References(103)
[1]
D. Schuler, Social computing, Commun. ACM, vol. 37, no. 1, pp. 28–29, 1994.
[2]
B. Dear, The Friendly Orange Glow: The Untold Story of the Rise of Cyberculture. Knopf Doubleday Publishing Group, 2017.
[3]
G. Cormode and B. Krishnamurthy, Key differences between Web 1.0 and Web 2.0, First Monday, vol. 13, no. 6, 2008.
[4]
B. Abrahao, P. Parigi, A. Gupta, and K. S. Cook, Reputation offsets trust judgments based on social biases among Airbnb users, Proc. Natl. Acad. Sci. USA, vol. 114, no. 37, pp. 9848–9853, 2017.
[6]
N. Hemmatazad, Social computing, in Encyclopedia of Information Science and Technology, 3rd ed. M. Khosrow-Pour, ed. Hershey, PA, USA: Information Science Reference, 2015, pp. 6754–6761.
[7]
F. Turner, From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism. Chicago, IL, USA: University of Chicago Press, 2010.
[8]
M. Biazzini and B. Baudry, May the fork be with you: Novel metrics to analyze collaboration on GitHub, in Proceedings of the 5th International Workshop on Emerging Trends in Software Metrics, Hyderabad, India, 2014, pp. 37–43.
[10]
J. Grudin, Computer-supported cooperative work: History and focus, Computer, vol. 27, no. 5, pp. 19–26, 1994.
[12]
R. B. Myerson, Optimal auction design, Mathematics of OR, vol. 6, no. 1, pp. 58–73, 1981.
[13]
I. Roussaki, M. Louta, and L. Pechlivanos, An efficient negotiation model for the next generation electronic marketplace, in Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521), Dubrovnik, Croatia, 2004, pp. 615–618.
[14]
D. Gale and L. S. Shapley, College admissions and the stability of marriage, null, vol. 69, no. 1, pp. 9–15, 1962.
[15]
S. T. Kirsch, Secure, convenient and efficient system and method of performing trans-internet purchase transactions, US Patent 5963915, Oct. 5, 1999.
[16]
S. P. Lalley and E. G. Weyl, Quadratic voting: How mechanism design can radicalize democracy, AEA Papers and Proceedings, vol. 108, pp. 33–37, 2018.
[17]
A. E. Roth and E. Peranson, The redesign of the matching market for American physicians: Some engineering aspects of economic design, Am. Econ. Rev., vol. 89, no. 4, pp. 748–780, 1999.
[19]
A. Shaw, Teaching socially intelligent computing principles in introductory computer science courses, in Proceedings of the 50th Annual Southeast Regional Conference, Tuscaloosa, Alabama, 2012.
[20]
D. Retelny, S. Robaszkiewicz, A. To, W. S. Lasecki, J. Patel, N. Rahmati, T. Doshi, M. A. Valentine, and M. Bernstein, Expert crowdsourcing with flash teams, in Proceedings of the 27th annual ACM symposium on User interface software and technology, Honolulu, HI, USA, 2014, pp. 75–85.
[21]
D. Adjodah, Y. Leng, S. K. Chong, P. Krafft, and A. Pentland, Social Bayesian learning in the wisdom of the crowd, arXiv: 1712.09960v1, 2017.
[22]
K. Patel, Incremental journey for World Wide Web: introduced with Web 1.0 to recent Web 5.0—A survey paper, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 10, 2013.
[23]
W. J. Ashworth, The calculating eye: Baily, Herschel, Babbage and the business of astronomy, Br. J. Hist. Sci., vol. 27, no. 4, pp. 409–441, 1994.
[24]
C. E. Shannon, A symbolic analysis of relay and switching circuits, Univ. Polytehnica Bucharest Sci. Bull. Ser. C: Electr. Eng., vol. 57, no. 12, pp. 713–723, 1938.
[25]
A. M. Turing, Computing machinery and intelligence, Mind, .
[26]
D. Brooks, The Social Animal: The Hidden Sources of Love, Character, and Achievement. Random House Trade Paperbacks, 2012.
[27]
Y. N. Harari, Sapiens: A brief history of humankind. Random House, 2014.
[28]
C. Babbage, On the economy of machinery and manufactures, null, vol. 1, no. 3, pp. 208–213, 1832.
[29]
A. Currin, K. Korovin, M. Ababi, and K. Poper, Computing exponentially faster: Implementing a non-deterministic universal Turing machine using DNA, J. R. Soc. Interface, vol. 14, no. 128, p. 20160990, 2017.
[31]
D. C. North, Institutions and economic growth: An historical introduction, World Dev., vol. 17, no. 9, pp. 1319–1332, 1989.
[32]
T. Hobbes, Leviathan (1651), Glasgow 1974, 1980.
[33]
M. Granovetter, Economic action and social structure: The problem of embeddedness, Am. J. Sociol., vol. 91, no. 3, pp. 481–510, 1985.
[34]
H. Mercier and D. Sperber, The Enigma of Reason. Harvard University Press, 2017.
[35]
J. C. Tang, M. Cebrian, N. A. Giacobe, H.-W. Kim, T. Kim, and D. Wickert, Reflecting on the DARPA Red Balloon Challenge, Commun. ACM, vol. 54, no. 4, pp. 78–85, 2011.
[36]
T. Connolly, Micromotives and macrobehavior, Administrative Science Quarterly, vol. 24, no. 3. pp. 500–504, 1979, .
[37]
J. M. Epstein and R. Axtell, Growing Artificial Societies: Social Science from the Bottom Up. Washington, DC, USA: Brookings Institution Press, 1996.
[38]
J. M. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, NJ, USA: Princeton University Press, 2006.
[39]
D. Lazer, A. Pentland, L. Adamic, S. Aral, A. L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, et al., Life in the network: The coming age of computational social science, Science, vol. 323, no. 5915, pp. 721–723, 2009.
[40]
D. M. J. Lazer, A. Pentland, D. J. Watts, S. Aral, S. Athey, N. Contractor, D. Freelon, S. Gonzalez-Bailon, G. King, H. Margetts, et al., Computational social science: Obstacles and opportunities, Science, vol. 369, no. 6507, pp. 1060–1062, 2020.
[41]
J. Evans and J. G. Foster, Computation and the sociological imagination, Contexts, vol. 18, no. 4, pp. 10–15, 2019.
[42]
J. Inglese and D. S. Auld, High Throughput Screening (HTS) techniques: Applications in chemical biology, in Wiley Encyclopedia of Chemical Biology, T. P. Begley, ed. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007, p. 1.
[43]
W. Wang, D. Rothschild, S. Goel, and A. Gelman, Forecasting elections with non-representative polls, Int. J. Forecast., vol. 31, no. 3, pp. 980–991, 2015.
[44]
N. Sengupta, N. Srebro, and J. Evans, Simple surveys: Response retrieval inspired by recommendation systems, Social Science Computer Review, .
[45]
G. Grossman and D. Baldassarri, The impact of elections on cooperation: Evidence from a lab-in-the-field experiment in Uganda, Am. J. Pol. Sci., vol. 56, no. 4, pp. 964–985, 2012.
[46]
S. Goel, A. Anderson, J. Hofman, and D. J. Watts, The structural virality of online diffusion, Manag. Sci., vol. 62, no. 1, pp. 180–196, 2016.
[47]
P. S. Park, J. E. Blumenstock, and M. W. Macy, The strength of long-range ties in population-scale social networks, Science, vol. 362, no. 6421, pp. 1410–1413, 2018.
[48]
F. Shi and J. Evans, Science and technology advance through surprise, arXiv: 1910.09370, 2019.
[49]
M. J. Salganik, I. Lundberg, A. T. Kindel, C. E. Ahearn, K. Al-Ghoneim, A. Almaatouq, D. M. Altschul, J. E. Brand, N. B. Carnegie, R. J. Compton, et al., Measuring the predictability of life outcomes with a scientific mass collaboration, Proc. Natl. Acad. Sci. USA., vol. 117, no. 15, pp. 8398–8403, 2020.
[50]
P. M. Krafft, M. Macy, and A. Pentland, Bots as virtual confederates: Design and ethics, in Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, Portland, OR, USA, 2017, pp. 183–190.
[51]
J. W. Suchow and T. L. Griffiths, Rethinking experiment design as algorithm design, in Proc. 30th Conf. Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 2016, pp. 1–8.
[52]
J. Kleinberg, J. Ludwig, S. Mullainathan, and Z. Obermeyer, Prediction policy problems, American Economic Review, vol. 105, no. 5, pp. 491–495, 2015.
[53]
S. Athey and G. Imbens, Recursive partitioning for heterogeneous causal effects, Proc. Natl. Acad. Sci. USA, vol. 113, no. 27, pp. 7353–7360, 2016.
[54]
J. Pearl, The seven tools of causal inference, with reflections on machine learning, Commun. ACM, vol. 62, no. 3, pp. 54–60, 2019.
[55]
D. J. Watts, Should social science be more solution-oriented? Nature Human Behaviour, vol. 1, no. 1, p. 0015, 2017.
[56]
Y. Hochberg and Y. Benjamini, More powerful procedures for multiple significance testing, Statistics in Medicine, vol. 9, no. 7. pp. 811–818, 1990.
[57]
A. M. Hein, S. B. Rosenthal, G. I. Hagstrom, A. Berdahl, C. J. Torney, and I. D. Couzin, The evolution of distributed sensing and collective computation in animal populations, Elife, vol. 4, p. e10955, 2015.
[58]
M. L. Jones, How we became instrumentalists (again) data positivism since World War II, Hist. Stud. Nat. Sci., vol. 48, no. 5, pp. 673–684, 2018.
[59]
M. Marvin, The Society of Mind. New York, NY, USA: Simon and Shusier, 1985.
[60]
M. Niepert, M. Ahmed, and K. Kutzkov, Learning convolutional neural networks for graphs, in International Conference on Machine Learning, 2016, pp. 2014–2023.
[61]
F. Papadopoulos, M. Kitsak, M. Á Serrano, M. Boguñá, and D. Krioukov, Popularity versus similarity in growing networks, Nature, vol. 489, no. 7417, pp. 537–540, 2012.
[62]
I. Chami, R. Ying, C. Ré and J. Leskovec, Hyperbolic graph convolutional neural networks, Adv. Neural Inf. Process. Syst., vol. 32, pp. 4869–4880, 2019.
[63]
J. X. You, J. Leskovec, K. M. He, and S. N. Xie, Graph structure of neural networks, arXiv: 2007.06559, 2020.
[64]
S. N. Xie, A. Kirillov, R. Girshick, and K. M. He, Exploring randomly wired neural networks for image recognition, in Proc. 2019 IEEE/CVF Int. Conf. Computer Vision, Seoul, South Korea, 2019, .
[65]
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, .
[66]
J. Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. New York, NY, USA: Doubleday, 2004.
[67]
A. Kittur and R. E. Kraut, Harnessing the wisdom of crowds in wikipedia: Quality through coordination, in Proc. 2008 ACM Conf. Computer Supported Cooperative Work, San Diego, CA, USA, 2008, pp. 37–46.
[68]
J. Lorenz, H. Rauhut, F. Schweitzer, and D. Helbing, How social influence can undermine the wisdom of crowd effect, Proc. Natl. Acad. Sci. USA, vol. 108, no. 22, pp. 9020–9025, 2011.
[69]
H. L. Chen, P. De, Y. Hu, and B. H. Hwang, Wisdom of crowds: The value of stock opinions transmitted through social media, The Review of Financial Studies, vol. 27, no. 5, pp. 1367–1403, 2014.
[70]
F. Shi, M. Teplitskiy, E. Duede, and J. A. Evans, The wisdom of polarized crowds, Nat. Hum. Behav., vol. 3, no. 4, pp. 329–336, 2019.
[71]
A. E. Mannes, R. P. Larrick, and J. B. Soll, The social psychology of the wisdom of crowds, in Frontiers of Social Psychology. Social Judgment and Decision Making, J. I. Krueger, ed. New York, NY, USA: Psychology Press, 2012.
[72]
J. Becker, D. Brackbill, and D. Centola, Network dynamics of social influence in the wisdom of crowds, Proc. Natl. Acad. Sci. USA, vol. 114, no. 26, pp. E5070–E5076, 2017.
[73]
J. Becker, E. Porter, and D. Centola, The wisdom of partisan crowds, Proc. Natl. Acad. Sci. USA, vol. 116, no. 22. pp. 10 717–10 722, 2019.
[74]
B. Golub and M. O. Jackson, Naïve learning in social networks and the wisdom of crowds, American Economic Journal: Microeconomics, vol. 2, no. 1, pp. 112–149, 2010.
[75]
S. E. Page, The Diversity Bonus: How Great Teams Pay Off in the Knowledge Economy. 2nd ed. Princeton, NJ, USA: Princeton University Press, 2019.
[76]
A. Rzhetsky, J. G. Foster, I. T. Foster, and J. A. Evans, Choosing experiments to accelerate collective discovery, Proc. Natl. Acad. Sci. USA, vol. 112, no. 47, pp. 14569–14574, 2015.
[77]
V. Danchev, A. Rzhetsky, and J. A. Evans, Meta-research: centralized scientific communities are less likely to generate replicable results, eLife, vol. 8, pp. e43094, 2019.
[78]
A. L. Samuel, Some studies in machine learning using the game of checkers, IBM J. Res. Dev., vol. 3, no. 3. pp. 210–229, 1959.
[79]
L. Itti and P. Baldi, Bayesian surprise attracts human attention, Vision Res., vol. 49, no. 10, pp. 1295–1306, 2009.
[80]
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proc. 27th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672–2680.
[81]
G. Kasparov, The chess master and the computer, New York Rev. Books, vol. 57, no. 2, pp. 16–19, 2010.
[82]
D. Chan, The AI that has nothing to learn from humans, Atlantic, vol. 20, 2017.
[83]
H. Shirado and N. A. Christakis, Locally noisy autonomous agents improve global human coordination in network experiments, Nature, vol. 545, no. 7654, pp. 370–374, 2017.
[84]
W. R. Ashby, An Introduction to Cybernetics. New York, NY, USA: J. Wiley, 1956.
[85]
D. E. Englebart, Augmenting human intellect, originally published in “The Augmentation Papers”, Bootstrap Institute, pp. 64–90, 1962.
[86]
J. C. R. Licklider, Man-computer symbiosis, IRE Transactions on Human Factors in Electronics, vol. HFE-1, no. 1, pp. 4–11, 1960.
[87]
D. Stark, The Sense of Dissonance: Accounts of Worth in Economic Life. Princeton, NJ, USA: Princeton University Press, 2011.
[88]
R. I. Sutton, Weird Ideas That Work: 11 1/2 Practices for Promoting, Managing, and Sustaining Innovation. New York, NY, USA: Free Press, 2002.
[89]
S. Aral and M. Van Alstyne, The diversity-bandwidth trade-off, Am.J. Sociol., vol. 117, no. 1, pp. 90–171, 2011.
[90]
H. Garfinkel, Studies in Ethnomethodology. Englewood Cliffs, NJ, USA: Prentice-Hall, 1967.
[91]
L. F. Wu, D. S. Wang, and J. A. Evans, Large teams develop and small teams disrupt science and technology, Nature, vol, 566, no. 7744, pp. 378–382, 2019.
[92]
D. R. Swanson, Fish oil, Raynaud’s syndrome, and undiscovered public knowledge, Perspect. Biol. Med., vol. 30, no. 1, pp. 7–18, 1986.
[93]
D. R. Swanson, Medical literature as a potential source of new knowledge, Bull. Med. Libr. Assoc., vol. 78, no. 1, pp. 29–37, 1990.
[94]
D. R. Swanson and N. R. Smalheiser, An interactive system for finding complementary literatures: A stimulus to scientific discovery, Artif. Intell., vol. 91, no. 2, pp. 183–203, 1997.
[95]
M. Weeber, H. Klein, L. T. W. De Jong-Van Den Berg, and R. Vos, Using concepts in literature-based discovery: Simulating Swanson’s Raynaud-fish oil and migraine-magnesium discoveries, J. Am. Soc. Inf. Sci. Technol., vol. 52, no. 7, pp. 548–557, 2001.
[96]
J. Evans and A. Rzhetsky, Machine science, Science, vol. 329, no. 5990, pp. 399–400, 2010.
[97]
R. B. Freeman and W. Huang, Collaboration: Strength in diversity, Nature, vol. 513, no. 7518, p. 305, 2014.
[98]
P. Forman, Weimar culture, causality, and quantum theory, 1918–1927: Adaptation by German physicists and mathematicians to a hostile intellectual environment, Hist. Stud. Phys. Sci., vol. 3, pp. 1–115, 1971.
[99]
V. Tshitoyan, J. Dagdelen, L. Weston, A. Dunn, Z. Q. Rong, O. Kononova, K. A. Persson, G. Ceder, and A. Jain, Unsupervised word embeddings capture latent knowledge from materials science literature, Nature, vol. 571, no. 7763, pp. 95–98, 2019.
[100]
, Life of Theseus. Independently Published, 2019.
[101]
V. Bush, Science the endless frontier: A report to the President by Vannevar Bush, Director of the Office of Scientific Research and Development, United States Government Printing Office, Washington DC, USA, 1945.
[102]
N. Rosenberg and R. Nathan, Exploring the Black Box: Technology, Economics, and History. Cambridge, UK: Cambridge University Press, 1994.
[103]
I. Wills, Edison and science: A curious result, Stud. Hist. Philos. Sci. B Stud. Hist. Philos. Modern Phys., vol. 40, no. 2, pp. 157–166, 2009.