Open Access
Issue
Published:
30 September 2023
Research on Intelligence Evaluation Method for Crowd Collaboration System
)
Download citation
205
Views
20
Downloads
0
Crossref
N/A
WoS
0
Scopus
N/A
CSCD
Crowd collaboration system, originating from cooperation among animals in nature, is composed of intelligent subjects, characterized by complex dynamic interactions, and has many applications in daily life. In the fields of psychology and computing, scientists have tried to quantify individual intelligence with performance on tasks. In this paper, we explore the main factors affecting group performance for small production factories from the perspective of intelligence. Based on the individual daily efficiency and the average process efficiency, we evaluate individual intelligence level and interaction intensity by integrating group size and efficiency difference, and thus propose crowd intelligence evaluation method. The rationality of the method is analyzed from overall group performance and change in the average individual performance. In the future, the intelligence evaluation method can be applied to more general production scenarios, and the impact of external uncertainty on the intelligence can be studied with simulation to achieve real-time and quantitative optimization of intelligence level of the crowd collaboration system.
menu
Research on Intelligence Evaluation Method for Crowd Collaboration System
)
Abstract
Crowd collaboration system, originating from cooperation among animals in nature, is composed of intelligent subjects, characterized by complex dynamic interactions, and has many applications in daily life. In the fields of psychology and computing, scientists have tried to quantify individual intelligence with performance on tasks. In this paper, we explore the main factors affecting group performance for small production factories from the perspective of intelligence. Based on the individual daily efficiency and the average process efficiency, we evaluate individual intelligence level and interaction intensity by integrating group size and efficiency difference, and thus propose crowd intelligence evaluation method. The rationality of the method is analyzed from overall group performance and change in the average individual performance. In the future, the intelligence evaluation method can be applied to more general production scenarios, and the impact of external uncertainty on the intelligence can be studied with simulation to achieve real-time and quantitative optimization of intelligence level of the crowd collaboration system.
References(25)
W. Xie, Z. Wang, F. Wang, and L. Zhang, A review on cooperation from the point of view of evolutionary psychology, Adv. Psychol. Sci., vol. 21, no. 11, pp. 2057–2063, 2013.
J. Henrich and M. Muthukrishna, The origins and psychology of human cooperation, Annu. Rev. Psychol., vol. 72, pp. 207–240, 2021.
A. P. Melis, The evolutionary roots of human collaboration: Coordination and sharing of resources, Ann. New York Acad. Sci., vol. 1299, no. 1, pp. 68–76, 2013.
S. Duguid and A. P. Melis, How animals collaborate: Underlying proximate mechanisms, Wiley Interdiscip. Rev., vol. 11, no. 5, p. e1529, 2020.
C. Wagner, Wiki: A technology for conversational knowledge management and group collaboration, Commun. Assoc. Inf. Syst., vol. 13, pp. 265–289, 2004.
A. Desai, J. Warner, N. Kuderer, M. Thompson, C. Painter, G. Lyman, and G. Lopes, Crowdsourcing a crisis response for COVID-19 in oncology, Nat. Cancer, vol. 1, pp. 473–476, 2020.
P. Lorenz-Spreen, S. Lewandowsky, C. R. Sunstein, and R. Hertwig, How behavioural sciences can promote truth, autonomy and democratic discourse online, Nat. Hum. Behav., vol. 4, no. 11, pp. 1102–1109, 2020.
K. S. McGrew and B. J. Wendling, Cattell–horn–carroll cognitive-achievement relations: What we have learned from the past 20 years of research, Psychology in the Schools, vol. 47, no. 7, pp. 651–675, 2010.
S. Legg and M. Hutter, Universal intelligence: A definition of machine intelligence, Minds Mach., vol. 17, no. 4, pp. 391–444, 2007.
J. Hernández-Orallo and D. L. Dowe, Measuring universal intelligence: Towards an anytime intelligence test, Artif. Intell., vol. 174, no. 18, pp. 1508–1539, 2010.
K. Kovacs and A. R. A. Conway, A unified cognitive/differential approach to human intelligence: Implications for IQ testing, J. Appl. Res. Mem. Cogn., vol. 8, no. 3, pp. 255–272, 2019.
A. W. Woolley, C. F. Chabris, A. Pentland, N. Hashmi, and T. W. Malone, Evidence for a collective intelligence factor in the performance of human groups, Science, vol. 330, no. 6004, pp. 686–688, 2010.
A. Bruhin, K. Janizzi, and C. Thöni, Uncovering the heterogeneity behind cross-cultural variation in antisocial punishment, J. Econ. Behav. Organ., vol. 180, pp. 291–308, 2020.
M. S. Granovetter, The strength of weak ties, Am. J. Sociol., vol. 78, no. 6, pp. 1360–1380, 1973.
K. Donahue, O. P. Hauser, M. A. Nowak, and C. Hilbe, Evolving cooperation in multichannel games, Nat. Commun., vol. 11, p. 3885, 2020.
S. Schoenmakers, C. Hilbe, B. Blasius, and A. Traulsen, Sanctions as honest signals–The evolution of pool punishment by public sanctioning institutions, J. Theor. Biol., vol. 356, pp. 36–46, 2014.
E. Fehr and S. Gächter, Cooperation and punishment in public goods experiments, Am. Econ. Rev., vol. 90, no. 4, pp. 980–994, 2000.
A. Li, L. Zhou, Q. Su, S. P. Cornelius, Y. Y. Liu, L. Wang, and S. A. Levin, Evolution of cooperation on temporal networks, Nat. Commun., vol. 11, p. 2259, 2020.
M. A. Nowak, Five rules for the evolution of cooperation, Science, vol. 314, no. 5805, pp. 1560–1563, 2006.
Publication history
Copyright
Acknowledgements
We thank Tongda Zhang for the data support. This work was supported by the National Key Research and Development Program of China (No. 2021YFF0900801).
Rights and permissions
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
FEEDBACK 
TOP