Citation Analysis

Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making
Liu He
https://arxiv.org/abs/2601.08247
26
Citation mentions
15
Cited references
10
Sections
4,180
Words (approx)

References by Citation Intensity

Ordered by composite index (descending). Higher values indicate more intensive citation.

# Reference Year Mentions Breadth Sec. Wtd Share Composite Main %
1 Kara, Alara Uyvar 2025 3 3 3.5 0.115 0.843 100%
2 Kanapickienė, Rasa and Vasiliauskaitė, Deimantė an... 2024 3 3 3.5 0.115 0.843 100%
3 Pricope, Tidor-Vlad 2021 3 3 3.5 0.115 0.843 100%
4 Xu, Maochun and Lan, Zixun and Tao, Zheng and Du, ... 2023 3 3 3.5 0.115 0.843 100%
5 Turgay, Safiye and Aydın, Abdülkadir 2025 3 3 6.0 0.115 0.843 100%
6 Wang, Bingqing 2023 2 2 2.5 0.077 0.644 100%
7 Afjal, Mohd 2024 1 1 2.0 0.038 0.406 100%
8 Garg, Sara 2025 1 1 2.0 0.038 0.406 100%
9 Vetrin, Roman Leonidovich and Koberg, Karl 2024 1 1 0.5 0.038 0.406 100%
10 Hong, Joey and Dragan, A. and Levine, S. 2023 1 1 0.5 0.038 0.406 100%
11 Zhong, Yuejia and Shi, Weiyang and Dong, Zhenwei a... 2025 1 1 0.5 0.038 0.406 100%
12 Banerjee, Sounak and Cornelisse, Daphne and Gopina... 2025 1 1 0.5 0.038 0.406 100%
13 Hu, Bowen 2025 1 1 0.5 0.038 0.406 100%
14 Cheridito, Patrick and Dupret, Jean-Loup and Wu, Z... 2025 1 1 0.5 0.038 0.406 100%
15 Watkins, Christopher J. C. H. and Dayan, Peter 1992 1 1 0.5 0.038 0.087 0%
Measures: Mentions = total in-text citations; Breadth = distinct sections; Sec. Wtd = section-weighted count (body ×2, lit review/appendix ×0.5); Share = mentions / total citations in paper; Composite = geometric mean of normalised count, breadth, and main-text ratio; Main % = fraction of mentions in main text (excl. appendix). (self) = self-citation.