Robust Sequential Learning in Random Order Networks

2026-02-09

AI Summary

This work addresses the instability of sequential learning in social networks, where convergence to the true state is often disrupted by minor perturbations in decision order, network topology, or private signals. The paper introduces the notion of “asymptotic truth learning under random update orders” as a robustness criterion, characterizes necessary network conditions for achieving this property, and constructs graph structures that support diverse learning dynamics. Leveraging tools from graph theory, probabilistic analysis, and combinatorial optimization, the authors design efficient algorithms with provable approximation guarantees to transform arbitrary networks into robust ones via minimal edge or node modifications. The proposed approach operates in polynomial time and ensures resilience against bounded adversarial perturbations, thereby guaranteeing asymptotic convergence to the truth even under stochastic decision sequences.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

In the sequential learning problem, agents in a network attempt to predict a binary ground truth, informed by both a noisy private signal and the predictions of neighboring agents before them. It is well known that social learning in this setting can be highly fragile: small changes to the action ordering, network topology, or even the strength of the agents'private signals can prevent a network from converging to the truth. We study networks that achieve random-order asymptotic truth learning, in which almost all agents learn the ground truth when the decision ordering is selected uniformly at random. We analyze the robustness of these networks, showing that those achieving random-order asymptotic truth learning are resilient to a bounded number of adversarial modifications. We characterize necessary conditions for such networks to succeed in this setting and introduce several graph constructions that learn through different mechanisms. Finally, we present a randomized polynomial-time algorithm that transforms an arbitrary network into one achieving random-order learning using minimal edge or vertex modifications, with provable approximation guarantees. Our results reveal structural properties of networks that achieve random-order learning and provide algorithmic tools for designing robust social networks.

Problem

• sequential learning
• social learning
• random-order learning
• network robustness
• asymptotic truth learning

Innovation

• random-order learning
• robust social learning
• network transformation
• asymptotic truth learning
• adversarial resilience