Embedded Files
Tutorial Session - ECC'25
Wednesday June 25, 2025; 14:10-16:10
M1-Rehearsal Hall, Thessaloniki Concert Hall
The problem of optimal incentive design is classically a static one. Instances of this problem include mechanism design for truthful revelation of private preferences, optimal road pricing to alleviate congestion, optimal electricity pricing for peak shaving and demand side management, and many other socio-technical allocation problems. Viewing these problems as single-shot and static leads to classical impossibilities that are well-known in economics, and money is a commonly employed instrument to circumvent these impossibilities. However, there are many applications in which the use of financial instruments is sensitive or undesirable, and designers may want to allocate resources in ways that are not proportional to users' economic capacity and sensitivity to money. This tutorial introduces recent advances demonstrating that, by embracing the repeated and dynamic nature of many socio-technical allocation problems, e.g., commuters need roads every day, energy prosumers require energy continuously, etc., new types of incentives become possible. Topics include mean-field game theory for resource allocation; karma economies, a novel class of dynamic incentive schemes that provide fair and efficient alternatives to monetary policies; and other state of the art contributions in the emerging area of dynamic incentive design. In addition to theory, the tutorial will feature human behavioral insights, as well as a coding exercise. A main goal is to attract the interest of young control researchers in this untapped area with important societal implications.
Learning Objectives
Learning Objectives
Recall the limitations of classical, single-shot and static incentive design from economics.
Demonstrate, with the example of karma economies, that repetition and dynamics enable overcoming these limitations; and designing new fair and efficient incentive schemes.
Understand the fundamentals of mean-field game theory as applied to dynamic resource allocation problems; and the connection between mean-field games and static population games.
Apply mean-field game theory to model different variants of karma economies and compute their mean-field equilibria.
Deduce resource allocation fairness and efficiency properties from the karma mean-field equilibrium.
Gain exposure to other recent advances and approaches in dynamic incentive design.
Program
Program
14:10-14:50: Modelling and Control with Karma Economies (Ezzat Elokda)
14:50-15:10: Human Learning in Dynamic Games – A Behavioral Perspective (Heinrich Nax)
15:10-15:30: A Fair and Efficient Bottleneck Congestion Management with CARMA (Carlo Cenedese)
15:30-15:50: Designing Truthful Two-Stage Contracts for Non-Myopic Agents under Information Asymmetry (Umar Niazi)
15:50-16:10: Discussion and Q&A
This tutorial-style talk introduces dynamic incentive design for socio-technical resource allocation problems that arise in transportation, energy, communication, and many other critical applications. The focus is on theoretical tools for modelling and control using karma economies, a novel dynamic incentive scheme that achieves fair and efficient socio-technical resource allocations without relying on financial instruments. The tutorial covers the following topics: a) Fundamental limitations of static incentive design in jointly achieving fairness and efficiency, and how dynamics enable overcoming these limitations; b) basic constituents of a dynamic socio-technical resource allocation problem and its connection to mean-field games; c) introduction to mean-field games and their reduction to (static) population games; d) computation of mean-field equilibria using evolutionary dynamics; e) modelling of karma economies as a mean-field game: existence of karma mean-field equilibria, and mean-field game models for first price vs. second price karma auctions; and f) analysis of karma mean-field equilibria and the resulting resource allocation fairness and efficiency. Interactive teaching elements will be used, including a coding exercise and an online experiment.
We review the experimental learning in games literature, and identify avenues for behavioral mechanism design. The focus of this review will be on recent results that investigate the effects of feedback and information on the behavior of humans in dynamic environments involving other humans and algorithms. These dynamic environments include repeated double-sided auctions and karma auctions. We also review methods to quantify dynamic effects in experimental data, and discuss new behavioral trends that arise due to the dynamics.
This talk demonstrates the use of CARMA (=karma for cars) as a fair solution to the morning commute congestion. We consider heterogeneous commuters traveling through a single bottleneck that differ in the value of time (VOT), generalizing the notion of VOT to vary dynamically on each day (e.g., according to trip purpose and urgency) rather than being a static characteristic of each individual. In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion. Commuters use karma to bid for access to the fast lane, and those who get outbid or do not participate in the scheme instead use the slow lane. At the end of each day, karma collected from the bidders is redistributed, and the process repeats day by day. We specialize the karma economy mean-field game model to this setting and analyze pthe roperties of its mean-field equilibrium. Unlike existing monetary schemes, CARMA is demonstrated to achieve (a) an equitable traffic assignment with respect to heterogeneous income classes and (b) a strong Pareto improvement in the long-term average travel disutility with respect to no policy intervention. Moreover, CARMA can retain the same congestion reduction as an optimal monetary tolling scheme under uniform karma redistribution and even outperforms tolling under a well-designed redistribution scheme.
Umar Niazi: Designing Truthful Two-Stage Contracts for Non-Myopic Agents under Information Asymmetry (15:30-15:50)
Umar Niazi: Designing Truthful Two-Stage Contracts for Non-Myopic Agents under Information Asymmetry (15:30-15:50)
Strategic agents and automated decision-making systems increasingly interact in modern infrastructure systems, raising challenges around trust and inducing truthful behavior for the system operators. We study a Stackelberg game where a principal (or a system operator) designs a two-stage contract for a non-myopic agent whose type is unknown to the principal. While the agent's first-stage action can reveal their type under truthful play, they may misrepresent themselves to gain higher second-stage incentives. We show that when the agent is non-myopic and their type is in a continuous space, simultaneously learning agent behavior and optimizing incentives is impossible for the principal under linear contracts. However, there is a possibility of achieving this task with discrete types. We interpret this result by resorting to arguments from adverse selection and moral hazard in contract theory. To address this limitation, we develop a novel nonlinear contract design incorporating an adjustment mechanism that penalizes inconsistent behavior across stages, which is shown to induce truthful behavior by forcing the agent to be consistent. This approach successfully mitigates information asymmetry and ensures truthful play while allowing for more flexible incentive functions.
Material
Material
Page updated
Google Sites
Report abuse