Advertisement

The Artificial Facilitator: Guiding Participants in Developing Causal Maps Using Voice-Activated Technologies

  • Thrishma Reddy
  • Philippe J. GiabbanelliEmail author
  • Vijay K. Mago
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11580)

Abstract

Complex problems often require coordinated actions from stakeholders. Agreeing on a course of action can be challenging as stakeholders have different views or ‘mental models’ of how a problem is shaped by many interacting causes. Participatory modeling allows to externalize mental models in forms such as causal maps. Participants can be guided by a trained facilitator (with limitations of costs and availability) or use a free software (with limited guidance). Neither solution easily copes with large causal maps, for instance by preventing redundant concepts. In this paper, we leveraged voice-activated virtual assistants to create causal models at any time, without costs, and by avoiding redundant concepts. Our three case studies demonstrated that our artificial facilitator could create causal maps similar to previous studies. However, it is limited by current technologies to identify concepts when the user speaks (i.e. entities), and its design had to follow pre-specified rules in the absence of sufficient data to generate rules by discriminative machine-learned methods.

Keywords

Amazon Alexa Causal maps Mental models Participatory modeling Virtual assistant 

Notes

Acknowledgments

The authors are indebted to Mitacs Canada for providing the financial support which allowed TR to perform this research at Furman University, while mentored by PJG (local advisor) and VKM (home advisor). Publication costs are supported by an NSERC Discovery Grant for VKM.

Contributions

TR implemented the software and produced the videos demonstrating its functioning. PJG wrote the manuscript and designed the workflow of the virtual facilitator. TR was advised by PJG and VKM, who jointly initiated the study. All authors read and approved of this manuscript.

References

  1. 1.
    Heitman, K.: Reductionism at the dawn of population health. In: El-Sayed, A.M., Galea, S. (eds.) Systems Science and Population Health, chap. 2, pp. 9–24. Oxford University Press (2017)Google Scholar
  2. 2.
    Fink, D.S., Keyes, K.M.: Wrong answers: when simple interpretations create complex problems. In: El-Sayed, A.M., Galea, S. (eds.) Systems Science and Population Health, chap. 3, pp. 25–36. Oxford University Press (2017)Google Scholar
  3. 3.
    Giabbanelli, P.J.: Analyzing the complexity of behavioural factors influencing weight in adults. In: Giabbanelli, P.J., Mago, V.K., Papageorgiou, E.I. (eds.) Advanced Data Analytics in Health. SIST, vol. 93, pp. 163–181. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-77911-9_10CrossRefGoogle Scholar
  4. 4.
    Giabbanelli, P.J., Baniukiewicz, M.: Navigating complex systems for policymaking using simple software tools. In: Giabbanelli, P.J., Mago, V.K., Papageorgiou, E.I. (eds.) Advanced Data Analytics in Health. SIST, vol. 93, pp. 21–40. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-77911-9_2CrossRefGoogle Scholar
  5. 5.
    Riley, B., et al.: Systems thinking and dissemination and implementation research. In: Brownson, R.C., Colditz, G.A., Proctor, E.K. (eds.) Dissemination and Implementation Research in Health: Translating Science to Practice, chap. 9, p. 143. Oxford University Press (2017)Google Scholar
  6. 6.
    Verigin, T., Giabbanelli, P.J., Davidsen, P.I.: Supporting a systems approach to healthy weight interventions in British Columbia by modeling weight and well-being. In: Proceedings of the 49th Annual Simulation Symposium, Society for Computer Simulation International, p. 9 (2016)Google Scholar
  7. 7.
    Dubé, L., et al.: From policy coherence to 21st century convergence: a whole-of-society paradigm of human and economic development. Ann. N. Y. Acad. Sci. 1331(1), 201–215 (2014)CrossRefGoogle Scholar
  8. 8.
    Lavin, E.A., et al.: Should we simulate mental models to assess whether they agree? In: Proceedings of the Annual Simulation Symposium, Society for Computer Simulation International, p. 6 (2018)Google Scholar
  9. 9.
    Gupta, V.K., Giabbanelli, P.J., Tawfik, A.A.: An online environment to compare students’ and expert solutions to ill-structured problems. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2018. LNCS, vol. 10925, pp. 286–307. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91152-6_23CrossRefGoogle Scholar
  10. 10.
    Giabbanelli, P.J., Tawfik, A.A.: Overcoming the PBL assessment challenge: design and development of the incremental thesaurus for assessing causal maps (ITACM). Technol. Knowl. Learn. 24, 161–168 (2019)CrossRefGoogle Scholar
  11. 11.
    Voinov, A., et al.: Tools and methods in participatory modeling: selecting the right tool for the job. Environ. Model. Softw. 109, 232–255 (2018)CrossRefGoogle Scholar
  12. 12.
    Giabbanelli, P.J., Crutzen, R.: Using agent-based models to develop public policy about food behaviours: future directions and recommendations. Comput. Math. Methods Med. 2017 (2017). https://www.hindawi.com/journals/cmmm/2017/5742629/abs/CrossRefGoogle Scholar
  13. 13.
    Maglio, P.P., Mabry, P.L.: Agent-based models and systems science approaches to public health. Am. J. Prev. Med. 40(3), 392–394 (2011)CrossRefGoogle Scholar
  14. 14.
    Giabbanelli, P.J., Jackson, P.J.: Using visual analytics to support the integration of expert knowledge in the design of medical models and simulations. Procedia Comput. Sci. 51, 755–764 (2015)CrossRefGoogle Scholar
  15. 15.
    Xue, H., et al.: Applications of systems modelling in obesity research. Obes. Rev. 19(9), 1293–1308 (2018)CrossRefGoogle Scholar
  16. 16.
    de Pinho, H.: Generation of systems maps: mapping complex systems of population health. In: El-Sayed, A.M., Galea, S. (eds.) Systems Science and Population Health, chap. 6, pp. 61–76. Oxford University Press (2017)Google Scholar
  17. 17.
    Giabbanelli, P.J., Crutzen, R.: Creating groups with similar expected behavioural response in randomized controlled trials: a fuzzy cognitive map approach. BMC Med. Res. Methodol. 14(1), 130 (2014)CrossRefGoogle Scholar
  18. 18.
    Lich, K.H., et al.: Extending systems thinking in planning and evaluation using group concept mapping and system dynamics to tackle complex problems. Eval. Program Plan. 60, 254–264 (2017)CrossRefGoogle Scholar
  19. 19.
    Frerichs, L., et al.: Mind maps and network analysis to evaluate conceptualization of complex issues: a case example evaluating systems science workshops for childhood obesity prevention. Eval. Program Plan. 68, 135–147 (2018)CrossRefGoogle Scholar
  20. 20.
    Gray, S., et al.: The structure and function of angler mental models about fish population ecology: the influence of specialization and target species. J. Outdoor Recreat. Tour. 12, 1–13 (2015)CrossRefGoogle Scholar
  21. 21.
    Befus, D.R., et al.: A qualitative, systems thinking approach to study self-management in women with migraine. Nurs. Res. 67(5), 395–403 (2018)Google Scholar
  22. 22.
    Yourkavitch, J., et al.: Interactions among poverty, gender, and health systems affect womens participation in services to prevent HIV transmission from mother to child: a causal loop analysis. PloS One 13(5), e0197239 (2018)CrossRefGoogle Scholar
  23. 23.
    Giabbanelli, P.J., Torsney-Weir, T., Mago, V.K.: A fuzzy cognitive map of the psychosocial determinants of obesity. Appl. Soft Comput. 12(12), 3711–3724 (2012)CrossRefGoogle Scholar
  24. 24.
    Zhang, S., de la Haye, K., Ji, M., An, R.: Applications of social network analysis to obesity: a systematic review. Obes. Rev. 19(7), 976–988 (2018)CrossRefGoogle Scholar
  25. 25.
    Finegood, D.T., Merth, T.D., Rutter, H.: Implications of the foresight obesity system map for solutions to childhood obesity. Obesity 18(S1), S13–S16 (2010)CrossRefGoogle Scholar
  26. 26.
    Jebb, S., Kopelman, P., Butland, B.: Executive summary: foresight tackling obesities: future choices project. Obesity Reviews 8, vi–ix (2007)CrossRefGoogle Scholar
  27. 27.
    Drasic, L., Giabbanelli, P.J.: Exploring the interactions between physical well-being, and obesity. Can. J. Diabetes 39, S12–S13 (2015)CrossRefGoogle Scholar
  28. 28.
    Allender, S., et al.: A community based systems diagram of obesity causes. PLoS One 10(7), e0129683 (2015)CrossRefGoogle Scholar
  29. 29.
    McGlashan, J., et al.: Comparing complex perspectives on obesity drivers: action-driven communities and evidence-oriented experts. Obes. Sci. Pract. 4, 575–581 (2018)CrossRefGoogle Scholar
  30. 30.
    McGlashan, J., et al.: Quantifying a systems map: network analysis of a childhood obesity causal loop diagram. PloS One 11(10), e0165459 (2016)CrossRefGoogle Scholar
  31. 31.
    Knapp, E.A., et al.: A network approach to understanding obesogenic environments for children in pennsylvania. Connections (02261766) 38(1) (2018).  https://doi.org/10.21307/connections-2018-001
  32. 32.
    Owen, B., et al.: Understanding a successful obesity prevention initiative in children under 5 from a systems perspective. PloS One 13(3), e0195141 (2018)CrossRefGoogle Scholar
  33. 33.
    Giabbanelli, P., et al.: developing technology to support policymakers in taking a systems science approach to obesity and well-being. Obes. Rev. 17, 194–195 (2016)Google Scholar
  34. 34.
    Rwashana, A.S., et al.: Advancing the application of systems thinking in health: understanding the dynamics of neonatal mortality in uganda. Health Res. Policy Syst. 12(1), 36 (2014)CrossRefGoogle Scholar
  35. 35.
    Giabbanelli, P.J., Tawfik, A.A., Gupta, V.K.: Learning analytics to support teachers’ assessment of problem solving: a novel application for machine learning and graph algorithms. In: Ifenthaler, D., Mah, D.-K., Yau, J.Y.-K. (eds.) Utilizing Learning Analytics to Support Study Success, pp. 175–199. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-64792-0_11CrossRefGoogle Scholar
  36. 36.
    Singh, M., et al.: Building a cardiovascular disease predictive model using structural equation model & fuzzy cognitive map. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1377–1382. IEEE (2016)Google Scholar
  37. 37.
    Pillutla, V.S., Giabbanelli, P.J.: Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps. Appl. Soft Comput. 76, 459–472 (2019)CrossRefGoogle Scholar
  38. 38.
    Pratt, S.F., et al.: Detecting unfolding crises with visual analytics and conceptual maps emerging phenomena and big data. In: 2013 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 200–205. IEEE (2013)Google Scholar
  39. 39.
    Ozawa, S., Paina, L., Qiu, M.: Exploring pathways for building trust in vaccination and strengthening health system resilience. BMC Health Serv. Res. 16(7), 639 (2016)CrossRefGoogle Scholar
  40. 40.
    Lukoianova, T., Rubin, V.L.: Veracity roadmap: is big data objective, truthful and credible? Adv. Classif. Res. Online 24, 4 (2014)CrossRefGoogle Scholar
  41. 41.
    Jetter, A.J.: Fuzzy cognitive maps for engineering and technology management: what works in practice? In: PICMET (2006)Google Scholar
  42. 42.
    Jordan, R., et al.: Twelve questions for the participatory modeling community. Earth’s Future 6(8), 1046–1057 (2018)CrossRefGoogle Scholar
  43. 43.
    Giabbanelli, P.J., Adams, J., Pillutla, V.S.: Feasibility and framing of interventions based on public support: leveraging text analytics for policymakers. In: Meiselwitz, G. (ed.) SCSM 2016. LNCS, vol. 9742, pp. 188–200. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39910-2_18CrossRefGoogle Scholar
  44. 44.
    Binder, J.R., Desai, R.H.: The neurobiology of semantic memory. Trends Cogn. Sci. 15(11), 527–536 (2011)CrossRefGoogle Scholar
  45. 45.
    Lewis, P.: Rich picture building in the soft systems methodology. Eur. J. Inf. Syst. 1(5), 351–360 (1992)CrossRefGoogle Scholar
  46. 46.
    Sommerfeld, E., Sobik, F.: Operations on cognitive structures - their modeling on the basis of graph theory. In: Albert, D. (ed.) Knowledge Structures, pp. 151–196. Springer, Heidelberg (1994).  https://doi.org/10.1007/978-3-642-52064-8_5CrossRefGoogle Scholar
  47. 47.
    Johnson-Laird, P.N.: Mental models and human reasoning. Proc. Natl. Acad. Sci. 107(43), 18243–18250 (2010)CrossRefGoogle Scholar
  48. 48.
    Mago, V.K., et al.: Analyzing the impact of social factors on homelessness: a fuzzy cognitive map approach. BMC Med. Inform. Decis. Mak. 13(1), 94 (2013)CrossRefGoogle Scholar
  49. 49.
    Mago, V.K., et al.: Fuzzy cognitive maps and cellular automata: an evolutionary approach for social systems modelling. Appl. Soft Comput. 12(12), 3771–3784 (2012)CrossRefGoogle Scholar
  50. 50.
    Axelrod, R.: Decision for neoimperialism: the deliberations of the British Eastern Committee in 1918. In: The Cognitive Maps of Political Elites, Structure of Decisions, chap. 4, pp. 77–95 (1976)Google Scholar
  51. 51.
    Axelrod, R.: Results. In: The Cognitive Maps of Political Elites, Structure of Decisions, chap. 9, pp. 221–250 (1976)Google Scholar
  52. 52.
    Ross, S.: Complexity and the presidency. In: The Cognitive Maps of Political Elites, Structure of Decisions, chap. 5, pp. 96–112 (1976)Google Scholar
  53. 53.
    Williams, J., Raux, A., Henderson, M.: The dialog state tracking challenge series: a review. Dialogue Discourse 7(3), 4–33 (2016)Google Scholar
  54. 54.
    Laranjo, L., et al.: Conversational agents in healthcare: a systematic review. J. Am. Med. Inform. Assoc. 25(9), 1248–1258 (2018)CrossRefGoogle Scholar
  55. 55.
    Henderson, M.: Machine learning for dialog state tracking: a review. In: Proceedings of The First International Workshop on Machine Learning in Spoken Language Processing (2015)Google Scholar
  56. 56.
    Canalys: Amazon reclaims top spot in smart speaker market in Q3 2018 (2018)Google Scholar
  57. 57.
    Harwell, D.: The accent gap. The Washington Post (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Thrishma Reddy
    • 1
  • Philippe J. Giabbanelli
    • 2
    Email author
  • Vijay K. Mago
    • 1
  1. 1.Department of Computer ScienceLakehead UniversityThunder BayCanada
  2. 2.Computer Science DepartmentFurman UniversityGreenvilleUSA

Personalised recommendations