This research did not receive any external funding.

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 technical setup
<ol>
	<li>Prepare online consent forms and questionnaires
	<ol>
		<li>Prepare an online consent form in an online questionnaire tool.</li>
		<li>If applicable, prepare an online questionnaire in an online questionnaire tool. 
		NOTE: It is possible to include a short questionnaire within the HoneyComb program (see step 1.3.5). To use longer questionnaires, use a separate online questionnaire tool instead. Examples for online questionnaire tools are given in the Table of Materials.</li>
	</ol>
	</li>
	<li>Prepare remote desktop server
	<ol>
		<li>Install a Linux-based operating system on a remote server. If possible, ask technical assistants about the available resources at the institution. Otherwise, follow an installation guideline39.</li>
		<li>Create different users on this server40.
		<ol>
			<li>Create a user admin which has root permissions and is accessed solely by the technical lead in the experiment.</li>
			<li>Create a user experimenter which has permissions to create shared folders, import and export data, and can be accessed by all personnel collecting data (including students/research assistants, etc.).</li>
			<li>Create multiple users named participant-1, participant-2, etc. 
			NOTE: Researchers will only be able to test as many participants in one experimental session as users that are created.</li>
		</ol>
		</li>
		<li>Execute the command java -version on the admin user to ensure that a Java runtime environment is available on the server. If not, install the most recent Java version before continuing and make sure all users can access it.</li>
		<li>Install the program
		<ol>
			<li>Download the program. 
			NOTE: The program can be downloaded as a zip-file HC_CTG.zip containing 1) the runnable HC.jar, 2) three files for configuration (hc_server.config, hc_panel.config, and hc_client.config), and 3) two subfolders named intro and rawdata.</li>
			<li>Create a folder on the experimenter user and share it with the other users41. Extract the files from the compressed file HC_CTG.zip into this folder.</li>
			<li>For each participant user, access this shared folder and check that the user can access the files.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Open the three configuration files.
	<ol>
		<li>Edit hc_server.config and save the edited file.
		<ol>
			<li>Configure the number of players by setting n_Pl to the desired number. For example, enter 4 behind the =.</li>
			<li>Configure the number of rounds to play (playOrder) by repeating the game number 54a (e.g., 54a, 54a, 54a, 54a for four rounds). 
			NOTE: i54a stands for the instructions and should not be deleted in the configuration file.</li>
			<li>Configure whether a questionnaire should be shown in HoneyComb by including 200 at the end of playOrder. Delete 200 if a separate online questionnaire tool is used.</li>
			<li>Configure the investment scale. To configure the scale for investors (iscale), enter which values should be available as investment steps (e.g., 0, 12, 24, 36, 48, 60, 72). Use integers that are multiples of three so that payouts are also integers. 
			NOTE: These configured values are also displayed as possible investment steps to the investors.
			<ol>
				<li>Configure the display scale for trustees (tlabel) by choosing which values should be displayed as possible returns on the playfield (e.g., 0, 1/6, 2/6, 3/6, 4/6, 5/6, 1). NOTE: This scale does not influence the calculation of payouts.</li>
				<li>Configure the scale for trustees (tscala) by choosing which return values should be possible as returns (e.g., 0, 0.166666, 0.3333, 0.5, 0.6666, 0.833331, 1). Use digital values only (i.e., no fractions). 
				NOTE: These values are used to calculate payouts and are NOT displayed on the playfield.</li>
			</ol>
			</li>
			<li>Configure the time-ins (timeInI for investors, timeInT for trustees) and time-outs (timeOutI for investors, timeout for trustees) in seconds. For example, timeInI = 0, timeOutI = 30, timeInT = 30, and timeout = 45.</li>
			<li>Configure the amount of money investors and trustees are endowed with in each round in cents (r52).</li>
			<li>Configure the factor with which the investment is multiplied before being sent to the trustee (f52).</li>
			<li>Configure whether the group has to reach a unanimous decision (set bUnanimous to true) or not (set unanimous to false)</li>
			<li>Configure whether the group is paid out in equal parts (set bCommon to true) or according to how much each investor has contributed to the investment (set bCommon to false).</li>
			<li>If bUnanimous is set to true, configure the penalty-the amount of money deducted from the investors if a unanimous decision is not reached (p52).</li>
		</ol>
		</li>
		<li>Edit hc_client.config if necessary. Make sure to set ip_nr to localhost so that the clients can connect to the experimenter.</li>
		<li>Edit hc_panel.config.
		<ol>
			<li>Adjust the size of the hexagons (radius) according to the screen resolution. Test the experiment on multiple different screens to ensure that the experiment will be visible on a wide variety of screens.</li>
			<li>Adjust the text that is displayed on the playfield under labels (e.g., Your role is: investor, Account Balance, etc.)</li>
		</ol>
		</li>
		<li>Adjust and/or translate the instructions, if necessary. To do so, edit and save the simple HTML-files (Figure 2A) in the &#34;intro&#34; folder within the HoneyComb program folder.</li>
		<li>If you want to use the questionnaire within the HoneyComb program, adjust and/or translate the questionnaire in the file qq.txt and save the file.</li>
		<li>Keep this setup constant across all experiment sessions (within one experiment condition). Document all configurations.</li>
	</ol>
	</li>
</ol>
2. Participant recruitment
<ol>
	<li>Online advertisement
	<ol>
		<li>Recruit participants over available channels (e.g., social media, university blog, flyer with QR-code). Name important information about the experiment, such as its purpose, duration, and maximum payment calculated according to game behavior. 
		&#8203;NOTE: The sample presented here was recruited via an online blog for psychology students at the University of G&#246;ttingen as well as unpaid advertisements in social media groups. An example flyer can be seen in Supplemental Figure 1.</li>
		<li>Make potential participants aware that participation will require usage of personal laptops/PCs with a stable internet connection and in a quiet, secluded area. Make participants aware that they might need to install a program to establish the Remote Desktop connection. 
		NOTE: Participation via mobile phones or tablets is not possible.</li>
		<li>Make sure the participants meet the experiment&#39;s inclusion criteria such as language requirements or color sightedness.</li>
		<li>Make sure the participants have not taken part in previous experiments on the CTG.</li>
	</ol>
	</li>
	<li>Book experimental sessions with the participants
	<ol>
		<li>Ask the participants to book time-slots for their participation.</li>
		<li>Use a participant management software to send automated invitation or reminder e-mails.</li>
		<li>Overbook time-slots by at least one participant to ensure enough participants are present to run the experiment.</li>
	</ol>
	</li>
	<li>Send participants a confirmation e-mail with the following details: guide on computer setup, installation of Remote Desktop Connection Tool, and establishing connection to Remote Desktop. Make sure to NOT send any log in information yet, in order to avoid technical issues due to earlier log in.</li>
	<li>Send participants reminder e-mails about 24 h prior to the experiment, including the link to the video conferencing platform. Include the information about installation that was sent in the confirmation e-mail.</li>
</ol>
3. Experimental setup (before each experimental session)
<ol>
	<li>Prepare the video conferencing platform (Figure 3)
	<ol>
		<li>Make sure the participants are blocked from sharing their microphone or camera. Make sure the participants cannot see each other&#39;s names.</li>
		<li>Share the experimenter&#39;s microphone and camera, and share the screen with minimal instructions on the video conferencing platform (Figure 3).</li>
	</ol>
	</li>
	<li>Prepare the remote desktop
	<ol>
		<li>User experimenter
		<ol>
			<li>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.</li>
			<li>Start the server program HC_Gui.jar by typing the command java -jar HC_Gui.jar in the terminal and pressing ENTER.</li>
		</ol>
		</li>
		<li>Users participant-1, participant-2, etc.
		<ol>
			<li>Establish a remote desktop connection with users participant-1, participant-2, .... Open the shared folder and start a terminal in this folder as before.</li>
			<li>Start the client programs for each user by typing the command java -jar HC.jar in the terminal and pressing ENTER.</li>
			<li>Check whether the connections are established correctly on all participant users. 
			NOTE: The participant users&#39; screens should display the message Please wait. The computer is connecting to the server. It is recommended to have as many laptops present as users (Figure 4).</li>
		</ol>
		</li>
		<li>User experimenter
		<ol>
			<li>Check that a line appears in the server GUI, displaying the IP address of each of the participant users. When all participant users are connected, check that the server program displays the message All Clients are connected. Ready to start?. Click on OK.</li>
			<li>Check that the screens of the participant users display the welcome screen of the experiment (first instructions page). 
			&#8203;NOTE: The experimenter can prepare the session up to this point.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Experimental procedure
<ol>
	<li>Admit participants to the video conference at the scheduled experiment time-slot. Welcome all participants using a standardized text. Explain the technical procedure to participants.</li>
	<li>Share the link to the online consent form. Check that all participants have given written consent.</li>
	<li>Guide participants to open the Remote Desktop Connection tool and send each participant their individual login data via personal chat in the video conference. 
	NOTE: When the participants log in to the participant users, the notebooks in the laboratory will lose connection to the participant users. From here on, the experiment runs automatically until the participants reach the final page, instructing them to return to the video conference.</li>
	<li>Have participants confirm that they have read the first instructions page by clicking on OK. Once all participants have confirmed, wait until the participants have completed the game. 
	NOTE: The participants can page through the instructions at their preferred pace. Once all participants have confirmed that they have read the instructions, the CTG automatically commences. The game progresses automatically through as many rounds as indicated in the server.config file.</li>
	<li>Testing phase
	<ol>
		<li>Assign participants to one of two roles: investor or trustee. 
		NOTE: Multiple participants can be assigned the same role.</li>
		<li>Have investors start on the bottom-most field (indicated investment of 0) and trustees on the upmost field (indicating return of 0) (Figure 1B).</li>
		<li>Instruct participants to move their avatar by left-click into an adjacent hexagon field. Instruct participants that only adjacent fields can be chosen and fields cannot be skipped. Instruct participants that their avatar will display a small tail for 4000 ms after each move that indicates the last direction from which they moved to the current field (Figure 1C).</li>
		<li>Allow investors to move from the beginning (time-in = 0) to indicate through movement how much they would like to invest. After a certain amount of time, prohibit the movement of investors (time-out). 
		NOTE: The field on which they stand will then indicate how much is invested. In the middle of the playfield, a blue number will additionally show the amount sent to the trustee. If the experiment is set up to require unanimous invests, investments will only be made if all participants stand on the same field.</li>
		<li>Explain in the instructions that the invested amount is multiplied by a factor (e.g., three) 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.</li>
		<li>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 also indicated in the middle of the playfield by a yellow number (Figure 1D).</li>
		<li>Have the pop-up window display the amount of money the person has earned at the end of the round (Figure 1E).</li>
		<li>Repeat the game round as needed (i.e., as indicated in the server.config file).</li>
		<li>Once all rounds are completed, 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.</li>
		<li>After participants have generated the code, display a screen which instructs participants to return to the video conference and close the Remote Desktop connection. 
		&#8203;NOTE: The experimental procedure (section 4 in this protocol with 15 game rounds) takes 35 min.</li>
		<li>If technical issues or failure of a participant require that the experiment session is aborted, refrain from restarting the experiment with the same participants.</li>
	</ol>
	</li>
	<li>Post-testing phase
	<ol>
		<li>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.</li>
		<li>While the participants are filling out the questionnaires, close the server program on the experimenter user by clicking on Stop &#38; Exit. This will also close the program on the participant users.</li>
		<li>Thank participants for their time and explain how and when their earnings will be transferred to them. Make sure all participants have left the video conference, especially if another experiment time-slot is scheduled directly afterwards.</li>
	</ol>
	</li>
</ol>
5. Finishing the experiment
<ol>
	<li>Transfer and back up the data (e.g., in the cloud), in the form of one *.csv and one *.txt file per group and experiment time-slot, marked by a day- and time-stamp of the experiment.</li>
	<li>Close all Remote Desktop connections.</li>
</ol>

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<li>Boos, M., Pritz, J., Belz, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+HoneyComb+paradigm+for+research+on+collective+human+behavior%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%22%5BJournal%5D)">The HoneyComb paradigm for research on collective human behavior.</a> Journal of Visualized Experiments. (143), e58719(2019).</li>
<li>Ritter, M., Wang, M., Pritz, J., Menssen, O., Boos, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((How+collective+reward+structure+impedes+group+decision+making%3A+An+experimental+study+using+the+HoneyComb+paradigm%5BTitle%5D)+AND+%22PLOS+One%22%5BJournal%5D)">How collective reward structure impedes group decision making: An experimental study using the HoneyComb paradigm.</a> PLOS One. 16 (11), 0259963(2021).</li>
<li>Kocher, M., Sutter, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Individual+versus+group+behavior+and+the+role+of+the+decision+making+process+in+gift-exchange+experiments%5BTitle%5D)+AND+%22Empirica%22%5BJournal%5D)">Individual versus group behavior and the role of the decision making process in gift-exchange experiments.</a> Empirica. 34 (1), 63-88 (2007).</li>
<li>Ickinger, W. J. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22A+behavioral+game+methodology+for+the+study+of+proxemic+behavior%22">A behavioral game methodology for the study of proxemic behavior</a>. , Doctoral Dissertation (1985).</li>
<li>Deligianis, C., Stanton, C. J., McGarty, C., Stevens, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+impact+of+intergroup+bias+on+trust+and+approach+behaviour+towards+a+humanoid+robot%5BTitle%5D)+AND+%22Journal+of+Human-Robot+Interaction%22%5BJournal%5D)">The impact of intergroup bias on trust and approach behaviour towards a humanoid robot.</a> Journal of Human-Robot Interaction. 6 (3), 4-20 (2017).</li>
<li>Haring, K. S., Matsumoto, Y., Watanabe, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((How+do+people+perceive+and+trust+a+lifelike+robot%5BTitle%5D)+AND+%22Proceedings+of+the+World+Congress+on+Engineering+and+Computer+Science%22%5BJournal%5D)">How do people perceive and trust a lifelike robot.</a> Proceedings of the World Congress on Engineering and Computer Science. 1, 425-430 (2013).</li>
<li>Gintis, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Behavioral+game+theory+and+contemporary+economic+theory%5BTitle%5D)+AND+%22Analyse+%26+Kritik%22%5BJournal%5D)">Behavioral game theory and contemporary economic theory.</a> Analyse & Kritik. 27 (1), 48-72 (2005).</li>
<li>Weimann, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Individual+behaviour+in+a+free+riding+experiment%5BTitle%5D)+AND+%22Journal+of+Public+Economics%22%5BJournal%5D)">Individual behaviour in a free riding experiment.</a> Journal of Public Economics. 54 (2), 185-200 (1994).</li>
<li>How to install Xrdp server (remote desktop) on Ubuntu 20.04. Linuxize. , Available from: 
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<li>How to create users in Linux (useradd Command). Linuxize. , Available from: 
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<li>How to create a shared folder between two local user in Linux. GeeksforGeeks. , Available from: 
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<li>Johanson, G. A., Brooks, G. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Initial+scale+development%3A+Sample+size+for+pilot+studies%5BTitle%5D)+AND+%22Educational+and+Psychological+Measurement%22%5BJournal%5D)">Initial scale development: Sample size for pilot studies.</a> Educational and Psychological Measurement. 70 (3), 394-400 (2010).</li>
<li>Glaeser, E. L., Laibson, D. I., Scheinkman, J. A., Soutter, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Measuring+trust%5BTitle%5D)+AND+%22The+Quarterly+Journal+of+Economics%22%5BJournal%5D)">Measuring trust.</a> The Quarterly Journal of Economics. 115 (3), 811-846 (2000).</li>
<li>Mayring, P. Qualitative Content Analysis: Theoretical Background and Procedures. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aMayring%2C+P+%22Approaches+to+Qualitative+Research+in+Mathematics+Education%3A+Examples+of+Methodology+and+Advances+in+Mathematics+Education%22">Approaches to Qualitative Research in Mathematics Education: Examples of Methodology and Advances in Mathematics Education</a>. Kikner-Ahsbahs, A., Knipping, C., Presmed, N. , Springer. Dordrecht. 365-380 (2015).</li>
<li>Chandler, J., Paolacci, G., Peer, E., Mueller, P., Ratliff, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Using+nonnaive+participants+can+reduce+effect+sizes%5BTitle%5D)+AND+%22Psychological+Science%22%5BJournal%5D)">Using nonnaive participants can reduce effect sizes.</a> Psychological Science. 26 (7), 1131-1139 (2015).</li>
<li>Belz, M., Pyritz, L. W., Boos, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Spontaneous+flocking+in+human+groups%5BTitle%5D)+AND+%22Behavioural+Processes%22%5BJournal%5D)">Spontaneous flocking in human groups.</a> Behavioural Processes. 92, 6-14 (2013).</li>
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</ol>

The authors have nothing to disclose.

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><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: &lt;em&gt;A Language and Environment for Statistical Computing.&lt;/em&gt; 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 &quot;Prairie Trillium&quot;</td><td>RStudio Team &lt;em&gt;RStudio: Integrated Development Environment for R.&lt;/em&gt; 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. &lt;em&gt;ggplot2: Elegant Graphics for Data Analysis.&lt;/em&gt; 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. &lt;em&gt;cowplot: Streamlined Plot Theme and Plot Annotations for &amp;ldquo;ggplot2.&amp;rdquo;&lt;/em&gt; 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&amp;nbsp;</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&amp;ndash;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 &quot;Focal Fossa&quot;</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...

Watch this Scientific Journal Video about The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm at JoVE.com

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-an-online-group-adaptation-trust-game-based

Research

JoVE Journal

Behavior

2.0K Views.  University of Goettingen. De Collective Trust Game is een online multi-agent aanpassing van het trustspel. in het Honingraatparadigma. Het stelt onderzoekers in staat om collectief vertrouwen en het ontstaan ervan in groepen holistisch te onderzoeken.Dit spel combineert de hoge interne validiteit van een economisch spel met rijke ruimtelijke temporele gegevens, zodat emergente groepsprocessen in detail kunnen worden onderzocht. Het is belangrijk om de deelnemers te verbieden hun scherm, camera, microfoon en de ...

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

A Push-pull Protocol to Reduce Colonization of Bird Nest Boxes by Honey Bees

Introduction of the invasive Africanized honey bee (AHB) into the Neotropics is a serious problem for many cavity nesting birds, specifically parrots. These bees select cavities that are suitable nest sites for birds, resulting in competition. The difficulty of removing bees and their defensive behavior makes a prevention protocol necessary. Here, we describe a push-pull integrated pest management protocol to deter bees from inhabiting bird boxes by applying a bird safe insecticide, permethrin, to repel bees from nest boxes, while simultaneously attracting them to pheromone-baited swarm traps. Shown here is an example experiment using Barn Owl nest boxes. This protocol successfully reduced colonization of Barn Owl nest boxes by Africanized honey bees. This protocol is flexible, allowing adjustments to accommodate a wide range of bird species and habitats. This protocol could benefit conservation efforts where AHB are located.

Preventing colonization of bird nest boxes by invasive Africanized honey bees is important for conservation efforts of nest-site limited birds. We provide an integrated pest management approach to &#34;push&#34; bees away from nest boxes with a repellant insecticide, permethrin, and &#34;pull&#34; them toward pheromone baited swarm traps.

Introduction of the invasive Africanized honey bee (AHB) into the Neotropics is a serious problem for many cavity nesting birds, specifically parrots. ...

Environment

The HoneyComb Paradigm for Research on Collective Human Behavior

Collective human behavior such as group movement frequently shows surprising patterns and regularities, such as the emergence of leadership. Recent literature has revealed that these patterns, often visible at the global level of the group, are based on self-organized, individual behaviors that follow several simple local parameters. Understanding the dynamics of human collective behavior can help to improve coordination and leadership in group and crowd scenarios, such as identifying the ideal placement and number of emergency exits in buildings.
In this article, we present the experimental paradigm HoneyComb, which can be used to systematically investigate conditions and effects of human collective behavior. This paradigm uses a computer-based multi-user platform, providing a setting that can be shaped and adapted to various types of research questions. Situational conditions (e.g., cost-benefit ratios for specific behavior, monetary incentives and resources, various degrees of uncertainty) can be set by experimenters, depending on the research question. Each participant's motions are recorded by the server as hexagonal coordinates with timestamps at an accuracy of 50 ms and with individual IDs. Thus, a metric can be defined on the playfield, and movement parameters (e.g., distances, velocity, clustering, etc.) of participants can be measured over time. Movement data can in turn be combined with non-computerized data from questionnaires garnered within the same experiment setup.
The HoneyComb paradigm is paving the way for new types of human movement experiments. We demonstrate here that these experiments can render results with sufficient internal validity to meaningfully deepen our understanding of human collective behavior.

Here, we present the computer-based, multi-agent game HoneyComb, which enables experimental investigations of collective human movement behavior via black-dot-avatars on a virtual 2D hexagonal playfield. Different experimental conditions, like variable&#160;incentives on goal fields or vision radius, can be set, and their effects on human movement behavior can be investigated.

Collective human behavior such as group movement frequently shows surprising patterns and regularities, such as the emergence of leadership. Recent ...

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker bees spend the first half of their life inside the dark hive, where they are nursing the larvae or building the regular hexagonal combs for food (e.g. pollen or nectar) and brood3. The antennae are extraordinary multisensory feelers and play a pivotal role in various tactile mediated tasks4, including hive building5 and pattern recognition6. Later in life, each single bee leaves the hive to forage for food. Then a bee has to learn to discriminate profitable food sources, memorize their location, and communicate it to its nest mates7. Bees use different floral signals like colors or odors7, 8, but also tactile cues from the petal surface9 to form multisensory memories of the food source. Under laboratory conditions, bees can be trained in an appetitive learning paradigm to discriminate tactile object features, such as edges or grooves with their antennae10, 11, 12, 13. This learning paradigm is closely related to the classical olfactory conditioning of the proboscis extension response (PER) in harnessed bees14. The advantage of the tactile learning paradigm in the laboratory is the possibility of combining behavioral experiments on learning with various physiological measurements, including the analysis of the antennal movement pattern.

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

In this protocol we show how to condition harnessed honey bees to tactile stimuli and introduce a 2D motion capture technique for analyzing the kinematics of fine-scale antennal sampling pattern.

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker ...

Neuroscience

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

We present two methods for observing bumblebee choice behavior in an enclosed testing space. The first method consists of Radio Frequency Identification (RFID) readers built into artificial flowers that display various visual cues, and RFID tags (i.e., passive transponders) glued to the thorax of bumblebee workers. The novelty in our implementation is that RFID readers are built directly into artificial flowers that are capable of displaying several distinct visual properties such as color, pattern type, spatial frequency (i.e., &#8220;busyness&#8221; of the pattern), and symmetry (spatial frequency and symmetry were not manipulated in this experiment). Additionally, these visual displays in conjunction with the automated systems are capable of recording unrewarded and untrained choice behavior. The second method consists of recording choice behavior at artificial flowers using motion-sensitive high-definition camcorders. Bumblebees have number tags glued to their thoraces for unique identification. The advantage in this implementation over RFID is that in addition to observing landing behavior, alternate measures of preference such as hovering and antennation may also be observed. Both automation methods increase experimental control, and internal validity by allowing larger scale studies that take into account individual differences. External validity is also improved because bees can freely enter and exit the testing environment without constraints such as the availability of a research assistant on-site. Compared to human observation in real time, the automated methods are more cost-effective and possibly less error-prone.

This video describes Radio-Frequency Identification (RFID) and motion-sensitive video recording methods to monitor choice behavior by bumblebees.

We present two methods for observing bumblebee choice behavior in an enclosed testing space. The first method consists of Radio Frequency Identification ...

A 3D Printed Pollen Trap for Bumble Bee (Bombus) Hive Entrances

To verify the plant sources from which bumble bees forage for pollen, individuals must be collected to remove their corbicular pollen loads for analysis. This has traditionally been done by netting foragers at nest entrances or on flowers, chilling the bees on ice, and then removing the pollen loads from the corbiculae with forceps or a brush. This method is time and labor intensive, may alter normal foraging behavior, and can result in stinging incidents for the worker performing the task. Pollen traps, such as those used on honey bee hives, collect pollen by dislodging corbicular pollen loads from the legs of workers as they pass through screens at the nest entrance. Traps can remove a large quantity of pollen from returning forager bees with minimal labor, yet to date no such trap is available for use with bumble bee colonies. Workers within a bumble bee colony can vary in size making size selection of entrances difficult to adapt this mechanism to commercially reared bumble bee hives. Using 3D printing design programs, we created a pollen trap that successfully removes the corbicular pollen loads from the legs of returning bumble bee foragers. This method significantly reduces the amount of time required by researchers to collect pollen from bumble bee foragers returning to the colony. We present the design, results of pollen removal efficiency tests, and suggest areas of modifications for investigators to adapt traps to a variety of bumble bee species or nest box designs.

A 3D Printed Pollen Trap for Bumble Bee Bombus Hive Entrances

We present a non-lethal and automated mechanism to collect pollen from bumble bee (Bombus) workers returning to a hive. Instructions for producing, preparing, installing and using the devices are included. By using 3D-printed objects, modification to the design was timely, efficient and allowed for quick turnaround for testing.

To verify the plant sources from which bumble bees forage for pollen, individuals must be collected to remove their corbicular pollen loads for analysis.  ...

Biology

Group Behavior

<h4>Life in a Population</h4>
Organisms in populations interact with one another in complex ways, where individuals compete for resources such as food, shelter, and mates. These interactions are costly since individuals invest energy to acquire the resource, therefore there are a variety of strategies that organisms adopt to gain an advantage over their competitors. This is observed clearly with polymorphic mating systems, in which one sex, predominantly males, display multiple mating strategies. For example, dominant male sea lions defend harems of females up on the beach, whereas non-dominant males try to increase their chances of mating by staying in or near the water, where they mate with females that have left the beach temporarily 1. Other types of strategies may determine how often an individual will fight for a resource, or how willing they are to cooperate with others.
<h4>Game Theory and Evolutionarily Stable Strategies</h4>
In order to understand how different behavioral strategies evolve, ecologists turn to the game theory, which is a mathematical modeling approach that investigates the outcomes of multi-individual interactions, in which the payoff for any one individual depends its own strategy as well as the strategies of the others 2. In this approach, the relative cost of the interaction and the benefits obtained from the resource determines the net gain or, in some cases, loss incurred by the organism. Different strategies can be assigned costs and benefits based on those faced by an organism. For example, fighting for control of a resource may provide a large benefit, but it also comes with costs that reduce an organism&#8217;s net gain. On the other hand, a strategy to avoid conflict will reap fewer benefits but incurs no costs.
The organisms with the best interaction strategy maximize their net gain, which in turn will contribute to their fitness. Therefore, over the course of evolutionary time, one strategy may arise that outcompetes all others in a population. This is called an evolutionary stable strategy (ESS) 2. Populations evolve to adopt this strategy once it arises through mutation or is introduced through migration. Therefore, these strategies are mostly genetic or adopted at a young age, and changes in the strategies used by a population over time are determined by the action of natural selection. This concept is often illustrated using the Hawk-Dove game, which compares the success of two strategies for obtaining resources 3. In this example, &#8220;hawks&#8221; are aggressive, and always fight for resources. &#8220;Doves,&#8221; on the other hand, are passive, and never fight for resources. In an interaction between two doves, resources are split evenly. When a hawk and a dove interact, the hawk always wins and obtains all of the resources. However, when two hawks interact, they split resources evenly and suffer the cost of their conflict as well 3. Assessing these interactions over consecutive interactions allows modeling of how competing strategies in an evolving population fare against each other, thus emergence of ESS in an experimental setting.
As seen with the doves in the Hawk-Dove example, organisms not only compete against each other, but also display cooperative behaviors. The risk of being a population of 100% doves is the arrival of a cheater, or an individual that does not cooperate 4. Cheaters can invade and outcompete the residents, therefore many cooperating populations have strategies in place to prevent invasion, including the ability to switch strategy as necessary or to identify and cheaters, and in some cases, to communicate this information to others in their group to decrease the chances of cheater success 4.
<h4>Social Interaction in Nature</h4>
The existence of altruism, or a reduction in an organism&#8217;s immediate fitness in order to benefit another&#8217;s, in wild populations has been held up as a counterpoint to the theory of natural selection, however game theory shows how altruism can evolve under certain conditions. Assuming organisms can identify one another or reasonably expect to interact again in the future, acts of apparent altruism may actually be beneficial over time, as an organism can count on the favor being returned. This is seen when flocks of birds or herds of mammals feed collectively &#8211; one individual may sound an alarm call when they spot a predator, making them more vulnerable to attack 5. The net benefit, however, of others frequently doing the same makes this act adaptive. Similarly, a vampire bat may regurgitate its food to feed hungry individuals. When it is unable to find food in future, it may benefit from the same behavior of another vampire bat 6.
In addition to the interactions within a single species, the evolution of social interactions can take place between species as well. Besides predator-prey interactions, different species can compete for the same resources and develop strategies to gain advantage over others. However, individuals of a species may also interact cooperatively with members of another species. Interspecies interaction that require cooperation include mutualisms, or situations in which two organisms provide mutual benefits to one another. Many plants form mutualisms with nitrogen-fixing bacteria in the soil, whereby the plants provide complex sugars to bacteria in exchange for nitrogen 7. Should bacteria fail to provide nitrogen, then plants can reduce the amount of sugars available.
Since cooperative interactions depend on either being able to identify or retaliate against cheaters, it is possible for invaders to take advantage of existing mutualisms in new environments. Invasive species have the potential to be closely related enough to local species that they are able to form mutualisms with other local species, but distantly related enough that existing recognition or defense mechanisms are ineffective. In Hawaii, an invasive dinoflagellate that forms a mutualism with coral takes more resources than local dinoflagellates. This has a negative impact on coral instead of the expected beneficial one 8. Therefore, studying intra- and interspecies interactions not only allows understanding the development of behavioral strategies in evolving populations but is also essential to assess the behavioral phenotypes of invasive organisms to develop effective strategies against them.
<h4>References</h4>
<ol>
	<li>P&#246;rschmann, UlrichUniBi, et al. Male reproductive success and its behavioural correlates in a polygynous mammal, the Gal&#225;pagos sea lion (Zalophus wollebaeki). Molecular Ecology. 2010, Vol. 19, 12 (2574-86).</li>
	<li>Price, J. M Smith &#38; G. R. The Logic of Animal Conflict. Nature. 1973, 246 (15-18).</li>
	<li>Sigmund K, Nowak MA. Evolutionary game theory. Curr Biology. 1999, Vol. 9, 14 (R503-5).</li>
	<li>Rose, M. R. Cheating in Evolutionary Games. J. Theor. Biol. 1978, 75 (21-34).</li>
	<li>Trivers, R. L. Evolution of reciprocal altruism. Q. Rev. Biol. 1971, Vol. 46, 1 (35-57).</li>
	<li>Wilkinson, G. S. Reciprocal food sharing in the vampire bat. . Nature. 1984, 308(181-84).</li>
	<li>Francis M. Martin, St&#233;phane Uroz, David G. Barker. Ancestral alliances: Plant mutualistic symbioses with fungi and bacteria. Science. 2017, Vol. 356, 6340 (eaad4501).</li>
	<li>Tracy D.Ainsworth, Rebecca VegaThurber, Ruth D.Gates. The future of coral reefs: a microbial perspective. Trends in Ecology &#38; Evolution. 2010, Vol. 25, 4 (233-40).</li>
</ol>

Group Behavior: Hawk-Dove Game and Trading Exercise - Concept

Life in a Population
Organisms in populations interact with one another in complex ways, where individuals compete for resources such as food, shelter, ...

Education

JoVE Lab Manual

Ecology

Creating the Minimal Group Paradigm

Source: Julian Wills &#38; Jay Van Bavel&#8212;New York University
The study of intergroup relations, such as prejudice, conflict, and discrimination, has always been a central topic in social psychology. Does discrimination stem from competition with other groups, a history of conflict, or derogatory stereotypes? Despite an abundance of real-world examples, the ingredients that lead to intergroup discrimination are often unclear. 
To help solve this problem, a group of psychologists created "minimal groups" to strip away confounds like monetary self-interest and a history of conflict that are normally involved in intergroup discrimination. In minimal groups, participants are randomly assigned to completely novel groups. Thus, any consequences emerging from this minimal group induction must stem from identifying with a social group and separating the social world into "us" and "them." Research using minimal groups has shown that, despite the arbitrary nature of group membership, participants willingly discriminate by favoring members of their in-group over members of the out-group.
The minimal group paradigm is widely used in social psychology to study the most basic elements of intergroup relations. This method was first introduced in a 1971 paper called Social Categorization and Intergroup Behaviour by Henri Tajfel and colleagues.1 Across three experiments, the authors documented the in-group favoritism that emerges from a rather minimal group induction. This video will demonstrate how to produce the minimal group induction in a manner similar to the first experiment, where groups were ostensibly created based on dot estimation tendencies.

Source: Julian Wills &#38; Jay Van Bavel&#8212;New York University
The study of intergroup relations, such as prejudice, conflict, and discrimination, has ...

JoVE Science Education

Psychology

Social Psychology

Eusociality and Division of Labor

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Simulating Eusocial and Solitary Behavior
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">Note: In this simulation, you will portray either a eusocial or solitary animal. The goal of this simulation is to maximize your fitness by reproducing as many offspring as possible during the allotted five minutes. To do this, you must forage for resources and reproduce within the constraints of your assigned role.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Assuming a class size of 10, 4 individuals should form the solitary, or the carpenter bee group, and the remaining 6 students should form the eusocial, or the honeybee group. Note: The eusocial honeybee group will include one queen, one drone, and four workers. The queen and the drone will reproduce and protect the resources, while the workers will forage and steal resources. The solitary carpenter bee students must perform all of the tasks in this activity, with the exception of reproduction, by themselves.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Store your resources for reproduction in the nest bowl provided by your instructor. These resources are represented by assorted beans that can be collected by foraging in the environment buckets or by stealing from other nests.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">The blue cups are male and the red cups are female, and they will be used to produce offspring. This is done by combining all four bean types in one red or blue cup to symbolize the resources needed to reproduce, and then stacking one completed cup inside a cup of the opposite color that also contains one of each bean type, to simulate a mating that produces two-cup offspring, one from each parent.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">To reproduce, the solitary students will need to seek out another solitary student with an opposite-colored cup. Conversely, the eusocial queen and drone always reproduce with each other. HYPOTHESES: The hypotheses for this lab activity are based on the possible benefits of eusocial groups over solitary individuals. The alternate hypothesis could be that one group, either eusocial or solitary, will have an advantage over the other, given the ecological constraints, and this will be reflected in the number of offspring per capita. The null hypothesis might be that there will be advantage conferred by either of the strategies.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">If you are a worker in the honeybee group or a solitary student in the carpenter bee group, walk to the other side of the room to search through the rice on the foraging side of the room for one bean and bring the bean back to your bowl on the nest side of the room. Note: Even though this activity has a 5-minute time limit, running is not permitted. 
</div></li><li data-sub-note-item="1-7"><div class="lab-step">If a solitary student is not present at a neighboring bowl, you can try to steal one bean at a time, using a cup to scoop up the bean without touching the bean or the bowl with anything but the cup. You must immediately stop trying to steal if the solitary student makes it back to their bowl before you finish stealing. Note: Neighbors cannot steal from eusocial nests because the queen and drone do not leave their nest.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">You can create offspring at any time during the activity. Remember to label each cup that is contributed to an offspring with your name to keep track of your reproduction. Note: Eusocial groups produce two offspring for their group every time they reproduce, while solitary students produce only one offspring per reproductive coupling.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">If you were a solitary carpenter bee, record the number of offspring you produced in the appropriate row of the table. <a href="https://www.jove.com/CDNSource/lm/labs/36/Table_1_Eusociality_and_division_of_labor.xlsx" style="display:block;margin:20px;" target="_blank">Click Here to download Table 1</a>
</div></li><li data-sub-note-item="2-2"><div class="lab-step">If you were a part of the eusocial honeybee group, record the number of offspring produced by your queen and drone.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">To calculate the fitness for a solitary carpenter bee student, simply count the number of offspring that the student produced.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">To calculate the fitness for a eusocial honeybee student, divide the number of offspring produced by the queen and drone by the number of members in the group, in this case 6, to get a per-student fitness value.
</div></li><li data-sub-note-item="2-5"><div class="lab-step">When each student has calculated their respective fitness value, calculate the average number of offspring produced for each social category and plot these as a bar graph. <a href="https://www.jove.com/CDNSource/lm/labs/36/Table_2_Eusociality_and_division_of_labor.xlsx" style="display:block;margin:20px;" target="_blank">Click Here to download Table 2</a>
</div></li><li data-sub-note-item="2-6"><div class="lab-step">Next, use Hamilton&#39;s equation to analyze the data. To do this, set r equal to the degree of relatedness, B to the class average eusocial fitness, and C to the class average solitary fitness. r * B > C
</div></li><li data-sub-note-item="2-7"><div class="lab-step">Use these results to determine if altruistic behavior should persist in your eusocial honeybee group. Discuss the variables in Hamilton&#39;s equation that may contribute to this observation. Consider circumstances under which you would expect to see different results. 
</div></li></ul></li></ol>

Simulating Eusocial and Solitary Behavior
ExpandNote: In this simulation, you will portray either a eusocial or solitary animal. The goal of this ...

Lab Manual Sub Articles

Cooperative vs. Non-Cooperative Games

In game theory, games are scenarios where players make decisions to maximize their outcomes while considering the possible actions of others. These games are classified into two main types: non-cooperative and cooperative.
In non-cooperative games, players act independently, without forming any agreements or commitments. Each player focuses on their own outcome, taking into account what others might do. This is often done by devising individual strategies, which are plans or actions that a player takes to achieve their desired outcome. An example of this is two rival tech companies launching new products at the same time. Each company independently sets its prices and marketing strategies, aiming to gain a competitive edge. While neither company cooperates, each must anticipate the other&#39;s decisions to succeed.
In cooperative games, players work together to achieve a better outcome for the group. They form agreements that benefit all participants involved. A typical example is seen in labor unions, where independent workers choose to collectively negotiate wages and working conditions with their employer. By forming a union, the workers cooperate to achieve a better overall result than they could by acting individually, thereby ensuring fair wages and benefits for the group.
The key difference between non-cooperative and cooperative games is whether or not formal agreements are made. In non-cooperative games, players rely solely on their own strategies, while in cooperative games, collaboration allows players to achieve shared benefits.
In summary, non-cooperative and cooperative games offer valuable insights into strategic decision-making. Understanding these two types of games is crucial in fields such as economics, business, and politics, where decision-making often involves balancing personal gain with potential cooperation.

In game theory, a game is a scenario where players make decisions to achieve their goals, considering the possible choices of others.
These games are ...

Business

JoVE Business

Microeconomics

Unit 1

Game Theory

KEY TERMS AND CONCEPTS

Altruism

Altruistic behaviors are &ldquo;unselfish&rdquo; behaviors&mdash;those that help another individual at the expense of the individual carrying out the behavior. Despite the negative consequences for the altruistic animal, these behaviors are thought to have evolved for several reasons.

<h4>Reasons for Altruistic Behaviors</h4>

First, most altruistic behavior occurs between individuals that are related. Altruism is particularly pronounced in animals that live in colonies with one reproductively capable female&mdash;the queen&mdash;such as bees and naked mole rats.

In these eusocial animals, all the members of a colony are very closely related. The non-reproductive members&mdash;for instance, worker bees&mdash;care for the queen and her offspring by engaging in altruistic behaviors, such as bringing back food and defending the colony. In the course of these activities, they may even sacrifice their own lives for the good of the queen and the colony.

However, because they are closely related to the queen, their self-sacrificing behavior increases the chances that the genes they share with her will be passed down through her offspring&mdash;ultimately preserving altruistic behavior in the population.

Altruism also occurs between related animals that are not eusocial, such as squirrels who warn other members of their group&mdash;some of whom are related to them&mdash;with alarm calls when a predator is near. This puts the squirrel giving the call at risk, but helps the group, and its genes, survive.

Altruism can also occur between unrelated individuals within a social group, such as when primates groom each other or share food. Although these behaviors may be costly to the altruistic animal in the short-term, they can be beneficial if the favor is repaid later. This is called reciprocal altruism and primarily occurs in animals that live in stable social groups in which individuals have many opportunities to &ldquo;repay&rdquo; individuals who have helped them in the past.

Animal Behavior: Altrusim and Community

Altruistic behaviors are &ldquo;unselfish&rdquo; behaviors&mdash;those that help another individual at the expense of the individual carrying out the ...

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

Samenvatting

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