "agency costs".

Optimization problem

In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set.

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Convex optimization

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets). Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard.

Duality (optimization)

In mathematical optimization theory, duality or the duality principle is the principle that optimization problems may be viewed from either of two perspectives, the primal problem or the dual problem. If the primal is a minimization problem then the dual is a maximization problem (and vice versa). Any feasible solution to the primal (minimization) problem is at least as large as any feasible solution to the dual (maximization) problem.

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