games MDPI
Article
Lab-Sophistication: Does Repeated Participation in Laboratory
Experiments Affect Pro-Social Behaviour?
Tiziana Medda 1
, Vittorio Pelligra2
and Tommaso Reggiani 3
'4
'5
'*
E S O M A S Department, University of Turin, Corso Uniove Sovietica 218,10134 Turin, Italy;
tiziana.medda@unito.it
Department of Economics and Business, University of Cagliari, V. le S. Ignazio, 17, 09123 Cagliari, Italy;
pelligra@unica.it
Cardiff Business School, Cardiff University, Colum Drive, Cardiff CF10 3EU, U K
M U E E L Lab, Masaryk University, Lipová 507/41a, 60200 Brno, Czech Republic
IZA—Institute of Labor Economics, Schaumburg-Lippe-StraiSe 5-9, 53113 Bonn, Germany
Correspondence: reggianit@cardiff.ac.uk
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updates
Citation: Medda, T.; Pelligra, V ;
Reggiani, T. Lab-Sophistication: Does
Repeated Participation i n Laboratory
Experiments Affect Pro-Social
Behaviour?. Games 2021, 22, 18.
https://doi.org/10.3390/gl2010018
Academic Editors: Riccardo Ghidoni
and Ulrich Berger
Received: 17 January 2021
Accepted: 19 February 2021
Published: 22 February 2021
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4.0/).
Abstract: Experimental social scientists working at research-intensive institutions deal inevitably
with subjects who have most likely participated in previous experiments. It is an important methodological
question to know whether participants that have acquired a high level of lab-sophistication
show altered pro-social behavioural patterns. In this paper, we focus both on the potential effect of
the subjects' lab-sophistication, and on the role of the knowledge about the level of lab-sophistication
of the other participants. Our main findings show that while lab-sophistication per se does not
significantly affect pro-social behaviour, for sophisticated subjects the knowledge about the counterpart's
level of (un)sophistication may systematically alter their choices. This result should induce
caution among experimenters about whether, in their settings, information about lab-sophistication
can be inferred by the participants, due to the characteristics of the recruitment mechanisms, the
management of the experimental sessions or to other contextual clues.
Keywords: lab-sophistication; experimental methodology; external validity; pro-social behaviour; co-
operation
JEL Classification: D03; D83; C91; C92
1. Introduction
Since its first appearance, experimental analysis of economic behaviour has provoked
sceptical reactions and criticism [1]. The methodological rigour has steadily increased
among the community of experimentalists as an antidote to this criticism. In fact, "Just as
we need to use clean test tubes in chemistry experiments, so we need to get the laboratory
conditions right when testing economic theory" ([2]). One of the most critical conditions
refers to the characteristics of the subjects with whom the experiments are run. They can,
in fact, affect the generalizability of the experimental results in various ways [3,4]. In particular,
a potential weakness derives from the predominant use of students as experimental
subjects. The extensive use of pools of students, in fact, may generate problems related to
their intrinsic characteristics (the majority of them are "WEIRD" college students,1
coming
from Western industrialized, rich and democratic countries2
), but the voluntary basis of the
See [5,15],
[16].
Games 2021,12,18. https://doi.org/10.3390/gl2010018 https://www.mdpi.com/journal/games
Games 2021,12,18 2 of 14
enrolment process may also produce self-selection that in turn may lead to the formation
of experimental pools with peculiar characteristics.3 , 4
A further potential source of modification arises from the combined effect of "location"
and "number": given the prevalent location of labs in university campuses5
and the everincreasing
number of experiments run in each of these labs, students tend to accumulate
game-specific and laboratory sophistication through repeated participation in experimental
sessions [22].
We investigate whether familiarity with the laboratory context, "lab-sophistication",
given by a long record of participations in laboratory-based studies, alters the subjects'
behaviour in a set of experimental games used to capture pro-social behaviour.
More precisely, by lab-sophistication, we do not mean the experience accumulated
in a specific game, but the general familiarity with the standard procedures that characterise
laboratory sessions and the framing of the decision situation usually implemented
in behavioural experiments [23,24]. We examine whether having taken part in several
experimental sessions—having gained familiarity with the lab context and certain knowledge
about the working of an experimental session—generates systematic variations in the
pro-social choices made by individuals.
To distinguish subjects in term of their lab-sophistication, we adopt the following criteria:
The highly sophisticated subjects (H-types) are subjects with 15 or more participations
in lab experiments; the unsophisticated subjects with low laboratory context familiarity
(L-types) are those whose number of participations in experiments is between 1 and 5.
Moreover, we explore a closely related issue: whether knowledge about the counterpart's
lab-sophistication plays a systematic role in shaping the behaviour of experimental
subjects in simple games focusing on cooperation and pro-sociality. Even though experimenters
do not provide explicitly information about the lab-sophistication of their
participants neither before nor during the experiment, due to peculiarities of the recruiting
system (e.g., small pool) or the management of the experimental sessions (e.g., waiting
times outside the lab and socialization of the participants), it cannot be excluded that
subjects infer lab-sophistication level of other subjects.
In this context, it becomes crucial to ask whether the knowledge about the other
players' level of lab-sophistication potentially plays a role in modifying subjects' choices.
Does knowing that the player with w h o m you are interacting is a lab-sophisticated (labunsophisticated)
player affect your choices? Further, if you are a lab-sophisticated (labunsophisticated)
player, does the knowledge that the player with whom you are interacting
is a lab-sophisticated (lab-unsophisticated) player affect your choices?
Thus, in this paper we focus on the role of subjects' lab-sophistication and on its
consequences on pro-social behaviour.
Our results show that, while lab-sophistication per se does not significantly affect the
behaviour of participants, information about the counterpart's level of lab-sophistication
may systematically alter the choices of those, among the participants, that are already
lab-sophisticated, especially in the situations like the ultimatum game and the trust game.
We do not have a theoretical explanation for this phenomenon which, however, we think
represents an important practical and methodological element that needs to be considered
when (i) recruiting subjects [25], (ii) designing, (iii) running a lab experiment, (iv) and when
interpreting or assessing its conclusions.
The remainder of the paper is organised as follows. In Section 2 we provide a brief
overview of the literature and outline our contribution with respect to the studies that most
closely relates to the present paper. In Section 3, the experimental design and procedures
3
See [17,20], among others.
4
Guillen et al. [21] show how males and well performing subjects (in monetary terms) are more likely to return in lab and this may introduce a bias to
the conclusions derived from observing their behavior.
5
According to a list drafted by the Laboratoire d'Economie Experimentale de Nice, only two out of 166 experimental-economics labs in the world are
not located on a university campus, and only one is independent and not related to academic activities (https://orsee.unice.fr/public/labs.php/,
last accessed on 5 March 2020).
Games 2021,12,18 3 of 14
are described. In Section 4, the testable hypotheses are derived. Section 5 describes the
statistical analyses and the main results. Section 6 concludes.
2. Related Literature
The need to clarify the role of laboratory experience in economic experiments has given
rise, in the last few decades, to a small, but growing, stream of studies: Harrison et al. [26]
and Benson and Faminov [27] discuss industrial organization experiments; Marwell and
Ames [28], Isaac et al. [29] and Bolton [30] focus on bargaining-game experiments; and,
more recently, Matthey and Regner [31], Capraro and Cococcioni [32], Xue et al. [33],
Benndorf et al. [34] and Conte et al. [35] have published on this subject.
Among the more recent contributions, Conte et al. [35] focus on a public good game
and show that subjects' contribution behaviour is affected by the number of previous
participations. However, the effect is stronger if subjects have gained such experience
taking part in experiments involving the same typology of game compared to the experience
gained in experiments implementing different games. Focusing on cooperation in one-shot
interaction, Capraro and Cococcioni [32] analyse the history-dependent dynamic process.
Authors run a standard two-person Prisoner's Dilemma in which participants are randomly
assigned to either of two conditions: (i) Time pressure condition, which measures intuitive
cooperation, and (ii) time delay condition, which measures deliberate cooperation. They
report that promoting intuition versus deliberation, has no effect on cooperative behaviour
among inexperienced subjects playing in a non-cooperative setting, and that experienced
subjects cooperate more than inexperienced subjects, but only under time pressure.
Xue et al. [33] test whether participants with greater experience in experiments will
have higher maximization rates in prepayment treatments. Their results show that individuals
who participated in many economics experiments do not choose differently to those
who are novices.
Matthey and Regner [31] and Benndorf et al. [34] provide the two contributions that
are closely related to ours. In the former, the authors consider data from three different
studies involving 'allocation games'; their results show that subjects with a higher number
of participations tend to be less generous, but this holds true only for participation in
experiments with similar tasks.
Benndorf et al. [34] main result is a null result, as they find no 'experience effect' for all
the games they consider, with the exception of the trust game, where they observe less trust
and trustworthiness from the experienced participants. Moreover, they find a moderate,
but significant, 'recruitment bias', in that experienced subjects tend to sign up for sessions
more often than inexperienced ones.
Our study contributes to the existing literature in several ways: first, we do not restrict
our scope to the effect of 'game-specific experience', as in [31,35]; we, in the contrary,
consider a multidimensional set of games to explore the broader effect generated by Tabsophistication'
intended as a certain degree of familiarity with laboratory's procedures
and settings. Second, our contribution differs from the previous studies, as far as the
quantitative definition of 'experience' is concerned. We denote, in fact, the subjects in our
pool as lab-sophisticated only if they have a considerably large number of lab participations
(at least 15). Matthey and Regner [31] consider subjects with at most 13 participations;
Conte et al. [35] define as 'experienced', subjects with, on average, 2.2 participations
in public-goods game and, 5.2 participations in experiments involving different games.
The paper by Benndorf et al. [34] is, in this respect, the closest to ours, as they consider
'experienced' subjects with at least 10 participations.
However, the most important difference between our study and the previous ones
concerns the role of information. From a methodological point of view this is a relevant
issue every time we study strategic interactions.
Games 2021,12,18 4 of 14
3. Experimental Design and Procedures
Our main goal is twofold. First, we investigate the effect of high versus low level of
lab-sophistication in decision-making experiments: we do this considering a set of simple
experimental games where fairness and reciprocity principles are concerned. Secondly,
we study the effect of the information about the counterpart's lab-sophistication level on
players' choices.
Exploiting data stored in the ORSEE recruitment system [36] of the University of
Cologne, we recruited, by design, participants with a very different number of participations
in previous experiments. We considered as lab-sophisticated subjects (H-types) individuals
with at least 15 previous participations in experiments, and as lab-unsophisticated
subjects (L-types) individuals with 1 to 5 participations.6
These two pools were chosen in
order to assure (i) an adequate number of subjects in each group and (ii) a sharp difference
in the level of individual laboratory sophistication between the two groups.
In our first experimental Condition 1 (CI), each participant was asked to make her
decisions in four standard experimental games, without receiving any information about
the level of lab-sophistication of the counterpart7
with whom she was randomly matched.
Here we investigated the effect of having acquired high lab-sophistication (H-types) vs.
low lab-sophistication (L-types), merely through repeated participations.
Moreover, we considered a within-subject design8
in which, in each session, each
subject played the same four standard games faced in C I , under the following conditions:
Condition 2 (C2): each participant is asked to make decisions in the four games knowing
that she is now randomly re-matched with a different subject having the same level of
lab-sophistication: H-type vs. H-type/L-type vs. L-type;
Condition 3 (C3): each participant is asked to make decisions in the four games knowing
that she is now randomly re-matched with a different subject (with respect to C I and C2)
having a different level of lab-sophistication: H-type vs. L-typ/L-type vs. H-type (see
Table 1).
Table 1. Experimental design.
Treatments Matching Rule Information
CI Random No information
Hvs. H
C2 and Information
L vs. L
Hvs. L
C3 and Information
Lvs. H
The baseline condition CI was always played as the first in all six sessions, while the
ordering of the other two experimental conditions was randomized.9
In C I , subjects were
randomly paired and no further information was given to them. In C2 and C3, we revealed
the following information in the instructions:
C2: for H-type [L-type]: " . . . in this situation, you will face a different counterpart
who has a H I G H [LOW] level of experience. That is, a subject who has participated in
many [in few] experiments";
We excluded from the recruiting phase the cluster of registered subjects having 0 participations simply because in relative terms this group represents
the less populated one (with the seasonal exception represented by the period in which freshers start their courses).
In the debriefing questionnaire, we asked to self-report about the number of experiments in which subjects had already participated in the past. The
correlation between this self-reported measure and the actual record provided by ORSEE is 0.89. This shows how subjects are quite aware about
their own individual level of lab-sophistication.
In our framework, the within-subject design entailed a substantial boost in terms of statistical power [37].
The randomization of C2 and C3 was important to enhance the robustness of our within-subject design [37] and the minimization of potential
cognitive experimental demand effect [38].
Games 2021,12,18 5 of 14
C3: for H-type [L-type]: " . . . in this situation, you will face a different counterpart
who has a L O W [HIGH] level of experience. That is, a subject who has participated in few
[in many] experiments".
Each session, by design, was balanced in terms of the number of H and L types. While
in CI, types were paired randomly, in C2 and C3 we used the ad-hoc assortative matching
rule, randomly pairing, within the same session, H (L) with H (L) or H (L) with L (H),
respectively, according to the specific condition.
We considered the following four games: Dictator Game, Ultimatum Game, Trust
Game and Prisoner's Dilemma Game1 0
. In the first three asymmetric games, all the subjects
played both role A (dictator/proposer/trustor) and role B (receiver/responder/trustee)
in a strategy-method fashion, and subsequent stages were not announced in advance. As
in [34], the games are played in a fixed sequence.1 1
At the end of each experimental session,
only one experimental condition, one game, one decision and one role for each player
(when considering the dictator game, ultimatum game and trust game) were randomly
selected and matched with their counterpart. Both players were paid in cash accordingly.
A l l subjects received EUR 2.50 as a show-up fee and got an average experimental
payment of E U R 7.50 for a 45-min lab session, including post-experimental surveys and
debriefing. The exchange rate between E C U (points) and Euros was 6 E C U = 1 Euro. A
total of six experimental sessions were conducted at the University of Cologne.1 2
The
experimental protocol was implemented using the Bonn Experiment System [39]. N o
feedback or results were received by participants before the end of the session.
4. Testable Hypotheses
According to [22], the subjects' behaviour changes over time as they get used to the
experimental setting. This fact represents an issue both in terms of intra-session learning
and inter-sessions familiarity and experience accumulation. A s far as it regards intrasession
learning, Binmore and Shaked [40] observe how the fact of getting used to the
experimental setting leads the subjects to converge to behavioural patterns that are closer to
the 'homo economicus' ones. Instead, Ding and Schotter [41] demonstrate how behaviour
evolves via social learning and may diverge dramatically from that envisioned by the
designer: experience with an incentive-compatible mechanism may not foster truthful
revelation if that experience is achieved via social learning. Nevertheless, it is not clear
whether this is also the case for inter-sessions sophistication. Matthey and Regner [31] and
Benndorf et al. [34] follow the same line in devising their hypothesis. From the conjecture
that lab-sophisticated players are more inclined to play equilibrium strategies than labunsophisticated
ones, and given our focus on fairness and reciprocity, we can derive the
following general hypotheses:
General Hypotheses: Highly lab-sophisticated individuals are more likely to play the
payoff-maximizing strategies than low lab-sophisticated ones. That means that we should
observe the following for high lab-sophisticated players compared to low lab-sophisticated
players1 3
: (a) a smaller offer in the Dictator Game; (b) a smaller average offer and a lower
minimum acceptable offer (MAO) in the Ultimatum Games; (c) a smaller average trust rate
and a smaller average level of trustworthiness in the Trust Game; and, finally, (d) a higher
defection rate in the Prisoner's Dilemma Game.
In testing these general hypotheses, we look separately at two possible channels
of influence: sophistication and information. The "pure effect of lab-sophistication" is
considered by comparing the behaviour of subjects with high and low lab-sophistication
when they have no explicit information about their own and their counterpart's level of
The tasks are provided in the Appendix A in the Supplementary Materials.
The logical sequence "Dictator Game —> Ultimatum Game —> Trust Game", moving from the baseline case (DG) to the more complicated (TG)
interaction, is implemented in order to favour the comprehension of the games and to avoid confusion. The Prisoner's Dilemma game is placed at
the end of the sequence in order to reduce the priming effect and because of the different nature of its dynamics.
The instructions are provided in the Appendix B in the Supplementary Materials.
The following specific predicitons are implied by the assumptions of selfish preferences and their being common knowledge.
Games 2021,12,18 6 of 14
sophistication. The "pure effect of information", on the contrary, is considered by looking at
changes in behaviour, given the same or different level of sophistication, when information
is provided.
Given our experimental design, the analysis is structured as follows:
The "pure effect of lab-sophistication" is detected by comparing H-types and L-types
in C I condition, where each participant is asked to make her decisions without receiving
any information about the level of laboratory sophistication of the counterpart;
The "pure effect of information" becomes apparent when we compare the behaviour
of one subject (e.g., H-type) when she is not informed about the type of the opponent (CI)
vs. the behaviour of the very same subject when matched with an opponent of the same
type (H-type in C2 condition), or when she is matched with a different type (L-type in
C3 condition).
5. Results
In Table 2, we summarize the characteristics of the participants. We recruited 134 subjects
(77 females and 57 males), with an average age of 25 and, balanced for the level of
laboratory sophistication: 67 H-type subjects (min. 15, max. 86 previous participations
in experiments, avg. 31) and 67 L-type subjects (min. 1, max. 5 previous participations
in experiments, avg. 3). Our conventional sample size was set before any data analysis.
Descriptive statistics show that, apart from the degree of laboratory sophistication, the two
pools are fairly homogeneous.
Table 2. Balance of the two experimental groups.
Characteristics
H Types
(n = 67)
L Types
(n = 67)
delta: A (H-L)
p-Value
Female, # (%) 37 (55.2%) 40 (59.7%) 0.60
Age, mean (min-max) 25.7 (19-60) 24.1 (18-65) 0.14
Behavioural Economics classes, # (%) 12 (18%) 11 (16.4%) 0.82
Games Theory classes, # (%) 23 (34.4%) 18 (26.9%) 0.35
5.1. Lab-Sophistication Effect
In the baseline condition (CI), players have no information about the lab-sophistication
level of the counterpart. This experimental condition allows us to address our first question
about the hypothesis of higher selfishness in H-type subjects with respect to L-type subjects.
Tables 3 and 4 report plain average outcomes by lab-sophistication level for the
different games. In all the games, average outcomes generated by H-types and L-types look
quite aligned and no systematic differences can be detected. The battery of between-subject
non-parametric tests systematically fails in rejecting the null hypotheses of equality of
means by level of sophistication (see Figure la-f).
Games 2021,12,18 7 of 14
Table 3. Summary and results (Dictator Game, Ultimatum Game and Trust Game by sophistication levels—CI).
Games Subjects Type Mean Std. Dev Median Min Max MWU—Z p-Value
Dictator Game
H-type
L-type
27.5
31.1
22.6
20.6
30
40
0
0
80
60
0.815 0.415
Ultimatum
Game Proposer
H-type
L-type
39.6
37.9
10.4
10.4
40
40
10
0
60
100
-0.755 0.450
Ultimatum
Game MAO Responder
H-type
L-type
29.3
26.6
13.9
14.7
30
30
0
0
50
50
-1.022 0.307
Trust Game Trustor
H-type
L-type
23.6
21.3
16.2
16.6
20
20
0
0
50
50
-0.946 0.344
Trust Game Trustee
Individual average return
H-type
L-type
21.7
24.4
14.0
13.4
26
29
0
0
40
45
1.072 0.283
Notes: Columns (8) and (9) are the results of a Wilcoxon-Mann-Whitney Nonparametric test.
Table 4. Summary and results (Prisoner's Dilemma by sophistication levels—CI).
Games Subjects/Type Defection Share Std. Dev Median Min Max X2
p-Value
Prisoner's
Dilemma
H-type
L-type
0.57
0.63
0.5
0.49
1
1
0
0
1
1
0.496 0.481
Notes: Columns (8) and (9) are the results of Chi-square test, Pearson chi2(l).
30
45
40
. 35
I 30
; «
! 20
:
15
10
5
0
Dictator Game: Offers by Sophistication Level
p = 0.415
31.09
27.49
L-iype H-type
(a) Dictator Game
Ultimatum Game: Offers by Sophistication Level
100
90
80
"S 70
I »
£. 50
I 40
0
1 30
20
10
0
p = 0.450
37.91 39.55
L-type H-type
(c) Ultimatum Game (Proposer)
Figure 1. Cont.
\
0.9
_0.8
£0.7
= 0.6
'•£ 0.5
So.4
8 0.3
u
0.2
0.1
0
Prisoner's Dilemma: Defection by Sophistication
Leve
p = 0.481
'00
90
I 8 0
° ™
1 60
I 50
< 40
1 30
I 20
10
0
(b) Prisoner's Dilemma
Ultimatum Game: MAO by Sophistication Level
p = 0.307
(d) Ultimatum Game (Responder)
Games 2021,12,18 8 of 14
Trust Game: Investment by Sophistication Level
p = 0.344
1
0.9
a 0.8 -
2 0.7
1 0.6
'S 0.5
% 0.4
§ 0.3
I 0.2
0.1
0
Trust Game: Sent Back by Sophistication Level
p = 0.283
I I
(e) Trust Game (Trustor) (f) Trust Game (Trustee)
Figure 1. (a-f) Choices in the four games by sophistication levels.
Table 5 goes one step further. It reports OLS regressions, allowing for a more comprehensive
parametric analysis. In this second analytical assessment, all the outcomes under
CI are regressed—in a between-subjects fashion—on a dummy variable, named H-dummy,
that identifies subjects with a high level of lab-sophistication. This coefficient informs us
about the different behaviour of H-type subjects compared to L-types ones captured in the
constant term.1 4
We further take into account a battery of covariates including risk attitude,
exposition to experimental/behavioural economics or game theory classes, gender, age, as
well as more conventional controls, such as monthly budget1 5
and nationality.1 6
Table 5. Sophistication effect (parametric analysis).
(1) (2) (3) (4) (5) (6)
Outcome DG UG_roleA MAO_UG_roleB TG_roleA TG_mean_roleB PD
H-dummy -3.789 2.041 3.378 3.145 -3.485 -0.078
(3.886) (2.302) (2.543) (2.904) (2.523) (0.090)
risk_attitude -1.123 ** -0.755 ** 0.442 0.264 -0.352 -0.011
(0.509) (0.301) (0.335) (0.380) (0.330) (0.012)
exp_class -1.785 1.142 6.347 * -6.284 0.648 -0.166exp_class
(5.485) (3.248) (3.566) (4.099) (3.561) (0.127)
game_class -2.006 -1.337 1.065 1.507 0.547 0.064
(4.603) (2.726) (3.001) (3.440) (2.988) (0.107)
male -6.928 * 4.277 * 3.899 8.051 *** 1.049 0.053
(3.943) (2.335) (2.600) (2.947) (2.560) (0.091)
age -0.127 0.055 0.038 -0.489 * 0.338 -0.001
(0.367) (0.217) (0.239) (0.274) (0.238) (0.009)
controls yes yes yes yes yes yes
constant 41.359 *** 39.993 *** 16.881 ** 30.437 *** 18.816 *** 0.887 ***
(L-type)
(10.431) (6.178) (6.772) (7.796) (6.772) (0.242)
Obs. 134 134 134 134 134 134
R-squared 0.109 0.139 0.145 0.136 0.049 0.081
Significance levels: * p < 0.10; * * p < 0.05; * * * p < 0.01. Standard deviations reported in parenthesis.
In Column (1), dictator-game allocations are analysed. The coefficient of H-dummy is
not statistically significant at any conventional level. High-type dictators do not implement
more greedy allocations compared to Low-type dictators. The allocation is marginally
lower for males and partially affected by the individual degree of risk aversion.
While the size of the estimates associated with L-type (constant terms) and H - d u m m y coefficients do not quite match the absolute level of the
corresponding averages reported in Table 4, due to partial effects captured by the control variables, from a qualitative perspective plain averages
and parametric estimates are compatible.
Dummy variables for different budget ranges: (i) E U R 0-300; (ii) E U R 301-1000; (iii) E U R 1501-2000; (iv) more than E U R 2001.
Dummy variable =1 if non-German.
Games 2021,12,18 9 of 14
Column (2) addresses ultimatum-game offers (role A). H-type proposers turn out
to be neither more generous nor more selfish than the L-type ones. A s for the dictators'
allocations, ultimatum proposals are marginally affected by risk attitude, and at the same
time marginally larger for male subjects. A s far as it concerns the minimal acceptable
offer (MAO) for ultimatum-game receivers (role B), the same tendency is confirmed (see
Column (3)).
Column (4) focuses on trust-game offers (role A). Additionally, in this case, H-type
subjects show a behavioural pattern that is comparable with the one exhibited by L-type
subjects. Trust appears to be significantly higher in males and marginally negatively
affected by age.
The same result is confirmed when the behaviour of trustees (role B) is addressed.
In Column (5), the outcome is represented by the mean amount returned by responders
and, it is computed at individual level, averaging the trustees' multiple responses under
the different trust profiles (strategy method). As for the previous outcomes, it is not
possible to detect any significant difference in the estimated return rates of the two different
populations; this indicates the absence of any differential effect due to a higher level of
lab-sophistication.
Column (6) analyses prisoner's dilemma strategies, implementing a linear probability
model. L-type subjects defect in the majority of the cases (constant term), and the H dummy
variable does not prove statistically significant difference between the two subjects'
population.
The general behavioural pattern that emerges from this first analysis proves to be
quite consistent across the six different archetypal interactions: H-type subjects do not
prove to be systematically more selfish than L-type subjects. A high individual level of
experimental lab-sophistication per se does not systematically push subjects towards more
selfish choices.
5.2. Information Effect
Exploiting within-subject outcome variations, we now focus on how individual behaviour
changes when information about the type of the opponent is revealed. In order to
do that, we contrast the outcome observed under the two further experimental manipulations,
C2 and C3, against the baseline outcome observed under C I , where the sophistication
level of the opponent in unknown.
Table 6 reports GLS regressions with random effects at subject level, as we are now
addressing within-subject outcome variations (see [42]).
L-types and H-types subjects are analysed separately. A l l the outcomes are regressed
on the two "condition" dummies, C2—same and C3—different, which identify the opponent's
level of laboratory sophistication (L or H types). Under C2 condition, subjects
are matched with players of the same type (H- types vs. H-types/ L-types vs. L-types),
while under C3 condition the matching is heterogeneous (H-types vs. L-types/ L-types
vs. H-types). We further control for a conventional battery of covariates, including risk
attitude, exposition to experimental/behavioural game-theory classes, gender, age, and
controls such as monthly budget and nationality.
Games 2021,12,18 10 of 14
Table 6. Information effect (parametric analysis).
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
n r i v U G _ U G _ U G _ M A O _ U G _ M A O _ TG_ TG_ TG_mean_ TG_mean_ r>n r>n
Outcome 1A i _ roleA_ roleA_ roleB_ roleB_ roleA_ roleA_ roleB_ roleB_ 1 tJ_ 1 tJ_Outcome
L-type H-type L-type H-type L-type H-type L-type H-type L-type H-type L-type H-type
C2—same -1.194 -1.522 -3.134* -2.388 ** -2.894 ** -0.299 0.001 -0.448 -0.567 -1.352 ** 0.030 0.001
(2.46) (1.95) (1.85) (1.09) (1.34) (0.85) (1.38) (1.38) (1.13) (0.66) (0.06) (0.05)
C3—different -4.075 * -2.343 -3.433 * -4.179 *** -0.313 0.299 -0.896 -4.030 *** -1.552 -2.101 *** -0.001 0.075 *
(2.46) (1.95) (1.85) (1.09) (1.33) (0.85) (1.38) (1.38) (1.13) (0.66) (0.06) (0.05)
risk_attitude -0.460 -1.212 -0.048 -0.484 0.788 * 0.150 0.613 0.346 0.340 -0.897 * -0.026 * 0.001
(0.64) (0.76) (0.48) (0.42) (0.48) (0.49) (0.53) (0.56) (0.50) (0.53) (0.01) (0.02)
exp_class -4.505 -0.827 -7.905 1.795 4.898 4.397 15.470 *** 0.024 -8.048 5.368 0.055 -0.249exp_class
(6.76) (8.01) (5.02) (4.47) (5.01) (5.15) (5.63) (5.97) (5.26) (5.57) (0.15) (0.18)
game_class -5.333 -0.455 -0.334 -2.302 -0.710 0.915 1.857 0.084 0.280 -3.332 0.021 0.027game_class
(5.75) (6.19) (4.27) (3.46) (4.26) (3.98) (4.79) (4.61) (4.48) (4.31) (0.12) (0.14)
male -1.073 -4.968 2.799 4.741 1.388 4.386 1.982 9.806 ** 3.964 0.164 -0.076 0.068
(5.03) (5.27) (3.74) (2.94) (3.74) (3.39) (4.20) (3.93) (3.92) (3.66) (0.11) (0.12)
age -0.299 -0.020 0.220 -0.070 0.163 -0.001 0.123 -0.591 * -0.091 0.050 0.006 0.001age
(0.80) (0.43) (0.60) (0.24) (0.60) (0.28) (0.67) (0.32) (0.63) (0.30) (0.02) (0.01)
controls yes Yes yes yes Yes yes yes Yes yes yes yes yes
random effects yes Yes yes yes Yes yes yes Yes yes yes yes yes
constant (CI) 49.796 *** 28.674 ** 38.602 *** 42.178 *** 10.527 27.997*** 22.984 30.950 *** 29.238 ** 21.461 ** 0.627 0.694 **
(19.07) (14.44) (14.18) (8.06) (14.12) (9.26) (15.88) (10.76) (14.82) (10.02) (0.41) (0.32)
Obs. 201 201 201 201 201 201 201 201 201 201 201 201
R-squared 0.097 0.094 0.098 0.127 0.175 0.134 0.165 0.158 0.07 0.153 0.073 0.094
Significance levels: * p < 0.10; **p < 0.05; * * * p < 0.01. Standard deviations reported in parenthesis.
Games 2021,12,18 11 of 14
The first two columns address dictator-game allocations. Column (1) addresses L-type
dictator allocations. In this case, the C2—same dummy informs us about the change in the
L-type dictator allocation when she is informed of being matched whit a L-type recipient.
The coefficient associated with C2—same turned out to be small in its magnitude and not
statistically significant at any conventional level. This means that, on average, the behaviour
of L-type dictators does not change when the recipient's low level of sophistication is
disclosed. Following the same line of reasoning, the C3—different dummy informs us
about the change in the L-type dictator's allocation when the dictator is informed of being
matched with a H-type recipient. The coefficient for C3—different turned out to be negative,
but rather small in its magnitude (—4 points) and, only marginally statistically significant
(p < 0.10). This means that, on average, L-type dictators tend to give a bit less when the
recipient's high level of lab-sophistication is disclosed. Column (2) reports the very same
assessment for H-type dictators. Both in the case in which it is revealed she is interacting
with an H-type and the case in which it is revealed she is dealing with an L-type, no
statistically significant differences in dictator's allocations are detected.
Columns (3) and (4) address ultimatum-game allocations (role A) for L-type and
H-type subjects, respectively In column (3) for L-type proposers, both coefficients of
C2—same and C3—different dummies prove to be negative, only marginally statistically
significant at the 10% level and similar in their own sizes (—3 points, Wald test p > 0.80).
This common departure from the baseline (CI condition) is irrespective of the type of
the opponent. In column (4) for H-type proposers, both coefficients of C2—same and
C3—different dummies are negative and statistically significant at the 5% and 1% level,
respectively H-type proposers give less when information is provided, irrespectively of
the type of opponent; while the C2—same dummy coefficient brings a negative effect by
—2 points, and C3—different by —4 points, the Wald test fails to reject the null hypothesis of
equality (p > 0.10).
Columns (5) and (6) analyse the minimal-acceptable-offer (MAO) for L-type and H type
ultimatum-game recipients (role B), respectively. On the one hand, L-type recipients
matched with L-type proposers are willing to accept slightly smaller offers (—2.8 points)
than in the baseline condition. The negative effect associated with the C2—same dummy
coefficient is significant at the 5% level. O n the other hand, when an L-type recipient
is interacting with a H-type proposer (captured by C3—different dummy coefficient), no
differential effect can be detected with regard to the baseline case (column (5)). The
same analysis for H-type ultimatum-game recipients is described in column (6). Both the
estimates for the variables dummies C2—same and C3—different turn out to be negligible in
their magnitudes and no significant statistical differences can be observed compared to the
benchmark condition.
Columns (7) and (8) focus on trust-game offers (role A) of L-type and H-type subjects,
respectively. The L-type trustor, in column (7), does not seem to be sensitive to the level of
sophistication of the trustee with whom she is interacting. Both coefficients of C2—same and
C3—different dummies are small in size and not statistically significant at any conventional
level. As a side remark, we detect the negative effect associated with the dummy variable
signalling the previous exposition to experimental-economics classes. In column (8), H-type
trustors seem to trust less when they face L-type trustees. The coefficient of C3—different
dummy, in fact, is negative and statistically significant at the 1% level. The negative effect
has an estimated magnitude of —4 points sent to L-type trustees. Males turn out to be
significantly more trustful, while age brings a marginal negative.
Trustworthy responses by L-type and H-type trustees (role B) are reported in columns
(9) and (10), respectively. While L-type responders apply the very same level of trustworthiness
in the three different conditions (in column (9), the coefficients of C2—same
and C3—different dummies are small and not statistically different from the constant term
CI), H-type subjects marginally decrease their level of trustworthiness when the trustor's
type is revealed. In column (10), the estimate for C2—same dummy variable is equal to
Games 2021,12,18 12 of 14
— 1.3 points and —2 points for C3—different dummy variable. In both cases, the negative
effect turns out to be statistically significant at the 5% and 10% level, respectively.
Column (11) focuses on prisoner's dilemma strategies1 7
for L-types. The defection
strategy proves to be the most adopted one under the three different experimental conditions.
No significant departure from the baseline can be observed, either under C2 or in the
C3 condition. Column (12) looks at H-type subjects' behaviour. Additionally, in this case,
the defection strategy is the most frequently implemented option, with just a marginal
variation. Under C3 condition, captured by C3—different dummy coefficient, H-types tend
to be marginally (p < 0.10) more cooperative (+7 percentage points) when matched with an
L-type.
In sum, although no clear and consistent behavioural pattern emerges, we can say
that the exogenous provision of information about the level of lab-sophistication of the
counterpart seems to affect the H-type participants' choices mostly. In particular:
• in the ultimatum game, for the H-type proposers, both coefficients for C2 (same type
match: H-type vs. H-type) and C3 (different type match: H-type vs. L-type) prove to
be negative and statistically significant at the 5% and 1% level, respectively. H-type
proposers give less when information is provided, irrespective of the type of opponent;
• in the trust game, H-type trustors invest less in L-type trustees, and H-type trustees
send back less when the trustor's type is revealed, irrespective of her type;
• finally, in the prisoner's dilemma game, H-types tend to be more cooperative when
they know they are interacting with an L-type.
As far as it concerns the L-type participants, we only find that L-type dictators tend to
give a bit less to H-type recipients (p < 0.10); in the Ultimatum Game, as far as the receivers'
behaviour is concerned, L-types are willing to accept lower offers when they know that
they come from L-type proposers (p < 0.05).
6. Conclusions
The generalizability of conclusions drawn from lab experiments is still a debated issue
in economics. It is, of course, a multifaceted problem that refers to many dimensions of
the experimental practices and methods: the artificiality of the situations considered in the
lab, the small size of the incentives and the lack of representativeness of the experimental
subjects are only a few of the problematic elements. In particular when we consider the
reliability of the conclusions drawn from experiments involving a convenience pool of
students, we should also ask whether the repeated participation into different experiments
by these subjects might have a lasting effect on their behavioural tendencies in the lab,
even regardless of the nature of the tasks they were involved in the previous experiments.
Were this to be true, in fact, lab-sophisticated subjects would constitute an even less representative
pool whose behaviour patterns could not be reliably generalized. In this paper,
we precisely addressed this point. By design, we investigated whether having repeatedly
taken part in previous experiments consistently modifies the behaviour of individuals,
when compared to sophisticated (H-type) or unsophisticated (L-type) participants, in a
set of widely used games: dictator game, ultimatum game, trust game and prisoner's
dilemma game. We considered a between-subjects design to compare the behaviour of
sophisticated (H-type) or unsophisticated (L-type) subjects in the four games, and we
exploited a within-subjects design to explore the extent to which knowledge about the
level of laboratory sophistication of the counterpart affects players' decisions.
While sophistication per se seems not to have any significant effect on participants' prosocial
and cooperative choices, our data suggest how knowing the level of sophistication of
the co-players may systematically alter the behaviour of H-type participants, especially in
the ultimatum game and in the trust game. We do not advance any theorical explanation
for this phenomenon; we only stress that fact that if such information can be inferred by the
participants, due to the characteristics of the recruitment mechanisms or the management
The binary outcome assumes value 1 in case of defection, 0 in case of cooperation.
Games 2021,12,18 13 of 14
of the experimental sessions, this could have a negative effect on the reliability of the
laboratory data. This result should induce experimenters to take into account a further
methodological prescription and to pay attention to neutralize all contextual and procedural
details from which each individual participant could draw information about the level
of sophistication of all the other participants in order to minimize the risk of measuring
systematically distorted behavioural.
Supplementary Materials: The following are available online at tinyurl.com/kOnzupwb, Appendix
AandB.
Author Contributions: Conceptualization, T.M., V.P. and T.R.; methodology, T.M., V.P. and T.R.;
software, T.M., V.P. and T.R.; formal analysis, T.M., V.P. and T.R.; writing—original draft preparation,
T.M., V.P. and T.R.; writing—review and editing, T.M., V.P. and T.R. All authors have read and agreed
to the published version of the manuscript.
Funding: This research was funded by Masaryk University, grant number MUNI/G/0985/2017 and
University of Cologne, grant number DFG/FOR1371.
Institutional Review Board Statement: The study was conducted according to the guidelines of
the Declaration of Helsinki, and approved within the research framework University of CologneDFG/FOR1371
(CLER Lab).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Data available upon request.
Acknowledgments: We thank Enrique Fatas for his encouragement and stimulus in the early stages of
this project; the staff of CODEBE and CLER Lab of the University of Cologne for research assistantship;
Valerio Capraro, Miloš Fišar, Francesco Guala, Andrea Isoni, Ro'i Zultan and the participants at the
"Methods" parallel session of 2015 ESA European Conference (Heidelberg), for their comments and
suggestions. We are grateful to the Editors and three anonymous referees for their comments and
suggestions that helped us to improve the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Sugden, R. Experiment, theory, world: A symposium on the role of experiments in economics. /. Econ. Methodol. 2005,12,177-184.
[CrossRef]
2. Binmore, K. Why Experiment in Economics? Econ. f. 1999,109,16-24. [CrossRef]
3. Guala, F.; Mittone, L. Experiments in economics: External validity and the robustness of phenomena. /. Econ. Methodol. 2005,12,
495-515. [CrossRef]
4. Schram, A. Artificiality: The tension between internal and external validity in economic experiments. /. Econ. Methodol. 2005,12,
225-237. [CrossRef]
5. Cooper, D.J.; Kagel, J.H.; Lo, W.; Gu, Q.L. Gaming Against Managers in Incentive Systems: Experimental Results with Chinese
Students and Chinese Managers. Am. Econ. Rev. 1999, 89, 781-804. [CrossRef]
6. Peterson, R.A. On the Use of College Students in Social Science Research: Insights from a Second-Order Meta-analysis. /. Consum.
Res. 2001, 28,450-461. [CrossRef]
7. Fehr, E.; List, J.A. The Hidden Costs and Returns of Incentives. Trust and Trustworthiness among CEOs. /. Eur. Econ. Assoc. 2004,
2, 743-771. [CrossRef]
8. Carpenter, J.; Burks, S.; Verhoogen, E. Comparing students to workers: The effects of social framing on behavior in distribu-tion
games. In Field Experiments in Economics (Research in Experimental Economics); Carpenter, J., Harrison, G., List, J., Eds.; IAI/Elsevier:
Greenwich, CT, USA; London, UK, 2005; pp. 261-290.
9. Bellemare, C ; Kröger, S. On representative social capital. Eur. Econ. Rev. 2007,51,183-202. [CrossRef]
10. Danielson, AJ.; Holm, HJ. Do You Trust Your Brethren? Eliciting Trust Attitudes and Trust Behavior in a Tanzanian Congrega-tion.
/. Econ. Behav. Organ. 2007, 62,255-271. [CrossRef]
11. Alatas, V; Cameron, L.; Chaudhuri, A.; Erkal, N.; Gangadharan, L. Subject pool effects in a corruption experiment: A compari-son
of Indonesian public servants and Indonesian students. Exp. Econ. 2009,12,113-132. [CrossRef]
12. Belot, M.; Duch, R.M.; Miller, L.M. Who Should be Called to the Lab? A Comprehensive Comparison of Students and Non-students in
Classic Experimental Games; CESS Discussion Paper Series 2010-001; Centre for Experimental Social Sciences, Nuffield College,
University of Oxford: Oxford, UK, 2010.
13. Cappelen, A.W.; Nygaard, K.; Sorensen, E.0.; Tungodden, B. Social Preferences in the Lab: A Comparison of Students and a
Representative Population. Scand. f. Econ. 2015,117,1306-1326. [CrossRef]
Games 2021,12,18 14 of 14
14. Anderson, J.; Burks, S.V.; Carpenter, J.; Gotte, L.; Maurer, K.; Nosenzo, D.; Potter, R.; Rocha, K.; Rustichini, A. Self-selection and
variations in the laboratory measurement of other-regarding preferences across subject pools: Evidence from one college student
and two adult samples. Exp. Earn. 2012,16,170-189. [CrossRef]
15. Frechette, G.R. Laboratory Experiments: Professionals Versus Students. SSRN Electron, f. 2011, 360-390. [CrossRef]
16. Henrich, J.; Heine, S.J.; Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 2010, 33, 61-83. [CrossRef]
17. Eckel, C ; Grossman, P.J. Volunteers and pseudo-volunteers: The effect of recruitment method in dictator experiments. Exp. Econ.
2000, 3,101-120. [CrossRef]
18. Falk, A.; Meier, S.; Zehnder, C. Do Lab Experiments Misrepresent Social Preferences? The case of self-selected student sam-ples. /.
Eur. Conomic. Assoc. 2010,11, 839-852. [CrossRef]
19. Cleave, B.L.; Nikiforakis, N.; Slonim, R. Is there selection bias in laboratory experiments? The case of social and risk preferences.
Exp. Econ. 2012,16, 372-382. [CrossRef]
20. Exadaktylos, F; Espin, A.M.; Branas-Garza, P. Experimental subjects are not different. Sci. Rep. 2013, 3,1213. [CrossRef]
21. Guillen, P.; Veszteg, R.F On "lab rats". /. SocioEcon. 2012, 41, 714-720. [CrossRef]
22. Friedman, D.; Cassar, A. Economics Lab: An Intensive Course in Experimental Economics; Routledge: London, UK, 2004.
23. VandenBos, G.R. (Ed.) APA Dictionary ofPsychology: 'Subject Sophistication'; American Psychological Association: Washington, DC,
USA, 2007.
24. Kruglanski, A.W. The Human Subject in the Psychology Experiment: Fact and Artifact. In Advances in Experimental Social
Psychology; Elsevier BV: Amsterdam, The Netherlands, 1975; Volume 8, pp. 101-147.
25. Greiner, B.; Stephanides, M . Subject pools and recruitment. In Handbook of ResearchMethods and Applications in Experimental
Economics; Edward Elgar Publishing: Vilnius, Lithuania, 2019.
26. Harrison, G.W.; McKee, M.; Rutstrom, E.E. Experimental evaluations of institutions of monopoly restraints. In Advances in
Behavioral Economics; Green, L., Kagel, J.H., Eds.; Ablex Press: Norwood, NJ, USA, 1987; pp. 54-94.
27. Benson, B.L.; Faminow, M. The impact of experience on prices and profits in experimental duopoly markets. /. Econ. Behav. Organ.
1988, 9,345-365. [CrossRef]
28. Marwell, G.; Ames, R.E. Experiments on the Provision of Public Goods. II. Provision Points, Stakes, Experience, and the Free-Rider
Problem. Am. }. Sociol. 1980, 85, 926-937. [CrossRef]
29. Isaac, M.; Walker, J.M.; Thomas, S.H. Divergent Evidence on Free Riding: An Experimental Examination of Possible Explana-tions.
Public Choice 1984,43,113-149. [CrossRef]
30. Bolton, G.E. A comparative model of bargaining: Theory and evidence. Am. Econ. Rev. 1991, 81,1096-1136.
31. Matthey, A.; Regner, T. On the independence of history: Experience spill-overs between experiments. Theory Decis. 2012, 75,
403^19. [CrossRef]
32. Capraro, V ; Cococcioni, G. Social setting, intuition, and experience in lab experiments interact to shape cooperative deci-sionmaking.
Proc. R. Soc. B Biol. Sci. 2015,282,20150237. [CrossRef] [PubMed]
33. Xue, L.; Sitzia, S.; Turocy, T.L. Mathematics Self-Confidence and the "Prepayment Effect" in Riskless Choices. /. Econ. Behav.
Organ. 2017,135,239-250. [CrossRef]
34. Benndorf, V ; Moellers, C ; Normann, H.-T. Experienced vs. inexperienced participants in the lab: Do they behave differently? /.
Econ. Sci. Assoc. 2017, 3,12-25. [CrossRef]
35. Conte, A.; Levari, M.V; Montinari, N. Experience in public goods experiments. Theory Decis. 2019, 86, 65-93. [CrossRef]
36. Greiner, B. Subject pool recruitment procedures: Organizing experiments with ORSEE. /. Econ. Sci. Assoc. 2015, 1, 114-125.
[CrossRef]
37. Charness, G.; Gneezy, U.; Kuhn, M.A. Experimental methods: Between-subject and within-subject design. /. Econ. Behav. Organ.
2012, 81,1-8. [CrossRef]
38. Zizzo, D.J. Experimenter demand effects in economic experiments. Exp. Econ. 2010,13, 75-98. [CrossRef]
39. Seithe, M . Introducing the Bonn Experiment System (BoXS). 2012. Available online: http://boxs.uni-bonn.de/boxs_seithe.pdf
(accessed on 22 February 2021).
40. Binmore, K.; Shaked, A. Experimental economics: Where next? /. Econ. Behav. Organ. 2010, 73, 87-100. [CrossRef]
41. Ding, T.; Schotter, A. Learning and mechanism design: An experimental test of school matching mechanisms with intergenerational
advice. Econ. }. 2019,129,2779-2804. [CrossRef]
42. Hennig-Schrnidt, H ; Irlenbusch, B.; Rilke, R.M.; Walkowitz, G. Asymmetric outside options in ultimatum bargaining: A
system-atic analysis. Int. f. Game Theory 2018, 47, 301-329. [CrossRef]