This document proposes using a prediction market game with a comment system to enhance knowledge sharing among salespeople. It describes preliminary experiments comparing prediction market sessions with and without the comment system. The sessions with comments had significantly more transactions, suggesting comments activated more frequent information and knowledge sharing. Further experiments are needed to better evaluate the game and identify improvements, such as capturing comment relationships. The goal is to develop a serious gaming approach that motivates truthful information sharing to foster collaboration.

1. Hajime Mizuyama
A Prototype Prediction Market Game for
Enhancing Knowledge Sharing among Salespeople
Hajime Mizuyama & Kazuto Yamamiya
Aoyama Gakuin University
mizuyama@ise.aoyama.ac.jp
ISAGA 2014 7/July/2014

2. Hajime Mizuyama
Competitive atmosphere
• In a company running a BtoB business, such as IT system integration, orders
are usually obtained through negotiations with customer companies.
• The mission of its sales division is
– to keep winning an appropriate number
of orders through negotiations
– to accurately forecast the total number
of orders to be achieved
Research background
Information and
knowledge sharing
in the division
Information and
knowledge sharing
in the division

3. Hajime Mizuyama
• In a company running a BtoB business, such as IT system integration, orders
are usually obtained through negotiations with customer companies.
• The mission of its sales division is
– to keep winning an appropriate number
of orders through negotiations
– to accurately forecast the total number
of orders to be achieved
– to foster collaborative atmosphere
in the division
Research background
Information and
knowledge sharing
in the division
Collaborative atmosphere

4. Hajime Mizuyama
The objective of this research is to develop a serious gaming approach using
prediction markets for motivating truthful information and knowledge sharing
and thereby fostering collaborative environments in a sales division.
• Reward the members of the division for providing
valuable information or knowledge truthfully.
• Properly forecast the probability of winning a sales
negotiation based on the information provided.
• Improve/control the probability of winning a sales
negotiation based on the knowledge provided.
Research objective

5. Hajime Mizuyama
• Research background and objective
• Problem setting
• What are prediction markets?
• Proposed game design
• Preliminary experiments and their results
• Conclusions
Agenda

6. Hajime Mizuyama
Problem setting
Sales division
Sales negotiation 1
Sales negotiation 3
Sales negotiation 2
Sales negotiation 4

7. Hajime Mizuyama
Forecast accurately the probability
of winning the negotiation.
Improve/control the probability
of winning the negotiation.
Collaborative information/knowledge acquisition and sharing
Problem setting
Sales negotiation 1
Owner
Other participants

8. Hajime Mizuyama
• Research background and objective
• Problem setting
• What are prediction markets?
• Proposed game design
• Preliminary experiments and their results
• Conclusions
Agenda

9. Hajime Mizuyama
An ordinal prediction market setting
Central Market Maker
(LMSR)
Market prices
= Probabilities
of being
elected
Candidate
A
Candidate
B
Candidate
C
WTA securities
A fixed posterior
payoff only for
the elected
candidate
Bid and ask orders

10. Hajime Mizuyama
Automated market maker LMSR by R. Hanson (2003)
 
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 
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i bQbC
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/explog)(Q
)()(Cost QqQ CC 
 
 
 N
j
j
i
i
i
bQ
bQ
dQ
dC
p
1
/exp
/exp)(Q
Numberofsec.：Q
Cost function
How much to charge for buying Δq
Unitary price of ith security
Whole numbers sold so far
Unitaryprice：p
Candidates
Candidates

11. Hajime Mizuyama
• Research background and objective
• Problem setting
• What are prediction markets?
• Proposed game design
• Preliminary experiments and their results
• Conclusions
Agenda

12. Hajime Mizuyama
Outline of proposed system
Central Market Maker
(LMSR)
Bid/ask orders
Market prices
= Probabilities of
whether it will
be won or lost
Win
Lose
Win/Lose securities
A fixed posterior
payoff is given only for
that corresponding
to the actual
outcome.
Owner Other participants
price

13. Hajime Mizuyama
Outline of proposed system
Central Market Maker
(LMSR)
Bid/ask orders & Comments
Market prices
= Probabilities of
whether it will
be won or lost
Comments
= Grounds for
the prediction
Win
Lose
Win/Lose securities
A fixed posterior
payoff is given only for
that corresponding
to the actual
outcome.
Highly evaluated
comments are
also rewarded.
Owner Other participants
comment comment
comment comment
comment comment
comment comment
Provide basic information.
Set initial prices for the securities.
Answer questions (Q&A function to be introduced).
price

14. Hajime Mizuyama
Collection and evaluation of comments
Win Lose
It will be effective to change the
sales strategy such that…
It will be effective to modify the
proposal such that …
The proposal does not seem
competitive because …
The competitor A has a stronger
connection with the customer.
Buy shares Buy shares

15. Hajime Mizuyama
Collection and evaluation of comments
Win Lose
It will be effective to change the
sales strategy such that…
It will be effective to modify the
proposal such that …
The customer will accept our
offer because …
The proposal does not seem
competitive because …
The competitor A has a stronger
connection with the customer.
Buy 3 shares Buy shares

16. Hajime Mizuyama
Collection and evaluation of comments
Win Lose
It will be effective to change the
sales strategy such that…
It will be effective to modify the
proposal such that …
The customer will accept our
offer because …
The proposal does not seem
competitive because …
The competitor A has a stronger
connection with the customer.
Buy shares Buy shares
✔
2

17. Hajime Mizuyama
Collection and evaluation of comments
Win Lose
It will be effective to modify the
proposal such that …
It will be effective to change the
sales strategy such that…
The customer will accept our
offer because …
The proposal does not seem
competitive because …
The competitor A has a stronger
connection with the customer.
Buy shares Buy shares

18. Hajime Mizuyama
Collection and evaluation of comments
Win Lose
UncontrollableControllable
A competitor has decided to
withdraw their proposal.
If the owner does this, the
probability will increase.
A new company had decided
to enter the competition.
If the owner does not do this,
the probability will remain
low.

19. Hajime Mizuyama
Posterior wealth
= Payoff from owned security
− Cost of buying security
+ Revenue from selling security
Comment contribution
Game scores
= evaluation counts of comment
𝑐𝑜𝑚𝑚𝑒𝑛𝑡𝑠
𝑒𝑛𝑡𝑒𝑟𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑝𝑙𝑎𝑦𝑒𝑟

20. Hajime Mizuyama
• Research background and objective
• Problem setting
• What are prediction markets?
• Proposed game design
• Preliminary experiments and their results
• Conclusions
Agenda

21. Hajime Mizuyama
• Participants were 6 undergraduate students in Aoyama Gakuin University.
• Only fixed participation fee, no additional monetary incentive depending on
the game score, is provided.
• Simple either-or problems:
– Red-and-white-ball setting
– Imaginary sales negotiation scenario
• Before a game session started, the players were secretly provided different
information. Then, they enjoyed the session for about 15 min.
• Market game sessions with and without the comment system are compared.
Experimental setting

22. Hajime Mizuyama
Red-and-white-ball setting
60
70
80
90
100
110
120
130
140
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Number of transactions
赤証券
白証券
60
70
80
90
100
110
120
130
140
1 4 7 10 13 16 19 22 25 28 31 34 37 40
Number of transactions
赤証券
白証券
WithoutcommentsystemWithcommentsystem
PricePrice
Red
White
Red
White
60
70
80
90
100
110
120
130
140
1 4 7 101316192225283134374043
Number of transactions
赤証券
白証券
60
70
80
90
100
110
120
130
140
1 2 3 4 5 6 7 8 9 10 11 12 13
Number of transactions
赤証券
白証券
PricePrice
Red
White
Red
White
60
70
80
90
100
110
120
130
140
1 4 7 10 13 16 19 22 25 28 31 34
Number of transactions
赤証券
白証券
60
70
80
90
100
110
120
130
140
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Number of transactions
赤証券
白証券
PricePrice
Red
White
Red
White

23. Hajime Mizuyama
Imaginary sales negotiation scenario
60
70
80
90
100
110
120
130
140
1 3 5 7 9 111315171921232527293133353739414345
Number of transactions
60
70
80
90
100
110
120
130
140
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Number of transactions
With comment systemWithout comment system
Win
Lose
Win
Lose
Price
Price

24. Hajime Mizuyama
Without comment
system
With comment
system
Red-and-white-ball #1 14 42
Red-and-white-ball #2 13 43
Red-and-white-ball #3 14 35
Imaginary sales
negotiation scenario
14 46
Average 13.75 41.50
Number of transactions in each game session

25. Hajime Mizuyama
• Research background and objective
• Problem setting
• What are prediction markets?
• Proposed game design
• Preliminary experiments and their results
• Conclusions
Agenda

26. Hajime Mizuyama
• A prediction market game with a comment system is proposed for enhancing
information and knowledge sharing among the salespeople of a company.
• A simple prototype of the game is developed and preliminary laboratory
experiments using it found that the comment system activates transactions
and hence the frequencies of sharing information and knowledge.
• Further experiments are necessary for properly evaluating the game and
identifying how to improve it.
• It is also an important challenge how to capture the relationships among the
comments, for example, by introducing gIBIS structure.
Conclusions

27. Thank you for your kind attention!
Questions and comments are welcome.