Knowledge Representation: Expertise in Negotiations
Ron Caneel
rcaneel@media.mit.edu
Massachusetts Institute of Technology
Abstract
Do people who perform well in negotiation have a
different representation of important factors in their
mind? To answer this question, I analyzed the objective
negotiation outcome for creating and claiming value in
regard to the knowledge representation of subjective
measurements. I used the Thurstone approach and
individual difference scaling (INDSCAL) to find an
answer to this question. The only finding that can be
reported is that claiming value seems to have a more
distinct separation in knowledge representation between
skill groups than creating value.
Introduction to Negotiations
Negotiation is viewed by many people as art and by no
fewer as science. Usually when we hear the word
negotiation, we think about two or more business
partners with the goal to make a favorable deal. But
many situations in our daily lives can be viewed as
negotiations: deciding with a friend where to have
dinner, arguing with ones child who wants the latest
toy, or finding a vacation destination that matches the
taste of both a wife and her husband. The most
dominant and obvious way to evaluate a negotiation is
the objective outcome. Was the goal achieved? How
well did I compare to the other person? But there are
other important factors. Often a negotiation is not only
a one-time event. People have to continue to work or to
live with each other. In an ongoing relationship, both
private and business, a person often does care what the
other party thinks about him/her. In addition to the
relationship factor, a person’s own feelings should not
be neglected. This is true on an emotional as well as on
a moral level.
Looking again at the objective outcome there are two
important dimensions: creating value and claiming
value. Creating value is beneficial for both parties. By
enlarging the pie for both sides this aspect is often
called a win-win solution. By working together it is
important to discover the hidden opportunities that
increase the share for both parties. Claiming value
refers to the aspect of how the total available value is
distributed. Each party tries to get as much as possible
from the fixed pie. In this case we have a win-lose
situation; the better one side performs, the worse the
other performs. So, when looking at the outcome of a
negotiation, both dimensions have to be considered. A
person could successfully create value but then leave all
the shares to the other person, or the opposite could
occur: from the little value that was created, a person
can claim most of it.
The purpose of this paper is to analyze how knowledge
representation of different values can help a person to
do well on either of the two objective outcomes.
Background
The standard answer for what distinguishes an expert
from a novice is her/his knowledge. This is definitely
true but what does this exactly mean? For most people
this answer is related to content. Experts in their field
simply know more than novices. They have cumulated
more knowledge over time and have it at hand when
necessary. Based on their greater knowledge, they are
also able to better distinguish between different items
and have a clearer understanding of the material. In
addition to the difference in amount of knowledge, past
research also discovered difference in how the data is
internally structured and accessed. Here, we talk about
knowledge representation. In this case, experts and
novices
use
conceptually
different
systems.
Traditionally there are two ways to analyze different
skill levels. The first is recall performance. In chess [3],
for example, experts show faster recall for meaningful
situations than novices. Salomon [6] explains this with
the chess master’s different sense of how pieces are
clustered. The disadvantage of the recall method is that
it doesn’t give us any insight into how knowledge is
structured. The other category is the measurement of
knowledge proximity between different items. For this
approach subjects have to rate the similarity between
different objects. The researchers’ goal is then to
identify underlying structure that could explain the
differences. Several analytical techniques can be used
to extract the underlying structure: Multidimensional
Scaling (MDS), principal component analysis (PCA) or
hierarchical clustering. Not all of them are suited for
measuring skill level differences. In addition to the
three techniques mentioned above Ye [9] reports the
use of correlation techniques, which can be used to
study skill groups. By comparing the intra- and intergroup correlation coefficient, the different groups can
be identified. The weakness of this method is the lack
of indication of statistical significance. The standard
MDS does not provide the necessary tools for
comparing multiple people or groups since it only
displays the dominant dimension for one subject. The
metric method introduced by Caroll [2] solves this
problem. Her method of Individual Difference Scaling
(INDSCAL) considers the weighting of the axes
between multiple similarity matrices.
Past research analyzed several domains where the
different skill levels between experts and novices can
be explained with knowledge representation. As
mentioned earlier, de Groot [4] discovered differences
between experts and novices chess players. Solomon
[6] studied conceptual difference in wine expertise. He
found experts would use more specific features when
describing wines and they tend to sort the wines
explicitly by grape types. Correlation techniques were
used by Shavelson [5] to understand different skill level
in physics students. In the computer domain Ye looked
at how programming concepts vary between experts
and beginners.
Research Design
I had performance data from a negotiation session that
was conducted with first-year Sloan students. It was a
version of the new recruit case study. One person
played the vice-president who hirers a new middle
manager for his company. The two parties have to come
to an agreement on eight different dimensions. Based
on the agreement, two objective scores were calculated:
one individual score (claiming value) and one dyadic
score (creating value). These values were taken to
distinguish between experts and novices. The
assumption was that a certain skill set and approach
helped them to generate a higher outcome.
As a next step I had to find some criteria for how
people would first evaluate their performances and
what their attitudes towards negotiating is. In his
research Curhan et al [3] found that people care about
four basic domains: feelings about instrumental
outcomes, feelings about themselves, feelings about the
process, and feelings about their relationships. They
created the Subjective Value Inventory (SVI), which
should help a person to conceptualize his negotiation
performance. The idea was that these dimensions not
only might be asked after a conducted negotiation to
evaluate the performance but that the weighting of these
values in general might be a indicator for the objective
outcome as well.
Instead of asking the subject to rate each question of the
SVI, I used Thurstone’s comparative judgment
approach [7]. Several reasons influenced me trying the
pairwise comparison approach. One problem with the
widely used Linkert scale is the ceiling effect. Also is
the Linkert scale more open to conscious interpretations
by the subject and less likely to be an indicator of the
real underlying structure. From economical psychology
we know that people are influenced by the anchoring
effect [8]. Derived from this we can conclude that it is
not easy for subjects to come up with a robust rating of
different values on a value scale. Ariely [1] discovered
in his research that subjects can easily tell which item
or activity they prefer in a pairwise comparison but are
very inconsistent when mapping them onto a monetary
scale. The Thurstone methodology is analogous to
knowledge proximity measures generally used for
MDS. Multidimensional scaling extracts a partial
representation of the knowledge structure, which can
be used to study different skill groups. I hoped to be
able to extract a hidden structure by pairwise weighting
the SVI questions. With the total number of SVI
questions this would mean one hundred twenty
comparisons. This is an overbearing and nearly
impossible amount of comparisons for MBA students. I
therefore reduced the sixteen questions to ten and
modified the expression in a way that they could be
compared with each other, e.g. “Do you feel your
counterpart(s) listened to your concerns” was changed
to “Feeling that your counterpart listened to you”. The
possible answers for each statement were ‘much more
important’, ‘more important’ or ‘slightly more
important’.
The final version (see figure 1) was on the web in the
form of an online survey where the subjects could
weight all forty-five pairs. The order of the
comparisons was randomly assigned.
Figure 1: Web form: example question
Results
5. Fair
The total number of completed survey was very small.
Only ten students participated in this study. Several
attempts to recruit more students failed. This means that
the dataset is to small to come up with any significant
findings. This implies as well that any differences that
could be found could just be random and no meaningful
interpretation can be derived from the data. The overlap
between the two different skills (creating and claiming
value) will also be big since I had to use the same data
for both dimensions.
Thurstone Analysis
Since the Thurstone analysis only uses binary
comparison as input, I transformed the ratings to binary
weights. (This was taken into consideration during the
survey design: An even rating scale prevented
participants from selecting a neutral answer).
Table 1 shows the preferred order for the creating value
dimension, for both the experts and novice groups. A zscore value of +1.0 means a support of 84%; a score of
0 means support of 50% and a score of –1.0 means a
support of 16%.
0.273
1. Competence
0.202
1. Competence
0.067
10. Trust
0.141
2. Consistent
-0.340
5. Fair
0.070
3. Balance
-0.408
8. Relationship
-0.135
10. Trust
-0.613
3. Balance
-0.408
6. Appropriate
-0.684
6. Appropriate
-0.684
INDSCAL
Even though the collected data was not similarity
ratings in its proper means, the pairwise rating on a
weighting scale allows running a multidimensional
scaling algorithm. I used the INSCAL version included
in the sas-application. Figure 2 and 3 show the different
mappings for the creating value dimension.
Table 1 Order for creating value
Expert order
z-value
Novice order
z-value
7. Lose Face (self)
0.751
7. Lose Face
1.035
4. Benefit (inst)
0.545
4. Benefit
0.918
2. Consistent (inst)
0.407
9. Listened
0.776
1. Competence (self)
0.337
5. Fair
0.259
8. Relationship (rel)
0.272
1. Competence
0.193
10. Trust (rel)
0.208
8. Relationship
0.077
5. Fair (proc)
0.003
3. Balance
-0.041
9. Listened (proc)
-0.137
10. Trust
-0.291
6. Appropriate (self)
-0.272
2. Consistent
-0.334
3. Balance (inst)
-0.472
6. Appropriate
-0.949
Figure 2 INDSCAL plot for experts in creating value
We can see some minor differences in the ranking but
at the same time, the order of the two highest ranked
items is the same.
Table 2 shows the weighting for the claiming value
dimension. In this case, a difference between the
experts and the novice group seems to be more
reasonable.
Table 2 Order for claiming value
Expert order
z-value
Novice order
z-value
7. Lose Face
1.229
4. Benefit
1.021
9. Listened
1.091
2. Consistent
0.684
8. Relationship
0.478
7. Lose Face
0.340
4. Benefit
0.343
9. Listened
0.205
Figure 3 INDSCAL plot for novice in creating value
The key for the “term_nr” pair is included in the
appendix. The number does not have any meaning; it is
just based on the random order of the questions. We can
see several differences between the two figures. But
there is no clear, obvious distinction for the two plots
that could indicate two different structure with a higher
then chance possibility.
Figure 4 and 5 display the results for the claiming value
dimension. Again, similar to the Thurstone analysis, we
see a clearer difference between the two skill groups.
Discussion
As mentioned before the response to my call for
participation in this project remained mostly
unanswered. I tried different methods: snow ball
method, handing out little notes with URL after asking
students directly and sending personalized emails. But
the success rate remained very low.
Now after the project I also question my approach to
use weighting instead of similarity to extract knowledge
representation. Even though I mentioned advantages to
this method earlier in the paper, I have to conclude that
it does not work. In order to get meaningful scaling the
proximity measurement should be taken.
It might also be that the assignment to experts and
novices was not really appropriate. First, I only used the
performance of the subgroups to determine the skill
level. Second, there was a 50% overlap between the
two objective dimensions due to the small answer size.
References
[1] D. Ariely, G. Loewenstein and D. Prelec, "Coherent
arbitrariness: Stable demand curves without stable
preferences," Quarterly Journal of Economics, No.118 (1),
73-105, 2003.
[2] J.D. Caroll and J.J Chang " Analysis of individual
differences in multidimensional scaling via an n-way
generalization of " Eckart-Young " decomposition ",
Psychometrika, 35, 238-319, 1979.
Figure 4 INDSCAL plot for experts in claiming value
[3] Curhan, J. R., Elfenbein, H. A., & Xu, A. (2004). What
do people care about when they negotiate? Mapping the
domain of subjective value in negotiation, Manuscript in
preparation (www.subjectivevalue.com)
[4] A. D. de Groot, Thought and Choices in Chess. The
Hague, The Netherlands: Mouton, 1965.
[5] R.J, Shavelson. Some aspects of the correspondence
between content structure and cognitive structure in physics
instruction. Journal of Educational Psychology, Vol. 63, 225234, 1972.
[6] G. E., Solomon. Conceptual change and wine expertise.
Journal of the Learning Sciences, 6 (1), 41–60, 1997.
[7] Thurstone LL: "A law of comparative judgment".
Psychological Review; 34: 273-286, 1927.
Figure 5 INDSCAL plot for novice in claiming value
A clustering according to the SVI values cannot be
identified although many times at least two items seem
to be in close to each other.
[8] T.D., Wilson, C. Houston, K.M. Etling,, &N.A. Brekke,
new look at anchoring effects: Basic anchoring and its
antecedents. Journal of Experimental Psychology: General. 4,
387-402, 1996.
[9] N. Ye. The MDS-ANAVA technique for assessing
knowledge representation differences between skill groups.
IEEE Transactions on Systems, Man, and Cybernetic Part A
28(5): 586-600, 1998.
APPENDIX
SVI
Nr Full Text Question
dimension
1 'Feeling competent as a negotiator'
self 1
2 'Feeling like your outcome is consistent with principles of legitimacy or objective criteria'
instr 1
3 'Feeling satisfied with the balance between your own outcome and your counterpart's outcome' instr 2
4 'Feeling satisfied with the extent to which the terms of your agreement benefit you'
instr 3
5 'Feeling that the negotiation process was fair'
process 1
6 'Feeling that you behaved appropriately in terms of your own principles and values'
self 2
7 'Feeling that you didn't "lose face" (i.e., damage your sense of pride)'
self 3
8 'Feeling that you have a good relationship with your counterpart'
relationship 1
9 'Feeling that your counterpart listened to you'
process 2
10 'Feeling trust for your counterpart'
relationship 2