Pub Date : 2023-12-22DOI: 10.26599/IJCS.2023.9100024
Yueting Chai;Jun Qian;Muhammad Younas
Metaverse is a collective term for all economic and social activities in the space where the physical world, digital world, and consciousness world are interactively integrated and mutually empowered. The metaverse is the advanced stage of digital civilization and the future formation of human society. The basis for developing metaverse is general digital technologies such as high-performance network, high-performance storage, high-performance computing, high-performance security, and artificial intelligence. On the basis of the above, the key to developing the metaverse lies in researching core technologies such as digital life technologies, trusted collaborative network technologies, natural interaction technologies, ubiquitous operating system technologies, technologies and methods for computational experiments, and theories and technologies for crowd intelligence science. We should take typical metaverse application scenarios as entry points, such as the key fields of agriculture, industry, service industry, military, social governance, and other economic and social areas, to break through key metaverse technologies and implement pilot demonstration projects of metaverse. Through the demonstration, we can systematically promote the application of metaverse in economy and society from point to line, and continuously iterate and evolve the metaverse technology to advance the metaverse to a higher stage. This paper systematically analyzes the current development status and future directions of metaverse from the concept, key technology, and vision of metaverse, paving the way for the subsequent research of metaverse.
{"title":"Metaverse: Concept, Key Technologies, and Vision","authors":"Yueting Chai;Jun Qian;Muhammad Younas","doi":"10.26599/IJCS.2023.9100024","DOIUrl":"10.26599/IJCS.2023.9100024","url":null,"abstract":"Metaverse is a collective term for all economic and social activities in the space where the physical world, digital world, and consciousness world are interactively integrated and mutually empowered. The metaverse is the advanced stage of digital civilization and the future formation of human society. The basis for developing metaverse is general digital technologies such as high-performance network, high-performance storage, high-performance computing, high-performance security, and artificial intelligence. On the basis of the above, the key to developing the metaverse lies in researching core technologies such as digital life technologies, trusted collaborative network technologies, natural interaction technologies, ubiquitous operating system technologies, technologies and methods for computational experiments, and theories and technologies for crowd intelligence science. We should take typical metaverse application scenarios as entry points, such as the key fields of agriculture, industry, service industry, military, social governance, and other economic and social areas, to break through key metaverse technologies and implement pilot demonstration projects of metaverse. Through the demonstration, we can systematically promote the application of metaverse in economy and society from point to line, and continuously iterate and evolve the metaverse technology to advance the metaverse to a higher stage. This paper systematically analyzes the current development status and future directions of metaverse from the concept, key technology, and vision of metaverse, paving the way for the subsequent research of metaverse.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 4","pages":"149-157"},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10371287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139022169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Research on Intelligence Evaluation Method for Crowd Collaboration System","authors":"Jinwei Miao;Xiao Sun;Jun Qian;Ziyang Wang;Yueting Chai","doi":"10.26599/IJCS.2023.9100008","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100008","url":null,"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.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"120-130"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10269819.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67844787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/IJCS.2023.9100009
Depei Yang;Leiju Qiu
With the rapid economic development, megacities have gathered a large number of population and industries, and a series of “urban diseases” have also emerged. To alleviate these problems, various administrative measures have been taken to control population and optimize industrial distribution. Meanwhile, home-purchase restriction (HPR) has been introduced to control the soaring housing prices. Existing research focuses on the impact of policy on its own market, without paying attention to the linkage between markets and spillover effect. We take Beijing, the capital of China, as an example to study the impact of the HPR on the population distribution and industry development of megacities. By analyzing the industry location quotient and population economy matching degree, we conclude that HPR effectively promotes the efficiency of population and industry dispersal, but increases the mismatching between industry and population. City is a typical intelligent system that gathers various intelligent agents, and the development of its population and industry is the fundamental evolution of the system. This paper explores the role of policies in the evolution of urban intelligent systems, and therefore has important theoretical and practical significance for intelligent systems.
{"title":"Home-Purchase Restriction, Urban Population, and Industry Development: Evidence from Beijing","authors":"Depei Yang;Leiju Qiu","doi":"10.26599/IJCS.2023.9100009","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100009","url":null,"abstract":"With the rapid economic development, megacities have gathered a large number of population and industries, and a series of “urban diseases” have also emerged. To alleviate these problems, various administrative measures have been taken to control population and optimize industrial distribution. Meanwhile, home-purchase restriction (HPR) has been introduced to control the soaring housing prices. Existing research focuses on the impact of policy on its own market, without paying attention to the linkage between markets and spillover effect. We take Beijing, the capital of China, as an example to study the impact of the HPR on the population distribution and industry development of megacities. By analyzing the industry location quotient and population economy matching degree, we conclude that HPR effectively promotes the efficiency of population and industry dispersal, but increases the mismatching between industry and population. City is a typical intelligent system that gathers various intelligent agents, and the development of its population and industry is the fundamental evolution of the system. This paper explores the role of policies in the evolution of urban intelligent systems, and therefore has important theoretical and practical significance for intelligent systems.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"137-147"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10269821.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67844789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/IJCS.2023.9100011
Zhida Shang;Hefeng Meng;Yibowen Zhao;Ronghua Xu;Yonghui Xu;Lizhen Cui
The evaluation and prediction of credit risk have always been a research hotspot to ensure the healthy and orderly development of the credit market. Most researchers use deep learning to predict credit risk. However, when training data are too small, deep learning models often lead to overfitting. Although we have a large amount of available training data, we often cannot ensure that the data are evenly distributed, which is still not conducive to model training. In addition, deep learning is often difficult to explain, and the unexplained model is often difficult to gain the trust of users, thus reducing the usefulness of the model. To solve these problems, we propose an integrated cross-domain credit default prediction network, called Transfer Light Gradient Boosting Machine (TrLightGBM), based on interpretable integration transfer. This network considers the weight of data from different domains in training and implements cross-domain credit default prediction by adjusting the weight. The experiment shows that our method TrLightGBM not only achieves the interpretability of the model to a certain extent but also has good performance.
{"title":"Cross-Domain Credit Default Prediction via Interpretable Ensemble Transfer","authors":"Zhida Shang;Hefeng Meng;Yibowen Zhao;Ronghua Xu;Yonghui Xu;Lizhen Cui","doi":"10.26599/IJCS.2023.9100011","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100011","url":null,"abstract":"The evaluation and prediction of credit risk have always been a research hotspot to ensure the healthy and orderly development of the credit market. Most researchers use deep learning to predict credit risk. However, when training data are too small, deep learning models often lead to overfitting. Although we have a large amount of available training data, we often cannot ensure that the data are evenly distributed, which is still not conducive to model training. In addition, deep learning is often difficult to explain, and the unexplained model is often difficult to gain the trust of users, thus reducing the usefulness of the model. To solve these problems, we propose an integrated cross-domain credit default prediction network, called Transfer Light Gradient Boosting Machine (TrLightGBM), based on interpretable integration transfer. This network considers the weight of data from different domains in training and implements cross-domain credit default prediction by adjusting the weight. The experiment shows that our method TrLightGBM not only achieves the interpretability of the model to a certain extent but also has good performance.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"106-112"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10269817.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67844792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Product Map Analysis from a Crowd of Small- and Medium-Sized E-Commerce Sites: A Bottom-Up Approach","authors":"","doi":"","DOIUrl":"https://doi.org/","url":null,"abstract":"","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10271249.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67844788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/IJCS.2023.9100003
Jia Gu;Youyang Du;Chi Zhang;Yunsen Tang;Huiguo Zhang;Yonghui Xu;Lizhen Cui
Digital music has various characteristics, such as melody, rhythm, timbre, and harmony. According to these characteristics, music can be classified using artificial intelligence (AI). Music can reduce cognitive dissonance and improve memory in humans; however, occasionally, dissonant music can cause negative effects, such as aggravating depression. Therefore, music can be classified using technical methods and used selectively for human mood regulation, sleep improvement, disease relief, and treatment. Herein we present a survey of the fields of music, AI, and health to shed light on the digitization of music. In this survey, we (1) summarize the various characteristic elements of music, such as melody, rhythm, timbre, and harmony; (2) discuss the role of neural networks in classifying music based on these musical characteristics; (3) summarize the positive and negative effects of music with respect to five areas: sleep, memory, attention, mood, and movement; (4) summarize the therapeutic effect of music intervention with respect to various illnesses; and (5) present the future of music therapy as well as provide a few suggestions with respect to music therapy.
{"title":"Music Intervention in Human Life, Work, and Disease: A Survey","authors":"Jia Gu;Youyang Du;Chi Zhang;Yunsen Tang;Huiguo Zhang;Yonghui Xu;Lizhen Cui","doi":"10.26599/IJCS.2023.9100003","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100003","url":null,"abstract":"Digital music has various characteristics, such as melody, rhythm, timbre, and harmony. According to these characteristics, music can be classified using artificial intelligence (AI). Music can reduce cognitive dissonance and improve memory in humans; however, occasionally, dissonant music can cause negative effects, such as aggravating depression. Therefore, music can be classified using technical methods and used selectively for human mood regulation, sleep improvement, disease relief, and treatment. Herein we present a survey of the fields of music, AI, and health to shed light on the digitization of music. In this survey, we (1) summarize the various characteristic elements of music, such as melody, rhythm, timbre, and harmony; (2) discuss the role of neural networks in classifying music based on these musical characteristics; (3) summarize the positive and negative effects of music with respect to five areas: sleep, memory, attention, mood, and movement; (4) summarize the therapeutic effect of music intervention with respect to various illnesses; and (5) present the future of music therapy as well as provide a few suggestions with respect to music therapy.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"97-105"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10269820.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67844791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/IJCS.2023.9100016
{"title":"Erratum to “Individual Behavior Modeling and Transmission Control During Disease Spread: A Review”","authors":"","doi":"10.26599/IJCS.2023.9100016","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100016","url":null,"abstract":"","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"148-148"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10271251.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68006870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/IJCS.2023.9100005
Guoqing Zhang;Hongbo Sun
To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part's surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications.
{"title":"Part Deviation Correction Method Based on Geometric Feature Recognition","authors":"Guoqing Zhang;Hongbo Sun","doi":"10.26599/IJCS.2023.9100005","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100005","url":null,"abstract":"To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part's surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"113-119"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10269818.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68006871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/IJCS.2023.9100006
Xin Li;Tongda Zhang;Xiao Sun;Yongsheng Ma
The study of product maps in e-commerce has garnered significant attention from academics and practitioners, as they provide insights into the relationship between products, such as complementarity and competition. However, existing studies have focused on the perspectives of large manufacturers and retailers, using data from these central sources. This paper adopts a bottom-up approach based on crowd intelligence, with small- and medium-sized e-commerce (SME) sites serving as independent data providers. This approach allows for the decentralized processing of data and enables the aggregation of diverse perspectives and insights from a large number of independent sources. A graph term frequency-inverse document frequency method is proposed, which can measure the similarities of products and build a product map. The method was employed to find a hierarchical community structure using data from over 90 000 products from 52 SME sites. The results showed that products within the same site tend to be distributed across the same community. Our findings can assist e-commerce sites in making informed decisions about pricing and product offerings, leading to more diversified production.
{"title":"Product Map Analysis from a Crowd of Small- and Medium-Sized E-Commerce Sites: A Bottom-Up Approach","authors":"Xin Li;Tongda Zhang;Xiao Sun;Yongsheng Ma","doi":"10.26599/IJCS.2023.9100006","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100006","url":null,"abstract":"The study of product maps in e-commerce has garnered significant attention from academics and practitioners, as they provide insights into the relationship between products, such as complementarity and competition. However, existing studies have focused on the perspectives of large manufacturers and retailers, using data from these central sources. This paper adopts a bottom-up approach based on crowd intelligence, with small- and medium-sized e-commerce (SME) sites serving as independent data providers. This approach allows for the decentralized processing of data and enables the aggregation of diverse perspectives and insights from a large number of independent sources. A graph term frequency-inverse document frequency method is proposed, which can measure the similarities of products and build a product map. The method was employed to find a hierarchical community structure using data from over 90 000 products from 52 SME sites. The results showed that products within the same site tend to be distributed across the same community. Our findings can assist e-commerce sites in making informed decisions about pricing and product offerings, leading to more diversified production.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 3","pages":"131-136"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10269816/10269822.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68006872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-22DOI: 10.26599/IJCS.2022.9100035
Andy E. Williams
Increasing the number, diversity, or uniformity of opinions in a group does not necessarily imply that those opinions will converge into a single more “intelligent” one, if an objective definition of the term intelligent exists as it applies to opinions. However, a recently developed approach called human-centric functional modeling provides what might be the first general model for individual or collective intelligence. In the case of the collective intelligence of groups, this model suggests how a cacophony of incoherent opinions in a large group might be combined into coherent collective reasoning by a hypothetical platform called “general collective intelligence” (GCI). When applied to solving group problems, a GCI might be considered a system that leverages collective reasoning to increase the beneficial insights that might be derived from the information available to any group. This GCI model also suggests how the collective reasoning ability (intelligence) might be exponentially increased compared to the intelligence of any individual in a group, potentially resulting in what is predicted to be a collective superintelligence.
{"title":"Turning the Cacophony of the Internet's Tower of Babel into a Coherent General Collective Intelligence","authors":"Andy E. Williams","doi":"10.26599/IJCS.2022.9100035","DOIUrl":"10.26599/IJCS.2022.9100035","url":null,"abstract":"Increasing the number, diversity, or uniformity of opinions in a group does not necessarily imply that those opinions will converge into a single more “intelligent” one, if an objective definition of the term intelligent exists as it applies to opinions. However, a recently developed approach called human-centric functional modeling provides what might be the first general model for individual or collective intelligence. In the case of the collective intelligence of groups, this model suggests how a cacophony of incoherent opinions in a large group might be combined into coherent collective reasoning by a hypothetical platform called “general collective intelligence” (GCI). When applied to solving group problems, a GCI might be considered a system that leverages collective reasoning to increase the beneficial insights that might be derived from the information available to any group. This GCI model also suggests how the collective reasoning ability (intelligence) might be exponentially increased compared to the intelligence of any individual in a group, potentially resulting in what is predicted to be a collective superintelligence.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"7 2","pages":"55-62"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9736195/10159625/10159626.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46559436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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