Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.12931

Network Topology Shapes AI Intervention Effectiveness on Deception: Truth-Telling vs. Investigative Agents

Zhao Song·Stefan Sarkadi·The Anh Han
View original source ↗Read source document ↗Export metadata ↓

Abstract

Deploying AI agents to combat dishonest communication is an intuitive response to deception in hybrid human-AI societies. However, the efficacy of such interventions in realistic networked settings remains unclear. We study this problem using an evolutionary game-theoretic model of the Deceiver's Game by extending it to structured networks, where agents can adopt deception, truth-telling, and investigation strategies. We introduce committed AI agents (zealots) fixed to either truth-telling or investigative strategies and conduct agent-based simulations across three network topologies. Our key findings are: (1) passive committed truth-telling agents can amplify adaptive deception by creating exploitable honest partners, whereas investigative agents increase adaptive truth-telling in the oscillatory regime; (2) intervention effects are non-monotonic in deployment density because committed agents reshape rather than simply suppress the underlying oscillatory dynamics; and (3) response peaks depend on topology, with sampled scale-free populations showing sharp low-density responses and fully connected populations requiring larger committed fractions. Overall, the results highlight the need to distinguish passive truth-telling from active investigation when using committed agents to influence networked deception dynamics.

Explore related subjects

cs.GT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhao Song, Stefan Sarkadi, The Anh Han. 2026-10-09. Network Topology Shapes AI Intervention Effectiveness on Deception: Truth-Telling vs. Investigative Agents. https://arxiv.org/abs/2610.12931

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Co-opetition Equilibrium in Adversarial Team Games with Heterogeneous Utilities

The United Nations' 2030 Agenda for Sustainable Development requires all countries to collaborate against adversarial factors, a scenario that can be formalized as an adversarial team game. However, existing solution concepts assume team players share identical utility functions---an assumption inconsistent with real-world settings where countries have divergent objectives. This paper argues that studying adversarial team games must account for heterogeneous utilities among team players. We show that ignoring utility differences can render computed equilibria unstable in the original game, and we formalize this degradation via the Price of Ignoring Heterogeneity, which can be unbounded. To address this, we introduce the Co-opetition Equilibrium (CoE), where team players with heterogeneous utilities correlate their strategies (cooperation) against an adversary (competition). We establish existence via reduction from Nash equilibrium, and prove that finding a CoE is PPAD-complete while computing a Team-Maximizing CoE (TMCoE) is NP-hard. Nevertheless, we identify a broad class of zero-sum adversarial team games---those satisfying a consistent-constraint condition that generalizes identical utilities---where TMCoEs are exchangeable and computable in polynomial time via linear programming. We outline directions for algorithm design, MARL, and extensions to extensive-form and multi-team games.

cs.GT↗
arXiv2026

The False Allure of "Security by Obscurity": A Game-Theoretic Approach

"Security by obscurity," or the idea that hiding how a system works helps protect it from adversaries, has long been poorly regarded in the security community. Yet its intuitive appeal persists, and it has resurfaced repeatedly in industry practices and public debates over the years. The logic, on the one hand, is that open scrutiny of systems is the best way to find and respond to problems; and on the other hand, that too much openness can increase risk by giving adversaries a roadmap for attack. This isn't purely a technical question, but a human one: it's about how attackers and good-faith security researchers respond to openness in the real world. As such, security experts base their stance on decades of community experience; however, the field lacks empirical or theoretical foundations to back up these long-standing beliefs. We provide the first rigorous modeling of "security by obscurity," aiming to capture how rational actors will respond to openness or obscurity in fallible systems. We prove that in equilibrium under realistic model conditions, "obscured" systems are more likely to suffer successful attacks than "open" systems --- establishing theoretical support for the longstanding received wisdom.

cs.GT↗
arXiv2026

Stackelberg POMDP: Learning to Lead via Reinforcement Learning

Many real-world domains--including e-commerce platform design, security planning, and multi-agent coordination--feature leader-follower problems where one decision-maker commits to a policy and others react strategically. We develop a reinforcement learning framework for such interactions in sequential environments with partial observations and multiple followers. Followers may adapt through no-regret learning or reinforcement learning, potentially departing from equilibrium behavior. The framework embeds follower adaptation into the leader's environment to construct a single-agent partially observable Markov decision process--the Stackelberg POMDP. For policy-interactive response algorithms, which access the leader's policy through queries, we prove that an optimal policy based only on the leader's game history yields an optimal commitment under the specified response procedure. We use proximal policy optimization with a centralized critic and train contextual meta-followers to respond across leader policies. In indirect mechanism design, mechanisms using buyer messages achieve higher social welfare than optimal standard sequential price mechanisms across all tested type counts, with responses certified as approximate Bayesian coarse correlated equilibria. In platform design, learned display rules increase mean consumer surplus by 8.4% over an optimized fixed price cap while accommodating hidden seller costs. In Atari bilateral trade, meta-learned follower responses support joint learning of visual gameplay and economic decisions; assigning leadership to the seller or buyer shifts transaction prices and payoffs in that agent's favor. Controlled ablations examine how response credit, policy consistency, and reward timing affect learning.

cs.GT↗