arXiv subjects

Oliver Baumann

Publications and source records attributed to Oliver Baumann.

3 recordsLinked to original sources

arXiv2025

The Blind Men and the Elephant: Mapping Interdisciplinarity in Research on Decentralized Autonomous Organizations

Decentralized Autonomous Organizations (DAOs) are attracting interdisciplinary interest, particularly in business, economics, and computer science. However, much like the parable of the blind men and the elephant, where each observer perceives only a fragment of the whole, DAO research remains fragmented across disciplines, limiting a comprehensive understanding of their potential. This paper assesses the maturity of interdisciplinary research on DAOs by analyzing knowledge flows between Business & Economics and Computer Science through citation network analysis, topic modelling, and outlet analysis. Our findings reveal that while DAOs serve as a vibrant topic of interdisciplinary discourse, current research remains predominantly applied and case-driven, with limited theoretical integration. Strengthening the alignment between organizational and technical insights is crucial for advancing DAO research and fostering a more cohesive interdisciplinary framework.

cs.DC↗

arXiv2025

Imitating AI agents increase diversity in homogeneous information environments but can reduce it in heterogeneous ones

Recent developments in large language models (LLMs) have facilitated autonomous AI agents capable of imitating human-generated content, raising fundamental questions about how AI may reshape democratic information environments such as news. We develop a large-scale simulation framework to examine the system-level effects of AI-based imitation, using the full population of Danish digital news articles published in 2022. Varying imitation strategies and AI prevalence across information environments with different baseline structures, we show that the effects of AI-driven imitation are strongly context-dependent: imitating AI agents increase semantic diversity in initially homogeneous environments but can reduce diversity in heterogeneous ones. This pattern is qualitatively consistent across multiple LLMs. However, this diversity arises primarily through stylistic differentiation and variance compression rather than factual enrichment, as AI-generated articles tend to omit information while remaining semantically distinct. These findings indicate that AI-driven imitation produces ambivalent transformations of information environments that may shape collective intelligence in democratic societies.

cs.CY↗

arXiv2024

How to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics

Traditional recommendation proposals, including content-based and collaborative filtering, usually focus on similarity between items or users. Existing approaches lack ways of introducing unexpectedness into recommendations, prioritizing globally popular items over exposing users to unforeseen items. This investigation aims to design and evaluate a novel layer on top of recommender systems suited to incorporate relational information and suggest items with a user-defined degree of surprise. We propose a Knowledge Graph (KG) based recommender system by encoding user interactions on item catalogs. Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations. We hypothesize that surprisingness correlates with certain network metrics, treating user profiles as subgraphs within a larger catalog KG. The achieved solution reranks recommendations based on their impact on structural graph metrics. Our research contributes to optimizing recommendations to reflect the metrics. We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles. We find that reranking items based on complex network metrics leads to a more unexpected and surprising composition of recommendation lists.

cs.IR↗