A Negotiation Support Tool Using Emotional Factors
Masahide YUASA, Yoshiaki YASUMURA, Katsumi NITI’A
Department of Computational Intelligence and Systems Science
Tokyo Institute of Technology, Japan
E-mail: {yuasa,yasumura,nitta} @ntt.dis.titech.ac.jp
Abstract
In this paper, we introduce a new support tool for participants of negotiation through a computer network
using emotional factors. The emotional factors are important in negotiation, since the participant’s decision
is affected by emotional factors such as their emotional disturbances, the impression of opponent’s attitude
and so on. In order to consider a negotiation strategy using emotional factors, we constructed a new stategraph model. This model describes how participants’emotional status is affected by the opponent’s proposal and the opponent’s attitude. Based on this model,
we made the negotiation support tool by which users
exchange their proposal and their emotion by facial expression of an interface agent. This tool shows the user
the proper action to take next by considering the opponent’s proposal and the facial expression. We showed
the availability of negotiation strategy using the statetransition model by a computer simulation.

1 Introduction
Negotiation is a process to reach an agreement by exchanging participant’s proposals each other. In recent years, several software tools to support negotiation
through Internet have been developed. These tools support participants by giving advice based on the only
utility value [ 11.
However, we often select the next proposal using not
only the utility value but also various kinds of factors inpractice [2,3,4]. For example, when we negotiate about
compensation of a car accident, our decision makingin negotiation is influenced by the feeling against theopponent and the emotional disturbance.
Therefore, in order to support decision making in negotiation, we made the new model including emotional
factors. Based on the new model, we made the negotiation support tool which gives users the advice about
the next action, considering not only the opponent’s

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Buyeisutility

dueA

100

Buyer’s

proposals

Xl

Seller’s

proposals

Figure I. The utility values in negotiation

proposal but his/her emotion.
Section Two introduces the state graph model of negotiation which describes the relation among the opponent’sproposal, the feeing against the opponent’s attitude andthe user’s action. Section Three describes an overview
of a negotiation support tool using this model. In Section Four, we evaluate the quality of advice of the negotiation support tool by a computer simulation. Finally,Section Five summarizes our research.

2. Negotiation Models
At first, we introduce the model of negotiation basedon the utility value. Next, we show a new model basedon Heider’s A-B-X model [2] which represents humanrelation in social psychology.
2.1. The Modelthe Utilityof Negotiation
Value

BasedonWe will show the model of negotiation based on theutility value by a simple example [S, 61. In this example,
two persons play the roles of buyer and seller. Figure
1 shows the typical change of the utility value of their
proposals during a negotiation. Proposals are sent from
one to the other in turn. X0,X2,X4... mean proposals

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A--y+BA-6(1)

UnbalancedstatesXXA-B(5)

ct,(31X

ATB

A---,BA,,B(21X

A-B

A-B
(6)

BalancedA(6)statesFigure 2. Balanced states and unbalanced states

A-B
0Figur 4. The state graph model using emotional factors

tors, we use the A-B-X model, which represent humanrelations.

Figure 3. The changes of the state in negotiationby the seller, and Xl ,X3,X5... mean proposals by thebuyer. In this way, the difference of their utility values
decreases with time. Finally they reach an agreement
when there is no difference.
Based on the model, most negotiation support tools
select the next proposal. The negotiation strategy isfocused on how the participants reach the agreementwith the higher utility value.

3. The Model of Negotiation Based on Heider’s A-B-X Model
The model of negotiation based on the utility value
doesn’t consider the emotional factors. In order to construct the model of negotiation including emotional fac-0-7803-7078-3/01/$10.00 (C)2001 IEEE.

A-B-X model describes a social relation among twopersons, labeled A and B, and one object, labeled X.
The relation between two of these entities is indicatedby the plus or minus sign. The plus sign means aperson has positive feeling for the other. The minus
sign means a person has negative feeling. Using thisnotation, social relations among two persons and one
object are represented one of 8 (2 x 2 x 2) status. Amongthem, 4 status are balanced ones and the others areunbalanced ones, as in Figure 2. And the unbalanced
states tend to move to the balanced states.
In the case of negotiation, we regard A and B as participants, and X as a current proposal.
When B offers the proposal X, the state becomes oneof (2), (3), (5) or (6). (2) means that A is satisfied withX and A has negative feeling to B. Since this state isunbalance, the state moves to the balanced state (5) or(6) in Figure 3. If A changes the feeling against B, thestate moves to (5), which is an agreement state. On
the other hand, if A is not satisfied with X because of
the negative feeling against B, the state moves to (6).
(6) means that negotiation is continued. Similarly, (3)Page: 2907

means that A is not satisfied with X and A has positive
feeling against B, after B offers the proposal X to A.
And then A changes the feeling against B or to X, thestates moves to (5) or (6).
In this way, there are three types of routes to reach anagreement after the opponent offers the proposal. The
first is the route that the user is satisfied with the proposal and positive feeling for the opponent at once. The second is the route that the user has positive feeling affectedby which the user is satisfied with the proposal((2)-(5)).
The third is the route that the user is satisfied with theproposal affected by the user’s positive feeling((3)-(5)).
The third type means that the user’s decision affectedby the user’s feeling.
Figure 4 shows that the state graph model combined
these types. In Figure 4, the state (6)’ means that A
offers an initial proposal X. If B offers another proposal
X, the state moves to this state (6). At the state (6), if B
offers a new proposal X, A is satisfied with that proposal
and A has negative feeling to B, the state moves to thestate (2). And if B offers a new proposal X, A is notsatisfied with that proposal and A has positive feelingto B, the state moves to the state (3). Since the state(2) and (3) are the unbalanced state, the state moves
to the balanced state (6) to continue, or the agreement
state (5). Similarly, the agreement state (5)’ means B issatisfied with the proposal after A offers it. The same
applies to (2)’ and (3)‘. When A and B are not satisfied
with any proposal, A or B has negative feeling strongly
against the opponent or the proposal, or the time runs
out, the state moves to breakdown (9).

4. An Overview of Negotiation Support Tool
using Emotional Factors
Our system consists of a Message Control Server andNegotiation Support Tools (Figure 5).

l

Message Control ServerThis server is connected from the negotiation support tools to manage the participants and exchange
their messages.

l

Negotiation Support Tool
The support tool consists of two modules: Interface Module and Generating Advice Module.
(1) Interface ModuleThis module receives the opponent’s proposal and the facial expression of an interface

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Figure 5. The architecture of the tool
agent from the opponent’s tool and displaysthem on the screen. And it receives the advice from the Generating Advice Module andshows them. This module also displays amenu table on the screen and receives theuser’s proposal and facial expression.
(2) Generating Advice ModuleThis module receives the next proposal fromInterface Module and recommends the next
facial expression based on the state graphmodel.

5. Learning the Strategy Using Facial Expressions
In order to generate the advice, we need to know theprobability of the state transition of the state graph model. We acquired the probability by observing players
actions in MONOPOLY game. Based on the probability, we made MONOPOLY agent to negotiate and weevaluate the quality of advice by a computer simulation.

5.1. An Overviewof MONOPOLYgameThe MONOPOLY is a board game played by a few
players. Each player in turn puts the player’s token tothe place according to the dice. If the player’s token
stops at the place which belongs to no one, the player
may purchase the place and get a card which is a deedof the title of the place. If the player’s token stops at thesomeone’s place, the player’s must pay the rental fee tothe owner of the card. To each card, a color (red, blue,green and so on) is attached. In order to get the muchmoney, players must gather cards of the same color.
Good players negotiate with others and gather cards ofPage: 2908

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5.2.2

ResultIn the experiment, we obtained 56 cases including 292
proposals and 46 cases without facial expressions including 126 proposals. Table 2 shows that the probability of the players’ actions when they received theproposal and the facial expressions from the opponents.
In this table, the player’s rating for the proposal is classified into 3 levels, low (-S-4), middle (-3--l) andhigh (O-5).
Table 2. The player’s action by the diflerent
facial expressions

On the other hand, in the case of ANGRY, when therating is high, the probability to reach an agreement islower (39%) than the case without facial expressions.
And when the rating is middle, the probability to break
down is higher (15%) than the case without facial expressions. In addition, in the case of HAPPY, when therating is middle, the probability to reach an agreement
is the highest (33%). And the probability to break down
is the lowest (11%) .
In short, in the case that the facial expression is HAPPY, even if the player received the rating is low, theprobability to break down is low.
5.3. The

Evaluationof Advice

0 The case without facial expressions

lRatinghighmiddle
lowAction
Agreement 1 Continue 1 Breakdown
91 19102177
1207327

Ratinghighmiddle
lowAction
Agreement Continue
3961
877080ANGRYBreakdown
015200 COOL
ActionContinue
51
9496

Ratinghighmiddle
lowAgreement
49
40Breakdown
024Ratinghighmiddle
lowAction
Agreement 1 Continue 1 Breakdown
901
10 103355
12089
11RatinghighmiddlelowAction
Agreement Continue Breakdown
50
50
01481
5086
140 HAPPY

ll

SAD

53.1

We obtained the probability of the state transition in thestate graph model. Using the probability, the negotiation support tool gives the user an advice about facial
expressions which the player should use when the player shows the next proposal to the opponent. To evaluate
the quality of advice, we need to play the MONOPOLY
game according to the advice repeatedly and examine
how much money we can get.
However, it takes much time to get the statistically meaningful data. Therefore, we conducted theevaluation of the advice by a computer simulation.
We developed a MONOPOLY agent which plays theMONOPOLY game using the negotiation support tool
instead of a human player.
When the MONOPOLY agent receives the opponent’s proposal and the facial expression, the agent acts
according to the probability (Table 2). If the agent
decides to continue the negotiation, it selects the next
proposal using a negotiation strategy based on the utility value, and selects the facial expression by following
strategies.

l

HAPPY-AGENT always selects the HAPPY face.

l

ANGRY-AGENT always selects the ANGRY face.

SURPRISED
Ratinghighmiddle
lowAgreement
60
70Action
Continue Breakdown
4008677525

In the case which players don’t use facial expressions,when the rating is high, the probability to reach an agreement is the highest (9 1%). And when the rating islow, the probability to break down is the highest (27%).

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MethodlAVERAGE-AGENT
randomly.

uses five facial expressions

We observed how the agents behave and which type ofagent gains the most benefit. The MONOPOLY agent
selects the next proposal using the following evaluation
value U.

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U=xfi+M
xi f; means amount of values of cards that the player
has. Subscript i means a kind of colors that the cards
belong to. M means money that the player has. fi
becomes very high if the agent has all cards of the color
i. Agents make proposals which increase the evaluation
values of both the agent and the opponent in the game.
We simulated two cases. In the case (a), one HAPPYAGENT and two AVERAGE-AGENTS participated inthe game. In the case (b), one ANGRY-AGENT andtwo AVERAGE-AGENTS participated.
Table 3. The trading to use the different facial
expressions(a)HAPPY vs AVERAGE x 2
AgentAgreement Continue Breakdown Evaluation Value
HAPPY
AVERAGEAVERAGE

266
212
162384503
468

828155514
5179
5115

(b)ANGRY vs AVERAGE x 2
AgentAgreement Continue Breakdown Evaluation ValueANGRY
AVERAGEAVERAGE

5.3.2

194
229
217

625
525
522

31
16175189
5413
5432

Result

The result is shown in Table 3. In the case of (a), 120
games including 368 cases were executed. The case toreach an agreement for HAPPY-AGENT is 151, the caseto continue is 232 and the case to break down is 4. And
the average of evaluation value at the end of one gameis 543 1. In the case of (b), 110 games including 3 11cases were executed. In the case of (a), the strategy touse HAPPY enables to increase agreement. At the caseof (b), the strategy to use ANGRY enables to increase
breakdown. In addition, HAPPY-AGENT gained themost benefit (55 14) in three agents. On the other hand,
ANGRY-AGENT’s result is the worst (5 189).
In short, the results show that the amount of agreement
and their benefit change by the difference of the strategy
using facial expression. The player is able to become
to take the advantage by selecting the suitable facial
expression in negotiation.

6. Conclusion

In order to consider emotional factors, we applied A-BX model as a model of human relation to negotiation.
Based on the model, we made the negotiation support
tool using the facial expressions. In order to confirm
the availability of the strategy using facial expressions,
we made MONOPOLY agents, which have the different
strategy. By the computer simulation we confirmed that
we can take the advantage in negotiation by selecting
the suitable facial expression.
In the future, we will consider the history of trading andthe previous facial expressions that the opponent uses.
By using the additional information [8], the advice canbe more suitable.

References[l] Katsumi Nitta, Akira Yamazaki, Takashi Kitou
and Yoshiaki Yasumura, “A Negotiation Support Tool using a Computer Network”, Technicalreport of IEICE, OFS98-19, AI98-28, ~~53-60,1998, (in Japanese).
[2] Isamu Saito, The Handbook of Experimental
Social Psychology 2: Interpersonal Attractionand Social Motives, Seishin Shobo, 1987, (inJapanese).
[3] Leight Thompson, Victoria Husted Medvec,Venessa Seiden, “Poker Face, Smiley Face,and Rant’n’ Rave: Myths and Realities about Emotion in Negotiation”, Blackwell Hankbook inSocial Psychology, Vo1.3, Group Processes,2000.
[4] Max H. Bazerman, Margaret A. Neale, Negotiating Rationally, The Free Press, 1997.
[5] Gregory Kersten,Sunil Noronha, “Supporting International Negotiation with a WWW-based System”, IIASA, IR-97-49, 1997.
[6] Gregory Kersten,Sunil Noronha, “Negotiation andthe Web: User’s Perceptions and Acceptance”,IIASA, IR-98-002, 1998.
[7] Osamu Hasegawa and Katsuhiko Sakaue, “CGTool for Constructing Anthropomorphic Interface
Agents”, Proc. IJCAI-97 WS (W.5), ANIMATEDINTERFACE AGENTS, Nagoya, Japan, 1997-8,
pp.23-26.
D3] Masahide YUASA, Yoshiaki YASUMURA, Katsumi NITTA, “A Negotiation support tool using Physiological Information”, IEEE SMC,
Nashville, USA, October 2000, ~~248-253.

We introduced the new negotiation support tool using
emotional factors.

0-7803-7078-3/01/$10.00 (C)2001 IEEE.

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