This paper introduces an LLM-Enhanced Agent-Based Influence Diffusion Simulation (LLM-AIDSim) framework that integrates large language models (LLMs) into agent-based modelling to simulate influence diffusion in social networks. The proposed framework enhances traditional influence diffusion models by allowing agents to generate language-level responses, providing deeper insights into user agent interactions. Our framework addresses the limitations of probabilistic models by simulating realistic, context-aware user behaviours in response to public statements. Using real-world news topics, we demonstrate the effectiveness of LLM-AIDSim in simulating topic evolution and tracking user discourse, validating its ability to replicate key aspects of real-world information propagation. Our experimental results highlight the role of influence diffusion in shaping collective discussions, revealing that, over time, diffusion narrows the focus of conversations around a few dominant topics. We further analyse regional differences in topic clustering and diffusion behaviours across three cities, Sydney, Auckland, and Hobart, revealing how demographics, income, and education levels influence topic dominance. This work underscores the potential of LLM-AIDSim as a decision-support tool for strategic communication, enabling organizations to anticipate and understand public sentiment trends.

Personalized recommendation systems homogenize user preferences, causing an extreme belief imbalance and aggravating user bias. This phenomenon is known as the filter bubble. This paper presents the Responsible Graph-based Recommendation (RGRec) system, designed to alleviate the filter bubble effect in personalized recommendation systems. Acting as an intermediate agency between users and existing preference-based recommendation systems, RGRec is composed of three collaborative modules: the Multi-faceted Reasoning-based Filter Bubbles Detection module (FBDetect), the Belief Nudging module, and the Generative Artificial Intelligence-based Recommendation Strategy Generation module (RecomGen). The FBDetect module identifies users with extreme belief imbalances based on their belief networks, which are represented as heterogeneous graphs. These graphs are then utilized in the Belief Nudging module, where a nudging strategy is employed to adapt prompts for the RecomGen module. Ultimately, the RecomGen module generates contextually rich items for recommendations. Experimental results demonstrate that RGRec can promote diverse content exploration based on user feedback and progressively stimulate interest in topics users initially showed less interest in, encouraging individual exploration.

Reasoning under uncertainty is a fundamental challenge in Artificial Intelligence. As with most of these challenges, there is a harsh dilemma between the expressive power of the language used, and the tractability of the computational problem posed by reasoning. Inspired by human reasoning, we introduce a method of first-order relational probabilistic inference that satisfies both criteria, and can handle hybrid (discrete and continuous) variables. Specifically, we extend sum-of-squares logic of expectation to relational settings, demonstrating that lifted reasoning in the bounded-degree fragment for knowledge bases of bounded quantifier rank can be performed in polynomial time, even with an a priori unknown and/or countably infinite set of objects. Crucially, our notion of tractability is framed in proof-theoretic terms, which extends beyond the syntactic properties of the language or queries. We are able to derive the tightest bounds provable by proofs of a given degree and size and establish completeness in our sum-of-squares refutations for fixed degrees.

While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of “filter bubbles.” These bubbles restrict the range of information users interact with, inadvertently reinforcing their existing viewpoints. Many studies have been dedicated to improving the recommendation algorithms to tackle this issue. Yet, approaches that maintain the integrity of the original algorithms remain largely unexplored. This article introduces the agent-based adaptive information neutrality (AAIN) model, grounded in Yin-Yang theory. The proposed novel approach targets the imbalance in information perception within existing recommendation systems. It is designed to integrate with these preference-based systems, ensuring the delivery of recommendations with neutral information. Our empirical evaluation of this model proved its effectiveness, showcasing its capacity to expand information diversity while respecting user preferences. Therefore, AAIN proves to be an effective model in reducing the adverse impact of filter bubbles on how information is consumed.