Collective Intelligence: A Taxonomy and Survey

Collective intelligence (CI) refers to the intelligence that emerges at the macro-level of a collection and transcends that of the individuals. CI is a continuously popular research topic that is studied by researchers in different areas, such as sociology, economics, biology, and artificial intelligence. In this survey, we summarize the works of CI in various fields. First, according to the existence of interactions between individuals and the feedback mechanism in the aggregation process, we establish CI taxonomy that includes three paradigms: isolation, collaboration and feedback. We then conduct statistical literature analysis to explain the differences among three paradigms and their development in recent years. Second, we elaborate the types of CI under each paradigm and discuss the generation mechanism or theoretical basis of the different types of CI. Third, we describe certain CI-related applications in 2019, which can be appropriately categorized by our proposed taxonomy. Finally, we summarize the future research directions of CI under each paradigm. We hope that this survey helps researchers understand the current conditions of CI and clears the directions of future research.

[1]  Yu Liu,et al.  A new bio-inspired optimisation algorithm: Bird Swarm Algorithm , 2016, J. Exp. Theor. Artif. Intell..

[2]  Brendan T. O'Connor,et al.  Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.

[3]  Martijn J. van den Assem,et al.  The wisdom of the inner crowd in three large natural experiments. , 2018 .

[4]  Yi Liu,et al.  Literature review on collective intelligence: a crowd science perspective , 2018 .

[5]  Nicholas Epley,et al.  Group discussion improves lie detection , 2015, Proceedings of the National Academy of Sciences.

[6]  Marc Weeber,et al.  Text-based discovery in biomedicine: the architecture of the DAD-system , 2000, AMIA.

[7]  Yaser Jararweh,et al.  Data and Service Management in Densely Crowded Environments: Challenges, Opportunities, and Recent Developments , 2019, IEEE Communications Magazine.

[8]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[9]  D. Helbing,et al.  How social influence can undermine the wisdom of crowd effect , 2011, Proceedings of the National Academy of Sciences.

[10]  Richard James,et al.  Swarm intelligence in humans: diversity can trump ability , 2011, Animal Behaviour.

[11]  Reid Hastie,et al.  The robust beauty of majority rules in group decisions. , 2005, Psychological review.

[12]  D. Swanson.  Migraine and Magnesium: Eleven Neglected Connections , 2015, Perspectives in biology and medicine.

[13]  Panagiotis G. Ipeirotis,et al.  Get another label? improving data quality and data mining using multiple, noisy labelers , 2008, KDD.

[14]  Amal El Fallah Seghrouchni,et al.  Smart Mobility Using Multi-Agent System , 2019, ANT/EDI40.

[15]  Stefan M. Herzog,et al.  The Wisdom of Many in One Mind , 2009, Psychological science.

[16]  Anita Williams Woolley,et al.  Do you see what I see? The effect of members cognitive styles on team processes and errors in task execution , 2013 .

[17]  Neil R. Smalheiser.  The Arrowsmith Project: 2005 Status Report , 2005, Discovery Science.

[18]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[19]  D. Meyer,et al.  Supporting Online Material Materials and Methods Som Text Figs. S1 to S6 References Evidence for a Collective Intelligence Factor in the Performance of Human Groups , 2022 .

[20]  Eshel Ben-Jacob,et al.  Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions , 2011, PLoS Comput. Biol..

[21]  J. Stock,et al.  Combination forecasts of output growth in a seven-country data set , 2004 .

[22]  Marc Weeber,et al.  Drug Discovery as an Example of Literature-Based Discovery , 2007, Computational Discovery of Scientific Knowledge.

[23]  Damon Centola,et al.  Network dynamics of social influence in the wisdom of crowds , 2017, Proceedings of the National Academy of Sciences.

[24]  Juho Salminen,et al.  Collective Intelligence in Humans: A Literature Review , 2012, ArXiv.

[25]  Nanning Zheng,et al.  Hybrid-augmented intelligence: collaboration and cognition , 2017, Frontiers of Information Technology & Electronic Engineering.

[26]  Eva Chen,et al.  Identifying Expertise to Extract the Wisdom of Crowds , 2015, Manag. Sci..

[27]  Louis B. Rosenberg.  Human swarming, a real-time method for parallel distributed intelligence , 2015, 2015 Swarm/Human Blended Intelligence Workshop (SHBI).

[28]  Stefan M. Herzog,et al.  Boosting medical diagnostics by pooling independent judgments , 2016, Proceedings of the National Academy of Sciences.

[29]  H. T. Mouftah,et al.  Empowering Reinforcement Learning on Big Sensed Data for Intrusion Detection , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[30]  D. Raban,et al.  Collective Problem-Solving: The Role of Self-Efficacy, Skill, and Prior Knowledge , 2015 .

[31]  Em?ke-Ágnes Horvát,et al.  Harnessing Collective Intelligence in P2P Lending , 2019, WebSci.

[32]  D. Swanson.  Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[33]  D. Berend,et al.  When is Condorcet's Jury Theorem valid? , 1998 .

[34]  Stephen K. McNees.  The uses and abuses of ‘consensus’ forecasts , 1992 .

[35]  Dirk Helbing,et al.  Optimal incentives for collective intelligence , 2016, Proceedings of the National Academy of Sciences.

[36]  Steven L. Goldman.  Reinventing Discovery: The New Era of Networked Science , 2014 .

[37]  Gaganmeet Kaur Awal,et al.  Leveraging collective intelligence for behavioral prediction in signed social networks through evolutionary approach , 2017, Information Systems Frontiers.

[38]  H. P. Sims,et al.  Top management team diversity, group process, and strategic consensus , 1999 .

[39]  Alexander G. Schwing,et al.  Diverse Generation for Multi-Agent Sports Games , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Joyce A. Mitchell,et al.  Using literature-based discovery to identify disease candidate genes , 2005, Int. J. Medical Informatics.

[41]  Furu Wei,et al.  Context preserving dynamic word cloud visualization , 2010, 2010 IEEE Pacific Visualization Symposium (PacificVis).

[42]  Aniket Kittur,et al.  The polymath project: lessons from a successful online collaboration in mathematics , 2011, CHI.

[43]  Carol Friedman,et al.  Exploiting Semantic Relations for Literature-Based Discovery , 2006, AMIA.

[44]  Claudio J. Tessone,et al.  Effects of Social Influence on the Wisdom of Crowds , 2012, ArXiv.

[45]  Tian Tian,et al.  Max-Margin Majority Voting for Learning from Crowds , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  Jeffrey T. Polzer,et al.  Capitalizing on Diversity: Interpersonal Congruence in Small Work Groups , 2001 .

[47]  François Bry,et al.  Human computation , 2018, it Inf. Technol..

[48]  Deepak Ganesan,et al.  mCrowd: a platform for mobile crowdsourcing , 2009, SenSys '09.

[49]  T. Gowers,et al.  Massively collaborative mathematics , 2009, Nature.

[50]  Jesse Shore,et al.  How intermittent breaks in interaction improve collective intelligence , 2018, Proceedings of the National Academy of Sciences.

[51]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[52]  H. Pashler,et al.  Measuring the Crowd Within , 2008, Psychological science.

[53]  Thar Baker,et al.  Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets , 2019, Journal of Grid Computing.

[54]  Jonathan Grudin,et al.  Computer-supported cooperative work: history and focus , 1994, Computer.

[55]  Stefan Krause,et al.  Swarm intelligence in animals and humans. , 2010, Trends in ecology & evolution.

[56]  Wanda Pratt,et al.  H.3.3 Information Search and Retrieval , 2022 .

[57]  C. E. Agnew,et al.  Bayesian consensus forecasts of macroeconomic variables , 1985 .

[58]  Thomas Ertl,et al.  Word Cloud Explorer: Text Analytics Based on Word Clouds , 2014, 2014 47th Hawaii International Conference on System Sciences.

[59]  Didier Sornette,et al.  Utility Rate Equations of Group Population Dynamics in Biological and Social Systems , 2012, PloS one.

[60]  Neil R. Smalheiser,et al.  Artificial Intelligence An interactive system for finding complementary literatures : a stimulus to scientific discovery , 1995 .

[61]  Pietro Michelucci,et al.  The power of crowds , 2016, Science.

[62]  F. Galton.  Vox Populi , 1907, Nature.

[63]  George Sugihara,et al.  Detecting Causality in Complex Ecosystems , 2012, Science.

[64]  Chun Liu,et al.  Social Influence Bias : A Randomized Experiment , 2014 .

[65]  Athanasios V. Vasilakos,et al.  Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks , 2015, IEEE Transactions on Computers.

[66]  Yong Tao.  Swarm Intelligence in Humans: A Perspective of Emergent Evolution , 2017 .

[67]  Foutse Khomh,et al.  Swarm Debugging: the Collective Intelligence on Interactive Debugging , 2019, J. Syst. Softw..

[68]  Gerry Stahl,et al.  A view of Computer-Supported Collaborative Learning research today , 2011, 2011 International Conference on Collaboration Technologies and Systems (CTS).

[69]  Victor Zarnowitz.  The Accuracy of Individual and Group Forecasts from Business Outlook Surveys , 1984 .

[70]  J. Baron,et al.  The Power of Social In fl uence on Estimation Accuracy , 2014 .

[71]  Mohamed Elhoseny,et al.  An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications , 2018, Journal of Ambient Intelligence and Humanized Computing.

[72]  Lu Hong,et al.  Groups of diverse problem solvers can outperform groups of high-ability problem solvers. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[73]  A. Timmermann,et al.  Combining expert forecasts: Can anything beat the simple average? , 2013 .

[74]  Burak Kantarci,et al.  On the Feasibility of Deep Learning in Sensor Network Intrusion Detection , 2019, IEEE Networking Letters.

[75]  Robert L. Winkler,et al.  Simple robust averages of forecasts: Some empirical results , 2008 .

[76]  Koo-Rack Park,et al.  Pest diagnosis system based on deep learning using collective intelligence , 2019, International Journal of Electrical Engineering & Education.