The slides for presentation in the conference of the ACM Collective Intelligence 2019 at CMU, US.

1. Agent that Facilitates Crowd Discussion
Takayuki Ito,
Daichi Shibata, Shota Suzuki, Naoko Yamaguchi,
Tomohiro Nishida, Kentaro Hiraishi, and Kai Yoshino
Nagoya Institute of Technology,
Japan.

2. Agent that facilitate crowd discussion
• Crowd-scale discussion platforms are receiving great attention as
next-generation democratic citizen platforms [Malone &
Klein 2007; Malone 2018]
• COLLAGREE [Ito CI2014, Ito CI2015, Sengoku &Ito CI2016, Ito
2018]
• Large-scale experiment with Nagoya-city in 2013.
• 266 users, 1151 opinions, and 18,466 views.
• Human facilitators promote crowd-scale online discussion.
• More than 30 other experiments
• Problem
• Human facilitators cannot scale
• Discussion often have over a thousand opinions posted
simultaneously.
• Many discussion threads become tangled with overlapping
opinions.
• We propose automated facilitation agent

3. D-agree : crowd-scale discussion support system
based on automated facilitation agent
Posting form
Discussion board
Theme
Points
Important keywords

5. D-agree :
Crowd-scale discussion support system based on
facilitation agent
Discussion structure
Question
mediation
Idea/Position
extraction
How can we solve
congestion in Nagoya
city?
How about to introduce
a traffic tax mechanism?
That is a good idea
What are merits for
this idea?
(facilitation)
Only people who
want to enter can
enter the city.
Discussion
Issue
extraction Issue
Idea
Pros/Cons
Pros/Cons
Idea
AutomatedFacilitationAgent
ELSI committee
Consensus DB
Knowledge graph
SNS, Twitter, etc.
IBIS
Arguments
extraction

6. Web server(Apache)
Discussion DB
(Amazon RDS)
Theme data
Browser
Posting data
User data
Keyword data
CoffeeScript
JavaScript
Spine.js
PHP
Keyword
Extraction
Incentive
Calculation
Keywords
Discussion
Data
AWS ( Amazon Web Service )
Specification
Visualization
OS Ubuntu 16 (on AWS)
Language PHP, Python
DB MySQL, AWS RDS
Web server Apache
Framework Ruby on Rails3, Spine.js
AWS LB
Posting
Observation
Data
Data
acquistion
Observing & Posting
mechanism
Structure extraction and
Visualization mechanism
Data
acquisition
visualize Structure data
Knowledge DB
Data
instruction
Automated Facilitation Agent
Python
Python
Sentence Word Relations
Data sharing by black board model
System Architecture

7. Node and Link identification with DNN
F issue idea pros cons
Apl - June 0.382 0.436
July - Aug 0.816 0.216 0.293 0.526
Sep - Oct 0.743 0.708 0.460 0.518
Nov 0.887 0.761 0.530 0.543
Node identification
Precision idea->issue pros->idea cons->idea issue->idea
Apl - June 0.070
July - Aug 0.648
Sep - Oct 0.678 0.573 0.735
Nov 0.895 0.818 0.918 0.900
Link identification
fastText Bi-LSTM
4 class
output
Dense
An
idea’s
fastText
Real
Issue1
Real
Issue2
Real
Issue3
An issue’s
Prediction
similarity
Bi-
LSTM
Dense

8. Social experiment

9. January 15, 2019 Intelligence Augmentation and Amplification 9
Nagoya-city Social Experiment in 2018
Date: November 1st – December 7th, 2018
1st phase: 30 days to discuss
2nd phase: 7 days to express agreement for ideas
5 Theme: How to realize plans in Nagoya city?
- Theme 1: Respectful city for human rights (FA: Human)
- Theme 2: Kind city to raise children (FA: Human)
- Theme 3 : Safe and secure city (FA: Auto)
- Theme 4 : City that has comfortable urban environment
and nature (FA: Auto)
- Theme 5 : City that attract people and companies from
the world (FA: Auto & Human)
Page views: 15199
Visited participants: 798
Registered participants: 157
Posts: 432

10. Theme
Postings
All Human FA Auto FA Participants
1: Human FA 81 43 0 38
2: Human FA 56 21 0 35
3: Auto FA 88 0 24 64
4: Auto FA 70 0 18 52
5: A & H FA 137 17 21 99
Sum 432 81 63 288
• Human FA is Human Facilitator. Auto FA is Automated Facilitation Agent.
• Auto FA (red numbers) successfully extracted more posts from participants than
Human FA (blue numbers).
Results: the number of postings

11. 3.4 3.4
3.6
3.2
3.7
0
1
2
3
4
5
Theme 1
Human FA
Theme 2
Human Fa
Theme 3
Auto FA
Theme 4
Auto FA
Theme 5
A & H FA
Average
score
“Are you satisfied with the discussion of city plan?”
• Questionnaire (N=20) results of “are you satisfied with the
discussion of the city plan?”.
• Satisfaction scores on discussion by Auto FA were satisfied at
same level as discussion by Human FA.
Results: satisfaction scores

12. Issue 1: We need to increase
interest in Nagoya worldwide
Automated Facilitation Agent:
How can we solve it?
Idea 1: Advertising on TV
Automated Facilitation Agent (Issue 2):
What is necessary for the idea?
Idea 2: More collaboration between
Nagoya City and TV production
companies was proposed.
Results: a successful real case
Automated Facilitation Agent:
Identified as an issue
Automated Facilitation Agent:
Identified as an idea

13. Discussion structure visualization
Issu ok Issue ok Idea ok
Pros ok
Pros ok
Cons ok
Cons
Cons
Cons
Idea ok
13

14. Conclusions
• D-agree : A crowd-scale discussion support system based on
an automated facilitation agent, which extracts discussion
structures from text discussions, analyzes them, and posts facilitation
messages.
• A large-scale experiment with Nagoya City’s local government
in which our automated facilitation agent worked quite well.
• Auto FA extracted more postings
• Users satisfactions were at the same label between Human FA & Auto FA
• 15199 page views, 798 visited participants, 157 registered participants, and 432
posts.
• Future work
• Measures for good discussoin
• The automated facilitation agent can be biased, which is related to
Ethics (ELSI). How much bias people can accept?

15. IBIS(Issue—Based Information System)
The elements of IBIS are: issues (questions that need to be answered), each
of which are associated with (answered by) alternative positions (possible
answers or ideas), which are associated with arguments which support or
object to a given position; arguments that support a position are called
"pros", and arguments that object to a position are called "cons”. In the
course of the treatment of issues, new issues come up which are treated
likewise. (Wikipedia)
15
- Rittel, Horst W. J.; Noble, Douglas E. Issue-based information systems for design. Berkeley:
Institute of Urban and Regional Development, University of California, Berkeley. OCLC 20155825.
492. Jan 1989.

16. Crowd-scale discussion
Facilitation agent
Visualizing
discussion
structure
Mediation
Discussion analysis
Managing risks
Requirements
・Visualize discussion structure
・Analyzing discussion
・Questioning and mediation
mechanism
・Consensus DB management
・Risk management
Automated Facilitation Agent
Cloud
Consensus DB