=Paper=
{{Paper
|id=Vol-2068/humanize3
|storemode=property
|title=Identifying Dominators and Followers in Group Decision Making based on The Personality Traits
|pdfUrl=https://ceur-ws.org/Vol-2068/humanize3.pdf
|volume=Vol-2068
|authors=Yong Zheng
|dblpUrl=https://dblp.org/rec/conf/iui/Zheng18a
}}
==Identifying Dominators and Followers in Group Decision Making based on The Personality Traits==
https://ceur-ws.org/Vol-2068/humanize3.pdf
Identifying Dominators and Followers In Group Decision
Making Based on The Personality Traits
Yong Zheng
School of Applied Technology
Illinois Institute of Technology
Chicago, IL, USA
yong.zheng@iit.edu
ABSTRACT to group decisions, but some of other users (i.e., domina-
Human factors, such as emotions, personality traits and tors) may play a dominant role in group decision making.
trust network, have been proved to play an important
The impact by personality in individual and group deci-
role in the decision making process. The impact by per-
sion making is still under investigation, especially in the
sonality in individual and group decision making is still
area of educational learning. For example, team work be-
under investigation, especially in the area of education-
comes more and more popular in the educational learn-
al learning. In this paper, we propose two approaches
ing. Students may be suggested to work together on the
to distinguish the “dominator ” and “follower” in group
assignments or projects. Decision making is involved in
decision making by using an educational data. Our ex-
such a scenario, e.g., how will the students build the
periments also show that the characteristics of these two
team, or which materials or topics should a team select
user roles can further be utilized in group recommender
to start learning, etc. Furthermore, it is also interest-
systems to produce better item recommendations.
ing to understand which group of the users may yield to
group decisions. In this paper, we discuss our analysis
Author Keywords to distinguish followers and dominators in the group de-
recommender system, personality, group decision cision by using an educational data. Our contributions
can be summarized as follows:
INTRODUCTION
Human factors, such as emotions, trust and personality, • We propose two approaches to identify the dominators
have been recognized as influential factors in the recom- and followers in the group decision making.
mender systems. For example, emotional reactions [18] • We discover and summarize the characteristics of these
can be treated as strong implicit feedbacks that can rep- user roles in terms of the personality traits.
resent user tastes. Trust network [8] can provide addi- • We infer the pattern in team building.
tional property to infer the user preferences. User per- • We demonstrate that these characteristics are useful
sonalities [10, 16] may directly affect a user’s decision, to improve the quality of group recommendations.
since people with different personalities may present dis-
tinct behavior patterns and preferences in the real world.
RELATED WORK
Recently, the importance of personality is realized not Personality has been successfully applied to improve de-
only for predicting the individual tastes, but also the cision making in different areas, such as tourism [2, 14],
group preferences. For example, a group of users may trading [3], career [4], etc. For example, the analysis on
decide which dishes should be ordered for a group lunch. economics behaviors by Ertac, et, al. [3] helps us under-
Or, a group of tourists would like to make a decision stand the role of personality in group decisions. Par-
about the list of points of interests for tomorrow’s trip. ticularly, they found that openness, agreeableness and
Researchers [15, 12] find out that user personality is one conscientiousness are the major three personality traits
of the key factors in group decisions. For instance, some that can affect the group decisions by distinguishing the
users (i.e. followers) in the group may yield their choice user roles as leaders and non-leaders. Furthermore, the
personality traits are found to be useful in recommender
systems, e.g., Delic, et, al. [2] observe significant patterns
in user behaviors based on the personality traits which
can improve the group recommender systems.
In educational learning, personality has been proved to
be influential. Komarraju, et al. [6] identify the impact
c 2018. Copyright for the individual papers remains with the authors. of personality on the academic achievements, such as
Copying permitted for private and academic purposes. GPA. Vedel, [17] focuses more on the group differences
HUMANIZE ’18, March 11, 2018, Tokyo, Japan
across academic majors. However, there are limited work • Collection of Individual Preferences: At the be-
that explore the impact of personality on individual and ginning, each student is required to fill the question-
group decision making in the learning environment. naire by himself or herself. Each subject should select
at least three liked and disliked topics of the projects,
In this paper, we are particularly interested in distin-
and provide an overall rating to them. In addition,
guishing the follower and dominators. Dominator(s) is they are asked to rate each selected project on three
defined as one or more members in a group who could criteria: how interesting the application area is (i.e.,
be the decision leaders. By contrast, follower(s) can be App), how convenient the data processing will be (i.e.,
viewed as the member who may yield to the group de- Data), how easy the whole project is (i.e., Ease). The
cisions. The notions are inspired by Recio-Garcia, et rating scale is from 1 to 5.
al. [13]. They propose five different modes for responding
to conflict situations – competing, collaborating, avoid- • Collection of Group Preferences: Finally, each
ing, accommodating and compromising. The domina- student has to decide whether they will complete the
tor in our paper is the user role in the competing mode, project individually. For the team work, they need
while the follower represents the user role in the compro- to find partners and build the team by themselves.
mising mode. However, their work relies on the Thomas- Each team will fill the same questionnaire from the
Kilmann Conflict Mode Instrument (TKI) test. The sub- perspective of a team based on the group discussions.
jects are required to take the test in order to be classified
into these five modes. In our paper, we ignore the TKI In addition to these preference data, we collect demo-
test and try to distinguish the dominator and followers graphic (e.g., age, gender, marriage status, home coun-
by the rating characteristics in the data. try) information and personality traits of each studen-
t. We choose the Big Five Factor (Big5) [9] which is
the most popular framework to represent the personality
ITMLEARNING PLATFORM traits. In the Big5 framework, the personality traits can
The impact of personality on individual and group de- be described by five dimensions [6]: Openness (O) is re-
cisions is under investigation in the area of educational flected in a strong intellectual curiosity and a preference
learning. But unfortunately, there are no available da- for novelty and variety. Conscientiousness (C) is exem-
ta sets for public research in this domain. Even in the plified by being disciplined, organized, and achievement-
general area of group recommendations, most of the re- oriented. Extraversion (E) is displayed through a higher
search may use the MovieLens data – the evaluation is degree of sociability, assertiveness, and talkativeness. A-
usually based on the simulated groups. In this case, we greeableness (A) refers to being helpful, cooperative and
start collecting our own data for the research purpose. sympathetic towards others. Neuroticism (N) indicates
ITMLearning platform is built for the department of in- the degree of emotional stability, impulse control, and
formation technology and management (ITM) at the Illi- anxiety. To collect the Big5 traits, we use the well-known
nois Institute of Technology in USA. The platform is a Ten-Item Personality Inventory (TIPI) [5].
technology-enhanced learning system which aims to: a) The full questionnaire includes the ten statements that
suggesting appropriate learning materials (e.g., books, are listed below, and the subjects are asked to give a
articles, tutorials, videos); b) recommending job posi- rating in scale 1 (strongly disagree) to 7 (strongly agree)
tions; c) assisting instructors in the teaching. to each of them.
One of the ongoing projects from this platform is collect- • I see myself as extraverted, enthusiastic.
ing students’ preferences on the topics of the projects in
order to better support learning and assist teaching [19]. • I see myself as critical, quarrelsome.
We start from three courses (i.e., database, data mining • I see myself as dependable, self-disciplined.
and data analytics) which require students to complete a • I see myself as anxious, easily upset.
project as the final evaluations. Students have their own
• I see myself as open to new experiences, complex.
choice to select a topic for the project, and each student
can complete the project by himself/herself or by a team • I see myself as reserved, quiet.
work. We ask student volunteers to complete the ques- • I see myself as sympathetic, warm.
tionnaires, in order to collect the subjects’ personality • I see myself as disorganized, careless.
traits and their preferences on the topics of the projects.
More specifically, the questionnaires are designed to col- • I see myself as calm, emotionally stable.
lect both individual and group tastes: • I see myself as conventional, uncreative.
• Topics of The Projects: First of all, we provide At this moment, we have collected data for a full year
a list of potential topics for each course respectively. – we obtain a data set with 194 individuals and 122
Take data analytics course for example, we provide groups. 81 out of 122 groups are composed of more
the information about 50 data sets that are available than one members. More specifically, 60% of these 81
on Kaggle.com. Students should select one of them, groups are composed of two members, and the remain-
define the research problems, and figure out solutions ing groups are composed of three or four members. The
by using the data analytics skills. individuals leave 1951 ratings on the topics of projects,
while the groups leave 745 ratings in total. In addi- Furthermore, each subject is required to provide the in-
tion to the overall ratings, we collect their ratings on dividual preferences (i.e., user ratings) on the topics of
three criteria as introduced above. For the purpose of the projects. Alternatively, we can represent each sub-
personalization, this data is available for traditional rec- ject by his or her rating vector. The rating vector can be
ommender systems (i.e., recommendations for individu- filled by the overall rating or the multi-criteria ratings on
als), group recommender systems (i.e., recommendations −
→
app, data and ease respectively. In other words, Va and
for each group), and multi-criteria recommender system- →
−
Vb could be rating vectors based on the overall rating
s (i.e., recommendations based on multi-criteria decision or the multi-criteria ratings. The similarity between two
making), as well as context-aware recommendations (i.e., subjects can be obtained by the Equation 1 accordingly.
semester, year, course can be viewed as the context in-
formation). The project is still ongoing and we expect
1.0
to collect more data gradually.
0.8
ANALYSIS AND DISCUSSIONS
Intra−Group Similarity
Personality Traits by Gender
0.6
In our data, 42% of the subjects are female. We’d like to
explore whether there is a significant difference in their
0.4
personality traits in comparison with males. Table 1
presents the mean and standard deviation (SD) of the
0.2
scores in the Big5 factors for the overall, male and female
individuals respectively.
Big5 Rating App Data Ease
Table 1. Statistics About The Personality Traits (* indi-
cates significance at 95% confidence level by gender) Figure 1. Comparison of In-Group Similarities
O C E A N
Mean 5.22 5.05 4.63 4.85 4.11 As a result, we are able to produce the intra-group simi-
Overall
SD 1.28 1.34 1.49 1.47 1.53 larities by representing a user as the Big5 vector or a rat-
Mean 5.15 4.89* 4.52 4.64* 4.12 ing vector. We further analyze the distribution of these
Male
SD 1.23 1.38 1.4 1.49 1.44 intra-group similarities, and visualize them as box plots
Mean 5.32 5.27 4.78 5.14 4.1 in Figure 1. It is clear that the intra-group similarity is
Female
SD 1.34 1.26 1.61 1.39 1.66 significantly higher by the representations based on the
Big5 factors than the ones based on user’s rating vec-
In addition, we also observe that the standard deviation- tors. It implies that the subjects prefer to find the team
s in neuroticism and extraversion are significantly larg- members by the personality traits, even if their tastes on
er than other personality factors. The two-independent the projects may be different. The average intra-group
sample hypothesis tests reveal that the difference on con- similarity based on the rating vectors is actually below
scientiousness and agreeableness between males and fe- 0.5, which is surprising.
males are significant at the 95% confidence level.
Distinguish Dominators and Followers
Team Building It has been recognized that personality can affect group
We further analyze the 81 groups which are composed of decisions. Our goal is to find out distinct individuals
at least two members. Students actually find their own who react differently in group decisions. More specifi-
partners and build the team without intervention by the cally, we define dominator(s) as one or more members
instructors. We are pretty interested in how they build in a group who are the decision leaders, and follower(s)
a team or what are the most important criteria for them as the member who may yield to group decisions. We
to select partners. More specifically, we try to measure try to these two user roles from the perspective of user-
the intra-group similarities. group similarities and user-group conflicts which can be
First of all, each subject can be represented by the Big5 further discussed as follows. Also note that our following
vector. Cosine similarity, as shown by Equation 1, can analysis is based on the 81 groups which is composed of
be used to produce the similarity between two subjects at least two team members.
−→ →
−
Ua and Ub in a same team. The vectors Va and Vb are By User-Group Similarities
the Big5 vectors for Ua and Ub respectively. We obtain
similarity values of each pair of the subjects in a team, We have both individual and group preferences on the
and the mean similarity is viewed as the intra-group sim- topics of the projects. Each individual and group can
ilarity. be represented by the rating vectors. In this analysis,
we focus on the overall rating only and ignore the multi-
−
→ → − criteria ratings for simplicity. The similarity between a
Va · Vb group and an individual in the group can be computed by
Sim(Ua , Ub ) = −→ →
− (1)
||Va ||2 × ||Vb ||2 the cosine similarity of the representations based on the
Openness Openness*
6 6
5 5
4 4
3 3
Neuroticism 2 Conscientiousness Neuroticism 2 Conscientiousness
1 1
0 0
Agreeableness* Extraversion Agreeableness* Extraversion*
Global Dominantor Follower Global Dominantor Follower
Figure 2. Identifying User Roles by Rating Vectors Figure 3. Identifying User Roles by Latent Vectors
rating vectors. If this similarity value is relatively low, We figure out a way to alleviate this problem. More
it implies that this subject yields to the group decisions. specifically, we blend user ratings and group ratings to-
Subjects with higher user-group similarity can be viewed gether, while each group is viewed as a special user. We
as the “dominator”, while the subjects with user-group utilize a matrix factorization model based on this rating
similarity smaller than a threshold can be the “follower”. matrix to find the best model which can minimize the
squared prediction errors in the ratings. Finally, each us-
We have two strategies to define the thresholds: er and each group can be represented by a latent vector
• We can use the average value of the user-group simi- which is learned by the matrix factorization model. The
larities as a single threshold. The subjects will be split user-group similarity, therefore, can be calculated by the
into dominators and followers. cosine similarity of two latent factors. In our work, we
use the biased matrix factorization [7] as the algorithm,
and assign 10 latent factors so that each user and group
• Or, we will set two thresholds. For example, we obtain will be represented by a vector with size 10.
the 1st and 3rd quartile of the user-group similarities.
The subjects with user-group similarity larger than 3rd Figure 3 presents new comparisons of the BIG5 traits.
quartile will be viewed as dominators, while the users The dominators and followers are identified based on the
with user-group similarity smaller than 1st quartile same method as mentioned above, where we represent a
will be considered as the followers. team and a team member as the latent vectors learned
based on the matrix factorization model. We can observe
We found that the second way was better, therefore we that there are significant differences between dominators
only present these results in the following sections. and followers in openness, agreeableness and extraver-
Afterwards, we computer the mean Big5 vector for the sion based on the two-independent sample test at 95%
subjects as dominators and followers which can be de- confidence level. More specifically, dominators present
picted by the radar chart as shown in Figure 2. We higher values in the openness and extraversion, while the
use “Global” to represent the mean Big5 vector of al- agreeableness value is relatively higher in the followers
l the subjects. The “*” denotes a significant difference who may yield to the group decision. It is not surprising
(two-independent sample test at 95% confidence level) to see that a dominator could be more extraverted since
in a specific personality trait between dominators and he or she may be a talkative, confident and assertive per-
followers. We can observe that the significant difference son. In terms of the openness, one explanation could be
only shows up in agreeableness, while dominators actu- that dominator is usually the first person to start the
ally have larger degree of agreeableness. It sounds sur- discussions in a team, and they may produce novel ideas
prising to us, since we expect the followers may yield and lead the group decisions. By contrast, the follower-
to the group decisions and they should present relative s present larger degree of the agreeableness, which may
larger degree of agreeableness. infer that they tend to accept the group decisions even
if they have different opinions.
After a further investigation, we realize that the cosine
similarity based on the rating vectors relies on the num- By User-Group Conflicts
ber of co-ratings by a team and an individual in the team Alternatively, we can distinguish dominators and follow-
– the similarity may be not reliable if the number of co- ers based on the notion of “conflicts”. Recio-Garcia, et
rated items is limited. In our data, the average value of al. [13] summarized five different modes for responding to
co-ratings by the teams and the team members is 3.33 conflict situations in their work – competing, collaborat-
with standard deviation 2.45. We believe the results in ing, avoiding, accommodating and compromising. The
Figure 2 are not reliable due to the limited number of dominator in our paper is in the competing mode, while
co-ratings between a team and team member. the follower is in the compromising mode. However,
the work by [13] relies on the Thomas-Kilmann Conflict – one is by the similarity between the team and its team
Mode Instrument (TKI) test. In our work, we try to members, another one is by the conflicts between indi-
figure out another way to define the conflicts and avoid vidual and group preferences. We find that openness,
additional human efforts in the TKI test. agreeableness and extraversion are the three influential
factors to recognize the dominator and followers by using
More specifically, we define conflict as either the false
the user-group similarities. By contrast, agreeableness is
positive or the false negative case. A “false positive” the only crucial factor we find by using the method based
case can be described as the situation that a subject on the conflicts.
presents positive preference on one item, but his or her
group finally made a negative decision on the same item. Some previous research have also identified the impor-
Accordingly, the scenario that being positive on one item tant personality traits in the group decisions. For exam-
by group decision but negative by a team member will ple, Ertac, et, al. [3] tried to distinguish users as leaders
result in a “false negative” case. In our experiment, we and non-leaders, and they found that openness, agree-
use a rating threshold to define whether it is positive or ableness and conscientiousness are the three major per-
negative. More specifically, it is positive when individual sonality traits which affect the group decisions. But the
or group rating on one project is larger than 3. We com- openness only takes effect if the person is a leader. Our
pute the total number of conflicts (including both false findings are basically consistent with Ertac’s work. Neu-
positive and false negative cases) for each team member roticism is also pointed out as a key factor by [1, 4]. But
in a team. We find out that only 22.5% of the subjects we did not confirm its importance in our data.
present the conflicts in our data.
Accordingly, we obtain the mean, 1st and 3rd quartile RECOMMENDATIONS
of the number of conflicts, and set the threshold to dis- Once we identify the dominators and followers, we fur-
tinguish the followers and dominators. The process is ther exploit whether and how these findings are help-
similar to the one we used to identify different user roles ful in producing the group recommendations. There are
by using the user-group similarities – we can use a s- 515 ratings associated with the 81 groups which are com-
ingle threshold or two thresholds. In our experiments, posed of at least two team members. We conduct a 5-fold
we finally use the mean value of the number of conflicts cross validation based on these ratings – we split the 515
as the threshold to split the subjects to dominators and ratings into 5-folds. For each round evaluation, we selec-
followers. t one of the five folds as the testing sets, the remaining
four folds plus the data of individual ratings, information
The sparsity problem is involved again since the number about group members and the user’s BIG5 traits will be
of co-ratings by a team and a team member is limited. considered as the corresponding training set. We sim-
We use matrix factorization model to make prediction- ply examine the recommendation performance by rating
s on the unknown ratings for both subjects and teams. predictions and use mean absolute error (MAE) as the
As a result, 97% of the subjects present the conflict- evaluation metric. The rating prediction for a group g
ing behaviors. We use the single threshold to separate on an item t is represented by P (g, t). We adopt the
the subjects to dominators and followers, while the com- following strategy in the group recommendations:
parison in BIG5 can be depicted by Figure 4. We can
observe that the statistical significance only presents in • Average (AVG): P (g, t) is the average predicted rating
the agreeableness, while the followers usually have higher by all of the team members on the same item t.
values in agreeableness. • One user choice (ONE): P (g, t) is equivalent to the
preference by the dominator on the item t. If there
Openness are more than one dominators, we use their average
6
5 rating predictions 1 . We set up a baseline setting for
4 the ONE method – assuming that we do not know the
3 dominators, P (g, t) will be the preference by a random
Neuroticism 2 Conscientiousness team member on the item t.
1
0 • Least misery (LM): It is used to minimize the misery
for the group members. P (g, t) is the minimal predict-
ed rating by the team members.
• Most pleasure (MP): It tries to maximize the happi-
Agreeableness* Extraversion ness or pleasure for the group members. P (g, t) is the
maximal predicted rating by the team members.
Global Dominantor Follower
For the purpose of rating predictions, we use the biased
Figure 4. Identify User Roles by Conflicts matrix factorization [7] as the recommendation model.
Recall that we figure out two ways to identify the dom-
Summary inators and followers, we finally adopt the ones shown
We try to distinguish the dominators and followers in
1
the group decision making by two proposed approaches Note that this does not happen in our experiments.
by Figure 3 and 4 – we simply name them as “By Sim- explore the corresponding solutions in the future. Fur-
ilarity” and “By Conflicts” in the following discussion- thermore, we evaluate the recommendation performance
s. To take advantage of the identified dominators and based on simple group recommendation strategies, but
followers, we simply ignore the contributions by the fol- there are several advanced work which can directly in-
lowers when we execute the four recommendation strate- corporate personality in the group recommenders, such
gies mentioned above. Take the AVG recommendation as the work by [11]. We will figure out how to incorpo-
method for example, we will ignore the ratings by the rate the identified dominators and followers into these
identified followers when we try to calculate the average advanced group recommendation models.
value of the member’s rating predictions. Similar opera-
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