Supporting Online Material for

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1 Supporting Online Material for Indirect Punishment and Generosity Toward Strangers Aljaž Ule,* Arthur Schram, Arno Riedl, Timothy N. Cason *To whom correspondence should be addressed. This PDF file includes: Materials and Methods Figs. S1 to S5 Tables S1 to S3 References Published 18 December 2009, Science 326, 1701 (2009) DOI: /science

2 Supporting Online Material for Indirect Punishment and Generosity towards Strangers Aljaž Ule *, Arthur Schram, Arno Riedl, Timothy N. Cason * To whom correspondence should be addressed. a.ule@uva.nl Content: Materials and Methods 1. Experimental Design and Procedures 2. Written Instructions and Computer Screen Captures for Experiment 3. Details of Strategy Classification 4. Punishment in Indirect Reciprocity and Public Goods Games 5. Statistical Details for Main Hypothesis Tests Figures S1 to S5 Tables S1 to S3 Notes and References 1

3 Materials and Methods 1. Experimental Design and Procedures The experimental sessions were conducted in 2006 at the CREED laboratory at the University of Amsterdam. In total 140 subjects participated. Participation was voluntary. Each experimental session lasted approximately 60 minutes. Subjects' total earnings were determined by the sum of francs (the experimental monetary unit) earned over all the rounds plus 3000 francs received at the start of the session. The conversion rate was 3 Euro per 1000 francs (the benefits and costs listed below are all in francs). The average earnings were Euro. Two subjects accumulated negative earnings that were slightly higher than their starting balance of 3000 and were dismissed without earnings. Subjects were recruited through announcements. Each subject participated in only one session and none had previously participated in a similar experiment. Twenty subjects were recruited for every session and were, unknown to them, randomly divided into two independent cohorts of 10 subjects. Subjects were seated in separated cubicles, which ensured anonymity during the experiment. We consider each cohort of 10 subjects as one independent observation. The experiment was computerized using software developed at CREED by Jos Theelen. At the beginning of a session, subjects were told the rules of conduct and provided with detailed instructions. The instructions were computerized and presented as a series of htmlpages (see section 2 of this SOM) through which the subject could page at her or his own pace. The experiment started only when all subjects confirmed they had finished with the instructions and had no further questions. At the end of the experimental session subjects were paid their earnings in private and in cash in a separate room, and departed. Care was taken to minimize differences in the instructions for different treatments. (In the translation of instructions below the five words and one number on one page that differ between treatments are emphasized). In all treatments, subjects were informed of the matching procedure, the number of rounds, and how to calculate the payoff. The game was neutrally framed. The donor s choices were called blue (help), green (pass) and purple (hurt). The recipient s history was shown on the donor s screen as the list of her last three chosen colors. Similarly, the 2 nd order information was shown as a list of three colors. In the initial rounds the history and the 2 nd order information might show a list of less than three colors because the recipient made or observed less than three decisions. Each session consisted of 100 rounds of the following game. In each round each 10- subjects cohort was randomly partitioned in five donor recipient pairs. A recipient received no information about her donor and made no decision. A donor, however, learned the most recent three decisions made by her recipient, unless the recipient made less than three decisions, in which case the donor observed all recipients past decisions. These recent decisions chosen by her recipient were shown on the donor s computer screen as colored words. The donor could click on any of these three words and thus learn about the (2 nd order) information that the recipient had when she made this decision. More specifically, a click on any of the recipient s decisions shown to the donor revealed (up to) three colors 2

4 which the recipient observed when she made that decision. The donor incurred a cost of 2 francs whenever she looked up the 2 nd order information behind any of her recipient s decisions. The donor could choose to investigate all, some or none of this information, incurring between 0 and 6 francs in costs. Next, by choosing between three colors the donor decided whether she would help, hurt or pass the recipient. By choosing blue (help) the donor lost 200 and the recipient earned 250 francs. By choosing green (pass) neither the donor nor the recipient earned or lost any francs. By choosing 'purple' (hurt) the donor lost 50 francs while the payoff consequences for the recipient differed between our treatments: in HP the recipient lost 250 francs while in SP she neither lost nor earned any francs. The benefit to cost ratio for helping (5:4) is lower than in previous experiments with the helping game, which used ratios of 5:3 (S2) and 5:2 (S3). In these previous experiments, high levels of helping were observed. We therefore decided to make helping more costly in an attempt to make it a less obvious choice. The relatively high hurt to cost ratio (5:1) in HP was chosen to give punishment a good chance to affect the helping behavior in the experiment (S1). The cost of choosing purple in SP is also 50, in order to maintain comparability with HP. Treatment SP is a control for HP because it identifies differences in behavior between an environment where indirect punishment has material consequences for the recipient and an environment where it does not, while holding the action set and the donors cost of each action constant across treatments (S4). A standard indirect reciprocity treatment without the punishment option would have reduced the action set from three to two elements. This in itself may affect behavior. An alternative control treatment could make punishment costless for the donor (S5).This would change two parameters, compared to the HP: the cost to the donor and the harm to the recipient. Therefore, we would not be able to unambiguously relate changes in behavior to the possibility to inflict harmful punishment, something that our SP treatment allows us to do. The donor always saw the payoff consequences of all three possible actions on her computer screen and had to confirm each of her decisions. The recipient learned her donor s chosen color and her own earnings in each round only after all donors had made and confirmed all their decisions. Note that although the punished recipient in SP is not materially hurt, she does see that the donor chose the purple action. The round number was always visible and both the donor and the recipient could always access the information they observed and their decisions in all past rounds. 3

5 2. Written Instructions and Computer Screen Captures for Experiment This appendix gives the English translation of the original Dutch instructions. The instructions were programmed as html pages. Horizontal lines indicate page separations. The experiment started after all subjects had confirmed their understanding of the instructions and had asked all of their questions. Fig. S1 shows an example of the computer screen. [We give the instructions for the HP treatment. These differ from those for the SP treatment in only one number. We point out this difference with two comments in square brackets in italics on page 3 of the instructions.] Instructions (page 1 of 10) You are about to participate in a decision making experiment. The instructions are simple. If you follow them carefully, you may make a considerable amount of money. Your earnings will be paid to you privately in cash at the end of today's session. In the experiment, earnings are denoted in 'francs'. At the end of the experiment, francs will be exchanged into Euro. The exchange rate will be 1 Euro for 333 francs. In other words, for every 1000 francs, you will receive 3 Euro. Your decisions are anonymous. They will not be attached to your name in any way. You are not allowed to speak with other participants or to communicate in any other way. If you want to ask a question, please raise your hand. Next page Rounds and Pairs (page 2 of 10) This experiment consists of 100 rounds. At the beginning of every round the participants will be randomly divided into pairs. The probability to form a pair with any specific other participant is the same for all participants in every round. However, the probability to form a pair twice in a row with the same participant is very small. One of the two participants in a pair will have role A, the other role B. Which role you have will also be determined randomly in every round. You will only have to make a decision in a round if you are appointed role A. In rounds where you have role B, you will not need to do anything. Previous page Next page Choices (page 3 of 10) If you have role A in a round, you will be asked to choose between three options, called blue, green and purple. Your choice affects your own earnings and the earnings of the other participant (in role B) you are paired with in that round. If you choose `blue', the participant that you are paired with will receive 250 francs and you will lose 200 francs. If 4

6 you choose green, neither of you will gain or lose money in that round. If you choose purple, the participant that you are paired with will lose 250 francs [in treatment SP: neither gain or lose money ] in that round and you will lose 50 francs. Throughout the experiment, these choices and their consequences will be shown on the left bottom corner of your monitor. This window you will see looks like this: [Translation (not shown to participants): keuze A = choice A, verdienste = earnings, blauw = blue, groen = green, paars = purple] [In treatment SP this screen shot shows 0 in the cell where row paars and column B cross] Previous page Next Page Indicating your choices (page 4 of 10) As a participant in role A, you will be asked to indicate your choice by clicking one of the options in the decision window on the center of the top half of your screen. The decision window looks like this: [Translation (not shown to participants): U vervult deze ronde rol A. Maak uw keuze. = In this round you are in role A. Make your choice, Bevestiging = confirmation] After you have clicked on one of the options, you will need to confirm by clicking on the 'confirm' button. Previous page Next page 5

7 Information about player B (page 5 of 10) If your role is A then you will receive before you are asked to make a choice information about what the participant in role B, that you are paired with in this round, has chosen in earlier rounds. Only participant A will get this information, participant B will only see how much (s)he earned in this round. You will see a summary of the 3 most recent choices that B made, when he or she was appointed role A in earlier rounds. In early rounds of the experiment, B may not have made 3 decisions yet. In that case you see all (that is, 0, 1, or 2) of his or her previous decisions. The information you will receive consists of (at most) three words in a window at the bottom right corner of your screen. For example, blue, purple and purple means that B chose blue once and purple twice in the previous three turns as a participant in role A. As an (arbitrary) example of the window that shows this information, consider the following: In this example, B chose blue twice and green once in previous rounds. NOTE: The choices of participant B may not be shown in the same order in which B made them. Blue choices will always be shown on the left, green choices to the right of the blue choices, and purple choices to the right of the green choices. Hence, you receive no information about the order in which these choices were made. Previous page Next page Information about your own choices (page 6 of 10) You will always see your own three most recent choices when in role A on your computer screen. This is shown directly below the window where you make your decisions, thus just above the center of the screen. It looks like this: [Translation (not shown to participants): uw recente 3 keuzes met rol A = your 3 recent choices when in role A] In this example your three most recent chosen colors were green, purple and blue. In this window the choices are shown in the order in which you made them, with your most recent choice shown on the left. 6

8 Previous page Next page More information about player B (page 7 of 10) In role A, you may want to know what information B had when B made certain choices in previous rounds. You will have the possibility to see what information B had when (s)he made these choices. If you click on one of these previous choices by B, the information B had will appear. For example, assume that you see that B chose each of the options blue, green and purple once in her or his previous 3 decisions as an A. If you click on each of these, you may see the following additional information. Below we will explain the costs of accessing this additional information. [Translation (not shown to participants): Als u de geschiedenis achter een keuze wilt zien, dan klik daarop. Dit kost u 2 punten. De meest recente keuzes van deze B in deze ronde: = If you want to see the history behind a choice, click on it. This will cost you 2 francs. The most recent choices of this B in this round:] This information indicates that: when B chose blue, (s)he was paired with a player who had previously chosen green three times in a row; when B chose green, (s)he was paired with a player who had previously chosen green twice and blue once; when B chose purple, (s)he was paired with a player who had previously chosen green twice and blue once. NOTE: the order in which the colors are shown is always the same. From the top to the bottom you first see the number of blue choices (if applicable), then the green choices (if applicable), and then the purple choices (if applicable). This means that the order of colors reveals no information about the order in which these colors were chosen. The only information you will receive is therefore how often the different colors were chosen. In the early rounds you might see less than three colors in a list. This means that player B saw less than three colors (her recipient did not yet make three choices) when she made her decision. In this case you will see dashes replacing the missing colors. Previous page Next page Costs of information (page 8 of 10) When you are in role A, we will automatically inform you about the previous three choices B made. There are no costs involved with this information. If you want to know what information B had when (s)he made a decision, you need to click on one of these decisions. 7

9 Every time you do so, we will charge you 2 francs. Hence, if you want to know everything B knew when making the previous three decisions, this will cost you 6 francs. Previous page Next page Information about the experiment (page 9 of 10) Throughout the experiment, you can keep track of the current round number and your earnings until then in a window at the top left on your screen: At the start of the experiment, we will provide you with a starting capital of 3000 francs, which will appear at "earnings" when round 1 starts. During the experiment you can page through previous rounds by clicking on arrow buttons at the center of your screen: This will display the precise screen you saw during the round concerned. Previous page Next page End of instructions (page 10 of 10) This brings you to the end of these instructions. When you think that you understood everything, please click the 'Ready' button at the bottom of this screen. This will let us know that you are ready. When you are finished, you might have to wait a while until all others are ready. Please wait silently and patiently until we continue with the experiment. Previous page Return to first page 8

10 Fig. S1: Computer screen in treatment HP. A donor s decision. 9

11 3. Details of Strategy Classification We begin with the complete pool of subjects and classify them in the following steps, as summarized in Fig. S2. All classification is based on the first 90 rounds to avoid noise introduced by end-game effects that were observed in the final 10 rounds of each session. The classification also excludes the earliest rounds of the session in which the donor did not have a three-round history available for the past actions of their recipient, since that history is needed for the classification rules. 1. The first step is to distinguish between reciprocal and non-reciprocal strategies. Subjects who employ reciprocal strategies consider the recipient s history when choosing an action. We identify such subjects using logit regression models estimated separately for each individual using the rounds when she is in the role of donor. Since we are interested in reciprocal strategies that help or hurt recipients depending on their history of helping decisions, at the top level we estimate logit models that use the decision to help as the binary dependent variable. The explanatory variable in these regressions is the number of helping choices made by the recipient in the previous three rounds. The coefficient estimate on this helping history is significantly positive when a subject systematically rewards recipients who have been helpful in the recent past. Additionally, to be classified as a reciprocal subject the individual could not withhold help too often. In particular, to be classified as Reciprocator the subject must: (a) have a significantly positive (5% 1-tail) coefficient estimate on the number of helps chosen by their recipient for their individual logit helping regressions; and (b) pass less than 60% of the time overall. Note, that reciprocal strategies are discriminate and other regarding because they base their actions on the recipients history. 2. Some reciprocal subjects tended to punish unkind recipients, while others simply tended to pass when encountering unkind recipients. To identify Punishers we estimated a different set of logit regression models, again separately for each individual reciprocal subject when in the role of donor, but with the decision to punish as the binary dependent variable. The explanatory variable in these regressions is again the number of helping choices made by the recipient in the previous three rounds. If a reciprocal subject systematically punishes recipients who have been unhelpful in the recent past, the coefficient estimate on this summary of the recipient s helping history will be significantly negative. Therefore, in order to conclude that a subject conditions her decision to punish on her recipient s history, the Punisher definition requires this negative correlation between punishing and the recipient s past helping frequency to be statistically significant. To be classified as a Punisher the subject also had to punish at a non-trivial frequency. In particular, to meet the definition of a Punisher a subject had to be a reciprocator (that is, fulfill the conditions in step 1.) and in addition, the subject s choices had to satisfy the following two conditions: (a) have a significantly negative (5% 1-tail) coefficient estimate on the number of helps chosen by their recipient for their individual logit punishing regressions; and (b) punish at least 10% of the time overall. 10

12 3. Rewarders were identified as subjects who were reciprocators (satisfying the criterion explained in step 1 above) but were not classified as a Punisher. In other words, Rewarders were usually helpful to (only) kind recipients and almost never punished. 4. Rewarders and Punishers are two different types of reciprocal strategies. Each can be subdivided further into categories based on whether they explore the second-order history of their recipient when choosing an action. Image scoring strategies depend only on the first-order history (actions chosen) by the recipient, so we classify a subject as an Image Reciprocator if she only infrequently looks up her recipients information underlying the pass or punish actions. Standing strategies prescribe an action that is dependent on the recipient s history as well as the underlying second-order information on which the recipient had based her previous choices. For example, an unkind action such as pass or punish only harms a subject s reputation if it is directed towards a recipient who has a good reputation. Therefore, standing strategies require knowledge of recipients information when they chose unkind actions. Hence, to implement a standing strategy the donor must sometimes obtain information about the recipients (2 nd -order) information when the recipient was unkind in the past. In particular, we classify a subject as a Standing Reciprocator if she investigates the history underlying the recipient s pass or punish actions in at least 20% of the rounds in which this is possible, otherwise she is classified as an Image Reciprocator. By combining this partition with the one in step 2 above we divide reciprocal subjects into Image Rewarders, Standing Rewarders, Image Punishers and Standing Punishers. 5. Turning to Non-reciprocators (left branch of Fig. S2), our procedure first identifies a sophisticated strategy that seeks to maintain a reasonably good public image while providing only limited help. Unlike the Reciprocators, Cautious Defectors do not care about the reputation of their recipient and are therefore not other-regarding. They sometimes help and sometimes pass, but they do not choose these two actions randomly. Instead, they tend to help when they have few helps in their own history, in order to maintain a non-zero helping history. To evaluate how systematic this behavior is, we again employ a series of logit regression models estimated separately for each individual subject. As with the Rewarders classification, the decision to help is the binary dependent variable, but we add an additional explanatory variable to summarize the donor s own history: the number of helping choices made by the donor in the previous two rounds. (We use only the previous two rounds, because the choice three rounds earlier is erased after the donor has submitted the current decision.) If a subject wants to maintain her own reputation, she will help more frequently when her current helping rate is low and thus the coefficient estimate on her own helping history will be significantly negative. Since these subjects need to adjust their behavior to changes in their own history, they should also not choose one action too frequently nor can they already be classified as a punisher or rewarder. Therefore, to meet the definition of a Cautious Defector the subject s observed strategy had to satisfy the following three conditions: (a) have a significantly negative (5% 1-tail) coefficient estimate on the number of their own helps for their individual logit helping regressions; (b) does not use any single action more than 85% of the time overall; and (c) is not classified as 11

13 Reciprocator. Note, that Cautious Defectors are discriminating and self-regarding strategies because they base their decisions on their own image score. 6. The final two strategy classifications are non-reciprocal and indiscriminate because they do not depend on either the recipient s or the donor s history. Some subjects either help almost all of the time or almost never help. In particular, Indiscriminate Defectors (a) help less than 20% of the time overall; and (b) are not classified as Punishers, Rewarders, or Cautious Defectors. Indiscriminate Altruists (a) help more than 80% of the time overall; and (b) are not classified as Punishers, Rewarders, or Cautious Defectors. None of our subjects frequently and indiscriminately chose to hurt. All remaining individuals are unclassified. Only a small number of remaining subjects (SP: 13.3%; HP: 5.0%) are Unclassified by these rules. This is an indication that strategies are stable. If subjects were to switch from one strategy to another in the course of the experiment, this would likely render the coefficients estimated to classify subjects statistically insignificant and lead to a larger number of subjects remaining Unclassified. Unfortunately, we cannot directly test for the stability of strategies. The number of rounds for which we observe an individual subject s donating choices (41.9 rounds on average) is insufficient to split the sample in two halves and classify separately for each subsample. For many subjects these subsamples would be too small to detect statistically significant effects and, hence, many subjects would remain unclassified in either or both subsamples. Table 1 in the main text summarizes the fraction of subjects in each of the treatments that are classified into these strategies. Fig. S3 displays the aggregated action choices for these strategies. For each strategy it shows the actions chosen by donors using this strategy when meeting recipients with different histories. Identifying standing strategies comes from the pattern of actions chosen given the donor s second-order information about what the recipient observed about her own past recipient. Fig. S4 shows how often the second-order information behind different actions was accessed. The donors investigate this information relatively infrequently and are particularly uninterested in learning what motivates their recipients to help. Fig. S5 shows donor reactions to the second-order information underlying their recipient s most recent decision to pass for the 32 subjects who routinely access second-order information and were thus classified as standing reciprocators. In both HP and in SP these donors willingness to help clearly increases with the number of passes their recipient had observed before deciding to pass. For example, the leftmost bar in SP (top panel) shows the donor's response after observing that the recipient had passed when she was recently paired with a subject who helped in all of the previous three rounds. The donor responds to this by passing 82.1% of the time. By contrast, the rightmost bar in SP shows the donor's response after observing that the recipient had passed when she was recently paired with a subject who passed in all of the previous three rounds. The donor responds to this by helping 80.6% of the time. 12

14 P(help) increases with Recipient.help and Pass < 60% no yes Non-reciprocator Reciprocator Cautious Defector yes P(hurt) decreases with Donor.help and Help < 85%, Pass < 85%, Hurt < 85% P(hurt) decreases with Recipient.help and Hurt > 10% no no yes Indiscriminate Defector yes Indiscriminate Help < 20% no Rewarder Punisher Investigates at least 20% of 2 nd -order information underlying Recipient.pass and Recipient.hurt Altruist yes Help > 80% no yes no Unclassified Image Standing Fig. S2: Subject classification procedure. Values of 'Help', 'Pass' and 'Hurt' give the frequencies with which this subject chose actions help, pass and hurt, respectively, across rounds Variable 'Donor.help' indicates the number of actions help in the donor s own recent 1 st -order history. Variables 'Recipient.help', 'Recipient.pass' and 'Recipient.hurt' indicate the numbers of actions help, pass and hurt, respectively, in the recipient s recent 1 st -order history. Variables 'P(help)', 'P(pass)' and 'P(hurt)' give the likelihoods with which this subject chooses actions help, pass and hurt, respectively, given the indicated history of the recipients or her own history as a donor. 13

15 Fig. S3: Donors actions. For each strategy displayed in Table 1 (main text), the bars show the distributions of actions chosen after having seen the recipients' histories. Blue (mediumgray), green (light-gray) and purple (dark-gray) indicate the aggregate rates at which donors chose to help, pass and hurt, respectively, depending on the frequency of recipients' helping (between 0 and 3, depicted on the horizontal axis). 14

16 SP HP Fig. S4: Second-order information access. For each treatment the blue (medium-gray), the green (light-gray) and the purple (dark-gray) bars indicate how frequently the donors investigated the information their recipients had when choosing to help, pass or hurt, respectively. Error bars indicate +/- one standard error of the mean rate across cohorts. 15

17 Fig. S5: Standing strategies. Actions chosen by the 13 (19) Standing rewarders and punishers in SP (HP) when they observed 2 nd -order information for their recipient s most recent choice of pass. This 2 nd -order information for the recipient' most recent pass choice is shown on the horizontal axis as (#help, #pass, #hurt). Only the 2 nd -order information cases that were observed ten or more times are shown. The donors action frequencies are shown in blue/medium-gray (help), green/light-gray (pass), and purple/dark-gray (hurt). 16

18 4. Punishment in Indirect Reciprocity and Public Goods Games Even though we have implemented a relatively high 5:1 hurt to cost ratio (cf. Section 1.2 of this SOM), punishment may still seem mild from the point of view of a potential recipient of punishment, as was pointed out by an anonymous referee. When deciding whether to help or pass, passing saves 200 (the cost of helping) but also risks a loss of 250 by getting punished. So the net loss is (only) 50 in case punishment actually occurs. Moreover, passing is also a form of punishment and it is cheaper. This may be one explanation for the relatively little punishment we observe in our experiments in spite of the 5:1 hurt to cost ratio. Another reason why we see less punishment than is usually observed in public goods games with punishment may be due to differences in the structure of these games in comparison to our indirect reciprocity game. Our game is bilateral and therefore passing is a way in which one can target a (mild form) of punishment to a specific other player at no direct costs to oneself. In public goods games, where groups usually consist of more than 2 players, passing (not contributing) hurts all other players, not just those that one intends to punish. In public goods games with punishment the only way in which one can target specific other players is by specifically punishing them. Moreover, in these games a low- or non-contributor can get punished by more than one group member, which in combination often gives strong enough incentives to increase contributions if the hurt to cost ratio is high enough (S1). Finally, punishment is observed to have a positive effect on contributions in repeated public goods games. Once established, it can also become a credible threat which implies that punishment does not have to be used to induce the disciplining effect. Of course, by its very nature, our indirect setting does not allow for direct punishment. In sum, the differences between our game and public goods games with punishment may well explain the lower level of punishment we observe, even with our high hurt to cost ratio. 17

19 5. Statistical Details for Main Hypothesis Tests Each subject earned an amount that depends on her actions when she is a donor, and her donor s actions when she was a recipient. To determine the earnings associated with each strategy, we calculated the average earnings separately for each individual subject when in the donor role, and when in the recipient role. These total average earnings, like the classification procedure, drop the final rounds in order to reduce noise introduced by end-game effects. Each subject is classified as using a particular strategy, using the strategy classification procedure described above. We can therefore aggregate these individual average earnings into average earnings associated with each strategy. Table S1 displays these average earnings, separately for the two treatments. We also averaged these two earnings amounts for each individual in the two roles to provide an overall earnings average for that subject, since each subject was equally likely to be in each role for each round. This is shown in the rightmost column of Table S1. Each individual subject influences the earnings and actions chosen by other subjects in her cohort of ten subjects. Therefore, non-parametric tests comparing earnings of the different strategies cannot use subjects as the unit of observation because subjects within a session cohort are not statistically independent. Each session cohort is statistically independent, however, and they serve as the unit of observation for the statistical tests. Separately for each cohort, we first calculate the average earnings of all subjects using a particular strategy. Table S2 displays these averages. Unfortunately, some cohorts do not have individuals classified as using certain strategies, so the number of strategy averages generally falls below the number of cohorts. This limits the statistical power of our tests. For the main statistical tests we therefore also aggregated across the types of Defectors, Rewarders and Punishers, as shown on the right side of Table S2. To test the null hypothesis that two different strategies earn the same, we subtract the average earnings from one strategy from the average earnings from the other strategy, separately for each independent cohort. These differences are compared to zero using the Wilcoxon signed rank test on paired observations. For example, the All Rewarders All Defectors earnings differences in the 8 independent cohorts of treatment HP are (2.01, 0.03, 9.58, 10.27, 2.81, 10.04, -8.70, 5.85). These differences are significantly different from zero (P=0.068) according to the Wilcoxon test. Comparisons across treatments are conducted using two-sample Mann-Whitney tests. For example, Indiscriminate Defectors earn (39.81, 40.70, 11.38, 15.19) in the 4 independent cohorts where they are present in SP, and they earn (1.15, 15.22, 11.51, -3.48, 4.26) in the 5 cohorts where they are present in HP. These two samples are significantly different (P=0.086) according to the Mann-Whitney test. Other tests discussed in the main text are constructed similarly. Table S3 provides individual subject average earnings in each role, sorted by their strategy classification. Panel A contains treatment SP and Panel B contains treatment HP. 18

20 Table S1: Average Earnings of Strategies in Donor and Recipient Roles (Through Period 90) SP Number of Classfied Average Earnings in: Individuals Donor Role Recipient Role Both Roles Altruist Indiscriminate Defector Image Rewarder Image Punisher Standing Rewarder Standing Punisher Cautious Defector Unclassified Pooled Strategy Classifications Indiscriminate and Cautious Defector Image and Standing Rewarder Image and Standing Punisher HP Number of Classfied Average Earnings in: Individuals Donor Role Recipient Role Both Roles Altruist Indiscriminate Defector Image Rewarder Image Punisher Standing Rewarder Standing Punisher Cautious Defector Unclassified Pooled Strategy Classifications Indiscriminate and Cautious Defector Image and Standing Rewarder Image and Standing Punisher

21 Table S2: Average Earnings by Strategy and Cohort (Donor and Recipient roles Weighted Equally, Through Period 90) Individual Strategy Classifications Pooled Strategy Classifications Indiscriminate Image and Image and Altruist Indiscriminate Image Image Standing Standing Cautious Unclassi- and Cautious Standing Standing treatment cohort Defector Rewarder Punisher Rewarder Punisher Defector fied Defector Rewarder Punisher SP SP SP SP SP SP HP HP HP HP HP HP HP HP Note: SP and HP stands for the treatment with symbolic and harmful punishment, respectively; a cohort consists of 10 subjects and is our independent unit of observation. 20

22 Table S3--Panel A (SP): Average Earnings by Role for Individual Subjects 21 Average Earnings in: Cohort Subject ID Strategy Classification Donor Role Recipient Role Both Roles Altruist Altruist Altruist Altruist Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder

23 Image Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Cautious Defector Cautious Defector Cautious Defector Cautious Defector Unclassified Unclassified Unclassified Unclassified Unclassified Unclassified Unclassified Unclassified

24 Table S3--Panel B (HP): Average Earnings by Role for Individual Subjects 23 Average Earnings in: Cohort Subject ID Strategy Classification Donor Role Recipient Role Both Roles Altruist Altruist Altruist Altruist Altruist Altruist Altruist Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Indiscrim. Defector Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder

25 Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Rewarder Image Punisher Image Punisher Image Punisher Image Punisher Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Rewarder Standing Punisher Standing Punisher Standing Punisher Standing Punisher Standing Punisher Standing Punisher Cautious Defector Cautious Defector Cautious Defector

26 Cautious Defector Cautious Defector Cautious Defector Cautious Defector Cautious Defector Unclassified Unclassified Unclassified Unclassified Note: A cohort consists of 10 subjects and is our independent unit of observation; Subject ID consists of the cohort a subject belongs to (first 3 digits) and a unique subject number for that cohort (last two digits). 25

27 Notes and References S1. N. Nikiforakis, H.-T. Normann, Exp. Econ. 11, 358 (2008) S2. I. Seinen, A.J.H.C. Schram, Eur. Econ. Rev. 50, 581 (2006) S3. D. Engelmann, U. Fischbacher, Games & Econ. Beh. 67, 399 (2009) S4. J. Carpenter, A. Daniere, L. Takahashi, J. Econ. Org. Beh. 55, 533 (2004). S5. D. Masclet, C. Noussair, S. Tucker, M.C. Villeval, Am. Econ. Rev. 93, 366 (2003). 26