AGE DIFFERENCES IN ECONOMIC DECISIONS: A NEW ULTIMATUM GAME
Jiaxi Wang
A Thesis
Submitted to the Graduate College of Bowling Green
State University in partial fulfillment of
the requirements for the degree of
MASTER OF ARTS
December 2013
Committee:
Yiwei Chen, Advisor
Richard Anderson
Steve Jex
ii
ABSTRACT
Yiwei Chen, Advisor
Due to its simplicity, the classic ultimatum game has been widely adopted by researchers
in economic decision making. However, as a tradeoff of its simplicity, the classic two-player
ultimatum game bears some limitations. A new two-proposer (one fair, one unfair) game was
created to address these limitations. Thirty younger (18-30) and thirty older (60 or above) adults
participated in this study. It was found that some participants chose to maximize their gain while
many others chose to accept offers from a specific proposer, even at the expense of lower profit.
Furthermore, a novel construct of Fairness Sensitivity (FS) was created to assess the participants’
sensitivity to fairness. Positive linear relationship was found between the participants’ FS scores
and their acceptance rates of the fair proposer during the game trials when the other proposer
made better offers. Lastly, no age difference was observed in participants’ fairness sensitivity
scores and their acceptance rate of the fair proposer.
iii
TABLE OF CONTENTS
Page
INTRODUCTION .................................................................................................................
1
The Classic Ultimatum Game ....................................................................................
1
The New Ultimatum Game ........................................................................................
4
Age Differences in the Ultimatum Game ..................................................................
6
The Present Study ......................................................................................................
8
EXPERIMENT……… ..........................................................................................................
9
Method……… ...........................................................................................................
9
Participant ......................................................................................................
9
The New Ultimatum Game ............................................................................
9
Procedure .......................................................................................................
11
Results ………............................................................................................................
12
Acceptance Rates of the Fair Proposers.........................................................
12
Fairness Sensitivity and Acceptance Rates ...................................................
12
Age Differences .............................................................................................
13
DISCUSSION……….. ..........................................................................................................
14
Contributions………..................................................................................................
15
Limitations and Future Directions……… .................................................................
16
REFERENCES ......................................................................................................................
18
APPENDIX A. TABLES ......................................................................................................
21
APPENDIX B. FIGURES ....................................................................................................
25
APPENDIX C. FORMS........................................................................................................
29
iv
APPENDIX D. HSRB APPROVAL LETTER .....................................................................
37
v
LIST OF TABLES
Table
Page
1
Participants Background Information ........................................................................
2
Illustration of Counter Balancing of Facial Photos and Session
3
22
Orders by Participant ID ............................................................................................
23
Acceptance Rates of the Unfair Proposers by Offer Level and Age Groups.............
24
vi
LIST OF FIGURES
Figure
Page
1
Screen Shot ................................................................................................................
26
2
Scatter Plot of Fairness Sensitivity Score and Acceptance Rate (Fair Proposer) ......
27
3
Acceptance Rate Distributions by Age ......................................................................
28
vii
LIST OF FORMS
Form
Page
1
Character Evaluation Scale ........................................................................................
30
2
The Background Questionnaire .................................................................................
34
3
Informed Consent (Old) .............................................................................................
35
4
Informed Consent (Young) ........................................................................................
36
1
INTRODUCTION
People frequently find themselves in interactive decision-makings involving two or more
offers or choices. No matter whether it is a young university student choosing what to major in,
or an older adult deciding where to live after retirement, those who are making such decisions
will always need to think of the consequences that their decisions or actions may bring to them.
It is not surprising that, within the social science realm, many researchers have devoted to
studying the properties of such a decision-making process. It is worth mentioning that, among
the different types of decision makings, financial decision-making has been an especially popular
topic of study among researchers in economics and psychology (e.g., Powell & Ansic, 1997;
Sanfey, 2007; Simon, 1959).
The Classic Ultimatum Game
Among the tasks used in economics and decision-making researches, the ultimatum game
has been attracting more and more researchers’ attention, especially in the past three decades
(e.g. Güth, Schmidt, & Sutter, 2007; Güth, Schmittberger, & Schwarze, 1982; Sanfey, Rilling,
Aronson, Nystrom, & Cohen, 2003). An ultimatum serves as a last warning, or last notice, such
that the party receiving the ultimatum will need to make a ‘take it or leave it’ decision. In
experiments, a classic two-player ultimatum game is often implemented to simulate ultimatum
bargaining situations.
When playing such a game, the first player- the proposer- would propose an offer of
certain amount, say X dollars, out of a total of N dollars to the second player – the responder.
The responder would then decide whether to accept or reject the offer. If the offer was accepted,
then the total amount would be divided as proposed. That is, the responder would receive X
2
dollars and the proposer receive the remainder (N-X) dollars. However, if the offer was rejected,
then neither of the players would receive any money.
Findings from the existing literatures on ultimatum bargaining consistently show that
people would often make decisions that are deemed irrational. As the rational choice theory
advocates, people should make decisions to maximize the expected utilities (Hewig et al., 2011).
In other words, as was described in the expected utility model, the rational way of making such a
decision should be that people calculate the expected utility of each choice available, and then
select the choice that would lead the most appropriate utility. However, just as the research
findings have suggested, people often do not conform to such a model while making decisions
(Bolton & Zwick, 1995; Paterson & Diekmann, 1988; Simon, 1993).
In the case of ultimatum game, the outcomes of laboratory studies of ultimatum
bargaining have been remarkably consistent; that is, the lower offers (e.g. less than 30%-40% of
the total amount) would be significantly more likely to be rejected by responders than any other
offers (Güth, Schmittberger, & Schwarze, 1982). Such a finding obviously indicates that the
responders do not always employ the expected utility model as they are making decisions.
Otherwise, they would simply accept all the offers that hold some positive amount as their
rejections would lead to expected utilities of zero gain (Bolton & Zwick, 1995).
A great number of speculations have been proposed and tested regarding the causes of
rejection behaviors in ultimatum bargaining. Among the well accepted speculations, the
perceived fairness of the offers was recognized as one of the most influential factors which
influenced people’s decision-making in such games. As expected, the perceived unfair offers
were suggested to be able to generate negative emotions in responders, which, in turn, might
3
result in the rejections of such offers (Dunn, Makarova, Evans, & Clark, 2010; Sanfey, 2007).
These negative emotions typically included anger, disgust and resentment. Pillutla and
Murnighan (1996) tested the wounded pride/spite model in their study. According to the
wounded pride/spite model, during an ultimatum bargaining, the responders would be likely to
perceive low offers as unfair offers if they were aware of the total sum of money to be divided.
In turn, they may experience negative emotions and thus reject the unfair offers (Straub &
Murnighan, 1995). Pillutla and Murnighan’s (1996) findings supported the wounded pride/spite
model. In their study, the participants were introduced to a full and a partial information
treatment. Under the full information treatment, the participants were informed of the total sum
of money, whereas, under the partial information treatment, the participants were not informed of
the total amount. The rejection rates of the full information treatment were significantly higher
than those of the partial information treatment when the offers were considered as unfair (e.g. $1
or $2 out of $10). Additionally, the participants expressed a significantly higher level of anger
under the full information treatment compared to that under the partial information treatment.
Based on the existing literature, it is reasonable to conclude that a strong correlation
exists among the perceived fairness of treatment, the anger level and the rejection rate. Dunn et
al. (2010) further supported this position. In their study, the particpants played a series of
ultimatum game trials. In half of the trials, the participants were told that they were playing
against another person. During these trials, a person’s facial photo would appear on the computer
screen to represent the identity of the proposer. In the remaining half of trials, the participants
were told that all the offers were computer generated. In addition, no facial photos were shown in
these trials. In each trial, the participants were asked to rate how angry they felt toward the offer.
The results were that, after receiving unfair offers, both the rejection rate and the participants’
4
level of anger were significantly higher when the offers were made by a “person” compared to
the rejection rate and the level of anger when the offers were computer generated.
The New Ultimatum Game
The classic ultimatum game is popular due to its simplicity. Using the game design,
researchers are able to gain insights into how people make decisions at the most basic level
(Croson, 1996). On the other hand, because of its simplicity, the classic ultimatum game suffers
from several limitations. The present study attempted to address some limitations in order to
generalize the findings to the real world.
One limitation is that the classic ultimatum games often have independent trials: the
identities of the proposers are often anonymous to the responders and the proposed amount is
independent from the previous trials. Consequently, the classic ultimatum games may have
excluded the learning effects of responders on proposers by having independent trials. For
example, if a proposer’s identity was consistent, then the way in which a responder would
perceive the proposer would impact his/her decision on whether to accept or reject the offer. It is
reasonable to assume that, if an individual had been repeatedly treated unfairly by a certain
individual, then the negative emotions generated from such treatments would be accumulated.
Whenever such a thing happens, the consequence would be that, after having received a number
of unfair offers from one particular proposer, the responder would become angry toward that
proposer, and subsequently reject the offers from the proposer in the oncoming encounters. This
phenomenon is common in daily life scenarios. For instance, a customer might stop using a
certain service provider due to previous unpleasant experience with that provider, say, poor
customer service. Ultimately, he might refuse to purchase more services from the provider in the
5
future regardless whether or not the provider’s customer service has been improved.
Unfortunately, the classic one-on-one ultimatum game was not designed to address such a
limitation.
Another limitation of the classic game design is that the design involves only one
proposal per bargaining trial. In everyday life, people make decisions toward multiple options on
multiple occasions. A typical example would be that a person who is planning to buy a house.
This person would usually spend a lot of time scheduling appointment with realtors and looking
at the properties as people would rarely visit only one property before making an offer. The
decision that people make will be based on multiple factors, such as price range, locations,
property condition, and/or neighborhood. Sometimes, it even depends on something as trivial as
the buyer’s mood or physical conditions while shopping. Therefore, the independent, one-on-one
bargaining trials fail to provide a realistic representation of people’s real life interactions: In real
life, people will often need to decide which one to choose between multiple ‘proposers’; or
negotiate with those ‘proposers’ repeatedly over a length of time.
In order to address the above limitations, a new game design was created and
implemented in this study. It was a new version of three-player ultimatum game. Through this
modified three-player ultimatum game, the present study investigated how people would respond
when facing multiple proposers simultaneously and repeatedly.
In this three-player game, the participants were asked to play the role of responders only
and to decide on the two offers presented to them simultaneously by the two proposers. One of
the proposers, referred to as the fair proposer (FP) for the sake of convenience, would always
make fair offers ($5 or $6 out of $10), while the other proposer, referred to as the unfair proposer
6
(UP), would mostly frequently make unfair offers (less than $3), except for few occasions when
he/she might make a generous offer ($8-$9). The purpose of implementing the FP and UP
concurrently was to investigate to what degree the fairness of a treatment could affect people’s
decision-making in such bargaining situations. Specifically, by having two proposers with one
being fair and the other unfair, this game design was expected to directly measure people’s level
of sensitivity to fairness. The fairness sensitivity (FS) construct in this study was operationalized
as the differences of fairness ratings of both FP and UP after the game. Alternatively speaking,
people who were more sensitive to fairness would rate the FP as more fair, and/or the UP as less
fair, in comparison with those who were insensitive to fairness.
In addition, the new game design aimed to measure the participants’ acceptance rates of
the FPs. Since the new game was designed to measure how the effect of proposers’ fairness of
play would influence people’s decisions to be made, the participants’ acceptance rates were
measured separately depending on whether the UPs were making lower or higher offers.
Age Difference in the Ultimatum Game
Among the existing literature on decision-making in ultimatum games, age differences
were rarely the focus. While majority of the researchers investigated the potential causes and real
world consequences of rejections in ultimatum games, only a few explored the properties of such
decision-making behaviors from a developmental perspectives (e.g. Harbaugh, Krause, & Liday,
2003; Hoffmann & Tee, 2006; Leman, Keller, Takezawa, & Gummerum, 2009). Furthermore, it
is worth noting that even fewer researchers focused on how older adults (i.e. those who were in
their late adulthood) may behave in such bargaining situations (e.g. Güth, Schmidt, & Sutter,
2007; Roalf et al., 2012).
7
A rapid population growth occurred in many societies during the post-World War II era
(e.g. 1946 – 1964). Today, the Baby Boomers are entering their late adulthood. Consequently,
studies in the factors that may influence the elder’s decision making began to gain more
popularity among researchers (Mata, 2007). As late adulthood is associated with decrease in
attention, working memory and processing speed, it is of critical importance to examine whether
one’s financial decision-making patterns would change as the person is getting older (Salthouse,
2009). By looking at how the aging process affects elderly adults to make financial decisions, the
government agencies and policy makers might be able to develop or modify some of the current
policies regarding this issue so as to fully meet the needs of older people (Lusardi & Mitchell,
2006; Poterba, 2004).
However, in spite of the popularity of aging-focused decision-making research, very
limited studies are focused on older adults’ performance in ultimatum games. The findings of the
few studies investigating age difference in decision making via ultimatum games suggested that
the older adults might be more sensitive to whether the treatment they have received is unfair;
thus they might be more likely to reject low offers in comparison to younger adults (Güth,
Schmidt, & Sutter, 2007; Roalf et al., 2012).
Güth, Schmidt and Sutter (2007) posited that older adults may have a higher preference
to fairness than do younger adults. In their study, the participants participated in a newspaper
ultimatum game. Each participant served as the proposer to split a total amount of $1,200 among
three players, including the participant and two hypothetical players. The proposals were sent to
the experimenters via mails, phone calls or internet. Güth and his colleagues discovered that the
older people are more equity oriented, such that significantly more old participants (65 or above)
in their study proposed an equal split offers ($400-$400-$400). Consistently, Güth et al. (2007)
8
concluded that the older people were more likely to reject the unequal splits than their younger
counterparts.
Roalf and his colleagues (2012) obtained similar findings in their study. Compared with
younger adults, older adults were found to be significantly more likely to reject low offers in
ultimatum games. In addition, consistent with what Güth, Schmidt and Sutter (2007) found,
Roalf et al. (2012) noticed that older adults were more likely to propose equal offers than
younger adults.
The Present Study
The main purposes of the present study were to further investigate the decision-making
behaviors in the new ultimatum game, to study the relationship between sensitivity to fairness
and decision-making, and finally to further explore potential age difference in the decisionmaking behaviors. Specifically, the following hypotheses were tested and explored.
I.
When the UP makes higher offers, some participants would still accept FP’s relatively
lower offers.
II.
Fairness Sensitivity (FS) would significantly predict the participants’ acceptance rate of
the FP during trials when the UP made higher offers. Specifically, the acceptance rates of
the FP and the FS scores would have a positive correlation.
III.
Older adults would have higher Fairness Sensitivity than younger adults. Consequently,
older adults would have higher acceptance rate of the FP than younger adults would
during trials when the UP made high offers.
EXPERIMENT
9
METHODS
Participants
Thirty younger and thirty older adults were recruited as participants for this study. The
older participants were all sixty years old or older (mean = 66.65 yrs., SD = 7.08 yrs.), the
younger participants were between 18 and 30 years old (mean = 21.53 yrs.; SD = 2.57 yrs.). Both
the young and older participants were healthy community dwellers from the Mid-Western
regions in the U.S. All the participants received $5 for participation upon completing the
experiment. The detailed background information for the two age groups is illustrated in the
Table 1.
The New Ultimatum Game
The new ultimatum game was programmed by E-Prime and conducted on PCs with 20
inch screens. There were two independent sessions with 30 trials each. The two sessions were
labeled “I” and “II” for convenience’s sake. In Session I, there were two images of female faces
(Player A, Player B) displayed on the left and the right half of the computer screen serving as
proposers in each trial. In Session II, two different images of male faces (Player C, Player D)
were displayed, making a total of four facial images. The two sessions followed the same
procedure. The order of the two sessions was counter balanced such that Participant One would
go through the Session I, followed by Session II; and Participant Two would go through Session
II, then session I. The roles of the faces were also counter balanced such that players A and C
served as the unfair proposers in half of all games; players B (D) served as the unfair proposer in
the other half of the games. The counter balancing is illustrated by the Table 2 in the appendix. A
screen shot of a decision-making trial is available in Figure 1.
10
The location arrangement for the images was randomized to eliminate possible locational
confounding effects: For each trial, the probability of player A (or C) being on the left half of the
screen with player B (or D) being on the right was equal to the probability of player B (or D) on
the left and player A (or C) on the right. The facial photos were taken from a photo set used in a
previous study on appearance and attractiveness (Kacir, Wang, Jones, & Nutt, 2010). All the four
players had relatively similar attractive ratings in the previous study, and were Caucasian adults
without abnormalities including unnatural hair colors, exotic piercings and visible tattoos.
Before the actual game, the participants were explained the rules of the game. After that,
they were told to play a practice session in order to get familiarized with the game and to resolve
any questions they had. During the actual game, the faces appeared first on the screen. Then,
after two seconds, a line stating “You can have $X and I will take the remaining $Y was
presented under each of the two faces. X represented the amount to be offered to the participants;
Y referred to the amount left for the proposer to take if his/her offer was accepted. After four
seconds, a button labeled “ACCEPT” appeared under the offer amount from both proposers.
Once the buttons appeared, the participants would make a decision to accept the offer from one
of the two players by clicking the ACCEPT button under that player. Accepting one of the two
offers would automatically reject the remaining offer and the game will proceed to the next trial.
Once the decision was made, a blank screen would appear for 2 seconds, and a new trial would
start.
The amounts from the FP offers were all within the fair range (e.g. $5-$6 out of $10)
while the amounts from the UP were 80 percent of the times lower than the fair range (e.g. $0-$3
out of $10) and 20 percent of the times higher than the fair range (e.g. $7-$10 out of $10). In
11
order to ensure a learning effect in the participants, the UP always made lower offers in the first
five trials in each session. The orders for the remaining trials were set to random.
A character evaluation scale survey was administered immediately after each session. On
the character evaluation scale, there were the facial photos of the proposers from the game
sessions. For each of the four photos, the participants were asked to evaluate the corresponding
player on several traits including how fair the proposer was. The other traits were implemented
to mask the true intentions of the evaluation. All ratings were done on a 5 point Likert Scale
ranged from 1- strongly disagree to 5 - strongly agree.
Procedure
After being explained the rules and benefits of the experiment, the participants were
given the consent form. After signing the consent form, the participants completed a general
background questionnaire, including items such as age, sex, ethnic background, level of
education, marital status and health status. Then they were escorted to a computer to play the
new ultimatum game and fill out the character evaluation scales. Finally, each participant was
debriefed, paid, and thanked for his/her participation.
12
RESULT
Acceptance Rates of the FPs
The participants’ acceptance rates of the FPs were calculated separately depending on
whether the UPs were making lower or higher offers than the fair range. As expected, during the
trials when the UP made lower offers than the FP, a vast majority of the participants chose to
accept the FP’s offers. The average acceptance rate for the FP was .967 (SD = .09) for both age
groups. During the trials when the UP made higher offers, the mean acceptance rate was .3719
with the standard deviation of .4129. A simple Student’s T-test showed that this was
significantly greater than 0 (T (58) =8.11, p<0.01). Table 3 shows the detailed report on the
acceptance rates.
Fairness Sensitivity and Acceptance Rates
The fairness sensitivity score was calculated by the formula below,
(
where
)
(
)
is the fairness rating for the fair proposer during the Session i; similarly,
is
the fairness rating for the unfair proposer during the Session i (i = 1 or 2).
A simple linear regression analysis was used to test whether there was a linear
relationship between the participants’ FS score and the acceptance rates of the FPs during the
trials when the UPs made higher offers. The results of the analysis suggested that a significant
linear relationship existed between the participants’ FS level and their acceptance rate of the FPs
13
(F (1, 58) = 8.044, p < 0.01). Specifically, the acceptance rates1 and the participants’ FS had a
positive correlation (R2 = .093). The linear relationship is illustrated by the following equation.
Figure 2 shows the graphic representation of the linear relationship between participants’ FS
score and their acceptance rate of the FP.
Age Difference in the FS Scores and Acceptance Rates
Independent sample T-tests were used to determine whether the age difference exists in
the FS scores and the acceptance rate of the FPs between the two groups. Contrary to what was
expected, no age difference was found in the participants’ FS scores (T (58) = .71, p = .48). The
older participants had a mean FS score of 1.76 (SD=1.12), in comparison to the younger
participants’ mean FS score of 1.93 (SD = .9). Correspondingly, no significant difference was
found between the two age groups for the acceptance rate of the FP (T (58) = -1.361, p = .178).
Moreover, as shown in Figure 3, the distributions of the acceptance rates appeared to be bimodal.
Therefore, a non-parametric test (Mann-Whitney U Test) was used to determine whether the
distribution of acceptance rates was the same between the two age groups. No statistical
significance was found (p = .357), which indicated that the distribution of the acceptance rates of
the FP appeared to be the same in the two age groups.
1
For the sake of convenience, at this point onward, unless specifically stated, the acceptance
rates of the FPs during trials when the UPs made higher offers will be expressed as “the
acceptance rates”.
14
DISCUSSION
In order to address the limitations possessed by the classic ultimatum game, this study
used a new ultimatum game to investigate people’s decision making when there were multiple
offers to choose from and with repeated trials. In addition, this study explored the potential
effects of fairness sensitivity on people’s decision-making behaviors through a new ultimatum
game. Lastly, this study investigated whether younger and older adults would behave differently
when making decisions.
As the studies of the classic ultimatum game have suggested, when being treated unfairly,
people would often experience negative emotions, and consequently may refuse to cooperate
(Dunn et al., 2010; Pillutla & Murnighan, 1996; Straub & Murnighan, 1995). This study adds to
the existing body of literature on ultimatum bargaining. After receiving unfair treatments
repeatedly, people would be more likely to continually refuse to work with the source from
which they have received unfair treatments.
Evidently, one could safely conclude that when the UPs made low offers, people would
overwhelmingly favor the FPs’ offers. This is consistent with both the Rational Choice Model
and the fairness preference hypotheses. However, contradictory to the Rational Choice Model,
when the UPs made higher offers than the FPs did, mixed results were obtained.
Majority of people demonstrated the two-distinct behavior patterns when making such
choices. As shown in the present study, about 41% of the participants only accepted the offers of
higher value; while about 23% of the participants only accepted the offers from the FPs, even
when the UPs made higher offers. It is clear that the participants who demonstrated the second
behavioral pattern violated the Rational Choice Model. In other words, by choosing the FPs
15
during when the UPs made higher offers, these participants were not making decision to
maximize their gain utilities. It is likely that, by perceiving the UPs as unfair, these participants
favored the FPs over the UPs even when the UPs made higher offers. This behavioral pattern is
often observable in people’s everyday lives (e.g. brand loyalty).
In addition, the present study contributed to the ultimatum game literature by adding a
direct, context-relevant measure of people’s sensitivity level to fairness. Based on the findings
from previous studies using the original game, it is suggested that a perceived unfairness may
lead to a higher rejection rate (Dunn et al., 2010).The findings of the present study further
suggested that a person’s sensitivity to fairness could partially predict his/her behavior in
ultimatum bargaining situations. In more detail, those who displayed a higher sensitivity to
fairness may be more inclined to be uncooperative to the unfair proposers.
Surprisingly, contrary to the speculation of the previous studies that older adults were
more fairness oriented than younger adults, this study did not find significant age difference on
the FS scores. In addition, the participants from the two age groups did not show significant
difference in their decision-rules during the game sessions.
Contributions
To sum up, through the use of the modified ultimatum game design, the present study
was able to contribute to the existing literature on how people would behave in a more complex
bargaining situation. Moreover, although a great number of previous studies considered fairness
as an influencing factor in people’s decision-making, attitudes toward fairness were rarely
measured and analyzed directly. In an attempt to address this, the present study examined
16
directly how a person’s sensitivity to fairness would influence his/her decision-making rules in
ultimatum bargaining.
Limitations and Future Directions
The present study had some limitations. It was difficult to distinguish the motivation
behind those participants who chose the FPs over the UPs when it would be more advantageous
to accept the UPs’ offers. Through the informal post-game interview, it was revealed that there
were two possible reasons why some participants chose the FPs over the UPs. Some people
expressed that they “did not like the way he/she [UP] played the game”, and thus refused to
accept the UPs’ offers; some expressed that they preferred playing with the FPs, because the FPs
were “fair and more consistent”. Based on the information gathered through the interview, it is
possible that punishing the UP, rewarding the FP or doing both may motivate people to choose
the FPs over the UPs.
For future directions, it would be useful to further investigate people’s reactions to such
multiple-player decision-making games. Specifically, it is important to determine the reasons
why some people choose the FPs rather than the UPs during trials when the UPs made higher
offers. Through modifying the game rules (i.e. rejection lead to proposers getting the full
amount), Abbink, Bolton, Sadrieh and Tang (2001) suggested that rejections in the classic
ultimatum game could be punishment toward the proposers. Similar modifications can be
implemented to the new game design to achieve better understanding of people’s behavioral
patterns. Furthermore, it is also important to continue investigating the nature of fairness
sensitivity, as well as the roles it plays in people’s decision-making processes. Additionally,
contrary to the previous studies, the present study did not find any significant age effect in
17
people’s decision-making behaviors. More research is needed to address why age effect has not
been found in the present study.
18
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21
APPENDIX A. TABLES
22
Table 1
Background Information
Survey Items
Old (N=30)
Age Groups
Young (N=30)
1. Age
Mean = 66.65
SD = 7.08
Mean = 21.53
SD = 2.57
2. Gender
Female: n=20 (66.6%)
Male: n=10 (33.3%)
Female: n=25 (83.3%)
Male: n=5 (16.7%)
3. Marital Status
Single: n=1 (2.5%)
Married: n=17 (56.6%)
Divorced: n=6 (20%)
Widowed: n=6 (20%)
Single: n=28 (93.3%)
Married: n=2 (6.7%)
4. Ethnic Background
Caucasian: n=23 (76.7%)
Asian: n=7 (23.3%)
Caucasian: n=20(66.7%)
Asian: n=8(26.7%)
Other: n=2 (6.7%)
5. Education (highest
degree achieved)
High school: n=7 (23.3%)
Bachelor: n=10 (33.3%)
Advanced: n=13 (43.3%)
High school: n=23 (76.7%)
Bachelor: n=6 (20%)
Advanced: n=1 (3.3%)
23
Table 2
Illustration of Counter Balancing of Facial Photos and Session Orders By Participant ID
Participant ID First Session Face A
Face B
Face C
Face D
1
I
Unfair
Fair
Unfair
Fair
2
II
Fair
Unfair
Unfair
Fair
3
I
Fair
Unfair
Fair
Unfair
4
II
Unfair
Fair
Fair
Unfair
Note. The above order cycled for every four participants, so participant 5 experienced the
same game procedure as participant 1 had.
24
Table 3
Acceptance rates of the UPs by offer level and age groups
Offer Level
Mean Acceptance rates (Standard Deviation)
Young
Old
Total
Lower
.9670 (.09)
.9756 (.09)
.971 (.09)
Higher
.3094 (.37)
.4344 (.44)
.3719 (.41)
Note. The offer level refer to whether the UPs made low (common) or high (rare) offers.
25
APPENDIX B. FIGURES
26
Figure 1 Screen Shot
Note. In the top screen shot, the Unfair Proposer (right) made a lower offer; in the second screen
shot, she (left) made a rare high offer.
27
Figure 2. Scatter Plot of FS Score and Acceptance rate (FP)
Note. Higher FS score represent higher sensitivity to fairness. The acceptance rate of the FP
referred to the acceptance rate during trials when the UP made higher offers.
28
Figure 3. Acceptance Rate Distributions by Age
Group
Note. The acceptance rates refer to the acceptance rate of the FPs during trials when the UPs
made high offers
29
APPENDIX C. FORMS
30
Character Evaluation Scale
Please rate the above proposer based on the following characteristics by checking the
corresponding box.
Strong
Disagree
Friendly
Handsome
Generous
Fair
Social
Untrustworthy
Strong Willed
Comments:
Disagree
No opinion
Agree
Strongly
Agree
31
Character Evaluation Scale
Please rate the above proposer based on the following characteristics by checking the
corresponding box.
Strong
Disagree
Friendly
Handsome
Generous
Fair
Social
Untrustworthy
Strong Willed
Comments:
Disagree
No opinion
Agree
Strongly
Agree
32
Character Evaluation Scale
Please rate the above proposer based on the following characteristics by checking the
corresponding box.
Strong
Disagree
Friendly
Beautiful
Generous
Fair
Social
Untrustworthy
Strong Willed
Comments:
Disagree
No opinion
Agree
Strongly Agree
33
Character Evaluation Scale
Please rate the above proposer based on the following characteristics by checking the
corresponding box.
Strong
Disagree
Friendly
Beautiful
Generous
Fair
Social
Untrustworthy
Strong Willed
Comments:
Disagree
No opinion
Agree
Strongly Agree
34
The Background Questionnaire
(Please circle your choice)
1. Age:
2. Gender:
M
F
3. Marital Status:
Single
Married
Divorced
Widowed
4. Ethnic Background:
African-American
Asian
Caucasian
Hispanic
Native American
Other (Specify):
5. Is English your first language?
Yes
No
6. Major/Occupation:
7. Highest degree achieved:
8. How would you rate your overall health at the present time?
Poor
Fair
Good
Excellent
9. Have you been diagnosed with stroke, neurological or psychiatric disorder,
head injury, and dementia?
No
Yes (Specify):
35
36
37
APPENDIX D. HSRB APPROVAL LETTER
38