0635531 20250603104624.420250526235959.9 105003665803 001485002800001 10.1016/j.ijar.2025.109454 15 s. P cav_un_epca*02567740888-613X184Elsevier Belief functions Entropy Mutual information Divergence cav_un_auth*0101118 Jiroušek Radim UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0216188 Kratochvíl Václav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ K Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2025/MTR/kratochvil-0635531-preprint.pdf https://www.sciencedirect.com/science/article/pii/S0888613X25000957?via%3Dihub This paper addresses the long-standing challenge of identifying belief function entropies that can effectively guide model learning within the Dempster-Shafer theory of evidence. Building on the analogy with classical probabilistic approaches, we examine 25 entropy functions documented in the literature and evaluate their potential to define mutual information in the belief function framework. As conceptualized in probability theory, mutual information requires strictly subadditive entropies, which are inversely related to the informativeness of belief functions. After extensive analysis, we have found that none of the studied entropy functions fully satisfy these criteria. Nevertheless, certain entropy functions exhibit properties that may make them useful for heuristic model learning algorithms. This paper provides a detailed comparative study of these functions, explores alternative approaches using divergence-based measures, and offers insights into the design of information-theoretic tools for belief function models. WOS BA 10000 10100 10103 2026 2 RVO:67985556 https://hdl.handle.net/11104/0366809 S 109454 C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 3.1 1 6.2 0.00481 0.819 5074 116 0.731 49.3 3.44 1543 Q2 2.600 146 Q2 52.2 0.55 Q3 52.2 2025 105003665803 SCOPUS PUBMED 001485002800001 WOS cav_un_epca*0256774 International Journal of Approximate Reasoning 184 1 2025 0888-613X 1873-4731 Elsevier