International Journal of Social Robotics (2021) 13:851–862
https://doi.org/10.1007/s12369-020-00658-5

Robot Likeability and Reciprocity in Human Robot Interaction: Using
Ultimatum Game to determinate Reciprocal Likeable Robot Strategies
Eduardo Benítez Sandoval1,2· Jürgen Brandstatter2 · Utku Yalcin2 · Christoph Bartneck2

Accepted: 7 May 2020 / Published online: 19 June 2020
© Springer Nature B.V. 2020

Abstract
Among of the factors that affect likeability, reciprocal response towards the other party is one of the multiple variables
involved in social interaction. However, in HRI, likeability is constrained to robot behavior, since mass-produced robots will
have identical physical embodiment. A reciprocal robot response is desirable in order to design robots as likeable agents forhumans. In this paper, we discuss how perceived likeability in robots is a crucial multi-factorial phenomenon that has a strong
influence on interactions based on reciprocal robot decisions. Our general research question is: What type of reciprocal robot
behavior is perceived as likeable for humans when the robot’s decisions affect them? We designed a between/within 2 × 2 × 2
experiment in which the participant plays our novel Alternated Repeated Ultimatum Game ( A RU G) for 20 rounds. The robot
used in the experiment is an NAO robot using four different reciprocal strategies. Our results suggest that participants tend toreciprocate more towards the robot who starts the game and using the pure reciprocal strategy compared with other combined
strategies (Tit for Tat, Inverse Tit for Tat and Reciprocal Offer and Inverse Reciprocal Offer). These results confirm that theNorm of the Reciprocity applies in HRI when participants play ARUG with social robots. However, the human reciprocal
response also depends on the profits gained in the game and who starts the interaction. Similarly, the likeability score isaffected by robot strategies such as reciprocal (Robot A) and generous (Robot C). and there are some discrepancies in the
likeability score between the reciprocal robot and the generous robot behavior.
Keywords Human–robot interaction · Reciprocity · Game Theory · Alternated Repeated Ultimatum Game · Cooperation

1 Introduction
Likeability is associated with friendly, cooperative and prosocial behaviours [2] such as extroversion, agreeableness,and lack of over-conscientiousness [3,4]. Moreover, likeability is a very complex phenomenon involving behaviours,manners, perceived intelligence, similar socio-cultural con-

B Eduardo Benítez Sandovale.sandoval@unsw.edu.au

Jürgen Brandstatterjuergen.brandstatter@pg.canterbury.ac.nz
Christoph Bartneck
christoph.bartneck@canterbury.ac.nz
1

University of New South Wales, Arts and Design, Sydney,
Australia

2

HITLab NZ, University of Canterbury, Christchurch, New
Zealandtext, interests, and even physical attractiveness, acceptability
and popularity. In other words, a person is considered likeable when he or she is emotionally well-adjusted and she or
he can be engaged in high-quality relationships.
Future acceptance of social robots will be associated with
their likeability. The measurement of likeability in robots ismostly associated with their degree of anthropomorphism[5] and the design of the embodiment. However, the likeability of state-of-art robots cannot be based on unique physicalfeatures. Robots of the same model will be mass produced;
therefore, they will have identical physical embodiment.
Sooner rather than later, they may lose their novelty effectand their appearance might become ordinary. Hence, thelikeability of the robots will be determined mostly by their
behaviours towards humans.
We propose that people will find robots likeable depending on three main conditions, each of which are independentof their external characteristics: (A) How successfully can
the robot perform tasks that users expect? (B) How does
the robot behaviour match the interest and personality of

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the users? For instance, does the robot present slightly
unexpected behaviour to keep the attention of a curious
user, and more predictable behaviour for users who prefer routine? (C) How does the human–robot interaction
emotionally benefit the user? This third point leads us to
ask if humans would drive the robot’s behaviours basedon their own self-interest or show reciprocal behaviour
towards the robots if they received benefits from doingso.
Several studies have been performed on the acceptanceand likeability of the robots. Very often, the research studying likeability in HRI has used images, videos of robots[7] or robots with limited interactivity in controlled conditions [8–10]. These studies have not considered the incentives
for the human and the reciprocally beneficial human-robot
interaction and its impact in perceived likeability. Our study
aims to contribute in this regards and focuses on measuring likeability when humans interact with a humanoid
robot.
On the other hand, very recently, some studies revealed
that humans tend to be reciprocal towards robots and computers when playing Decision Games and when asking for help
[11–14]. Furthermore, humans try to reciprocate to robots
even when this breaks the social rules as our study in bribery
describes [15]. These facts will be considered as possibly
contributors to the likeability of the robots. In our previous
work users found the anti-social behaviour of a robot briber
likeable [11]. Hence, we aim to investigate how different
robot behaviours, particularly reciprocal behaviours, could
have an effect in the robot-likeability.
We consider that reciprocity will determine how meaningful the interactions between humans and the agents will
be. Although we cannot claim that robots and humans willdevelop relationships as deep as friendship or love; we
consider that in the future robots with a more engaging, interesting and likeable behaviour will have a greater chance ofbeing accepted and popular among users despite their lackof physical attractiveness [16,17].
In this paper, our goal is to describe quantitatively the
relationships between robot-likeability defined in the Godspeed questionnaire [18] and the reciprocal interactions
between NAO robots and humans playing 20 rounds ofAlternated Repeated Ultimatum Game ( A RU G). Furthermore, we investigate two factors: robot’s reciprocal decisions(R R D) and robot’s reciprocal offer (R R O) over the courseof the interaction. We measure their impact in measurements such as: participant’s reciprocal decisions (P R D),
participant’s reciprocal offer (P R O), and participant’s profit(P P). We also measure the correlation between four different reciprocal strategies (R R D × R R O)used by the robot
playing A RU G and the likeability scores and preference
ranking.

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2 Literature Review
Likeability is an important topic in HHI and HRI because
humans tend to establish their relationships based on how
much they like (or dislike) certain kinds of people. The useof the term “likeable” is broad. Our short definition is: easy
to like and having pleasant or appealing qualities [6]. Extensive research has been done on likeability in human–humaninteraction. Some of this can be analogous to robot-likeability
research in that people try to find the way to be likeable when
they are apart of a new group or in a new environment. For
instance, likeability in adolescents [3,4] and social groups living in unfamiliar environments have been investigated [19].
In other cases, when a person starts a friendship or a romantic relationship they tend to do it based on likeability criteria.
Over time, physical attractiveness and other factors tend to
become less important in the building of a relationship, andfocus more on the emotional and material benefits mutuallyobtained.
However, likeability can be a contradictory phenomenon.
Apparently people can find likeable some behaviours that are
not necessarily reciprocal, cooperative and mutually beneficial. The nicest behaviour of a person is not necessarily
the most likeable for others; sometimes it is perceived as
boring. Conversely, in certain cases, a subject can be aggressive, arrogant and manipulative, but despite that, people
might still find them likeable [2]. Public figures such as
rock-stars, athletes and politicians sometimes show rude oreven disgusting behaviour but are still fascinating to the
public.
Apparently, our reciprocal relationships with robots arealmost as complex as our relationships with other humans.
The question we raise in the domain of HRI is: Can human
likeability be translated to a human–robot interaction? Inother words: Should robots show likeable/unlikeable reciprocal behaviour in order to be liked? Our previous studies
have shown that robots showing unexpected behaviours oreven unacceptable behaviours received higher scores in likeability as we discuss in [15] and [11].

2.1 Likeability and Reciprocity
Fehr and Gachter discuss reciprocity in terms of positive andnegative reciprocity [20–22]. They claim that in response
to friendly actions, people are frequently much nicer andmuch more cooperative than predicted by the self-interest
model; conversely, in response to hostile actions they are
frequently much more nasty and even brutal. Likeability andReciprocity are strongly connected; if we consider somebody
friendly and pleasant it is because generally we receive areciprocal treatment from this person or agent.
Although Kahn et. al. proposed reciprocity as a benchmark in the evaluation of future human–robot interaction

International Journal of Social Robotics (2021) 13:851–862[1], reciprocal behaviour in human–robot interaction has notbeen sufficiently explored. Robot designers have tried to
implement highly cooperative behaviours in robots but thisis not necessarily the best solution for encouraging social
interaction between humans and robots. In this paper, wesuggest that reciprocal behaviours (no necessarily cooperative behaviour) in robots can offer another approach in termsof an effective, useful and engaging social interaction.
In order to measure reciprocal behaviours related withlikeability in HRI, we use the insights of Game Theory.
Using this approach, it is possible to take quantitative measurements of profit obtained, number of cooperations andnumber of reciprocations in order to correlate them with certain human characteristics into simplified social scenarios
called decision games. Several studies have been done using
decision games (Ultimatum Game, Prisoner’s Dilemma andRock, Paper Scissors Game) as experimental setup in HRI[11,27–29].

2.2 Alternated Repeated Ultimatum GameThe Ultimatum Game has offered a valuable instrument tomeasure different psychological and economic measures. For
instance, Burnell et. al., have researched the optimal strategies without fairness when the Repeated Ultimatum Game isplayed [23] and Oosterbeek found common behavioural patterns regardless of cultural differences in a meta analysis into
the Repeated Ultimatum Game [24]. Besides, individual differences related with reputation [25], attractiveness [26], andthe strategies displayed during the Ultimatum Game (U G)
have been studied in depth in the economics field. These
concepts are strongly linked with the concept of likeability
that we use in this study (face-to-face setup). In our case,likeability is more related to the robot behaviour and its reputation during the game rather than its physical appearance,
anthropomorphism or embodiment.
Similarly, Repeated Ultimatum Game is a well-known
game used very often in Behavioural Economics experimental research [22]. In the original version, a proposer decides
how to distribute a certain amount of money and the recipient can decide whether or not to accept the distribution.
If accepted both players keep the money and conversely if
the recipient rejects the offer both players lose the money.
For this study we propose a novel configuration of the Ultimatum Game called Alternated Repeated Ultimatum Game(A RU G). In this version the mechanics of the game are thesame as in the original version. However, in our version theplayers alternate roles every round. For instance, player 1
is proposer in round 1 and recipient in round 2 and so on.
This is done in order to measure the inter-dependencies interms of cooperation and reciprocation created for the reciprocal responses and offers of our robots. In this study we had

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nine predetermined money distributions programmed into
the robot for each role it played.

3 Research Questions
The aim of the experiment is to analyse the responses interms of the robot’s likeability (R L), participant’s reciprocal decision (P R D), participant’s reciprocal offer (P R O),
and participant’s profit (P P). We further describe these measurements in Sect. 4.6. The A RU G involved two factors:
robot’s reciprocal decisions (R R D) and robot’s reciprocal
offer (R R O). Additionally, there is a between-condition
called group (G) that describes if the participant or the robot
starts the game (A RU G). In order to evaluate our aim we
propose four research questions:
1. Is Robot Likeability (R L) significantly affected by the
robot’s reciprocal decisions (R R D), robot’s reciprocal
offer(R R O) and the starter of the interaction (G) individually or interactively?
2. Is the participant’s reciprocal decision (P R D) significantly affected by the R R D, R R O and G individuallyor interactively?
3. Is participant’s reciprocal offer (P R O) significantly
affected by RRD, RRO and G individually or interactively?
4. Is participant profit (P P) significantly affected by the
R R D, R R O and G individually or interactively?
5. What is the correlation between robot likeability R L
and participant’s reciprocal decision P R D, participant’s
reciprocal offer P R O, and participant’s profit P P?
6. Do participants rank the robots differently depending on
the robot’s factors and is this difference significant?

4 Method
We use 20 rounds of A RU G as a decision game able to conduct a mix between/within 2 × 2 × 2 factors experiment. In
which the between factor is G, in other words, the starterof the session is human or robot. The within factors (2 × 2)
are robot’s reciprocal decisions (R R D) and robot’s reciprocal offer (R R O). R R D has two conditions: Tit for Tat’s
decision (T f T ) and Inverse Tit for Tat’s (I − T f T ). Similarly, R R O has two conditions: Reciprocal Offer (R O) andInverse Reciprocal Offer (I − R O).
T f T means that the robot follows the decision of the participant. For instance, if the participant accepted the robot’soffer in round X, the robot will accept the participant’s offer
in round X+1. Conversely, I − T f T consist of rejecting theoffer in the current round if the participant accepted the offer
in the previous round.

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International Journal of Social Robotics (2021) 13:851–862

Fig. 1 The figure illustrates the differences between R O and I − R O
in two consecutive rounds. In I − R O if the participant is selfish, therobot reciprocates generously and vice-versa

Table 1 The four factor used in the experiment
Robot/human starting

Robot’s Rec. Decision (RRD)

Robot’s Rec. offer (RRO)

TfT

I–TfT

RO

Robot A

Robot B

I–RO

Robot C

Robot DRO Reciprocal offer, I–RO inverse reciprocal offer, Tf tit for tat, I–TfT
inverse tit for tat

In R R O factor, R O condition means that the robot
matches the participant’s offer in terms of distribution. The
robot and participant have 9 predetermined options to distribute the dollars between them. The options are: Human 10
dollars:Robot 90 dollars, Human 20 dollars:Robot 80 dollars,…, Human 90 dollars:Robot 10 dollars. An additional condition exist when the robot starts the game. In this case therobot initiates his game with an offer of Human 50 dollars:
Robot 50 dollars. For example, in R O if the participant offers
a distribution such as: Human 10 dollars: Robot 90 dollars,
the robot offers the same reciprocal distribution in the next
round; i.e. Human 90 dollars: Robot 10 dollars. In I − R O
the robot offers a non-reciprocal distribution. To illustrate, if
the participant offers a distribution such as: Human 10 dollars: Robot 90 dollars, then the robot will offer an inverse
distribution such as Human 10 dollars: Robot 90 dollars in
the next round. See Fig. 1.
These factors are aimed to be perceived by the participants
as the individual strategies of four different robots (A, B, C,D) in the experimental conditions. We named the robots inthis way in order to make them easier to remember for theparticipants. The strategies deployed by the four robots were
the result of four unique combinations of the robot’s reciprocal decision (R R D) and robot’s reciprocal offer (R R O)
conditions; T f T × R O is Robot A, I − T f T × R O is Robot
B. T f T × I − R O is Robot C and I −T f T × I − R O is Robot
D. See Table 1. A video demonstrating how the A RU G was
played during this study can be found at this URL video
https://www.youtube.com/watch?v=a5B3A9LVwpE.

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Fig. 2 Setup of the experiment. The participant can choose from ninedifferent options and the robot can point out the options

4.1 Experimental Setup
We rotated the order of the robots using the Latin square
method, with interaction between the participant and therobot consisting of both visual and audio communication.
All four robots showed the same minimal level of verbal
interaction and animosity to minimise the emotional impact
on the perceived likeability of each robot. Offers where madeboth verbally and visually by the robot, first stating its offerand secondly point to the card containing its offer. The robot
received the participants responses to its offers verbally using
speech recognition. The participants presented their offers
visually by showing the robot a card containing their offer.
Apart from verbally relaying their offers/responses and general guidance, robots also verbally rephrased participant’s
actions. For instance, after a participant offered Human 70
dollars: Robot 30 dollars, the robot would say “You offer me
30. Ok, I accept it”.

4.2 Materials
We used one NAO robot programmed in Choreographe andPython, presented under the disguise of four different robots
to participants. Experiment layout had an “Accepted” area
and a “Rejected” area for the offers to be put into accordingly.
A fixed layout of cards with offer rates was placed before
the robot, to which it pointed with its finger to indicate its
offer. Twenty units of cards for each offer rate were placedin a similar fashion in front of the participants, and wereused for making offers to the robots and also for tracking theaccepted/rejected amounts. A laptop was placed on a nearby
desk for the online questionnaire. See Fig. 2.

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WelcomeConsent Form
Demographics

Session 3

Session 4

TIPI & Godspeed

TIPI & Godspeed

Robot Swap

Robot Swap

Player says
‘Yes’ or ‘No’

“Round X, Pleaseshow me your card”
1

5

3

2

Interaction Loop
Human: $40

Player holds
the card

Explanatory Clip

Robot: $60

2

A,B,C,D
Latin Square

4“You give me X.”“Ok. l accept”
3

Session 1

Session 2

Results

TIPI & Godspeed

TIPI & Godspeed

Winnings ($)

Robot Swap

Robot Swap

5

4

1

“Round X, l give you X.
Do you accept? Please
say Yes or No.”

“Ok. l reject”

Human starting
condition
Robot starting
condition

Fig. 4 Initialising the game depending if the human or the robot starts

Q&A

Fig. 3 Experimental proceduree

4.3 Process in Human Starting Condition
In both conditions, we performed individual sessions of four
A RU G games. Participants were welcomed and led into theexperiment room to receive a brief description of the experiment. After reviewing and signing a consent form, they wereasked to fill out an online questionnaire that gathered demographic data including their previous experience with robots.
Then, participants were then provided with an ID number andshown a short video that demonstrated the experimental process. When they were ready, the robot was activated throughits feet bumpers, after which it asked the for the ID numberof the participant, who replied verbally. After introducing
the mechanics of the experiment to participants, we started
the experiment and discretely observed the first 2 rounds
from outside of the room to make sure the participant was
not experiencing technical issues, and then we left the room.
After each session we came back to change the robot and calculate the results of the sessions to compare them with data
recorded in the robot and left again so that participants would
not feel pressured by having an observer. Participants filled
an online survey with the TIPI and Godspeed questionnaire
after each experiment and a comment section regarding theiropinions of the robot. After each session, the experimentercame to count the cards, re-stack them and pretend to replace
the robot with the next one. Once all four sessions were done,
participants filled out the ranking about how much they liked
each robot. Then any questions were briefly answered. At
the end of the experiment participants were compensated by
0.03% of their accumulated symbolic earnings which ranged
between $6.00 and $13.00. This experiment was approvedby the Human Ethics Committee of the University of Canterbury [HEC APPLICATION 2015/36/LR-PS]. See Fig. 3
to see the simplified flow of the rounds.
A NAO robot was introduced as the participant’s opponent
in A RU G. The robot wore a tag that displayed “A”, “B”, “C”
or “D” to emulate the perception that the participant was facing four different robots (whereas we used a single robot andreprogrammed it between sessions). The robot asked for the
ID number to start the session in each condition. Once thesession started the robot requested the participant to take thefirst turn, and asked the participant to show the card that displayed the offer they wanted to give. By default all four robots
were programmed to accept the first offer to prevent participants from identifying its action pattern on the first round.
Starting from the 2nd round the robot started its programmed
reciprocation patterns. We designed it in this way to be consistent with the assumption of the cooperative behaviour ofsocial robots. After each session the robot was taken out of
the room, and while the participant filled out the survey, wasreprogrammed for the next reciprocation pattern and its tag
replaced accordingly, then represented to the participant as
their new opponent.
At the beginning of each round the robot announced thenumber of the current round, and then if it was the robot’s turnto offer it pointed to the proper card. It then asked whether
the participant accepted or not and using the speech recognition system distinguished whether the participant respondedwith a Yes or No. If it was the participant’s turn to offer, therobot told the participant to hold the card bearing the offer,and then it gave its response based on its reciprocation pattern using its vision system. At the end of the session of 20
rounds, the robot announced that the session was over. The
participant then was asked to fill out a survey on their opinion
on the robot and their perceived earnings. After completingthe final survey they were given the amount of their earningon that session. See Fig. 4 to see the experimental process
per participant.

4.4 Process in Robot Starting Condition
In robot starting condition, once the experiment started therobot informed the participant that it would take the first turn.
It then proceeds to make a fair offer (Human 50 dollars: Robot
50 dollars) as default. This was used in all four sessions toprevent the participants from identifying its action pattern inthe first round. From the second round the robot began its
programmed reciprocation patterns.

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4.5 Participants
We contacted participants via university boards, dedicatedwebsites, and Facebook groups in the city. After disposingof the data from sessions which were not carried out successfully due to human error or a robotic malfunction, we
had 38 participants in our experiment: 20 in the robot starting condition and 18 in the human starting conditions. Halfof the participants were male. 42% of the participants hadprior experience in interacting with robots in demonstrations
and studies. 5% were high school graduates, 42% were currently in college, 21% had college/university degrees, 13%
were currently in graduate or professional school, and 18%
had graduate or professional degrees. 68% of the participants
currently had jobs. 37% were from Oceania (Australia, New
Zealand and other countries), 29% were from Asia (China,
India, Japan and others), 18% were from Europe and 16%
were from North and South America. The average age was
25 years old (SD = 6.99).

4.6 Measurements
The measurements in the experiment are: robot’s likeability (R L), which is an item of the Godspeed questionnaire
series [18], participant’s reciprocal decision (P R D), meansthat the participant follows the behaviour of the robot in theimmediate next round, participant’s reciprocal offer (P R O),
means that the participant matches the offer of the robot in theimmediate next round, and participant’s profit (P P) obtainedby the participant in each condition. Finally, participants dida general ranking of their favorite robots.

5 Results
We performed a three-way mixed ANOVA (2 × 2 × 2) inwhich the between factor is group G and the within factors
are robots’s reciprocal decision (R R D) and robot’s reciprocal offer (R R O). The measurements were robot’s likeability(R L), participant’s reciprocal decision (P R D), participant’s
reciprocal offer (P R O), and participant’s profit (P P). Seemeasurements, interaction effects, main effects, means andstandard deviations of each measurement in Tables 2, 3, 4
and 5.
The first research question; (Is Robot Likeability (R L)
significantly affected by the robot’s reciprocal decisions(R R D), robot’s reciprocal offer (R R O) and the starter ofthe interaction (G) individually or interactively?) investigated the effect of G, R R D, R R O on robot’s likeability(R L). There is a statistically significant three-way interaction effect between R R D, R R O, and G, (F(1, 36) = 6.072,
p = 0.019). The outliers were kept in the analysis. There
was a significant two-way interaction (F(1, 19) = 4.452,

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International Journal of Social Robotics (2021) 13:851–862

p = 0.048) between in R R D and R R O appeared in thehuman condition but not in the robot condition (F(1, 17) =1.930, p = 0.183). There is a significant simple main effect(F(1, 19) = 4.902, p = 0.039) of R R D in the human startergroup condition and a significant main effect of R R D in the
robot group, (F(1, 17) = 10.742, p = 0.004). See Table 2
for interaction effects, means and standard deviations. The
robot C (T f T × I − R O)received a high score in likeability(4.12 out of 5) when the participant started the interaction. In
this interaction, the robot reciprocates low offers with highoffers. Robot C might be perceived as a generous robot.
The second highest likebility score is granted to Robot A(T f T × R O) when the robot started the interaction. Robot
A has the second highest R L score when reciprocal to theoffers of the participant, R L = 4.01. Similarly, the results
suggest that there is a significant interaction effect because
the reciprocal strategy of the robot (R R D × R R O) affecting
the likeability score and independence of the starter of thegame (robot or human). Also, the starter of the game has asignificant impact in the robots’ likeability score. See 2.
The second research question (Is the participant’s reciprocal decision (P R D) significantly affected by the R R D,
R R O and G individually or interactively?) investigates theeffect of robot’s reciprocal decision (R R D), robot’s reciprocal offer (R R O) and group (G), on participant’s reciprocal
decision (P R D). A statistically significant three-way interaction between R R D, R R O and G that affects P R D wasfound, (F(1, 36) = 12.665, p = 0.001). There is a significant two-way interaction between R R D and R R O in thehuman condition, (F(1, 19) = 15.092, p = 0.001). However, there is a non significant two-way interaction between
R R D and R R O (F(1, 19) = 1.294, p = 0.271) in the robot
condition. There is a significant simple main effect of R R D
in the human condition, (F(1, 19) = 5.608, p = 0.029).
There is a significant simple main effect of R R O in thehuman condition, (F(1, 19) = 32.589, p < 0.001). Besides,
there is a significant simple main effect of R R D in the robot
condition, (F(1, 17) = 11.018, p = 0.004) and there is asignificant simple main effect of R O in the robot condition,(F(1, 17) = 104.171, p < 0.001). See Table 3 for interaction
effects, main effects, means and standard deviations. Outliers
were not removed from the data. These results suggest thatparticipants tend to reciprocate more towards Robot A when
the robot started the game with a fair offer. Participants reciprocate on average 9 times during the game. Robot A used a
Tit for Tat strategy and Reciprocal offers (T f T × R O). Participants reciprocate on average 8.2 times towards Robot C(T f T × I − R O). Robot C was reciprocating low offers withhigh offers. Robot C has the highest likeability score of therobots. See Table 3.
In the third research question (Is participant’s reciprocal offer (P R O) significantly affected by robot’s reciprocal
decision R R D, robot’s reciprocal offer R R O and group G

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Table 2 Interaction effects, main effects, means and standard deviations of robot’s likeability (R L)
Measurement

Three-way interaction F(1, 36) =RL

G*RRD*RRO

6.072, p = 0.019

RL Human starts Two-way interaction
RRD*RRO
Main effects
RL Human starts RRD

Human*TfT*RO Human*TfT*I–RO Human*I–TfT*RO Human*I–TfT*I–RO
3.66 (0.85)

4.12 (0.81)

3.61 (0.85)

3.51 (1.08)

Robot*TfT*RO

Robo*TfT*I–RO

Robot*I–TfT*RO

Robot*I–TfT*I–RO

4.01 (0.69)

3.97 (0.79)

3.16 (0.85)

3.49 (0.84)

F(1, 19) =4.452, p = 0.048
F(1, 19) =4.902, p = 0.039
F(1, 17) =RL Robot starts

RRD

10.742, p = 0.004

G Group, R R O robot’s reciprocal offer, R R D robot’s reciprocal decision, T f T tit for tat, I − T f T inverse tit for tat, R O reciprocal offer, I − R O
inverse reciprocal offer, R L robot likeability
Table 3 Interaction effects, main effects, means and standard deviations of participant’s reciprocal decision (P R D)
Measurement

Three-way
interaction

PRD

PRD HumanF(1, 36) =Human*TfT*RO

Human*TfT*I–RO

Human*I–TfT*RO

Human*I–TfT*I–RO

12.665, p = 0.001

7.25 (2.17)

8.2 (2.04)

5.35 (1.73)

3.0 (1.65)

Robot*TfT*RO

Robo*TfT*I–RO

Robot*I–TfT*RO

Robot*I–TfT*I–RO

9 (0)6.94 (2.6)

2.89 (2.14)

1.78 (1.9)

Two-wayInteraction
effects

F(1, 19) =RRD*RRO

15.092, p = 0.001

Main effects

F(1, 19) =RRO

32.589, p < 0.001

6.1 (2.825)

5 (3.36)

TfT

I–TfT

RRD

5.608, p = 0.029

7.8 (2.1)

3.3 (2.245)

TfT*ROTfT*I–RO

I–TfT*ROI–TfT*I–RO

7.25 (2.17)

8.20 (2.04)

5.35 (1.73)

3.0 (1.65)

RO

I–RO

P R D participant’s reciprocal offer, R R O robot’s reciprocal offer, R R D robot’s reciprocal decision, T f T tit for tat, I − T f T inverse tit for tat,
R O reciprocal offer, I − R O inverse reciprocal offer, R L robot likeability

individually or interactively?), we investigate whether R R D,
R R O and G affects participant’s reciprocal offer (P R O). We
found that there is not an significant three-way interaction
effect (F(1, 36) = 0.824, p = 0.370). There is a statisticallysignificant main effect of R R O, (F(1, 36) = 4.151, p =0.049), a statistically significant main effect of the R D D,
(F(1, 36) = 8.775, p = 0.005) and a between subject mainsignificant effect of G, (F(1, 36) = 8.137 p = 0.007). There
are not interaction effect but we can observe that the participant’s reciprocal offer (P R O) is independently alignedwith with the pure reciprocal offer R O of the robot, the purereciprocal robot’s strategy (T f T ), and the G (robot/humanstarter). The highest number of participant’s reciprocal offer(P R O) happened when the robot started the interaction andthe second highest number of participant’s reciprocal offers
happened when the robot used R O offers. See Table 4 forthe means and standard deviations.

The fourth research question (Is participant profit (P P)
significantly affected by the R R D, R R O and G individually or interactively?), searchs for the strategies that affectparticipant’s profit (P P). There is not a statistically significant three-way interaction between strategy, offer andgroup, (F(1, 36) = 0.053, p = 0.819). Outliers were notremoved from the data. However, there is a statisticallysignificant two-way interaction between R R D and R R O,
(F(1, 36) = 34.006, p < 0.001). Statistically significant
main effects (F(1, 36) = 76.536 p < 0.001) of R R D were
found. In addition, R R O present a significant main effect(F(1, 36) = 66.515, p < 0.001). We can observe thatthe combination of R R O and R R D affects participant’s
profit. Robot C provides the highest profits to the participant(1219.21 hypothetical dollars) being reciprocal and accepting low offers paying them high (T f T x I − R O). The second
most profitable interaction P P = 752.63 happened with

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International Journal of Social Robotics (2021) 13:851–862

Table 4 Interaction effects,
main effects, means andstandard deviations ofparticipant’s reciprocal offer(P R O)PRO

Main effects

F(1, 36) =

RO

RRO

4.151, p = 0.049

2.7 (2.49)

1.6 (1.96)

TfT

I–TfT

2.4 (2.733)

1.9 (1.763)

Human

Robot

1.575 (1.833)

2.79 (2.6)

RRD

8.775, p = 0 .005

G

8.137 p = 0.007

I–RO

Table 5 Interaction effects, main effects, means and standard deviations of participant’s profit (PP)
Measurement

Two-way interaction effects

F(1, 36) =PP

RRD*RRO

34.006, p < 0.001

Main effects

F(1, 36) =RRO

66.515 p < 0.001

660.1 (211.297)

963.6 (384.106)

TfT

I–TfT

985.9 (400.46)

637.8 (130.57)

RRD

76.536 p < 0.001

Robot A using a pure reciprocal strategy of (T f T × R O).
See Table 5 for the means and standard deviations.
In order to answer our fifth research question ( What isthe correlation between robot likeability R L and participant’s reciprocal decision P R D, participant’s reciprocal
offer P R O, and participant’s profit P P?), we determine
the Spearman’s correlation between the P R D, P R O, P P
and R L. Preliminary analysis showed the relationships to be
monotonic, as assessed by visual inspection of the scatterplots. There was a positive moderate correlation between
P P and P R D, rho (152) = 0.513, p < 0.0001 and apositive moderate correlation between R L and P R D, rho(152) = 0.308, p < 0.0001. Similarly, there was a weak positive correlation between R L and P P, rho (152) = 0.226,
p < 0.005 and a negative weak correlation between R L
and P R O, rs(152) = −0.225, p < 0.005. These correlations led us to speculate that robot’s likeability is affectedby the profits obtained by the participants and participant’s
reciprocal decisions. In other words, the robot is more likeable when the interaction leads to higher profits and higher
number of participant’s reciprocal decisions but negatively
correlated with the participant’s reciprocal offer. Participant’s
reciprocal decision (P R D) and profit are correlated when theparticipants figure out the reciprocal patterns. However, these
correlations are not significant and it is not possible to draw
generalised conclusions from this analysis. See Table 6.
Finally, for the sixth question (Do participants rank the
robots differently depending on the robot’s factors and is thisdifference significant?), in order to determinate the favourite
robots, we asked the participants to rank them. We conducteda chi-square goodness-of-fit test to determine whether participants ranked one of the four robots significantly higher
than the other robots. The minimum expected frequencies
were 9.5 for the general ranking, 5 for the Human starter

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TfT*ROTfT*I–RO

I–TfT*ROI–TfT*I–RO

752.63 (251.93)

1219.21 (387.05)

567.63 (97.24)

707.89 (122.48)

RO

I–RO

Table 6 There are significant moderate and weak correlations amongrobot’s likeability (R L), participant’s reciprocal decision (P R D), participant’s reciprocal offer (P R O), and participant’s profit (P P)
p < 0.02

PRD

PRO

RL

0.308

−0.225

PRD

PP
0.226
0.513

group and 4.5 for the Robot starter group. The robot A using
T f T × R O (pure reciprocal) was ranked highest followed
by robot C using T f T × I R O as reciprocal strategy. All chisquare values for the four different robots are not significant.
However, these rankings could suggest that the Norm of Reciprocity is accomplished for reciprocal robots and generous
robots under this experimental setup. However, more studies
are required to generalise these conclusions. See Table 7.

6 Discussion and Conclusions
The robot’s likeability (R L) is affected by the three-way
interaction effect consisting of who starts the interaction (G),robot’s reciprocal decision (R R D), and robot’s reciprocal
offer (R R O). Hence, a two-way ANOVA was performedby separated groups. A two-way interaction effect between
R R O and R R D was found. The robots displaying a reciprocal decision T f T were rated higher in likeability than the
robots using a inverse reciprocal offer I − T F T . Indeed, therobot in the T f T and I − R O condition had a higher likeability score than the other robots in the human condition.
In this case I − R O is beneficial for the robot in the T f T
condition (Robot C) but not for the robot A in T f T and R O
condition (pure reciprocal). In the case of the robot starter

International Journal of Social Robotics (2021) 13:851–862

859

Table 7 Ranking of robot reciprocal conditions T f T × R O and I −T f T × R O received the best rankings due to probably the consistent
reciprocal strategy and the economic reward received by the participant
respectively
Favourite

1st

2nd

3rd

4th

T f T × RO

13

13

7

5

T f T × I − RO

9

9

9

11

I − T f T × RO

8

9

11

10

I − T f T × I − RO

8

7

11

12

General ranking of the robots

Human starter of ARUG
T f T × RO

5

8

3

4

T f T × I − RO

5

5

6

4

I − T f T × RO

5

5

5

5

I − T f T × I − RO

5

2

6

7

Robot starter of ARUG
T f T × RO

8

5

4

1

T f T × I − RO

4

4

3

7

I − T f T × RO

3

5

5

5

I − T f T × I − RO

3

4

6

5

condition, there is a main effect of R R D such that T F T(M = 3.98, SD = 0.73) lead to higher scores of likeability
than I − T f T (M = 3.32, SD = 0.85). In the robot startergroup the robot in the T f T and I − R O (Robot C) condition also had higher scores. The likeability of the robot due
to the T f T and I − R O could be explained by the unexpected behaviour of the robots towards the participants andthe nature of I − T f T that reciprocate low offers with higher
offers as is explained in the next paragraph.
The study performed in [15] shows similar results interms of the likeability of robots performing unexpectedbehaviours even when these behaviours were breaking social
rules. Moreover, these results slightly match with the resultsof the ranking of the robots after all the experimental sessions. The favourite robots in the ranking were firstly the
robot in T f T and R O (pure reciprocal) condition and thenT f T and I − R O condition. Probably the robot in T f T and
I − R O was perceived as a generous robot and that is why
people liked it. They didn’t expect that offering low pays
being rewarded with higher pays from the robot during the
A RU G. Participants liked the unexpected financial benefitand “nice” behaviour of the robot. On the other hand thepure reciprocal could be perceived as easier to understand.
This knowledge can be useful for the design of future robot
behaviours, offering unexpected robot behaviours and benefits to the users (low or high rewards) in order to keep to theuser engaged with the interaction.
In terms of participant’s reciprocal decision (P R D), a significant three-way interaction effect existed. Then, a two-way
ANOVA was performed for each group. A two-way interac-tion effect between R R O and R R D was found. Participants
reciprocated more towards the robots in the T f T condition than in the I − T f T in the human group. See Table 3
for means and standard deviations. Participants reciprocatedmore towards the robot in the T f T and I − R O condition in
the same group. These results are in line with our results inprevious studies [15] in terms that the Norm of Reciprocity[30] applies in HRI. People tend to reciprocate towards robots
that show evident reciprocal behaviours. Furthermore, theynaturally reciprocate more towards the robot in T f T and
I − R O because it offers higher financial benefits: the robot
made higher offers when the participant offered little money.
No cases of humans offering high amounts of money toreceive little money from the robot appeared during the experiment. In the case of the robot starter group, R D had a main
effect in the decisions of the participants. They reciprocate
more to the robot in T f T condition, (M = 7.97, SD = 2.09)
than the robot in I − T f T (M = 2.3, SD = 2.07). Similarly,
R O had an impact in P R D in the robot group. Participants
reciprocate more frequently when the robot used a reciprocal
offer in R O condition (M = 5.94, SD = 3.44), than when
the robot was using I − R O (M = 4.36, SD = 3.44). Similarly to the human group, reciprocal strategies play a role thatlead us to think that the Norm of Reciprocity rules the reciprocal behaviours in HRI. Moreover, the use of simultaneousreciprocal different strategies has a very defined outcome
in terms of P R D and R L. In this case, these interactive
behaviour could be attributed to the easy identification of areciprocal pattern along the 20 rounds of the A RU G. These
patterns are easy for the participant to comprehend, allowing
for easy prediction and manipulation of future benefits in thegame.
In terms of participant’s reciprocal offer (P R O) there are
no interaction effects at all. There is a main effect of R O. Participants reciprocated the offer of the robot more frequently
in R O (M = 2.7, SD = 2.49) than in I − R O (M = 1.6,
SD = 1.96). There is also a main effect of R D; participants reciprocate the robot’s offer more often in the T f T
condition (M = 2.4, SD = 2.733) than in the I − T f T ,(M = 1.9, p = 1.763). We also found that the initiator,
makes a significant difference in P R O. Participants reciprocate the offer less (M = 1.575, SD = 1.833) when they
start the A RU G than when is the robot who starts the game,(M = 2.79, SD = 2.6). Apparently, the robot is capable ofestablishing a reciprocal pattern when it starts the game thatis easily followed by the participant. These findings suggest
that, ideally, robots starting an interaction can establish thepatterns of interaction in an understandable way for the user.
There was not a three-way interaction effect affecting
participant’s profit (P P). However, there is a two-way interaction effect between R R O and R R D that can be explainedwith the main effects. In R R O condition participants had
a higher profit with the robot in the I − R O condition

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(M = 963.6, SD = 384.106) than in the R O condition,(M = 660.1, SD = 211.297). Similarly in R R D, participants had a higher profit with the robot in T f T condition,(M = 985.9, SD = 400.46) than with the robot in I − T f T ,(M = 637.8, SD = 130.57). In other words, the combinationof T f T and I − R O are the most profitable for the participant. The combination of the reciprocal movements and low
reciprocal offers made the participant quickly notice that they
can obtain higher profit if they keep making negative reciprocal offers (low offers) because the robot will offer high offers
in the next round. The main effect of the R R O made more
profitable the strategies that imply more R R D. For instance,a higher reciprocal offer coming from the robot made it easierfor the participant to accept it and do a reciprocal movementin the next round. Hence, it is possible to claim that humans
would prefer to receive higher benefits from the robot compared to what they offer it in other kinds of interaction. Thisis comparable to what happens in human–human interaction
when a person provides a service to another.
In terms of the correlations between robot’s likeability(R L), participant’s reciprocal decision (P R D), participant’s
reciprocal offer (P R O), and participant’s profit (P P), further studies are required due to the moderate and weak natureof the correlations.
Finally, participants ranked the robots at the end of theexperiment. They had a general view of all the possible
behaviours of the robots and freely decided their favourite
robot in their own terms as we can note in their final
comments. Although the chi-square analysis does not offer
significant results, the ranking gives some clue for futurestudies. People ranked T f T × R O as their favourite reciprocal strategy and T f T × I − R O as their second favourite.
In the case of T f T × R O, the pure reciprocal robot, this
could be explained due to the fact that they could detect a
reciprocal pattern easily compared to the other robots which
had more unexpected behaviour as we explained before. For
Robot C (T f T × I − R O), we observed a reciprocal patternperceived as generous due to the higher reciprocal offer of
the T f T × I − R O strategy when the participant made alow offer. This reciprocal strategy of Robot C gave to theparticipants who noticed it early more money compared tothe other strategies.

6.1 Conclusions
This study suggests that humans accomplish the Norm ofReciprocity proposed by Gouldner [30] in the domain of HRI
in terms of robot’s likeability, participant’s reciprocal decision, participant’s reciprocal offers, and participant’s profits.
We found that participants liked the pure reciprocal robot
strategy with T F T × R O and T f T × I − R O conditions
and obtained more benefits from the combination of thesestrategies. T f T and I − R O robot was likeable due to the123International Journal of Social Robotics (2021) 13:851–862

unexpected behaviour bringing economic benefits to the participant.
This study is in line with the results of previous studies[11] and [15]. It is possible to say that the Norm of Reciprocity rules the interaction of decision games in HRI in
terms of simultaneous use of reciprocal strategies in A RU G
which is a relatively complex interactive scenario. When thehuman starts the interaction, participants reciprocate towards
the robot that shows evident reciprocal behaviours, specifically with the robot in the T f T and I − R O condition due
to the higher economic benefits(the participant offers little
money to the robot and the robot made higher offers).
When the robot starts the interaction, participants reciprocate the offer (P R O) and the decision, (P R D) in the T f T
and R O conditions; more often than when the human starts
the interaction due to the robot establishing a pattern which iseasy to follow. Furthermore, if the robot starts the interaction
with a 50%:50% offer, it could be perceived as a fair offer.
This perception could be the cause of the significantly higher
reciprocation towards this robot. These findings could be useful in the future for designing complex reciprocal behaviours
for different social applications such as health-care, education or entertainment. Moreover, different layers of reciprocal
behaviours could work together in order to keep the attentionof the user and provide benefits by different means.
Besides, the participant’s profit (P P) is affected simultaneously by R R D, and R R O as main effects. Consequently
participants obtain a higher profit with the robot in the T f T
and I − R O condition. Although this high profit, our analysis
of correlations in the fifth question, shows a weak correlation
between PP and RL. In other words, Profit and Likeability
are not strongly correlated in Human Robot Interaction.
Although the people received a higher profit from the
Robot C using T f T and I − R O strategy they ranked Robot
A; the pure reciprocal robot (T f T and R O), higher when
they compare all the robots. This is likely because this robot
offered easily comprehensible and predictable outputs during
the A RU G. However, in the experimental session participants found more likeable the T f T and I − R O robot
condition. In other words, a likeable robot as Robot C would
not necessarily be the favourite robot when it is comparedwith other robots in a ranking. However, robots showing
in some reciprocity; such as the Robot A (T f T × R O)
and (T f T × I − R O) would be more beneficial for theusers than those that do not so. Again looks like reciprocal
robots are more likeable that generous robots when they arecompared with other robots showing less obvious reciprocal
behaviours.
These findings should be considered for the future designof interactive behaviours in social robots. More investigation is required to these findings to real interactive scenarios.
However, We expect that in the near future these interac-International Journal of Social Robotics (2021) 13:851–862

tive patterns based in games can be useful in more complex
human–robot interactions.

6.2 Limitations and Future Work
Considering the complexity of the reciprocal behaviors this
scenario presented, further studies are required in order todeterminate stronger correlations between likeability andreciprocity. Besides, future scenarios in the real world willoffer more challenging conditions for robot designers, requiring them to create even more complex robot behaviours. In
addition, the measurement and analysis of other items in the
Godspeed scale as well as other psychological measurements
could be added to the study and to similar experiments.
Acknowledgements The authors would like to acknowledge the support from NZi3 and NEC NZ Corp., in particular to David Hummand Glen Cameron. We also want to thank the support of the UC
International Doctoral Scholarship, CONACYT Scholarship and John
Templeton Foundation (Award ID 36617). Additionally, we wish to
thank the other members in the HIT Lab NZ for their useful advice andgood ideas. Thanks to Philippa Beckman and Julian Beiboer for theirextensive proofreading. This experiment was approved by the Human
Ethics Committee of the University of Canterbury under the reference
2013/23/LR-PS

Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict ofinterest.

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International Journal of Social Robotics (2021) 13:851–862
Utku Yalcin was a PhD student at the HITLab NZ, at the Universityof Canterbury. He is experienced in design from brief to final productdelivery (digital design, design for print, manufacture ready physicalproduct design, game design, animation).

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Eduardo B. Sandoval is Scientia Associated Lecturer at Universityof New South Wales, Sydney, Australia. Dr. Sandoval explores new
research fields in robot games and robot design in human–robot interaction (HRI). He holds a PhD in Human Interface Technology fromthe HIT Lab NZ at the University of Canterbury, New Zealand. Dr.
Sandoval aims to develop a model of reciprocity in HRI to makesocial robots useful and engaging. This could be applied in research
related to influence, altruism and fair treatment of robots and encourage long-term human–robot interaction. Eduardo has a Master degree
in Industrial Design by the National Autonomous University of Mexico (UNAM) and a Bachelor degree in Bionic Engineering by the
National Polytechnic Institute of Mexico (IPN). He was visiting student in the Laboratory of Intelligent Robotics in Osaka University(2011). Dr. Sandoval has served as an guest editor for the International
Journal of Social Robotics. He has been reviewer for the HRI, HAI,
RO-Man, DIS other conferences and journals for several years. He wasawarded by the UNAM with the Alfonso Caso medal in 2014 as thebest student of his postgraduate program. Since 2020 he is part of the
Mexican National Research System because his contributions in thearea of HRI.

Jürgen Brandstatter holds a PhD in Human Interface Technology fromthe HIT Lab NZ and works on humanoid robots and its influenceon humans when using the oral language. He studied at the Vienna
University of Technologies and Copenhagen University and holds a
master in Computer Science. In his master thesis Dr. BRandstetter
looked at a new computer game type called “Positive Impact Game”
and worked on a rehabilitation games for humans who need to learn
how to work with an arm prosthesis. He is also a full-stack developer,
android developer, prototyping enthusiast, User-experience designer,entrepreneur and design thinking tutor.

123

Dr. Christoph Bartneck is an associate professor and director of postgraduate studies at the HIT Lab NZ of the University of Canterbury.
He has a background in Industrial Design and Human-Computer Interaction, and his projects and studies have been published in leading
journals, newspapers, and conferences. His interests lie in the fieldsof Social Robotics, Design Science, and Multimedia Applications.
He has worked for several international organizations including theTechnology Centre of Hannover (Germany), LEGO (Denmark), Eagle
River Interactive (USA), Philips Research (Netherlands), ATR (Japan),
Nara Institute of Science and Technology (Japan), and The Eindhoven
University of Technology (Netherlands). Dr. Bartneck is a memberof the New Zealand Institute for Language Brain and Behavior, theIFIP Work Group and ACM SIGCHI. He has an outstanding publication record, including leading journals and conferences. Dr. Bartneck has served as an associate editor for the International Journalof Social Robotics and the International Journal of Human Computer
Studies. Furthermore, he is the editor in chief of the Entertainment
Robotics section of the Entertainment Computing journal. He has beena member of the program committee of the human–robot interaction
conference for several years. He has organized several workshops at
the CHI conference and is frequently invited as a speaker for symposia and conferences. He has been invited to present his work by
Carnegie Mellon University, the Future University Hakodate, the University of Venice, the Stedelijk Museum and the Pictopia Festival. The
press regularly reports on his work, including the New York Times,
New Scientist, Natuurwetenschap and Techniek, Volkskrant and Dutch
national television and radio stations.