Advertisement

Human Factors in the Age of Algorithms. Understanding the Human-in-the-loop Using Agent-Based Modeling

  • André Calero Valdez
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10914)

Abstract

The complex interaction of humans with digitized technology has far reaching consequences, many of which are still completely opaque in the present. Technology like social networks, artificial intelligence and automation impacts life at work, at home, and in the political sphere. When work is supported by decision support systems and self-optimization, human interaction with such systems is reduced to key decision making aspects using increasingly complex interfaces. Both, algorithms and human operators become linchpins in the opaque workings of the complex socio-technical system. Similarly, when looking at human communication flows in social media, algorithms in the background control the flow of information using recommender systems. The users react to this filtered flow of information, starting a feedback-loop between users and algorithm—the filter bubble. Both scenarios share a common feature: complex human-algorithm interaction. Both scenarios lack a deep understanding of how this interaction must be properly designed. We propose the use of agent-based modeling to address the human-in-the-loop as a part of the complex socio-technical system by comparing several methods of modeling and investigating their applicability.

Keywords

Agent-based modeling Social simulation Cognitive simulation Opinion forming Industrie 4.0 Internet of things Internet of production 

Notes

Acknowledgments

This work was funded in part by the State of North Rhine-Westphalia under the grant number 005-1709-0006, project “Digitale Mündigkeit” and project-number 1706dgn017. The authors also thank the German Research Council DFG for the friendly support of the research in the excellence cluster “Integrative Production Technology in High Wage Countries”.

References

  1. 1.
    Schaller, R.R.: Moore’s law: past, present and future. IEEE Spectr. 34(6), 52–59 (1997)CrossRefGoogle Scholar
  2. 2.
    Alagöz, F., Valdez, A.C., Wilkowska, W., Ziefle, M., Dorner, S., Holzinger, A.: From cloud computing to mobile internet, from user focus to culture and hedonism: the crucible of mobile health care and wellness applications. In: 2010 5th International Conference on Pervasive Computing and Applications (ICPCA), pp. 38–45. IEEE (2010)Google Scholar
  3. 3.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  4. 4.
    Kraemer, F., Van Overveld, K., Peterson, M.: Is there an ethics of algorithms? Ethics Inf. Technol. 13(3), 251–260 (2011)CrossRefGoogle Scholar
  5. 5.
    Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform. 3(2), 119–131 (2016)CrossRefGoogle Scholar
  6. 6.
    Kamishima, T., Akaho, S., Sakuma, J.: Fairness-aware learning through regularization approach. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 643–650. IEEE (2011)Google Scholar
  7. 7.
    Waldrop, M.M., Gleick, J.: Complexity: The Emerging Science at the Edge of Order and Chaos. Viking, Info London (1992)CrossRefGoogle Scholar
  8. 8.
    Byrne, D.S.: Complexity Theory and the Social Sciences: An Introduction. Psychology Press (1998)Google Scholar
  9. 9.
    Dorigo, M., Birattari, M.: Ant colony optimization. In: Encyclopedia of Machine Learning, pp. 36–39. Springer, New York (2011)Google Scholar
  10. 10.
    Bertrand, M., Mullainathan, S., Shafir, E.: Behavioral economics and marketing in aid of decision making among the poor. J. Public Policy Market. 25(1), 8–23 (2006)CrossRefGoogle Scholar
  11. 11.
    Hasselblatt, B., Katok, A.: A First Course in Dynamics: With a Panorama of Recent Developments. Cambridge University Press, Cambridge (2003)Google Scholar
  12. 12.
    Lorenz, E.: Predictability: does the flap of a butterfly’s wing in Brazil set off a tornado in Texas? (1972)Google Scholar
  13. 13.
    Rickles, D., Hawe, P., Shiell, A.: A simple guide to chaos and complexity. J. Epidemiol. Commun. Health 61(11), 933–937 (2007)CrossRefGoogle Scholar
  14. 14.
    Brown, J.H., Gupta, V.K., Li, B.L., Milne, B.T., Restrepo, C., West, G.B.: The fractal nature of nature: power laws, ecological complexity and biodiversity. Philos. Trans. Roy. Soc. B: Biol. Sci. 357(1421), 619–626 (2002)CrossRefGoogle Scholar
  15. 15.
    Harte, J., Kinzig, A., Green, J.: Self-similarity in the distribution and abundance of species. Science 284(5412), 334–336 (1999)CrossRefGoogle Scholar
  16. 16.
    Song, C., Havlin, S., Makse, H.A.: Self-similarity of complex networks. Nature 433(7024), 392 (2005)CrossRefGoogle Scholar
  17. 17.
    Li, W.: Random texts exhibit zipf’s-law-like word frequency distribution. IEEE Trans. Inf. Theory 38(6), 1842–1845 (1992)CrossRefGoogle Scholar
  18. 18.
    Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. 99(suppl 3), 7280–7287 (2002)CrossRefGoogle Scholar
  19. 19.
    Sargent, R.G.: Verification, validation, and accreditation: verification, validation, and accreditation of simulation models. In: Proceedings of the 32nd Conference on Winter Simulation, Society for Computer Simulation International, pp. 50–59 (2000)Google Scholar
  20. 20.
    Wilensky, U.: Netlogo. Center for Connected Learning and Computer Based Modeling. Northwestern University (1999). http://ccl.northwestern.edu/netlogo
  21. 21.
    Rogers, E.M., Cartano, D.G.: Public Opin. Q, pp. 435–441. Methods of measuring opinion leadership, D.G. (1962)Google Scholar
  22. 22.
    Myers, J.H., Robertson, T.S.: Dimensions of opinion leadership. J. Market. Res., 41–46 (1972)CrossRefGoogle Scholar
  23. 23.
    Childers, T.L.: Assessment of the psychometric properties of an opinion leadership scale. J. Market. Res., 184–188 (1986)CrossRefGoogle Scholar
  24. 24.
    Suiter, J., Farrell, D.M., O’Malley, E.: When do deliberative citizens change their opinions? evidence from the irish citizens’ assembly. Int. Polit. Sci. Rev. 37(2), 198–212 (2016)CrossRefGoogle Scholar
  25. 25.
    Zaller, J.: Political awareness, elite opinion leadership, and the mass survey response. Soc. Cogn. 8(1), 125–153 (1990)CrossRefGoogle Scholar
  26. 26.
    Dimitrova, D.V., Shehata, A., Strömbäck, J., Nord, L.W.: The effects of digital media on political knowledge and participation in election campaigns: evidence from panel data. Commun. Res. 41(1), 95–118 (2014)CrossRefGoogle Scholar
  27. 27.
    Noelle-Neumann, E.: Die Schweigespirale. Riper [ie Piper] (1980)Google Scholar
  28. 28.
    Gearhart, S., Zhang, W.: ‘Was it something i said?’ no, it was something you posted!’ a study of the spiral of silence theory in social media contexts. Cyberpsychol. Behav. Soc. Network. 18(4), 208–213 (2015)CrossRefGoogle Scholar
  29. 29.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  30. 30.
    Pariser, E.: The Filter Bubble: What the Internet is Hiding From You. Penguin, UK (2011)Google Scholar
  31. 31.
    Dylko, I., Dolgov, I., Hoffman, W., Eckhart, N., Molina, M., Aaziz, O.: The dark side of technology: an experimental investigation of the influence of customizability technology on online political selective exposure. Comput. Hum. Behav. 73, 181–190 (2017)CrossRefGoogle Scholar
  32. 32.
    Nguyen, T., Hui, P.M., Harper, F., Terveen, L., Konstan, J.: Exploring the filter bubble: the effect of using recommender systems on content diversity, pp. 677–686 (2014)Google Scholar
  33. 33.
    Calero Valdez, A., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 123–126. ACM (2016)Google Scholar
  34. 34.
    Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)CrossRefGoogle Scholar
  35. 35.
    Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Technical report, National Bureau of Economic Research (2017)Google Scholar
  36. 36.
    Woolley, S.: Automating power: Social bot interference in global politics. First Monday 21(4) (2016)Google Scholar
  37. 37.
    Clemm von Hohenberg, B., Maes, M., Pradelski, B.S.: Micro influence and macro dynamics of opinions. SSRN (2017)Google Scholar
  38. 38.
    Macy, M.W., Kitts, J.A., Flache, A., Benard, S.: Polarization in dynamic networks: a hopfield model of emergent structure. In: Dynamic Social Network Modeling and Analysis, pp. 162–173 (2003)Google Scholar
  39. 39.
    Mark, N.: Beyond individual differences: social differentiation from first principles. Am. Soc. Rev., 309–330 (1998)CrossRefGoogle Scholar
  40. 40.
    Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks, pp. 462–470 (2008). cited By 331Google Scholar
  41. 41.
    Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. PNAS 110(15), 5802–5805 (2013)CrossRefGoogle Scholar
  42. 42.
    Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., Stillwell, D.: Personality and patterns of facebook usage. In: Proceedings of the ACM Web Science Conference, pp. 36–44. ACM New York (2012)Google Scholar
  43. 43.
    Valdez, A.C., Brauner, P., Ziefle, M.: Preparing production systems for the internet of things the potential of socio-technical approaches in dealing with complexity (2016)Google Scholar
  44. 44.
    Brauner, P., Calero Valdez, A., Philipsen, R., Ziefle, M.: Defective still deflective – how correctness of decision support systems influences user’s performance in production environments. In: Nah, F.F.-H.F.-H., Tan, C.-H. (eds.) HCIBGO 2016. LNCS, vol. 9752, pp. 16–27. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39399-5_2CrossRefGoogle Scholar
  45. 45.
    Arnold, V., Sutton, S.: The theory of technology dominance: Understanding the impact of intelligent decision aids on decision maker’s judgments. Adv. Account. Behav. Res. 1(3), 175–194 (1998)Google Scholar
  46. 46.
    Brauner, P., Calero Valdez, A., Philipsen, R., Ziefle, M.: How correct and defect decision support systems influence trust, compliance, and performance in supply chain and quality management. In: Nah, F.F.-H., Tan, C.-H. (eds.) HCIBGO 2017. LNCS, vol. 10294, pp. 333–348. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58484-3_26CrossRefGoogle Scholar
  47. 47.
    Brauner, P., Valdez, A.C., Philipsen, R., Ziefle, M.: On studying human factors in complex cyber-physical systems. In: Mensch und Computer 2016-Workshopband (2016)Google Scholar
  48. 48.
    Sun, R.: Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, Cambridge (2006)Google Scholar
  49. 49.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111(4), 1036 (2004)CrossRefGoogle Scholar
  50. 50.
    ACT-R Research Group: ACT-R Publications. Carnegie Melon University (2018). http://act-r.psy.cmu.edu/publication/
  51. 51.
    SOAR Research Group: Soar Website. University of Michigan (2018). https://soar.eecs.umich.edu/
  52. 52.
    Laird, J.E.: It knows what you’re going to do: adding anticipation to a quakebot. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 385–392. ACM (2001)Google Scholar
  53. 53.
    Sun, R.: Duality of the Mind: A Bottom Up Approach to Cognition. Lawrence, Mahew (2002)Google Scholar
  54. 54.
    Sun, R., Merrill, E., Peterson, T.: From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognit. Sci. 25(2), 203–244 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany

Personalised recommendations