Dynamic Information Disclosure Games: Equilibrium Strategies Analysis

Core Concepts

Equilibrium strategies in dynamic information disclosure games involve compressed information for decision-making.

Abstract

The content discusses equilibrium strategies in dynamic information disclosure games between a principal and a receiver. It explores the use of Canonical Belief Based (CBB) strategies, sequential commitment to experiments, and the impact on decision-making. The analysis includes examples and extensions to multi-agent scenarios.

Introduction

Information asymmetry in modern systems.
Importance of communication protocols.

Problem Formulation

Two-player dynamic game setup with long-term objectives.
Principal's experiment choices influence receiver actions.

Main Results

Theorem 1 provides a sequential decomposition procedure for equilibria.

Examples

Quickest detection game and a game between a principal and detector analyzed.

Discussion

Challenges in extending results to settings with multiple senders or public observations discussed.

Conclusion and Future Work

Summary of findings and potential research directions outlined.

Stats

"In many real-world dynamic systems, information exchange and decision making can happen repeatedly as the system/environment changes over time"
"Public companies disclose information periodically which impacts stockholders’ decisions"
"The principal’s value function seems to converge when t is further away from the horizon T"
"The value functions at equilibrium oscillate with a period of 4 instead of converging as t gets further away from the horizon T"

Quotes

"The main reason is that the CIB belief in the signal picking game is strategy-independent, just like in [32]."
"Unlike the CIB-belief-based sequential decomposition procedures of [34], [35], [36], the sequential decomposition procedure of Theorem 1 always has a solution."

Key Insights Distilled From

Information Compression in Dynamic Information Disclosure Games

by Dengwang Tan... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12204.pdf

Deeper Inquiries

How can equilibrium strategies be adapted for infinite horizon games

Equilibrium strategies in dynamic games can be adapted for infinite horizon games by considering the long-term implications of actions and information disclosure. In an infinite horizon game, players need to account for the impact of their decisions over an extended period. This requires a deeper analysis of how strategies evolve over time and how they affect the overall outcome of the game.
One approach to adapting equilibrium strategies for infinite horizon games is to consider discounted payoffs or average payoffs over time. By incorporating discount factors or averaging mechanisms, players can evaluate the long-term consequences of their actions and make strategic choices accordingly.
Another adaptation involves introducing memory into player strategies. In an infinite horizon game, players may need to remember past actions and outcomes to inform their current decisions effectively. Strategies that take into account historical information can lead to more sophisticated gameplay dynamics in prolonged interactions.
Overall, adapting equilibrium strategies for infinite horizon games requires a comprehensive understanding of how player decisions unfold over time and how these decisions shape the overall equilibrium outcomes.

What challenges arise when extending results to settings with multiple senders

Extending results to settings with multiple senders poses several challenges due to increased complexity in decision-making dynamics:

Strategic Interactions: With multiple senders, each sender's strategy affects not only their own payoff but also impacts other senders' payoffs through strategic interactions. Analyzing equilibria becomes more intricate as each sender must anticipate others' responses based on their chosen strategies.

Information Sharing: Coordinating information sharing among multiple senders adds another layer of complexity. Ensuring that all senders have access to relevant information while maintaining individual incentives can be challenging.

Equilibrium Characterization: Identifying equilibria where all senders play optimal strategies simultaneously becomes more complex with multiple participants involved. The interplay between different sender behaviors complicates finding stable solutions that satisfy all parties.

4Communication Constraints: Communication constraints among multiple senders may limit coordination possibilities and influence equilibrium outcomes significantly.

How do public observations affect equilibrium strategies in dynamic games

Public observations introduce additional considerations into dynamic games:
1Impact on Information Asymmetry: Public observations reduce information asymmetry between players by providing common knowledge about certain aspects of the game environment or other players' actions.
2Influence on Strategic Choices: Players may adjust their strategies based on public observations as they now have access to shared information that influences decision-making processes.
3Enhanced Transparency: Public observations promote transparency within the game setting, potentially leading to more cooperative behavior or efficient outcomes as players are aware of common knowledge.
4Strategic Manipulation: However, public observations could also be manipulated strategically by one player if they control what is publicly observed, leading them towards achieving personal gains at others’ expense.
These factors interact dynamically within a game context when public observations are introduced, shaping equilibrium strategies and influencing overall gameplay dynamics."