Name: the collective trust game: an online group adaptation of the trust game based on the honeycomb paradigm
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Data collection and data analysis in this project have been approved by the Ethics Committee of the Georg-Elias-M&#252;ller Institute for Psychology of the University of G&#246;ttingen (proposal 289/2021); the protocol follows the guidelines on human research of the Ethics Committees of the Georg-Elias-M&#252;ller-Institute for Psychology. The CTG software can be downloaded from the OSF project (DOI 10.17605/OSF.IO/U24PX) under the link:&#160;https://s.gwdg.de/w88YNL.
1. Prepare technica...

<ol>
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The CTG provides researchers with the opportunity to adapt the classic BDM1 for groups and observe emergent processes within the groups in depth. While other work23,24,25,26 has already attempted to adapt the BDM1 to group settings, the only way to access group processes in these studies are laborious group interaction analyses of video-taped disc...

The Collective Trust Game (CTG) provides the opportunity to measure collective trust online within a group of humans. It generalizes the original Trust Game by Berg, Dickhaut, and McCabe1 (BDM) to the group level and can capture and quantify collective trust in its emergent qualities2,3,4, as well as related concepts such as fairness, reciprocity, or forward-signaling.
Previous research mostly conceptualizes trust as a solely interpersonal construct, for example, between a leader and a follower5,6, excluding higher levels of analysis. Especially in organizational contexts, this might not be enough to comprehend trust holistically, so there is great need to understand the processes by which trust builds (and diminishes) on a group level.
Recently, trust research has incorporated more multi-level thinking. Fulmer and Gelfand7 reviewed a number of studies on trust and categorized them according to the level of analysis that is investigated in each study. The three different levels of analysis are interpersonal (dyadic), group, and organizational. Importantly, Fulmer and Gelfand7 additionally distinguish between different referents. The referents are those entities at which trust is directed. This means that when &#34;A trusts B to X&#34;, then A (the investor in economic games) is represented by the level (individual, group, organizational) and B (the trustee) is represented by the referent (individual, group, organizational). X represents a specific domain to which trust refers. This means that X can be anything such as a generally positive inclination, active support, reliability, or financial exchanges as in economic games1.
Here, collective trust is defined based on Rousseau and colleagues&#39; definition of interpersonal trust8, and similar to previous studies on collective trust9,10,11,12,13,14; collective trust comprises a group&#39;s intention to accept vulnerability based upon positive expectations of the intentions or behavior of another individual, group, or organization. Collective trust is a psychological state shared among a group of humans and formed in interaction among this group. The crucial aspect of collective trust is therefore the sharedness within a group.
This means that research on collective trust needs to look beyond a simple average of individual processes and conceptualize collective trust as an emergent phenomenon2,3,4, as new developments in group science show that group processes are fluid, dynamic, and emergent2,15. We define emergence as a &#34;process by which lower level system elements interact and through those dynamics create phenomena that manifest at a higher level of the system&#34;16 (p. 335). Proposedly, this should also apply to collective trust.
Research that reflects the focus on emergence and dynamics of group processes should use appropriate methodologies17 to capture these qualities. However, the current status of collective trust measurement seems to lag behind. Most studies have employed a simple averaging technique across the data of each individual in the group9,10,12,13,18. Arguably, this approach has only little predictive validity2 as it disregards that groups are not simply aggregations of individuals but higher-level entities with unique processes. Some studies have tried to address these drawbacks: A study by Adams19 employed a latent variable approach, while Kim and colleagues10 used vignettes to estimate collective trust. These approaches are promising in that they recognize collective trust as a higher-level construct. Yet, as Chetty and colleagues20 note, survey-based measures lack incentives to answer truthfully, so research on trust has increasingly adopted behavioral or incentive-compatible measures21,22.
This concern is addressed by a number of studies which have adapted a behavioral method, namely the BDM1, to be played by groups23,24,25,26. In the BDM, two parties act as either investors (A) or trustees (B). In this sequential economic game, both A and B receive an initial endowment (e.g., 10 Euros). Then, A needs to decide how much, if any, of their endowment they would like to send to B (e.g., 5 Euros). This amount is then tripled by the experimenter, before B can decide how much, if any, of the received money (e.g., 15 Euros) they would like to send back to A (e.g., 7.5 Euros). The amount of money A sends to B is operationalized to be the level of trust of A toward B, while the amount that B sends back can be used to measure the trustworthiness of B or the degree of fairness in the dyad of A and B. A large body of research has investigated behavior in dyadic trust games27. The BDM can be played both as a so-called &#39;one-shot&#39; game, in which participants play the game only once with a specific person, and in repeated rounds, in which aspects such as reciprocity28,29 as well as forward-signaling might play a role.
In many studies that have adapted the BDM for groups23,24,25,26, either the investor, the trustee, or both roles were played by groups. However, none of these studies recorded group processes. Simply substituting individuals with groups in study designs does not meet the standards Kolbe and Boos17 or Kozlowski15 set up for investigations of emergent phenomena. To fill this gap, the CTG was developed.
The aim of developing the CTG was to create a paradigm that would combine the widely used BDM1 with an approach that captures collective trust as an emergent behavior-based construct that is shared among a group.
The CTG is based on the HoneyComb paradigm by Boos and colleagues30, that has also been published in the Journal of Visualized Experiments31 and has now been adapted for use in trust research. As described by Ritter and colleagues32, the HoneyComb paradigm is &#34;a multi-agent computer-based virtual game platform that was designed to eliminate all sensory and communication channels except the perception of participant-assigned avatar movements on the playfield&#34; (p. 3). The HoneyComb paradigm is especially suitable to research group processes as it allows researchers to record the movement of members of a real group with spatio-temporal data. It could be argued that, next to group interaction analysis17, HoneyComb is one of the few tools that allows researchers to follow group processes in great detail. In contrast to group interaction analysis, quantitative analysis of the spatio-temporal data of HoneyComb is less time-intensive. Additionally, the reductionist environment and possibility to exclude all interpersonal communication between participants except the movement on the playfield allows researchers to limit confounding factors (e.g., physical appearance, voice, facial expressions) and create experiments with high internal validity. While it is difficult to identify all influential aspects of a group process in studies employing group discussion designs33, the focus on basic principles of group interaction in a movement paradigm allows researchers to quantify all aspects of the group process in this experiment. Additionally, previous research has used proxemic behavior34-so reducing space between oneself and another individual-to investigate trust35,36.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63600/63600fig01.jpg" /> 
Figure 1: Schematic overview of the CTG. (A) Schematic procedure of one CTG round. (B) Initial placement of avatars at beginning of round. The three blue-colored investors are standing on the initial field &#34;0&#34;. The yellow trustee is standing on the initial field &#34;0&#34;. (C) Screenshot during the invest phase showing three investors (blue avatars) on the lower half of the playfield. One (big blue avatar) is currently standing on &#34;12&#34;, two investors are currently standing on &#34;24&#34;. Two avatars have tails (indicated by orange arrows). The tails are indicating from which direction they moved to their current field (e.g., one investor (big blue avatar) just moved from &#34;0&#34; to &#34;12&#34;). The avatar without a tail has been standing on this field for at least 4000 ms. (D) Screenshot during the return phase showing one trustee (yellow avatar) and the upper half of the playfield. The trustee is currently standing on &#34;3/6&#34; and has recently moved there from &#34;2/6&#34; as indicated by the tail. The blue number below (36) indicates the investment made by the investors. The yellow number, indicated by the arrow, is the current return (54) as depicted in the middle of the playfield. The return is calculated as follows: (invest (36 cents) x 3) x current return fraction (3/6) = 54 cent. (E) Pop-up window giving feedback to participants on how much they have earned during the round, displayed for 15 s after time-out of trustee expires. <a href="https://www.jove.com/files/ftp_upload/63600/63600fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The main procedure of the CTG (Figure 1A) is closely based on the procedure of the BDM1, in order to make results comparable to previous studies using this economic game. As the HoneyComb paradigm is based on the principle of movement, participants indicate the amount they would like to invest or return by moving their avatar onto the small hexagon field that indicates a certain amount of money or fraction to return (Figure 1C,D). Prior to each round, both the investors and trustees are endowed with a certain amount of money (e.g., 72 cents) with the investors being placed in the lower half of the playfield and the trustees being placed in the upper half of the playfield (Figure 1B). In the default setting, the investors are allowed to move first, while the trustees remain still. The investors move across the playfield to indicate how much, if any, of their endowment they would like to send to the trustee (Figure 1C). Through moving back and forth on the field, participants may also communicate to other investors how much they would like to send to the trustee. Depending on the configuration, participants need to reach a unanimous decision on how much they would like to invest by converging on one playfield when the time-out is reached. Unanimous decisions were required in order to enforce that investors need to interact with each other, instead of simply play alongside one another. If the investors do not reach a joint decision, a penalty (e.g., 24 cents) is deducted from their account. This was implemented to ensure that investors would be highly motivated to reach a shared level of collective trust. Once the investors&#39; time is up, the invested money is multiplied and sent to the trustees who are then allowed to move while the investors remain still. The trustees indicate through movement how much they would like to return to the investors (Figure 1D). The available return options are displayed as fractions on the playfield to keep cognitive load on trustees comparatively low. The playfield on which trustees stand once their allocated time runs out indicates which fraction (e.g., 4/6) is returned to investors. The round ends with a pop-up (Figure 1E) that summarizes for each participant how much they earned during that round and what their current account balance is.
Rounds should be repeated multiple times. Researchers should have participants play the CTG for at least 10 or 15 rounds in the same roles. This is necessary as collective trust is an emergent construct and needs to develop during repeated interactions within a group. Similarly, other concepts such as forward-signaling (i.e., reciprocating high returns from trustees with high investments in the next round) will only emerge in repeated interactions. It is crucial, however, that participants are unaware of the exact number of rounds to be played as it has been shown that behavior can drastically change when participants are aware that they are playing the last round (i.e., more unfair behavior or deflections in economic games37,38).
In this way, the CTG provides information about the emergence of collective trust on multiple levels. First, the level of collective trust exhibited in the final round should be a close representation of the shared level of trust investors hold towards the trustee(s). Second, the amount invested in each round can serve as a proxy for the emergence of collective trust over repeated interactions. Third, movement data sheds light on the group process that determines how much money is invested in each round.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Data Analysis Software and Packages</td>
			<td>R</td>
			<td>version 4.2.1 (2022-06-23 ucrt)</td>
			<td>R Core Team R: A Language and Environment for Statistical Computing. at [https://www.R-project.org/]. R Foundation for Statistical Computing. Vienna, Austria. (2020).</td>
		</tr>
		<tr>
			<td>Data Analysis Software and Packages</td>
			<td>R Studio</td>
			<td>version 2022.2.3.492 &#34;Prairie Trillium&#34;</td>
			<td>RStudio Team RStudio: Integrated Development Environment for R. at [http://www.rstudio.com/]. RStudio, PBC. Boston, MA. (2020).</td>
		</tr>
		<tr>
			<td>Data Analysis Software and Packages</td>
			<td>ggplot2</td>
			<td>version 3.3.6</td>
			<td>Wickham, H. ggplot2: Elegant Graphics for Data Analysis. at [https://ggplot2.tidyverse.org]. Springer-Verlag New York. (2016).</td>
		</tr>
		<tr>
			<td>Data Analysis Software and Packages</td>
			<td>cowplot</td>
			<td>version 1.1.1</td>
			<td>Wilke, C.O. cowplot: Streamlined Plot Theme and Plot Annotations for &#8220;ggplot2.&#8221; at [https://CRAN.R-project.org/package=cowplot]. (2020).</td>
		</tr>
		<tr>
			<td>OnlineQuestionnaireTool</td>
			<td>LimeSurvey</td>
			<td>Community Edition Version 3.28.16+220621&#160;</td>
			<td>Any preferred online questionnaire tool can be used. LimeSurvey or SoSciSurvey are recommended.</td>
		</tr>
		<tr>
			<td>Notebooks or PCs</td>
			<td>DELL</td>
			<td>Latitude 7400</td>
			<td>Any laptop that is able to establish a stable Remote Desktop Connection can be used.</td>
		</tr>
		<tr>
			<td>Participant Management Software</td>
			<td>ORSEE</td>
			<td>version 3.1.0</td>
			<td>It is recommended to use ORSEE (Greiner, B. [2015]. Subject pool recruitment procedures: Organizing experiments with ORSEE. Journal of the Economic Science Association, 1, 114&#8211;125. https://doi.org/10.1007/s40881-015-0004-4), but other software options might be available.</td>
		</tr>
		<tr>
			<td>Program to Open RemoteDesktop Connection</td>
			<td>Remote Desktop Connection (Program distributed with each Windows 10 installation.)</td>
			<td></td>
			<td>The following tools are recommended: RemoteDesktopConnection (for Windows), Remmina (for Linux), or Microsoft Remote Desktop (for Mac OS).</td>
		</tr>
		<tr>
			<td>Server to run RemoteDesktop Environment</td>
			<td>VMware vSphere environment based on vSphere ESXi</td>
			<td>version 6.5</td>
			<td>Ideally provided by IT department of university/institution.</td>
		</tr>
		<tr>
			<td>VideoConference Platform</td>
			<td>BigBlueButton</td>
			<td>Version 2.3</td>
			<td>It is recommend to use a platform such as BigBlueButton or other free software that does not record participant data on an external server. The platform should provide the following functions: 1) possibility to restrict access to microphone and camera for participants, 2) hide participant names from other participants, 3) possibility to send private chat message to participants.</td>
		</tr>
		<tr>
			<td>Virtual Machine running Linux-Installation</td>
			<td>Xubuntu</td>
			<td>version 20.04 &#34;Focal Fossa&#34;</td>
			<td>Other Linux-based systems will also be possible.</td>
		</tr>
	</tbody>
</table>

the collective trust game: an online group adaptation of the trust game based on the honeycomb paradigm

The need to understand trust in groups holistically has led to a surge in new approaches to measuring collective trust. However, this construct is often not fully captured in its emergent qualities by the available research methods. In this paper, the Collective Trust Game (CTG) is presented, a computer-based, multi-agent trust game based on the HoneyComb paradigm, which enables researchers to assess the emergence of collective trust. The CTG builds on previous research on interpersonal trust and adapts the widely known Trust Game to a group setting in the HoneyComb paradigm. Participants take on the role of either an investor or trustee; both roles can be played by groups. Initially, investors and trustees are endowed with a sum of money. Then, the investors need to decide how much, if any, of their endowment they want to send to the trustees. They communicate their tendencies as well as their final decision by moving back and forth on a playfield displaying possible investment amounts. At the end of their decision time, the amount the investors have agreed upon is multiplied and sent to the trustees. The trustees have to communicate how much of that investment, if any, they want to return to the investors. Again, they do so by moving on the playfield. This procedure is repeated for multiple rounds so that collective trust can emerge as a shared construct through repeated interactions. With this procedure, the CTG provides the opportunity to follow the emergence of collective trust in real time through the recording of movement data. The CTG is highly customizable to specific research questions and can be run as an online experiment with little, low cost equipment. This paper shows that the CTG combines the richness of group interaction data with the high internal validity and time-effectiveness of economic games.

This paper presents results of a pilot study conducted with the CTG with 16 participants (five men, 11 women; Age: M = 21, SD = 2.07). According to Johanson and Brooks42, this sample size is sufficient in a pilot experiment, especially when paired with a qualitative approach to reach a high information density about participants&#39; subjective experience during the experiment. It is recommended that whenever researchers intend to adapt the CTG to their specific research idea, fo...

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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The Collective Trust Game is a computer-based, multi-agent trust game based on the HoneyComb paradigm, which enables researchers to assess the emergence of collective trust and related constructs, such as fairness, reciprocity, or forward-signaling. The game allows detailed observations of group processes through movement behavior in the game.

The need to understand trust in groups holistically has led to a surge in new approaches to measuring collective trust. However, this construct is often ...

The Collective Trust Game is an online multi-agent adaptation of the trust game. in the Honeycomb paradigm. It allows researchers to investigate collective trust and its emergence in groups holistically.This game combines the high internal validity of an economic game with rich spatial temporal data so that emergent group processes can be investigated in detail. It is important to prohibit for the participants from sharing their screen, camera, microphone, and the chat between participants to ensure complete anonymity between participants. As long as the remote desktop is set up correctly and the experimenter and the participants have a stable internet connection, there should be no problem in running this experiment.To begin, start a remote desktop connection with the experimenter user. Open the shared folder and start a terminal by right clicking in the directory and choosing Open Terminal here. Then start the server program by typing the command in the terminal and pressing Enter.Next, establish a remote desktop connection with participant users. Open the shared folder and start a terminal in this folder as before. Start the client programs for each participant user by typing the command in the terminal and pressing Enter.Check that a line appears in the server GUI displaying the IP address of each of the participant users. Check whether the connections are established correctly for all participant users. When all participant users are connected, click on OK.Check that the screens of the participant users display the welcome screen of the experiment.Admit all participants to the video conference at the scheduled experiment time slot and send each participant their individual login data. Guide participants to open the remote desktop connection tool. Assign participants to one of the investor or trustee roles.Have investors start on the bottommost field and trustees on the upmost field. Instruct participants to move their avatar by left clicking into an adjacent hexagon field. Instruct participants that their avatar will display a small tail for 4, 000 milliseconds after each move that indicates the last direction from which they moved to the current field.Allow investors to move from the beginning to indicate through movement how much they would like to invest. After a certain amount of time, prohibit the movement of investors. Explain in the instructions that the invested amount is multiplied by a factor and sent to the trustees.Restrict the trustees from moving for as long as the investors are moving by setting the trustee time-in to the length of the trustee time-out. Instruct the trustees to move to indicate the fraction they would like to return to the investors. Once the trustee time-out is reached, the field on which the trustees stand is taken to indicate the fraction that is returned to the investors.The amount returned is indicated in the middle of the play field by a yellow number. Have the pop-up window display the amount of money the person has earned at the end of the round. Repeat the game round as needed.Once all rounds are complete, ask participants to generate a personal unique code so that the in-game earnings can be connected to their name while keeping the behavioral data anonymous. After participants have generated the code, display a screen which instructs participants to return to the video conference and close the remote desktop connection. Once the game is completed, make sure that all participants have closed the remote desktop connection.Have the participants fill out questionnaires as seen fit for a specific research question. While the participants are filling out the questionnaires, close the server program on the experimenter user by clicking on Stop and Exit. This will also close the program on the participant users.Transfer and back up the data in the form of csv and txt files per group and experiment time slot marked by a day and timestamp of the experiment. Close all remote desktop connections. In a pilot sample, participants reported the game to be quite intuitive and a 30 second time-out to be about right.Based on responses to open-ended questions of the pilot sample, participants were satisfied with the recruitment process and online procedure, preservation of anonymity in the experiment, and clarity of instructions and information provided, and the logic of the game. Most participants were satisfied with the design of the avatars, even though only half of those reported feelings sufficiently represented by their avatar. Participants reported behavioral strategies such as profit maximization or intentions to influence the behavior of coplayers.These strategies fit well with the economic game character of the Collective Trust Game. With a Collective Trust Game, trust and fairness behavior can be investigated as well as the processes that promote or impede their emergence.

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