Journal Home > Volume 1 , Issue 1

Social computing is ubiquitous and intensifying in the 21st Century. Originally used to reference computational augmentation of social interaction through collaborative filtering, social media, wikis, and crowdsourcing, here I propose to expand the concept to cover the complete dynamic interface between social interaction and computation, including computationally enhanced sociality and social science, socially enhanced computing and computer science, and their increasingly complex combination for mutual enhancement. This recommends that we reimagine Computational Social Science as Social Computing, not merely using computational tools to make sense of the contemporary explosion of social data, but also recognizing societies as emergent computers of more or less collective intelligence, innovation and flourishing. It further proposes we imagine a socially inspired computer science that takes these insights into account as we build machines not merely to substitute for human cognition, but radically complement it. This leads to a vision of social computing as an extreme form of human computer interaction, whereby machines and persons recursively combine to augment one another in generating collective intelligence, enhanced knowledge, and other social goods unattainable without each other. Using the example of science and technology, I illustrate how progress in each of these areas unleash advances in the others and the beneficial relationship between the technology and science of social computing, which reveals limits of sociality and computation, and stimulates our imagination about how they can reach past those limits together.


menu
Abstract
Full text
Outline
About this article

Social Computing Unhinged

Show Author's information James Evans( )
University of Chicago, Chicago, IL 60637, and Santa Fe Institute, Santa Fe, NM 87501, USA.

Abstract

Social computing is ubiquitous and intensifying in the 21st Century. Originally used to reference computational augmentation of social interaction through collaborative filtering, social media, wikis, and crowdsourcing, here I propose to expand the concept to cover the complete dynamic interface between social interaction and computation, including computationally enhanced sociality and social science, socially enhanced computing and computer science, and their increasingly complex combination for mutual enhancement. This recommends that we reimagine Computational Social Science as Social Computing, not merely using computational tools to make sense of the contemporary explosion of social data, but also recognizing societies as emergent computers of more or less collective intelligence, innovation and flourishing. It further proposes we imagine a socially inspired computer science that takes these insights into account as we build machines not merely to substitute for human cognition, but radically complement it. This leads to a vision of social computing as an extreme form of human computer interaction, whereby machines and persons recursively combine to augment one another in generating collective intelligence, enhanced knowledge, and other social goods unattainable without each other. Using the example of science and technology, I illustrate how progress in each of these areas unleash advances in the others and the beneficial relationship between the technology and science of social computing, which reveals limits of sociality and computation, and stimulates our imagination about how they can reach past those limits together.

Keywords:

social computing, complex systems, computer supported cooperative work, computational social science, artificial intelligence, human computer interaction, human-centered computing
Received: 28 September 2020 Accepted: 08 October 2020 Published: 28 October 2020 Issue date: September 2020
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.
[5]
I. Rogers, The Google pagerank algorithm and how it works, , 2002.
[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.
DOI
[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.
[9]
Y. B. Li and K. D. Joshi, The state of social computing research: A literature review and synthesis using the latent semantic analysis approach, , 2012.
[10]
J. Grudin, Computer-supported cooperative work: History and focus, Computer, vol. 27, no. 5, pp. 19–26, 1994.
[11]
P. H. Carstensen and K. Schmidt, Computer supported cooperative work: New challenges to systems design (1999), , 2020.
[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.
[18]
M. Iansiti and K. R. Lakhani, The truth about blockchain, , 2017.
[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.
[30]
G. K. Zipf, Human behavior and the principle of least effort, , 1950.
[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.
DOI
[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.
DOI
[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.
DOI
[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.
DOI
[85]
D. E. Englebart, Augmenting human intellect, originally published inThe 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.
DOI
[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.
Publication history
Copyright
Rights and permissions

Publication history

Received: 28 September 2020
Accepted: 08 October 2020
Published: 28 October 2020
Issue date: September 2020

Copyright

© The author(s) 2020

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/).

Return