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Preprint . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
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EQBSL: Against Trust Scores — Evidence, Uncertainty, and Trust Flow in Dynamic Networks

Authors: Hirst, Oliver C.;

EQBSL: Against Trust Scores — Evidence, Uncertainty, and Trust Flow in Dynamic Networks

Abstract

Most deployed reputation systems share a structural flaw: they present confident scalar scores whose derivation is neither traceable nor disputable. A number emerges; no one can interrogate its ledger. Evidence-Based Subjective Logic (EBSL) is the most principled existing corrective, anchoring trust computation in auditable evidence flows rather than opaque aggregation. EQBSL — Evidence-Quantised Belief Subjective Logic — is a systems-oriented extension of EBSL designed for the realities of modern distributed systems, where interactions are temporal, multi-party, and context-dependent. Building on the Subjective Logic formalism of Jøsang (2016) and the evidence-flow reformulation of Škorić et al. (2016), this paper introduces five coordinated extensions. First, scalar positive/negative evidence pairs (r, s) are replaced by evidence tensors e_ij(t) ∈ ℝ^m, capturing distinct categories of interaction — trade outcomes, attestation patterns, governance votes, dispute records — in a form that preserves their heterogeneity rather than collapsing them prematurely into a single scalar. Second, an explicit lift mapping Ψ translates evidence tensors into standard Subjective Logic opinion tuples (b, d, u, a) via application-defined aggregation functionals, making every design choice visible and disputable. Third, trust propagation is recast as a well-defined global update operator F acting on the time-indexed evidence state, replacing ad-hoc iterative convergence routines with a formal state-transition semantics amenable to batch and streaming deployment. Fourth, hypergraph-aware evidence handling is introduced to support natively multi-party interactions — DAO decisions, multi-signature executions, group swaps — without forcing them into pairwise fictions that erase accountability structure. Fifth, stable node-level trust embeddings are derived from the EQBSL evidence state, providing fixed-dimensional vector representations per agent with clear chain-of-custody provenance suitable for downstream machine learning pipelines. EQBSL is not a replacement for EBSL but a structured engineering extension of it: where EBSL provides the correct epistemic foundations, EQBSL adds the architectural scaffolding needed to deploy those foundations in systems that are temporal, multidimensional, hyperedge-native, and ML-adjacent. The framework has direct application in decentralised identity protocols, reputation-gated smart contract systems, distributed governance mechanisms, and cryptographically verifiable trust networks.

December 2025. Extension of Evidence-Based Subjective Logic (EBSL) for dynamic graphs and hypergraphs.

Keywords

trust networks, trust embeddings, zero-knowledge proofs, uncertainty quantification, EQBSL, dynamic graphs, evidence flow, EBSL, reputation systems, hypergraphs, evidence tensors, subjective logic, belief propagation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green