| Sign up

PDF (433.7 KB)

Cite

Collect

Submit Manuscript

Show Outline

Figures (5)

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Tables (6)

Table. I

Table. II

Table. III

Table. IV

Table. V

Research paper | Open Access

A novel simulation framework for crowd co-decisions

Xia Yao, Hongbo Sun(), Baode Fan

School of Computer and Control Engineering, Yantai University, Yantai, China

Show Author Information

Abstract

Purpose

The purpose of this paper is to aim mainly at social public decision-making problems, studies the corresponding relationship between different voting rule combinations and the final results, and discusses the quantitative relationships between group intelligence (final votes) and individual intelligence (everyone) to defend democracy under the circumstance of rapid development of network technology, and crowd intelligence becomes more complicated and universal.

Design/methodology/approach

After summarizing the crowd co-decisions of related studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations.

Findings

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations.

Originality/value

This paper will help researchers solve the social public decision-making problems in large-scale simulations.

Keywords

Crowd co-decisions Domain preference Simulation framework Voting mechanism

References

Almeida, E.J., Kokkinogenis, Z. and Rossetti, R.J.F. (2012), “NetLogo implementation of an evacuation scenario, information systems and technologies”, IEEE, Vol. 3 No. 68, pp. 373-377.

Google Scholar

Ao, L. Yun, L. Xia, L. and Zikas, V. (2018), “How private is your voting? A framework for comparing the privacy of voting mechanisms”.

Bonney, R., Cooper, C.B. and Dickinson, J. (2009), “Citizen science: A developing tool for expanding science knowledge and scientific literacy”, BioScience, Vol. 59 No. 11, pp. 977-984.

Crossref Google Scholar

Danishevskaya, A.Y., Filatova, O.A., Samarra, F.I.P., Miller, P.J.O., Ford, J.K.B., Yurk, H. and Hoyt, E. (2018), “Crowd intelligence can discern between repertoires of killer whale ecotypes”, Bioacoustics, Vol. 3 No. 6, pp. 1-13.

Google Scholar

Liu, Y., Zhang, X., Zhang, Y. and Guan, X. (2019), “Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach”, Chinese Journal of Aeronautics, doi: 10.1016/j.cja.2019.03.026.

Crossref Google Scholar

Mao, H., Fan, X., Guan, J., Chen, Y.-C., Su, H., Shi, W. and Xu, C. (2018), “Customer attractiveness evaluation and classification of urban commercial centers by crowd intelligence”, Computers in Human Behavior, doi: 10.1016/j.chb.2018.08.019.

Crossref Google Scholar

Michelucci, P. and Dickinson, J.L. (2016), “The power of crowds”, Science, Vol. 351 No. 6268, pp. 32-33.

Crossref Google Scholar

Rebello, M., Benito, R.M., Losada, J.C. and Galeano, J. (2018), “Robust distributed voting mechanism by consensus”, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), doi:10.1109/asonam.2018.8508703.https://doi.org/10.1109/ASONAM.2018.8508703

Crossref

Shih, T.-C., Ho, L.-P., Wu, J.-L., Chou, H.-Y. and Pai, T.-W. (2019), “A voting mechanism-based linear epitope prediction system for the host-specific iridoviridae family”, BMC Bioinformatics, Vol. 20 No. 7, doi: 10.1186/s12859-019-2736-2.

Crossref Google Scholar

Vratonjic, M., Singh, H., Kumar, G., Mohamed, R., Bajaj, A. and Gainey, K. (2018), “Power and performance aware memory-controller voting mechanism”, 2018 19th International Symposium on Quality Electronic Design (ISQED), doi: 10.1109/isqed.2018.8357276.https://doi.org/10.1109/ISQED.2018.8357276

Crossref

Wang, L., Gauthier, V., Chen, G. and Moreira-Matias, L. (2018), “Special issue on extracting crowd intelligence from pervasive and social big data”, Journal of Ambient Intelligence and Humanized Computing, Vol. 9 No. 6, doi: 10.1007/s12652-018-0680-z.

Crossref Google Scholar

Wang, F., Wang, F., Ma, X. and Liu, J. (2019), “Demystifying the crowd intelligence in last mile parcel delivery for smart cities”, IEEE Network, Vol. 33 No. 2, pp. 23-29, doi: 10.1109/mnet.2019.1800228.

Crossref Google Scholar

Xu, J., Wang, S., Bhargava, B. and Yang, F. (2019), “A blockchain-enabled trustless crowd-intelligence ecosystem on mobile edge computing”, IEEE Transactions on Industrial Informatics, 1-1, doi:10.1109/tii.2019.2896965.https://doi.org/10.1109/TII.2019.2896965

Crossref

International Journal of Crowd Science

Volume 4 Issue 1,
March 2020

Pages 2-16

DOI: 10.1108/IJCS-09-2019-0021

Cite this article:

Yao X, Sun H, Fan B. A novel simulation framework for crowd co-decisions. International Journal of Crowd Science, 2020, 4(1): 2-16. https://doi.org/10.1108/IJCS-09-2019-0021

Return

Table. IVoting mechanism statistics

Classification	Name
Positive points rule voting	Majority vote
	Borda rule
	K-yes vote
	Negative vote
Step process voting	Two-round run-off
Step process voting	Single measure shift voting
Condorcet extension rule	Maximum and minimum systemRanked Pairs

Table. IIMeaning of each part of the public wisdom unit

Noun	Description
Pattern	Pattern is a directed acyclic graph made up of decisions on time series
Affecter	The affecter is the influence of several proponents\' crowd-wisdom units, whose influence intensity is determined by the interconnection rule
Comparator	The comparator connects other crowd-intelligence units related to the crowd-intelligence unit and learns from the results of the behavior of other crowd-intelligence units
Decider	The decider takes resources and capabilities into account
Monitor	The monitor modifies deviations based on specific goals
Executor	The executor performs the selection based on the decisions of the decision-maker and the recommendations of the influencer

Table. IIIMeaning of each part of the public wisdom unit

Noun	Description
Decompositor	The decompositor is the decomposition capability for the original goal
Integrator	The integrator integrates the decomposed results of the resolver for the decision-maker to make the next decision

Table. IVBasic symbols

Symbols	Instructions
Vid	the ID of the decision task
Vname	Name of the decision task
Vtype VI	voting rule, atomic voting rule is empty particular voting question input set for decision tasks
VO	output set for decision tasks
Pre	the pre-dependent set Vid to perform the decision task
Bef	the post-dependency set of the task

Table. VBasic nouns and symbols

Nouns	Symbols	Instructions
Voter set	V = {v₁, v₂, v₃,…,v_n}	A limited set of voters
Candidate set	C = {c₁, c₂, c₃,…, c_m}	A limited set of candidates
Preference domain set	D = {d₁, d₂, d₃,…, d_u }	The area of preference involved in a particular voting question

Table. VIVoting mechanism statistics

Domain	d₁	d₂	d₃	TTL	w_k
d₁	0.5	1	1	2.5	0.56
d₂	0	0.5	1	1.5	0.33
d₃	0	0	0.5	0.5	0.11
total				4.5	1