On the Price of Transparency: A Comparison between Overt Persuasion and Covert Signaling
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Abstract (arXiv)
Transparency of information disclosure has always been considered an instrumental component of effective governance, accountability, and ethical behavior in any organization or system. However, a natural question follows: what is the cost or benefit of being transparent, as one may suspect that transparency imposes additional constraints on the information structure, decreasing the maneuverability of the information provider. This work proposes and quantitatively investigates the price of transparency (PoT) in strategic information disclosure by comparing the perfect Bayesian equilibrium payoffs under two representative information structures: overt persuasion and covert signaling models. PoT is defined as the ratio between the payoff outcomes in covert and overt interactions. As the main contribution, this work develops a bilevel-bilinear programming approach, called Z-programming, to solve for non-degenerate perfect Bayesian equilibria of dynamic incomplete information games with finite states and actions. Using Z-programming, we show that it is always in the information provider's interest to choose the transparent information structure, as 0≤ textrmPoT≤ 1. The upper bound is attainable for any strictly Bayesian-posterior competitive games, of which zero-sum games are a particular case. For continuous games, the PoT, still upper-bounded by 1, can be arbitrarily close to 0, indicating the tightness of the lower bound. This tight lower bound suggests that the lack of transparency can result in significant loss for the provider. We corroborate our findings using quadratic games and numerical examples.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2023.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 1.
9 papers.
- 2024Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Gamescited by 14
- 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 9
- 2024Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfarecited by 8
- 2023A First Order Meta Stackelberg Method for Robust Federated Learningcited by 7
- 2025Reasoning and Behavioral Equilibria in LLM-Nash Games: From Mindsets to Actionscited by 6
- 2025In-Context Reinforcement Learning via Communicative World Modelscited by 5
- 2023Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learningcited by 4
- 2025Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundationscited by 4
- 2026LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamicscited by 0
- 2026LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics27 Jun 2026
- 2025Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations28 Dec 2025
- 2025In-Context Reinforcement Learning via Communicative World Models8 Aug 2025
- 2025Reasoning and Behavioral Equilibria in LLM-Nash Games: From Mindsets to Actions10 Jul 2025
- 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks22 Oct 2024
- 2024Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare14 Mar 2024
- 2024Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games29 Feb 2024
- 2023Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning6 Oct 2023
- 2023A First Order Meta Stackelberg Method for Robust Federated Learning23 Jun 2023