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Cited by 890

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

A computational model of trust and reputation

Mui, L., Mohtashemi, M., Halberstadt, A.·2003·Proceedings of the 35th Annual Hawaii International Conference on System Sciences

Abstract

Despite their many advantages, e-businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents' trust and reputation scores.

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Metadata

Authors
Mui, L., Mohtashemi, M., & Halberstadt, A.
Venue
Proceedings of the 35th Annual Hawaii International Conference on System Sciences
Published
2003-10-01
Pages
2431-2439
ISSN
1530-1605 (ISSN-L)
OpenAlex
W2120239604
Cited by
890
References
56
FWCI
61.75
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A computational model of trust and reputation. https://app.cochat.ai/papers/10.1109/hicss.2002.994181
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Mui, L., Mohtashemi, M., & Halberstadt, A. (2003). A computational model of trust and reputation. Proceedings of the 35th Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/hicss.2002.994181

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.