Reciprocity in locating contributions: Experiments on the neighborhood public good game

Berninghaus, Siegfried; Güth, Werner; Schosser, Stephan Article Reciprocity in locating contributions: Experiments on the neighborhood public good game Games Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Berninghaus, Siegfried; Güth, Werner; Schosser, Stephan (2013) : Reciprocity in locating contributions: Experiments on the neighborhood public good game, Games, ISSN 2073-4336, MDPI, Basel, Vol. 4, Iss. 2, pp. 144-162, https://doi.org/10.3390/g4020144 This Version is available at: https://hdl.handle.net/10419/98561 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/ Games 2013,4, 144-162; doi:10.3390/g4020144 OPEN ACCESS games ISSN 2073-4336 www.mdpi.com/journal/games Article Reciprocity in Locating Contributions: Experiments on the Neighborhood Public Good Game Siegfried Berninghaus 1, Werner G¨ uth 2and Stephan Schosser 3,* 1Institute of Economic Theory and Statistics, Karlsruhe Institute of Technology, Zirkel 2, Karlsruhe, Germany 2Strategic Interaction Group, Max Planck Institute of Economics, Kahlaische Strasse 10, Jena, Germany 3Institute of Social Medicine and Health Economics, University of Magdeburg, Leipziger Strasse 44, Magdeburg, Germany *Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +49 (391) 67-24308; Fax: +49 (391) 67-24310. Received: 7 December 2012; in revised form: 1 March 2013 / Accepted: 4 April 2013 / Published: 26 April 2013 Abstract: In repeated public good experiments, reciprocity helps to sustain high levels of cooperation. Can this be achieved by location choices in addition to making contributions? It is more realistic to rely on an intuitive neighborhood model for community members who interact repeatedly. In our experiments, participants can locate their contribution, yielding a small benefit for the participant, who receives the contribution and a small disadvantage for the participant, at the opposite location. This mechanism of individually targeted sanctions helps to foster initial cooperation. It decreases over time, however. Location choices are used to reciprocate, but may not suffice to stabilize voluntary cooperation as an effect observed in the field. Keywords: public good game; neighborhood; cooperation; experimental analysis 1. Introduction In linear public good games, benefits from the common pool are smaller than the benefit of keeping everything for oneself, but investments in the common pool imply efficiency gains for the whole Games 2013,4145 population. In experimental studies of repeated voluntary public goods provision (see, e.g., [1] for an early investigation or [2] for a recent review), participants initially contribute to the common pool. However, cooperation decreases with the length of the experiment and the growing number of participants [3]. Allowing participants to repeatedly interact in the same group, i.e., by implementing a partner design, also allows for conditional cooperation [4]. Participants who cooperate conditionally adjust their contributions toward the average contribution of the whole group. This “reciprocal” adjustment is not perfect, but typically favors the adjusting participant, leading to a decline in contributions over time [5]. Several studies (e.g., [5,6]) report that the majority of participants resort to conditional cooperation, while a large fraction of participants free ride and a smaller fraction of them show unconditional cooperation. A mechanism ensuring cooperation over time is sanctioning [7,8], where, after each period, participants are informed of the contributions of all other participants and can impose a fine on those who did not fulfill the former’s expectations.1However, punishment has several drawbacks in addition to its potential demand effect: (1) It is costly for both, the punishing and the punished participant (see, e.g., [10]). (2) Some participants punish antisocial [11] participants who contributed more than they did. (3) Punishment is affected by an imposed and usually rather extreme relationship between the costs for the punisher and the fine imposed [12,13]. (4) The technology of such mandatory sanctions has no analogue in the field,2or more specifically, individual targeted mandatory sanctions by private agents may be useful for disentangling motives, but are rather unrealistic. These drawbacks do not apply to rewarding. Allowing to reward may also invoke a demand effect [14,15], where participants can transfer part of their earnings to other participants without loss of efficiency and, thereby, ensure cooperation over time. The advantages of both, punishment and rewarding, are combined in institutions in which participants can both punish and reward [14,15]. Rand et al. [15] conclude that “it is not costly punishment that is essential for maintaining cooperation in the repeated public goods game but instead the possibility of targeted interactions more generally.” In particular, they show that both punishment and rewarding with monetary transfers, i.e., investing part of one’s income to reduce or increase the income of another participant increases cooperation. In view of this, we modify the public good game by allowing for additional targeting of contributions. Our Neighborhood Public Good Game is played by four players located at the four corners of a square. Thus, each player has two direct neighbors (at adjacent corners) and one distant neighbor. Players do not only choose their contribution, but also the location (corner) of their contribution. What a player gains from a contribution depends on her distance to the location of the contribution. It is maximal (minimal) when the contribution is located at the (opposite) position of the player. Similar to monetary transfers (as used by Rand et al.), location choices are neutral with respect to efficiency—measured by the sum of contributions—and allow for both rewarding and punishment. For rewarding, a player locates his contribution at the position of the targeted player, while he chooses the opposite position when punishing. 1One wonders somewhat why this technology of monetary sanctioning is widely applied and apparently accepted in experimental economics, since modern democracies normally rely on democratically controlled sanctioning by the state (see [9] for an experimental comparison of decentralized and centralized sanctioning). 2In our view and in line with our experience, there seems to be no questioning of assumptions of former studies, even when they are outrageously unrealistic, what may be explained by habit information in science. Games 2013,4146 Having to locate one’s contributions in one’s neighborhood is quite natural. One example is locating facilities with positive or negative external effects for one’s neighbors.3Furthermore, the act of locating is neither an obvious device for sanctioning or rewarding with a self-serving location, i.e., locating one’s contribution at one’s location, as the obvious default. Will this allow voluntary cooperation by improved coordination? Since locating contributions for the whole group is efficiency neutral, this offers an intriguingly new aspect of repeated contributions in public goods experiments. To better disentangle whether being able to locate one’s contribution fosters voluntary cooperation, we first ran treatments with endogenous location choices and then treatments in which the same location choices are exogenously imposed. Thus, any difference in behavior in the main treatments and the control treatments can be unambiguously attributed to the discretion in locating one’s contribution. Only 16% of the participants always chose a self-serving location, confirming that they relied on location choices in spite of the self-serving location being the obvious default. Moreover, without the location option, there is less cooperation. Thus, assuming realistically that individual contributions are not just added in, but have to be located, captures a coordination device without any formal institution. In contrast to the literature on sanctioning with its demand effect [12,13], showing that punishment is more effective when the cost/fine ratio of punishment is low, we find the opposite effect, warning us against reaching general conclusions from strategies that employ highly unrealistic sanctioning schemes. In Section 2, we introduce the voluntary contribution game with its simple neighborhood structure and present our experimental design. The experimental data, comprising individual contributions and location choices, are analyzed in Section 3. Section 4discusses our results. 2. Experimental Design In this section, we first introduce the model of our Neighborhood Public Good Game and then describe our treatments and the experimental procedure. 2.1. Model The Neighborhood Public Good Game is repeated Ttimes. Four players (I={1,2,3,4}) interact, and each player, i∈I, is assigned to a different corner of a square. The two players occupying adjacent corners of the square are the direct neighbors of player, i, the other player is i’s distant neighbor. In each period, t∈ {1, ..., T }, each player, i, receives the same integer endowment, e > 0. Player i then chooses the size of the contribution, ct(i), with 1≤ct(i)≤eand its location, lt(i)∈I. Each player can locate ct(i)at the position of a neighbor or his own position and keeps the difference, e−ct(i), for himself. The location of contribution, lt(i), can be any corner of the square, i.e., the position of one of the four players. In the following, we also refer to lt(i)as the player located at corner, lt(i). Contributions have to be positive (ct(i)≥1for all i) to make their location always payoff relevantly. Further, the contribution, ct(i), can be located at one location, lt(i), only. By allowing only positive contributions at exactly one location, we intend to understand the motivation behind the contributions. If a player 3For instance, locating a chimney as far as possible from one’s neighbor would favor this neighbor, but harm the neighbor at the opposite end. Similarly, industrial production sites and emissions can be located with different effects for the neighboring firms. Games 2013,4147 could contribute nothing to the public good, his location choice would be meaningless, which we want to avoid. The position of the player in relation to contribution, lt(i), determines its constant marginal productivity α(k, lt(i)). For any player, k, the marginal productivity of an individual contribution, ct(i), is: α(k, lt(i)) =      αfor lt(i) = k αif kand lt(i)are direct neighbors αif kand lt(i)are distant neighbors where 1> α > α > α > 0and α+ 2α+α > 1. The payoffs are given by: ut(k) = e−ct(k) + X i α(k, lt(i))ct(i) for all players, k∈I, and all periods, t= 1, . . . , T with 1≤ct(i)≤efor all iand t. Although the location of contribution, lt(i), affects the individual gains from ct(i), it does not influence the aggregated payoff of all players, Piut(k), as the aggregated marginal benefit of a contribution is α+2α+αalways. Note that locating one’s contribution does not only target just one community member, but favors the one whose location is chosen and harms another who is at the opposite corner. In our view, this captures a usual aspect of most sanctioning devices in the field and questions individual targeting in sanctioning without external effects on others. 2.2. Treatment Design We conducted experiments to investigate the impact of location choices. In particular, we introduced treatments varying the values, α,αand α, without changing the sum, α+ 2α+α. Larger differences can, for instance, allow one to seriously harm the distant contact. Since we expected an increase in cooperation when participants have more discrimination power, we conducted two versions of the Neighborhood Public Good Game, Treatments, Mand M, using different values for α,α,α: M:α= 0.3, α = 0.5, α = 0.7and M:α= 0.4, α = 0.5, α = 0.6 In addition, we performed control Treatments, Cand C, of the Neighborhood Public Good Treatments, Mand M, where the location choices of participants are exogenously imposed, i.e. location choices were drawn from a uniform distribution. We did not inform participants of the control sessions how locations were determined—all the Cand Cinstructions say is “Locations are predetermined and independent of your behavior.” Table 1summarizes all four treatments. 2.3. Procedure We recruited participants using ORSEE [16] for our computerized (using z-Tree, [17]) experiment. At the beginning of a session, participants were randomly seated in the laboratory. We handed out Games 2013,4148 Table 1. Distinction of treatments. Intensity of punishment/rewarding low (α=.6; α=.5; α=.4) high (α=.7; α=.5; α=.3) Location by participant M M choice by random draw C C written instructions (see Appendix Afor an English translation), which were also read aloud to make them common knowledge. After privately answering questions concerning the instructions, we checked the understanding of the instructions by a control questionnaire. Participants who were not able to answer these questions were replaced (altogether, 15 participants). The participants played the four treatments of the Neighborhood Public Good Game for (T=)20 periods.4In the beginning of the first period, we assigned each participant to a group and a location, which did not change throughout all 20 periods. In each period, t, participants received an endowment of (e=)10 tokens. Each participant, i, decided how many tokens to contribute to the public good (ct(i)). In the MTreatments, each participant also had to specify where to locate it (lt(i)), while participants in the CTreatments were shown a location drawn from a uniform distribution where they could contribute. Each token was worth 10 points. Hence participants earned an integer number of points in each period, given the parameters in Table 1. At the end of each period, we informed the participants of their payoff, the contributions, ct(j), and the location choices, lt(j), of all participants in the current period. No additional information concerning other participants was given. Table 2. Overview of treatments conducted. Treatment Sessions Independent No. of No. groups per session observations participants M3 8 (Sessions 1 and 2), 23 92 7 (Session 3) M3 8 (all sessions) 24 96 C3 8 (all sessions) 24 96 C3 8 (all sessions) 24 96 Sum 12 95 95 380 A session lasted 60 to 90 minutes, including 25 minutes for reading and understanding the instructions. We paid participants privately at the end of each session. They earned points as payoffs (100 points = 0.20 euro). The average payoff was 14.02 euro. A participant who was replaced after not understanding the instructions received 2.50 euro in addition to the show-up fee of 2.50 euro. Three hundred and eighty students from various fields of study participated in the experiment at the laboratory of the Max Planck Institute of Economics in Jena. We conducted three sessions with 32 participants for each of the four Treatments (M,M,C,C) with eight groups and one session of Treatment Mwith 28 participants in 7seven groups (see Table 2). Hence, we generated 24, respectively 23, independent observations per treatment. The reduction to 23 independent observations in Treatment Mresulted from a hardware failure. 4After the 20 periods, we repeated the identical treatment with different groups. This repetition did not yield any additional insights. In this paper, we therefore focus on the results of the first repetition in this paper. Games 2013,4149 2.4. Hypotheses Clearly, the solution behavior (from backward induction, e.g. in the sense of repeated elimination of weakly dominated strategies or subgame perfect equilibria) for all four players, i, is c∗ t(i)=1and l∗ t(i) = iin all periods, t. For an efficient outcome, all participants must choose c+ t(i) = eand can make any location choice, lt(i), in all periods, t. However, results from experimental investigations show deviating behavior: in the Neighborhood Public Good Game, participants interact in a partners design. Previous studies (e.g., [18]) have shown that participants engage in initial cooperation, but reveal a downward sloping development of contributions with a further decline of cooperation in the terminal period. We expect qualitatively similar attempts for ct(i)in the Neighborhood Public Good Game, where our participants can additionally make self-serving location choices, lt(i) = i, but much less reliability of such attempts. In our view, this is mainly due to the unrealistic technology of individually targeted and highly detrimental monetary sanctions. Hypothesis 1 Cooperation decreases after the first period with: (a) contributions ct(i)decreasing rapidly over time and (b) an increasing number of self-serving location choices lt(i) = iin later periods. The higher variance between α,αand αin the Mtreatment than in the Mtreatment allows for higher discrimination power regarding location choices. In particular, the positive behavior of others can be more highly rewarded in the Mtreatment. As we expect reciprocity to be the driving force behind behavior, we also expect more cooperation in the Mthan in Mtreatment. Hypothesis 2 Participants contribute more in the Mtreatment than in the Mtreatment. Finally, only in Mtreatments can participants reciprocate the behavior of others, while in Ctreatments, the location choices are randomly determined. Hence, we derive the following hypothesis. Hypothesis 3 Participants contribute more in the Mthan in the Ctreatments. 3. Results 3.1. The Benefit of Location Choices The overall contributions (see Figure 1(b))5in the first period are 5.73 (in M) and 5.72 (in M) and are significantly higher than the contributions of 4.30 (in C) and 4.27 (in C) without location choices (Mvs. C(MW): p=0.016 / Mvs. C(MW): p=0.003). Although contributions in treatments with 5If not specified separately, all test results in this section are one-sided, with 23 (for Treatment M) or 24 (for Treatments M,Cand C) independent observations. We use MW as abbreviation for Mann-Whitney U Tests and WX as abbreviation for Wilcoxon Signed Rank tests. Games 2013,4150 location choices remain higher than contributions in treatments without location choices, they decrease at least for the first 10 periods (Mvs.C(MW): p=0.007 / Mvs.C(MW): p=0.016) until they approximate each other in the last period, clearly confirming Hypothesis 1 (a) (M: 3.50, M: 2.95, C: 2.41, C: 2.35). Compared to period 19, the last period only shows a weak endgame effect (in C) if any (M(WX): p=0.062 / M(WX): p=0.070 / C(WX): p=0.061 / C(WX): p=0.046). Figure 1. Development of contributions per treatment (a) Fraction of self-serving location choices (b) Height of average contrib. (c) Height of adjacent/distant contrib. (d) Height of self-serving contrib. Result 1 Being able to freely locate one’s contribution fosters initial cooperation, but cannot prevent its erosion over time. We attribute the decrease in contributions (mainly) to the increase in self-serving location choices. Both with high and low intensity of punishment or rewarding, significantly more (about 50%, see Table 4) participants invest in their own location than in experiments with random location choices (Mvs. C(MW): p=0.001 / Mvs. C(MW): p=0.026) and the fraction of investments in self-serving locations slightly increases over time, confirming Hypothesis 1 (b) (see Figure 1(a)). An analysis of contributions at adjacent/distant locations (see Figure 1(c)) and contributions at self-serving locations (see Figure 1(d)) in isolation confirms this. Contributions are significantly higher in treatments with location choices, both for self-serving (l(i) = i; Mvs. C(MW): p=0.001 / Mvs. C(MW): p=0.000) and other contributions (Mvs. C Games 2013,4151 (MW): p=0.045 / Mvs. C(MW): p=0.013). The effect even persists during the first 10 periods for contributions in self-serving (Mvs. C(MW): p=0.001 / Mvs. C(MW): p=0.003) and other locations (Mvs. C(MW): p=0.029 / Mvs. C(MW): p=0.033). Table 3. Comparison of first period contributions based on location choices. Treatment Other locations Own location Mean SD Mean SD M4.65 3.52 7.49 3.80 M4.35 2.88 7.34 3.85 C4.24 3.48 4.55 3.89 C4.25 3.43 4.33 3.41 However, the effects are stronger for self-serving location choices (in p-values). The difference between contributions at self-serving and distant location choices is most obvious when contributions in the first period are analyzed (see Table 3). Here, contributions at other locations are, on average, around four for all four treatments. However, in treatments allowing participants to choose the location of their contribution, the average contribution at self-serving locations is about twice as high. In addition, we find no endgame effect for contributions at self-serving locations (M(WX): p= 0.236 / M(WX): p= 0.091 / C(WX): p= 0.352 / C(WX): p= 0.415). Nevertheless, there is an endgame effect for contributions at other locations for Treatment Mand C(M(WX): p=0.063 / M(WX): p= 0.095 / C (WX): p= 0.135 / C(WX): p= 0.018). To sum up, contributions differ between treatments with and without location choices. This effect can be mainly attributed to the role of self-serving location choices. Whenever participants have the possibility, contributions at self-serving locations are more frequent and corresponding contributions are (at least initially) higher than choices for other locations. Result 2 Contributions at self-serving locations compensate this effect by being larger than contributions at distant locations, suggesting one can substitute free riding by self-serving location choices. 3.2. Impact of Costs for Rewarding and Punishment How fast cooperation decreases depends on the reward and punishment effects by location. While more than 40% of location choices fall on adjacent locations, only 5% choose distant neighbors (see Table 4). In particular, while no significant difference between the random (Treatments C) and the target mechanism (Treatments M) exists for adjacent locations (MW: p= 0.150), self-serving contributions at (distant) locations are significantly higher (lower) when participants choose these themselves (self-serving (MW): p= 0.000 / distant (MW): p=0.000). Thus, they refrain from punishment and rewarding of distant neighbors and focus on adjacent neighbors. The difference in impacts between Treatments Mand Mleads to differences in the effectiveness of this mechanism. While investments in the second half of the experiment are significantly higher in Treatment M than in Treatment C(MW: p= 0.010), no difference exists between Treatment Mand Treatment C(MW: p= 0.359). Although the benefit of our punishment and rewarding mechanism decreases in Games 2013,4158 7. Fehr, E.; G¨ achter, S. Altruistic punishment in humans. Nature 2002,415, 137–140. 8. Fehr, E.; G¨ achter, S. Cooperation and punishment in public goods experiments. Am. Econ. Rev. 2000,90, 980–994. 9. Fischer, S.; Grechening, K.; Meier, N. The law’s promise: An experiment on (de)centralized sanctions & (im)perfect information. 2012 10. Dreber, A.; Rand, D.G.; Fudenberg, D.; Nowak, M.A. Winners don’t punish. Nature 2008, 452, 348–351. 11. Herrmann, B.; Th¨ oni, C.; G¨ achter, S. Antisocial punishment across societies. Science (New York, N.Y.) 2008,319, 1362–1367. 12. Egas, M.; Riedl, A. The economics of altruistic punishment and the maintenance of cooperation. Proceedings of the Royal Society/Biological Sciences 2008,275, 871–878. 13. Nikiforakis, N.; Normann, H.T. A comparative statics analysis of punishment in public-good experiments. Experimental Econ. 2008,11, 358–369. 14. Sefton, M.; Shupp, R.; Walker, J.M. The effect of rewards and sanctions in provision of public goods. Econ. Inq. 2007,45, 671–690. 15. Rand, D.G.; Dreber, A.; Ellingsen, T.; Fudenberg, D.; Nowak, M.A. Positive interactions promote public cooperation. Science (New York, N.Y.) 2009,325, 1272–1275. 16. Greiner, B. The online recruitment system ORSEE 2.0: A guide for the organization of experiments in economics. 2004 17. Fischbacher, U. z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Econ. 2007,10, 171–178. 18. Keser, C.; van Winden, F. Conditional cooperation and voluntary contributions to public goods. Scand. J. Econ. 2000,102, 23–39. 19. Chaudhuri, A.; Paichayontvijit, T. Conditional cooperation and voluntary contributions to a public good. Econ. Bull. 2006,3, 1–14. Games 2013,4159 Appendix A. Instructions Welcome to this experiment and thank you very much for your participation. You will receive 2.50 euro for showing up in time. During the experiment, you will have the opportunity to earn additional money. Please stay quiet and switch off your mobile phone. Please read these instructions carefully, which are identical for all participants. Communication between the participants is not allowed. If you do not follow these rules, we have to exclude you from the experiment and, consequently, from any payment. To ensure that you understand the instructions, we ask you to answer several control questions before beginning the experiment. If you have any questions, please raise your hand. One of the experimenters will then come to you and answer your question in private. The endowment of 2.50 euro for showing up in time as well as any other amount of money you earn during the experiment will be paid to you in cash at the end of the experiment. We will pay you privately to ensure that no other participant becomes aware of the amount of your payment. Figure 5. Placement of participants within one group. Your payment depends on your own decisions as well as the decisions of other participants. The payoff in the experiment is measured in points. The points you earn during the experiment will be converted into euro at the end of the experiment and paid to you. You find the conversion rate at the end of this document. You and all other participants enter their decisions independently of other participants in individual computer terminals. Course of the Experiment At the beginning of the experiment, you are randomly assigned to three other participants, forming groups of four. Each member of your group will be randomly positioned in one corner of a square Games 2013,4160 (see Figure 5). The two other participants, who share one line of the square with you, are your direct neighbors, the fourth is your distant neighbor. The participants in one group will not necessarily sit side by side. The composition of the group and the positioning remain unchanged. In the following, “partners” stands for the participants in your group. The participants of other groups are not considered in the remainder of these instructions. Figure 5shows the arrangement of participants in one group. For example, the direct neighbors of the participant at position 1 are located at positions 2 and 4. The distant neighbor is located at position 3. This experiment consists of 20 periods. At the beginning of each period, you will receive 10 tokens. Subsequently, you have to make two decisions: •on the amount of your investment, i.e., the number of tokens ranging from 1, 2, ..., 9, 10, and •on where to place your investment, more precisely: in which partner, i.e., at your position, the position of a direct neighbor or a distant neighbor. While making your decision, you will see the following display on your computer screen (see Figure 6). Your position is visualized in the upper area of the screen. On the left side you decide on the amount of your investment, and on the right side you choose the partner in whom you want to place your investment. Figure 6. Decision screen. Games 2013,4161 Your Payoff At the end of each period, your payoff is calculated, based on the decisions of all participants. The result of this calculation is shown on the computer screen. The calculation of your payoff is as follows. For each token you have kept, you receive 10 points. You receive 7 points for every invested token assigned to you; for each token assigned to a direct neighbor you receive 5 points; and for every token assigned to the distant neighbor you receive 3 points. For the final payoff the points are added up over all periods. For 100 points you receive 0.20 euro. Payoff is rounded to the next higher amount divisible by 5 euro cent. This is repeated twenty times. Hence all groups and the groups’ assignment to positions in the square remain identical for all 20 periods. Please remain seated during all periods and only get up when asked. After these 20 periods the experiment is continued, and you will receive separate instructions. Figure 7. Decision screen. A.1. Differences in instructions 1.1.1. Differences between C and M Treatments During CTreatments, the three paragraphs between “This experiment consists of 20 periods. [..], in whom you want to place your investment.” are replaced with the following text: Games 2013,4162 “This experiment consists of 20 periods. In the beginning of each period, you will receive 10 tokens. Subsequently, you have to make one decision: on the amount of your investment, i.e., the number of tokens ranging from 1, 2, ..., 9, 10. In addition to the amount of the investment you specified yourself, the placement of your investment, i.e., in which partner, i.e., at your position, the position of a direct neighbor or the distant neighbor, is given by the computer terminal. The assignment is independent of your behavior. While making your decision, you will see the following display on your computer screen (see Figure 7). Your position is visualized in the upper area of the screen. On the left side you decide on the amount of your investment. Additionally, you see the result of the placement of your investment in a partner made by the computer terminal.” No other changes were made in the instructions. 1.1.2. Differences between M,Cand M,CTreatments For the M,CTreatments in the paragraph “The calculation of your payoff is as follows: [...] Payoff is rounded to the next higher amount divisible by 5 euro cent.” the multiplier of tokens was changed to 4, 5, and 6, respectively. No other changes were made in the instructions. c 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).