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Discussion Map with an Assistant Function for Decision-Making: A Tool for Supporting Consensus-Building

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11000)


In this paper, we propose a tool for supporting consensus-building in conversations with multiple participants. We call it “Discussion Map with Assistant (DMA)”. It consists of nodes and links. We classify the nodes into two types; alternatives and criteria. Alternatives represent what the participants are choosing between. Criteria are used to judge the alternatives. Each criterion contains an importance value. Each link between nodes also contains an importance value. The system estimates a ranking list of alternatives among participants from each map. We introduce a forgetting function to the model. The system also supports the decision-making process by using discussion maps from participants. It generates sentences and charts that describe the current state of the discussion. We evaluate the effectiveness of the discussion map system with DMA in a decision-making task experimentally.


  • Discussion map
  • Decision support system
  • Consensus estimation
  • Support with charts and sentences

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  • DOI: 10.1007/978-3-319-98743-9_1
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Fig. 1.
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Fig. 7.


  1. 1.

    Note that not all alternatives have a link. It depends on the participant that creates the DM.

  2. 2.

    The initial memory level is 100 in this formulation.

  3. 3.

    Note that three of them are hidden in this figure.

  4. 4.

    The difference of the score is 5% or less.

  5. 5.

    This is based on the average ranks among participants.

  6. 6.

    As conditions for the final decision, each discussion needs more than four alternatives and more than two criteria.

  7. 7.

    The assistant function, DMA, becomes active in five minutes although groups with our system can use the DM system from the start.

  8. 8.

    For instance, for G1, the number of alternatives with our system was 9 (19/2.11) while that without our system was 7 (21/3).


  1. Alonso, S., Herrera-Viedma, E., Cabrerizo, F.J., Chiclana, F., Herrera, F.: Visualizing consensus in group decision making situations. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2007, pp. 1–6 (2007)

    Google Scholar 

  2. Bautista, J., Carenini, G.: An integrated task-based framework for the design and evaluation of visualizations to support preferential choice. In: Proceedings of AVI 2006, pp. 217–224 (2006)

    Google Scholar 

  3. Ebbinghaus, H.: Memory: A Contribution to Experimental Psychology. Dover Publications, New York (1885)

    Google Scholar 

  4. El-Assady, M., Hautli-Janisz, A., Gold, V., Butt, M., Holzinger, K., Keim, D.: Interactive visual analysis of transcribed multi-party discourse. In: Proceedings of ACL 2017, System Demonstrations, pp. 49–54 (2017)

    Google Scholar 

  5. Gratzl, S., Lex, A., Gehlenborg, N., Pfister, H., Streit, M.: LineUp: visual analysis of multi-attribute rankings. IEEE Trans. Vis. Comput. Graph. 19(12), 2277–2286 (2013)

    CrossRef  Google Scholar 

  6. Hmelo-Silver, C.E.: Problem-based learning: what and how do students learn? Educ. Psychol. Rev. 16, 235–266 (2004)

    CrossRef  Google Scholar 

  7. Ito, T., Imi, Y., Ito, T., Hideshima, E.: COLLAGREE: a facilitator-mediated large-scale consensus support system. In: Proceedings of the 2nd Collective Intelligence Conference (2014)

    Google Scholar 

  8. Ito, T., Imi, Y., Sato, M., Ito, T., Hideshima, E.: Incentive mechanism for managing large-scale internet-based discussions on COLLAGREE. In: Proceedings of the 3rd Collective Intelligence Conference (2015)

    Google Scholar 

  9. Katsura, Y., Okada, S., Nitta, K.: Dynamic argumentation support tool using argument diagram. In: Proceedings of The 29th Annual Conference of the Japanese Society for Artificial Intelligence (2015). (in Japanese)

    Google Scholar 

  10. Masukawa, H.: Development of the reflective collaboration note: ReCoNote. In: Proceedings of the 29th Annual Conference of JSET (2013). (in Japanese)

    Google Scholar 

  11. Miyake, N., Shirouzu, H.: The dynamic jigsaw: repeated explanation support for collaborative learning of cognitive science. In: The Meeting of the 27th Annual Meeting of the Cognitive Science Society (2005)

    Google Scholar 

  12. Nagao, K.: Meeting analytics: creative activity support based on knowledge discovery from discussions. In: Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 820–829 (2018)

    Google Scholar 

  13. Nagao, K., Kaji, K., Yamamoto, D., Tomobe, H.: Discussion mining: annotation-based knowledge discovery from real world activities. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3331, pp. 522–531. Springer, Heidelberg (2004).

    CrossRef  Google Scholar 

  14. Sakaguchi, K., Shimada, K.: Cooperation level estimation of pair work using top-view image. In: Kim, S., Jung, J.-W., Kubota, N. (eds.) Soft Computing in Intelligent Control. AISC, vol. 272, pp. 77–87. Springer, Cham (2014).

    CrossRef  Google Scholar 

  15. Scardamalia, M., Bransford, J., Kozma, B., Quellmalz, E.: New assessments and environments for knowledge building. In: Griffin, P., McGaw, B., Care, E. (eds.) Assessment and Teaching of 21st Century Skills, pp. 231–300. Springer, Dordrecht (2012).

    CrossRef  Google Scholar 

  16. Shiota, T., Yamamura, T., Shimada, K.: Analysis of facilitators’ behaviors in multi-party conversations for constructing a digital facilitator system. In: Proceedings of the 10th International Conference on Collaboration Technologies (2018)

    Google Scholar 

  17. Suzuki, H., Funaoi, H., Kubota, Y.: Supporting “assemble & disperse” style collaborative learning using tablet terminals. Technical report of IEICE-ET2013-26, pp. 41–46 (2013). (in Japanese)

    Google Scholar 

  18. Takagi, H., Shimada, K.: Understanding level estimation using discussion maps for supporting consensus-building. Procedia Comput. Sci. 35, 786–793 (2014)

    CrossRef  Google Scholar 

  19. Villalon, J.J., Calvo, R.A.: Concept map mining: a definition and a framework for its evaluation. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 2008, pp. 357–360 (2008)

    Google Scholar 

  20. Yamasaki, K., Fukuda, H., Hirashima, T., Funaoi, H.: Kit-build concept map and its preliminary evaluation. In: Proceedings of The 18th International Conference on Computers in Education, ICCE 2010, pp. 290–294 (2010)

    Google Scholar 

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This work was supported by JSPS KAKENHI Grant Number 17H01840.

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Correspondence to Kazutaka Shimada .

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Kirikihira, R., Shimada, K. (2018). Discussion Map with an Assistant Function for Decision-Making: A Tool for Supporting Consensus-Building. In: Egi, H., Yuizono, T., Baloian, N., Yoshino, T., Ichimura, S., Rodrigues, A. (eds) Collaboration Technologies and Social Computing. CollabTech 2018. Lecture Notes in Computer Science(), vol 11000. Springer, Cham.

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