Question

Trust of service form indirect evidence?

Hi
i read in some article that the trust value can be caluclated from direct experience of a consomers or from referrals and ratings that are exchanged between users in a social network (indirect evidence)
i whant to know :
-how we can calculate the trust of a service(single and composite )using such referrals and exchanged information ?
-what is indirect evidence ?
-what are technique and approach that are used for estimating the trust value of a service using indirect evidence?
can someone help be
thanks

Top contributors to discussions in this field

Martin Gilje Jaatun
SINTEF Digital
Take a look at the web-of-trust concept in PGP - this might give you some ideas: https://ldlus.org/college/WOT/The_PGP_Trust_Model.pdf
Renaud Di Francesco
The Institution of Engineering and Technology
I have published on easily programmable and psychologically interpretable models of trust. In short someone tells you that something occurs with probability p, but you have a certain trust/mistrust of this person (or source of information) and instead of p, you say the probability must be p'. The function f(p) =p' gives a subjective probability from a source probability p. Read this:
Michael Toryila Tiza
University of Nigeria
Following to learn!

Similar questions and discussions

Do you like the New Research Topics on Social Simulations by E-CARGO and Group Role Assignment (GRA)?
Discussion
• Haibin Zhu
New Ideas in Simulation with E-CARGO and GRA
Haibin Zhu
1. Why does the President of the United States now oppose to globalization and hope to withdraw foreign investments back to the United States?
Large-scale capitals can be taken as agents, and different industries can be considered as roles. The United States can be taken as a Group, and Group Role Assignment (GRA) can be used to obtain the overall benefits, and similar industries in the world can be made into roles outside the United States Group. These capitals may be chosen to play the roles of other countries, because the benefits are greater. This situation means that the capitals are leaving the US group. A policy needs a certain amount of income (tax) returned to the US group. In this way, even if the GRA in the United States is optimized, the system benefits will be affected due to the departure of the agents. A reasonable agent departure algorithm can be designed, and a policy threshold (return to the United States tax) can be simulated. Below this threshold, the agents will tend to leave, and above this threshold, the agents will stay. Combined with the GRA algorithm, and Adaptive Collaboration within a certain period of time, the total revenue of the US Group will change over time. The possible result of the simulation may be that the President of the United States is for the benefit of the United States.
The assumption here, "capital is profit-seeking", which belongs to common sense, i.e., a reasonable assumption.
2. Team establishment: Original employees or new employees?
E-CARGO/GRA defines past roles, current roles, and potential roles. The Role Transfer problem comes from the conversion of current roles, and potential roles. A new problem can be formalized with past roles and current roles. This problem can be simulated with GRA.
Can past roles be considered during the evaluation period?
Past roles can be expressed as past assignment (original appointment), which can be transferred to a current assignment. Here, we may combine with the newly hired agents, which further bring up the trust problems.
The original hired agents have a clear trust levels, while we do not know the newly hired agents. Is it complicated? It needs to abstract, formalize, and find a solution.
3. Is the collectivist assignment principle a Pareto improvement?
GRA can be used to represent the Assignment of the optimal Group Performance under the principle of collectivism. A Pareto improvement is an improvement to a system when a change in allocation of goods harms no one and benefits at least one person.
We may simulate the GRA assignment by comparing with a random non-optimized assignment. Is the GRA a Pareto improvement compared with the random assignment?
Intuitively, no. But when can it be Pareto improvement? When is it not? Here we need to assume a reasonable payoff function for each agent under different assignments.
References：
[2] H. Zhu, “Agent Categorization with Group Role Assignment with Constraints (GRA+) and Simulated Annealing (SA),” IEEE Trans. on Computational Social Systems, 2020, DOI: 10.1109/TCSS.2020.3006381 (Early Access: https://ieeexplore.ieee.org/document/9139310).
[3] H. Zhu, “Group Multi-role Assignment with Conflicting Roles and Agents,” IEEE/CAA J. of Automatica Sinica, vol. 7, no. 6, Nov. 2020, pp. 1498-1510.
[4] X. Zhu, H. Zhu, D. Liu, and X. Z. Zhou, “Criteria Making in Role Negotiation”, IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 5, no. 10, Oct. 2020, pp. 3731 – 3740.
[5] H. Zhu, “Avoiding Critical Members in a Team by Redundant Assignment,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 50, no. 7, July 2020, pp. 2729-2740.
[6] H. Zhu, “Maximizing Group Performance while Minimizing Budget,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 50, no. 2, Feb. 2020, pp. 633-645.
[7] H. Zhu, Y. Sheng, X.-Z. Zhou, Y. Zhu, “Group Role Assignment with Cooperation and Conflict Factors”, IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 48, no. 6, June 2018, pp. 851 - 863.
[8] D. Liu, Y. Yuan, H. Zhu, S. Teng, and C. Huang, “Balance Preferences with Performance in Group Role Assignment”, IEEE Trans. on Cybernetics, vol. 48, no. 6, June 2018, pp. 1800 - 1813.
[9] [5] H. Zhu, “Avoiding Conflicts by Group Role Assignment”, IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 46, no. 4, April 2016, pp. 535-547.
[10] Y. Sheng, H. Zhu, X. Zhou, and W. Hu, ″Effective Approaches to Adaptive Collaboration via Dynamic Role Assignment″, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 1, Jan. 2016, pp. 76 - 92.
[11] H. Zhu, “Adaptive Collaboration Systems”, IEEE Systems, Man, and Cybernetics Magazine, vol. 1, no. 4, Oct. 2015, pp. 8-15.
[12] H. Zhu, “Role-Based Collaboration and the E-CARGO: Revisiting the Developments of the Last Decade, IEEE Systems, Man, and Cybernetics Magazine, vol. 1, no. 3, July 2015, pp. 27-35.
[13] H. Zhu, M. Hou, C. Wang, and M.C. Zhou, ″An Efficient Outpatient Scheduling Approach″, IEEE Transactions on Automation Science and Engineering, vol. 9, no. 4, Oct. 2012, pp. 701-709.
[14] H. Zhu, M. Hou, and M.C. Zhou, ″Adaptive Collaboration Based on the E-CARGO Model″, Int’l J. of Agent Technologies and Systems, vol. 4, no.1, 2012, pp. 59-76.
[15] H. Zhu, M.C. Zhou, and R. Alkins, ″Group Role Assignment via a Kuhn-Munkres Algorithm-based Solution″, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 42, no. 3,2012, pp. 739-750.
[16] H. Zhu, ″Fundamental Issues in the Design of a Role Engine, ″ in Proc. of The 9th International Symposium on Collaborative Technologies and Systems (CTS 2008), Irvine, California, 2008, pp. 399-407.
[17] H. Zhu, and M. Zhou, ″Role-Based Collaboration and its Kernel Mechanisms,″ IEEE Trans. on SMC, Part C: Applications and Reviews, vol. 36, no. 4, pp. 578-589, 2006.

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