Giving Advice in Negotiation using Physiological Information
Masahide YUASA, Yoshiaki YASUMURA, Katsumi NITTA
Dept. o f Computational Intelligence and Systems ScienceTokyo Institute o f Technology, Japan

Abstract

Section 4 describes the evaluation of the system.
Finally, Section 5 summarizes our research.

In this paper, we propose a method for giving advice
to participants of negotiation using physiologicalinformation. We apply Newcomb's A-B-X model as amodel human relation to negotiation. Based on themodel, we adopt the user's preference of a proposal,
an impression of opponent's attitude and a degree ofthe user's emotional disturbance in the generating
advice tool. We measure the user's perspiration andpulse rate to extract the user's emotional disturbance.
The tool learns the relation between the user's
preferences and the advice. The experimental results
show that the tool can improve the quality of advice.

12

We introduce Newcomb's A-B-X model [3], which
represent the human relation in psychology. Then we
explain the process of negotiation by using this model.

2.1 Newcomb's A-B-X Model
A-B-X model consists of two persons, labeled A and B,
and one impersonal entity, which could be a physicalobject or an idea, labeled X. The model represents theperson's cognitive structure of relations among A, Band X. The link sign is plus (+) when a person has
positive feeling and the link sign is minus (-) when aperson has negative feeling for one of these entities.
There are 16 states of the relation between A, B and X,
which are classified into balanced one and unbalanced
one. Figure 1 illustrates examples of these states. The
unbalanced states are unstable and tend to move to thebalanced states.

Introduction

Negotiation is a process to reach an agreement
between participants who have different goals byexchanging proposals. In order to negotiate efficiently,
several software tools to support negotiation throughInternet have been developed. Most of these tools use
the utility value to select a proposal.
However, we often decide the next proposal based on
various kinds of factors. For example, when wenegotiate about compensation of a car accident, weevaluate proposals by considering not only the utilityvalue but also the feeling about the opponent and theemotional disturbance.
Therefore, we use new psychological parameters suchas a user's emotional disturbances and an impressionof opponent's attitude, to select suitable proposals. The
user's emotional disturbance is extracted by using
perspiration and pulse rate.
In this paper, we will describe the method to give theusers advice by using physiological parameters andbased on the Newcomb's model for human relations.
Section 2 introduces Newcomb's model in negotiation.
Section 3 describes an overview of a negotiation
support system by using physiological information.

0-7803-6583-6/00/$10.00 © 2000 IEEE

H u m a n Relation in Negotiation

X<+>
A~-X

BA,,_Balancedstates

X

B<;-X

A.,~-- B

A.,,_Unbalanced

BstatesFigure 1. Balanced states and unbalanced states
2.2 The A-B-X model in Negotiation
To describe psychological relation among participants
and proposals of negotiation, we consider A and B as
participants, and X as a current proposal. The linkfrom A to B means A's impression of B's attitude and248the link A to X means A's preference of X.
Figure 2 shows state transition in negotiation. When A
offers a proposal X and B doesn't agree with X, thelink from A to X is plus and the link from B to X isminus. Because A's preference o f X is different fromB's, they have the negative feelings each other. Thisstate is represented as the state (~) in Figure 2.
Then B offers a new proposal but A doesn't agree withit. It is the state (~.By offering new proposals in turn,state changes from (~ to (~) and from (~) to (~). If
they agree with a certain proposal, then the state moveto (~). Because this state is unbalanced, the state moveto ® . This state means that they reach an agreement.
However, there are the other routes to reach anagreement. For example, if A changes the attitude in
the state (~), then the state will be the state (~). By thechange of A's attitude, B may change the preference o f
the proposal, and the state will be the state ® .
In this way, there are two types of route to reach anagreement, one is the change the preference ofproposals, and the other is the change of an impressionof the attitude. The preference of proposals and theimpression of the attitude are affected by theemotional disturbance. Therefore, we adopt thefollowing parameters in our negotiation support tool.
1) Preference of a proposal
2) Impression of an opponent's attitude
3) User's emotional disturbanceis used to select next advice.

. Breakdown•~.
X-/_\-/

A~BX.{_/_~_

B proposes-/\÷• BA proposes

AT - ~ BA43o,: \Xq®

;

Ixlx
÷/_\
.....

A4" B®

,,.

X%\+A ---~~==mll

B

®

®

Figure 2. State transition in negotiation

3 An Overview of Negotiation Support
System3.1

Functions o f the System

A user negotiates with an opponent through thecomputer network in our negotiation support system.
The system has two tools, the generating advice tool
and a video and audio conferencing tool, as in Figure 3.
The user inputs a preference of a proposal and animpression of an opponent's attitude, before offering aproposal. At the same time, the user's physiologicalinformation is measured by sensors. Then thegenerating advice tool gives the user advice from thepreference, the impression and the user's emotional
disturbance. The user can offer the proposals byreferring to the advice with the conferencing tool.
Then the user evaluates the advice and the evaluation

Figure 3. A video and audio conferencing tool andnegotiation support tool

249

....

iiiii[3.2 Architecture of the System
The system consists of the conferencing tool and thegenerating advice tool, as in Figure 4. The
conferencing tool enables us to use image and voicewith camera and microphone and hold face-to-faceconversations with an opponent in negotiation. And
the generating advice tool consists of the 3 modules, adialog box module, an extracting emotional
disturbance module, and a generating advice module.

ConferencinWtoolI'co1) Dialog Box ModuleThis module shows a dialog box and receives theuser's input; an impression of opponent's attitude anda preference of proposal. The user evaluates thesevalues from image and voice subjectively using theconferencing tool. These parameters are sent toGenerating Advice Module. And the dialog windowshows advice given from Generating Advice Module.
Then the user evaluates the advice and the evaluationis also sent to Generating Advice Module.

\2) Extracting Emotional Disturbance ModuleThis module receives physiological information suchas pulse rate and perspiration from sensors. And thismodule learns the relation between the user's
emotional disturbance and the sensor data by using
Probabilistic Neural Networks (PNN)[4][5]. The
information is sent to Generating Advice Module.

Figure 4. Architecture of System

3) Generating Advice ModuleThis module generates advice using a preference of a
proposal, an impression of opponent's attitude and theuser's emotional disturbance. The advices areclassified into three types: acceptance, compromise
proposal and hostile proposal. In order to provide
information suitable for the individual preferences ofuser, the system learns preference data by PNN.

Figure 5. Measuring equipment
3.3 Extracting Emotional Disturbance
The user's emotional disturbance is measured by thefollowing sensors.
1) Perspiration sensor
We use 'Perspiro201' by Suzuken as a perspiration
sensor as in Fig 5. This sensor measures mental
sweating, which occurs hands or thumb by emotional
disturbances.

Figure 6. Perspiration sensor

250

talking, thinking deeply and so on, it's difficult todiscriminate a psychological reason from the otherreasons.

The mental sweating occurs in response to excitement,
fear, anxiety, and a multitude of other disturbed states.

2) The pulse rate goes up by the emotional disturbancewithout the noise. However, it is slow to react to thedisturbance and not more sensitive than perspiration.

2) Pulse rate sensor
We use the pulse rate sensor made of infrared sensor
as in Fig 7. Generally, it's well known that emotional
disturbances quicken the pulse rate.

Considering these properties, we calculate a level o f
the emotional disturbance from the following threevalues:
1) The difference of value of the amount ofperspiration
The emotional disturbance agrees well with the peakof perspiration, which shows the broken line.
2) The moving average value of the amount ofperspiration
The shitt o f the amount of perspiration indicates theemotional disturbance approximately.

Figure 7. Pulse rate sensor~ / c m 2/mi.

beat#/m in1.41.2

Pul=e RaleX.

1~/0.80.6k,0.4.

-0.2/00v'krt-1001502003) The difference o f pulse rate
The pulse rate also indicates the emotional disturbanceapproximately.

1 tl

11,5It's difficult for us to find the relation among thevalues and the emotional disturbance. And the relation
changes for each. Therefore, we use PNN to learn therelation. PNN can learn data cumulatively
andquickly. PNN is suitable for the classification [7]. The
objective evaluation of a degree of user's emotionaldisturbance by subject is used as the supervised signal.

111
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50/~lOO

250

300II

Figure 8. Perspiration and pulse rate in negotiation

3.4 Generating Advice

Figure 8 shows values of pulse rate and perspiration ina example of negotiation [6]. The horizontal axis
means time, the vertical axis means sensor values eachtime. In this example, the status of negotiation
changed as follows. They exchanged the opinions
initially. However, his standpoint becomes worse
gradually. Though he tried to improve his standpoint,
he failed.
His emotional disturbance reflected to sensor value.
As the results, we can see the following properties:

The generating advice tool makes advice from an
impression of opponent's attitude, a preference of a
proposal and the user's emotional disturbance. The
tool gives the user one of three advices: acceptance,
compromise proposal and hostile proposal. The user
can negotiate by referring to the advice.
And we should consider differences among individuals.
Even if one person likes certain advice, another person
may like different one. Therefore, the tool learns thepreference by PNN. The user's preference of advice isused as the supervised signal. Since the user utilize thenegotiation support tool, the user needs to input test
patterns in advance. Figure 9 describes one of results

1) Perspiration is sensitive to the emotional
disturbance. However, because the mental sweating
occurs in response to the multitude of other factors:251

of learning training data. X-axis, Y-axis and Z-axis
means the user's preference o f a proposal, theimpression of an opponent's attitude and the user's
emotional disturbance.
Figure 10 and 11 are a cutting plane by Z-axis for two
different persons, A and B. As we can see thesefigures, the advice person wants are affected by theimpression of the opponent's attitude and the user's
emotional disturbance. It's mean that even if aperson's preference of a proposal is low, the good
relation between the person and the opponent cause
acceptance o f the person. And by comparing figure 10and i 1, we find that the differences among individualpersons.

Z=O.I

B

~acce~nc~~:~OOOOlDOD~~:~OOOOgOO0~O000OOOO~ooooooo|=~oooooo==|~oo(;](],==..

• acceptance[3 compromise propo#al
ho=tile propo=al4~=ooooo=,,==,,,,=,,,II||==l=ll"~X~*lOOOOOOO.*.|.oooo.
,,=,,=,,,=..=.....==,,====,,==,,====,,,,Ill|l||l=l')'X)X

Evaluation of Advice

We evaluate the effect of the system by comparing
with two advices. One is the advice generated fromonly the preference of proposals. Another is the advice
generated from the preference, an impression ofopponent and emotional disturbance. The results of theselection are shown in Table 1. The each colunm valuemeans 'support rate';
the number of advice which the user selects
the number of advice

acceptancecompromise proposal
hostile proposal\"\imwe~ionof opponent's
attitude~oooooo=|~ooooo====~O00===l||)X

support rate =y

Z=l.0~:~OOOOOOOO~OOO00OO0~aOOOO00

Figure 11. Cutting plane by Z-axis (person B)

alLI

Z=0.6

~x, O O O O D O O O~OOOOOOOO~:O~OOOgOa~*:~O000OOOO ~:~OOOOOO0' ~ ' ~ O 0 0 0 0 O ' '~o=o,°~rice\Z=04y\

only preference

ourtool

14/29
16/26
21/34
51/89

6/9
21/42
30/38
57/89

Table 1. Compare with two methods
As we can see from Table 1, the tool that used only
preference matched the user's move by 57%, our tool
matched by 64%.
The results mean that the user makes decisions by not
only preference of proposals but also an impression ofopponent's attitude and an emotional disturbance. Oneof the reasons that advice doesn't satisfy the user isthat our system failed to extract proper emotional
disturbance.

Figure 9. A distribution of advice
Z=O. I

Z=0.4

mn~:~oooooo=~;~OOO¢l===~ :•' ° =~ .I o' ' o' ' 'o=o". .

• hostilegropolll*) X

ocomlxomi~.~propo~zl

Z=O,O**~oo----oo..==o==="= ===' ', '='.'=' '.'. =Z=1.0~*==*===~=l==lllB=o" ',' ,' 'o' 'o' =o "o = = ="~ X,=||=,=,=,==|========. ==o' ','.' .' .' ','o =~/X"~ X

Figure 10. Cutting plane by Z-axis (person A)
5Conclusion

We proposed a method for giving advice to users innegotiation using physiological information.
We applied A-B-X model as a model o f humanrelation to negotiation. From the model, we decided to252

consider a preference of a proposal, an impression ofopponent's attitude and the user's emotional
disturbance. In order to extract the user's emotional
disturbance, we measured perspiration and pulse rate
as physiological information. Considering these results,
we made the negotiation support tool. Theexperimental results show that our tool can improve
the quality of advice by considering these parameters.
However, there is a problem that the user must input apreference of proposals and an impression ofopponent's attitude by the user's manual operations. It's
desired to improve the feature integrates user's data
automatically.
In this report, we use perspiration and pulse rate inorder to measure the emotional disturbance. We may
extract it from another sensors. By using the additional
information, the advice can be more suitability anddetailed.

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and Katsumi Nitta, On an Evaluation Method for Next
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