Binding the future boosts intergenerational sustainability

Many of the key problems humanity now faces, from climate change to the risk of asteroid impact, biodiversity and artificial intelligence (AI) safety, require cross-generational collaboration^1,2,3,4. The solution to such problems cannot be implemented instantaneously; it requires long-term, continuous effort by successive generations of governments, public leaders, and individuals. Coping with the climate crisis, for example, requires ongoing investment in both mitigation and adaptation (e.g., developing new energy storage and coastal defense technologies)^5,6,7,8,9. All these cases form a unique category of ‘inter-generational social dilemmas’ (IGSDs).

Developing collaborative solutions to intergenerational social dilemmas constitutes a complex problem, which involves both individual-level and group-level challenges. On the individual level, the challenge stems from the difficulty people face in contemplating optimal responses to decision problems that could impact their future well-being, such as savings, exercise and medical checks^10,11,12,13. One potential explanation for this difficulty is that the delayed, abstract, and statistical nature of future risks may limit their capacity to evoke strong visceral reactions^14,15. Construal level theory suggests that when psychological distance manifests across multiple dimensions its behavioral impact becomes stronger^16,17,18. This implies that people’s difficulty in contemplating the future may intensify when the lives of others are at stake, as this situation involves both social (self versus other) and temporal dimensions. Consistent with this idea, research indicates that sustainable cooperative decisions decline in intergenerational games where participants represent distinct generations and require to interact with present and future others, as opposed to settings with “infinitely-lived” participants who are not required to interact with future others¹⁹. Furthermore, reducing the psychological distance from future generations—by assigning participants the role of negotiators acting on behalf of future generations²⁰, or by asking participants to communicate with participants representing subsequent generations through written explanations of their behavior and advice²¹—has been shown to increase sustainable cooperative decisions.

Moving from the individual to the group level highlights additional challenges in fostering intergenerational cooperation. Like single-period dilemmas, intergenerational cooperation may be impeded by selfishness and fear of exploitation^22,23. Specifically, the present generation, as well as subsequent ones, may be tempted to exploit resources excessively (e.g., by escalating CO₂ emissions) in pursuit of short-term gains, while disregarding the anticipated costs imposed by their actions on future generations^21,22,24. Further, people may choose to withhold costly pro-climate efforts due to the fear of being exploited by future generations (i.e., if they believe their efforts would be in vain due to self-centered, anti-climate actions by future generations)^25,26,27.

A unique challenge of intergenerational social dilemmas is that the mechanisms that facilitate the evolution of cooperation in single-period interactions (reciprocity and third-party punishment) or compensate for its absence (formal centralized compliance institutions) are lacking. Specifically, due to the sequential and cross-temporal nature of intergenerational social dilemmas there are no opportunities for direct reciprocity^28,29; cooperators will not receive direct rewards, and free riders will not face social sanctions^30,31. The sequential and cross-temporal structure of intergenerational social dilemmas can also undermine efforts to promote cooperation through formal^32,33 and informal^34,35 compliance mechanisms that promote cooperation in single-period social dilemmas since future generations can amend or abolish such mechanisms rendering them ineffective^36,37. Finally, the absence of reciprocity and the weakness of stable compliance structures, which characterize intergenerational social dilemmas also pose obstacles to the cultivation of mutual trust, which plays a critical role in fostering cooperative behavior in single-period social dilemmas^38,39,40,41.

In the current work we experimentally examine the behavioral aspects of adopting credible commitment mechanisms to support the achievement of intergenerational collaboration. We argue that credible commitment mechanisms are critical for overcoming the above difficulties, and for devising optimal solutions to intergenerational social dilemmas. A commitment mechanism allows institutions of power (government, parliament, corporate management, municipal authority) to commit a future institution, including its future self, to a decision it has taken⁴². More accurately the creation of a commitment mechanism makes it more costly for the future agent to change course (relative to a counter-factual world without the commitment mechanism)⁴³. Credible commitment mechanisms are essential to tackling intergenerational social dilemmas, such as climate change. These mechanisms help sustain the ongoing efforts needed to counteract each generation’s temptation to ‘pass the buck’ to the future, by over-exploiting resources or under-investing in risk prevention for short-term gains. Additionally, they are crucial for building present-day political support, a prerequisite for initiating a cross-temporal collaborative chain, as they address concerns about potential exploitation by future generations⁴⁴. Citizens of the current generation might be willing to support costly mitigation policies (e.g., carbon tax), but they are less likely to want to do so if they think that future publics will defect from the cooperative plans necessary to make their costly effort worthwhile⁴⁵.

Commitment mechanisms can be implemented through either public or private law. In the public law context, this can be achieved by embedding climate goals within the constitution (constitutional entrenchment)^46,47, or through the creation of framework climate acts, which coordinate governments’ mitigation and adaptation strategies⁴⁸. Although climate acts are not constitutionally entrenched, they can still create a lock-in effect by setting long-term goals, establishing independent oversight bodies, and implementing monitoring and transparency measures^49,50. In private law, companies can utilize contractual mechanisms like sustainability-linked loans and bonds to establish long-term climate commitments^42,51.

Although governments and companies have various mechanisms for making credible climate pledges—publicly committing to reducing greenhouse gas emissions by a specific target date—they do not always use these legal tools effectively, and sometimes exploit legal loopholes to avoid fulfilling their commitments^52,53. This inconsistency has led critics to question the reliability of climate pledges made by both governments and companies^{54,55,56,57,58}.

Coping with the climate crisis requires governments and companies to make more responsible use of the commitment mechanisms provided to them by law. In the present work, we examine the feasibility and effectiveness of using a commitment mechanism in intergenerational social dilemmas through a behavioral experiment. Specifically, we examine whether individuals would be inclined to altruistically invest in a costly commitment mechanism that will obligate the next generation to collaborate with subsequent ones, and the impact of introducing such mechanism on the sustainability of a shared resource across several generations. By addressing these questions, our research may inform the application of credible commitment mechanisms in policy challenges involving intergenerational social dilemmas, particularly in the context of climate change.

In recent years, there has been a surge of interest in studying the behavioral aspects of intergenerational collaboration. Researchers from diverse fields, including experimental philosophy^59,60, policy studies²⁰, behavioral economics^61,62, and psychology^22,63, have explored this area from various perspectives. However, the behavioral impacts of introducing a commitment mechanism to address intergenerational dilemmas have remained largely unexplored. Most of these studies relied on versions of a common resource game that requires a cross-generational collaboration, either through investment or mitigated consumption, in order to sustain the resource across generations. Drawing on this basic setting, these studies examined the efficacy of various institutional interventions in enhancing resource sustainability. A consistent finding across most of these studies is that without some form of external intervention, the likelihood of preserving the shared resource beyond a few generations is significantly limited.

However, this prior research has also demonstrated that intergenerational sustainability can be positively influenced by various types of institutional mechanisms. Hauser et al., for example, showed that incorporating democratic voting in a decision dilemma about how much to extract from a common pool ensures that the resource is consistently sustained²². Furthermore, a follow-up study has found that voting, when it is binding for all, allows a majority of cooperators to restrain defectors, even in ambiguous settings⁶³. Kamijo et al. demonstrated that introducing an agent that represents the future generations (‘imaginary future generation’) improves intergenerational sustainability in group decision-making processes²⁰. Timilsina et al. found that intergenerational accountability, defined as the requirement for each generation to justify its decisions to the next, encourages more sustainable choices²¹. Finally, Lohshe and Waichman demonstrate that the presence of peer punishment within a generation enhances the probability of maintaining cooperation across successive generations⁶².

Whereas these studies have significantly advanced our understanding of the behavioral impacts of various potential climate intervention strategies, there remains a pressing need for research on multigenerational behavioral coping mechanisms⁶⁴. Furthermore, none of these studies has examined the impact of commitment mechanisms on intergenerational collaboration. This represents a significant gap in the existing literature, as commitment mechanisms are a vital policy tool that remains underutilized in the current regulatory landscape, despite their potential important role in counteracting the risk of future defection.

We conducted a preregistered study (link: https://aspredicted.org/939g-jgpc.pdf) to examine whether people are willing to (altruistically) invest in a costly commitment mechanism, and to explore the impact of introducing this option on the sustainability of a common pool across generations. We used a modified version of the intergenerational public goods game introduced by Hauser et al.²², in which participants were assigned to five-person groups (chains). Within each chain, each participant represented one of five generations and was endowed with a common pool of 100 units that replenished itself (i.e., refilled to 100 units) if 50 units were transferred to the next generation.

We compared two experimental conditions: a control condition and a commitment mechanism condition. In the control condition, each participant (generation) was presented with two options: (1) to extract all 100 units, which destroys the pool (set as 0 units), thus, none of the future participants (generations) can earn any bonus; or (2) to extract 50 units, which meets the threshold for replenishment (refills to 100 units), thus, the next participant (generation) can make their choice and also earn a bonus. In the commitment mechanism condition, on top of these two options, each participant (generation) was presented with a third option of implementing a commitment mechanism that locks-in one’s next generation to cooperate with the subsequent generation. Specifically, participants could extract only 40 units, investing in a commitment mechanism that cost them 10 units, which also guaranteed the future replenishment of the resource. The commitment mechanism limited the choices of the following generation (represented by the next participant) by excluding the option to extract the resource in full (all 100 units), thus, allowing them to choose between just two options: to extract 50 units or to extract only 40 units and invest 10 in the renewal of the commitment device. Therefore, the decision to invest in a commitment mechanism ensures that one’s third generation will also have access to the common pool resource by preventing the second generation from fully depleting it.

Our experimental design models the commitment mechanism as an instrument that limits the choice horizon of a future government, obliging it to preserve the common pool for the benefit of future generations. The mechanism design used in the experiment, which limits the lock-in period to apply only to the second generation while allowing the third generation to fully deplete the common pool, strikes a balance between rigidity and flexibility. This design is inspired by long-term contractual mechanisms, such as renewable power purchase agreements, which have extended but finite durations^42,65.

Building on prior research indicating that people are more inclined to cooperate altruistically with future generations rather than selfishly defect and ignore their needs^22,63, and that many are even willing to altruistically punish non-cooperators⁶², we reasoned that a significant portion of participants in the commitment mechanism condition would choose to altruistically invest in a costly commitment mechanism. That is, we expected to find a significant group who would demonstrate ‘long-sighted altruistic behavior’, willing to forego personal gain to enhance not only the welfare of the second generation but also that of the third one. Furthermore, we anticipated this trend to persist across generations. Thus, our main hypothesis was that introducing the option to invest in a commitment mechanism would increase participants’ cooperation with future generations in the game. Because the decision to cooperate helps sustain the common pool for the benefit of future generations, we will refer to participants’ cooperation rates across generations as the sustainability rate of the common pool.

We recruited a total of 990 participants that participated in five distinct sequential waves, to represent one of five generations. Within each wave (n = 198), we randomly assigned participants to either the control (n = 101) or the commitment mechanism (n = 97) conditions.

Our study focused on generations 1–3. We informed participants representing these generations of their position along the chain and provided those in generations 2–3 with information about the decision(s) of the previous generation(s) with whom they were assigned to interact (“You were assigned to be participant (generation) 2. The previous participant (generation 1) extracted 50 units from the common pool. Thus, the pool refilled to 100 units.”). In contrast, for participants representing generations 4-5, we did not provide information about their position along the chain since we did not want this information to bias their decision (the proximity to the end of the chain could have triggered more selfish decisions). Instead, we only presented these participants with the decision of their immediate previous generation (rather than all prior generations), and (“The previous participant (generation) extracted 50 units from the common pool. Thus, the pool refilled to 100 units.”). Consistent with our preregistered plans, the following analysis focuses on the behavior of generations 1-3. However, it should be emphasized that all significant effects were also observed in generations 4 and 5.

As we described in the method section and consistent with our preregistered plans, our study focused on generations 1-3. Thus, the following analysis focuses on the behavior of generations 1-3. However, it should be emphasized that all significant effects were also observed in generations 4 and 5. In what follows, we specify whether each analysis is preregistered or non-preregistered. All preregistered analyses (outlined in sections 5 and 8 of the preregistration questionnaire) include comparisons of participants’ decisions between the two experimental conditions, analyzed separately for each generation. These analyses involve simple comparisons between two proportions (binary data), and we used Chi-squared tests for this purpose. Although the preregistration did not specify a particular statistical test, alternative tests for binary data (e.g., Z-tests, logistic regressions) yield similar results, which is generally expected for simple comparisons. Additionally, to account for the increased risk of Type I errors due to multiple comparisons, we applied the Benjamini-Hochberg procedure to adjust the p-values for both preregistered (Chi-squared tests) and non-preregistered (Chi-squared tests and regressions) analyses. After adjustment, all significant p-values remained significant, and all non-significant p-values remained non-significant.

Figure 1 presents all the splitting paths and decision points of the first three generations in each of the two conditions, along with the distribution of participants’ choices. We found that among the first-generation participants in the commitment mechanism condition, 35.1% (34/97) chose to extract the entire 100 units, thereby depleting the pool (set to 0 units) and preventing future generations from benefiting. Additionally, 38.1% (37/97) chose to extract 50 units, enabling the pool to replenish (reset to 100 units) for the benefit of the second generation, but without committing them to continue cooperating with the third generation. Lastly, 26.8% (26/97) chose to bind the future by altruistically investing in a commitment mechanism, committing the second generation to continue cooperating with the third generation. Although this proportion is noteworthy, it could have resulted from a sampling error or randomness, rather than reflecting a genuine preference for the use of a commitment mechanism. To rule out this possibility and determine whether this reflects a true underlying tendency, we followed our preregistered plan and tested whether the proportion of participants who chose to invest in a commitment mechanism significantly differs from zero. This analysis revealed a significant difference, χ2(1) = 30.02, P < 0.001, w = .56, CI = (17.8%, 35.8%). Further, we found a similar rate of this ‘long-sighted’ altruistic behavior among second-generation participants [42.9% (27/63); χ2(1) = 34.36, P < 0.001, w = .74, CI = (30.3%, 55.4%)] and among third-generation participants [30.4% (17/56), χ2(1) = 20.04, P < 0.001, w = 0.60, CI = (17.9%, 42.8%)].

Distribution of participants’ choices, Sustainability (Sust.) rate, and Replenishment (Rep.) rate, in the Control condition (left side) and the commitment mechanism condition (right side).

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We tested our main hypothesis by assessing the impact of introducing the option to implement a commitment mechanism on the sustainability rate of the common pool, measured by the proportion of chains that maintained the common pool across all generations without fully depleting the resource. In accordance with our preregistered plan, we compared the sustainability rate between the two conditions (commitment mechanism and control), based on the decisions made by each of the first three generations. As can be seen in Fig. 2, the sustainability rate among first-generation participants was higher in the commitment mechanism condition [64.9% (63/97)] than in the control condition [57.4% (58/101)], although this difference was not statistically significant, χ2(1) = 1.18, P = 0.278. Importantly, consistent with our hypothesis, the difference in sustainability rates between the commitment mechanism and control conditions increased substantially and became statistically significant following the decisions of the second generation, χ2(1) = 5.82, P = 0.016, w = 17, and the third generation, χ2(1) = 12.01, P < 0.001, w = 25. Specifically, out of the 97 chains in the commitment mechanism condition, 56 (57.7%) survived after the decisions of the second generation, and 47 (48.5%) survived after the decisions of the third generation. In contrast, out of the 101 chains in the control condition, only 41 (40.6%) survived after the decisions of the second generation, and 25 (24.8%) survived after the third generation.

Sustainability and Replenishment rates as a function of condition and generation. Error bars represent 95% confidence intervals.

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To further explore the effect of introducing the option to implement a commitment mechanism on the common pool, we conducted additional, non-preregistered analyses to examine the replenishment rate within each generation. This rate reflects the proportion of active participants who cooperated with the subsequent generation, in each generation. Notably, beginning with the second generation, some of the participants in both conditions became inactive because their predecessors had fully depleted the common resource. Focusing on the decisions of active participants therefore provided deeper insights into the effect of the commitment mechanism. In the first generation, where all the participants were active, the replenishment rates in the commitment mechanism condition [64.9% (63/97)] and in the control condition [57.4% (58/101)] were identical to the sustainability rates, with no statistically significant difference, χ2(1) = 1.18, P = 0.278. However, from the second generation onward, the replenishment rates diverged, and the difference between conditions grew substantially, reaching statistical significance after both the second generation, χ2(1) = 6.29, P = 0.012, w = 0.23, and the third generation, χ2(1) = 6.52, P = 0.011, w = 0.26. Specifically, as shown in Fig. 2, 56 out of 63 participants (88.9%) in the second generation and 47 out of 56 participants (83.9%) in the third generation within the commitment mechanism condition chose to cooperate with the next generation. In contrast, only 41 out of 58 participants (70.7%) in the second generation and 25 out of 41 participants (61.0%) in the third generation within the control condition opted to cooperate.

Next, to assess whether the effect of the commitment mechanism on both sustainability and replenishment rates remained significant across generations, we conducted two separate non-preregistered two-way generalized probit regressions. The independent variables were the experimental condition (control, commitment mechanism) and generation (1, 2, 3), with sustainability rates as the dependent variable in the first regression (Reg1) and replenishment rates in the second (Reg2). In both regressions, the main effects of experimental condition (Reg1: Wald χ2(1) = 16.25, p < 0.001; Reg2: Wald χ2(1) = 13.48, p < 0.001) and generation (Reg1: Wald χ2(2) = 24.74, p < 0.001; Reg2: Wald χ2(1) = 14.26, p < 0.001) were significant, whereas the two-way interaction between these variables were not (Reg1: Wald χ2(2) = 2.97, p = 0.227; Reg2: Wald χ2(1) = 3.48, p = 0.176).

Another noteworthy finding relates to the emergence of a norm for investing in a commitment mechanism. Specifically, following our preregistered plan to examine the effect of the previous generation’s behavior (cooperation with or without a commitment device) on the subsequent generation’s decision to invest in such a mechanism, we found that the proportion of second-generation participants who chose to reinvest in a commitment mechanism after the first-generation did so [65.4% (17/26)] was more than double that of those who invested after the first-generation cooperated without investing in a commitment mechanism [27.1% (10/37)], χ2(1) = 7.67, P = 0.002, w = 0.35. This suggests that when one generation invests in a commitment mechanism, it significantly increases the likelihood that the next generation will follow suit.

However, due to the depletion of the common pool resource across generations and the increasingly fragmented paths, starting from the third generation, the number of participants at each decision point became too small to continue exploring the development of a norm for investing in a commitment mechanism (ranging between 9 to 20 participants in the third generation, and even fewer in subsequent ones). Therefore, we conducted two additional non-preregistered comparisons. First, we compared the proportion of first-generation participants who, despite not being bound by a commitment mechanism imposed by a previous generation, chose to invest in one [26/8% (26/97)], to the proportion of second-generation participants, who were obligated to cooperate due to the commitment mechanism imposed by their previous generation and chose to reinvest in the mechanism [65.4% 17/26)]. The latter group showed a significantly higher proportion of investment in a commitment mechanism, χ2(1) = 11.78, P < 0.001, w = 0.31. Second, we aggregated the choices of participants in the third, fourth, and fifth generations, finding a similar pattern to that observed in the second generation. Specifically, the proportion of participants who chose to reinvest in a commitment mechanism after their previous generation had done so [48.7% (28/60)] was more than twice that of participants whose previous generation cooperated without investing in a commitment mechanism [20.8% (16/77)], χ2(1) = 10.37, P = 0.001, w = 0.28.

Many of humanity’s most potentially destructive challenges are intergenerational problems that demand intergenerational solutions. Yet the mechanisms that facilitate the evolution of cooperation in single-period interactions: reciprocity, third-party punishment, and centralized compliance institutions, do not work in the intergenerational context. We argue that developing credible commitment mechanisms can contribute to achieving long-lasting intergenerational cooperation. Our research makes three important contributions to the study of commitment mechanisms and intergenerational social dilemmas.

First, we found that a significant number of individuals are willing to altruistically invest in a commitment mechanism, despite the added cost associated with such a decision. This result seems to reflect what we have termed ‘long-sighted altruism’ since these individuals were willing to forego personal gain in order to improve not just the welfare of the second generation but that of the third one as well. This long-sighted behavior may reflect a certain distrust in the capacity of the second generation to do the ‘right thing’, participate in upstream reciprocity^66,67, and maintain collaboration with future generations.

Our second key finding is that incorporating the commitment mechanism had a positive welfare effect across generations by increasing the sustainability rate. Specifically, as we moved beyond the first generation, offering the option of investing in a commitment mechanism increased both the proportion of active participants within each generation who cooperated with the subsequent generation (the replenishment rate) and the proportion of chains that sustained the common pool across all generations (the sustainability rate).

Finally, our study provides preliminary evidence for the development of a social convention supporting investment in a commitment mechanism. Specifically, after one generation invests in a commitment mechanism, the subsequent generation is more likely to invest in a similar mechanism, thereby reinforcing intergenerational cooperation along the generational chain. Future research should use a larger sample size to enhance its validity, as well as to allow further examination of this effect separately on each of the subsequent generations beyond the second one.

Our findings align with theoretical arguments for using commitment mechanisms to address intergenerational social dilemmas like climate change^68,69. By preventing each generation from “passing the buck” to the future, these mechanisms promote sustainable resource usage across generations. The development of a convention supporting such mechanisms could be interpreted as a social response to collective concerns about future exploitation.

Along with the benefits discussed in this work, implementing a commitment mechanism to facilitate intergenerational cooperation may also involve several potential drawbacks, which we elaborate on below. First, using a commitment mechanism involves a potential welfare loss: it requires the current generation to allocate resources that could have been used to enhance its own welfare in order to commit future generations to actions that will benefit the more distant future. This ‘wasteful’ investment could have been avoided if the present generation had trusted future generations to collaborate in preserving the public good (e.g., climate) for the benefit of the subsequent generations. Our findings, which are consistent with previous work^22,59,60,70 suggest, however, that this distrust of the future is well justified.

Second, employing a commitment mechanism introduces rigidity, potentially limiting future generations’ ability to adapt to evolving circumstances. For instance, committing to substantial CO₂ reductions now could impose disproportionately high costs on the future if, over time, unexpected technological advancements offer more efficient and cost-effective solutions. One approach to mitigating this concern is to embed flexibility within the commitment mechanism itself. Our experimental design, for example, incorporated a commitment mechanism with a limited duration of one generation, thereby constraining potential ‘rigidity’ costs while allowing for reassessment by future generations.

Lastly, using a commitment mechanism potentially conflicts with democratic principles, as it constrains the political autonomy of future generations. Our findings suggest that this encroachment on the freedom of future generations could be justified by the cross-generational welfare gains that are generated by using a commitment mechanism (reflected by the increased sustainability rate in the commitment condition). In the context of the climate crisis, this justification is particularly compelling, especially when we consider the dire consequences of a climate catastrophe that may arise from a failure to foster intergenerational cooperation. However, it is possible to minimize this encroachment without forfeiting the benefits of a commitment mechanism, by incorporating an ‘escape’ procedure into the commitment device^71,72,73. Subsequent studies could further explore the behavioral implications of using different ‘escape’ options.

How can policymakers establish credible commitments in the climate context? One option is constitutional entrenchment, which allows governments to create an indefinite commitment that can only be amended at a significant cost. This reflects the inherent difficulty of altering constitutional provisions compared to ordinary laws^73,74. Although constitutional entrenchment is usually used to protect the fundamental political structure of the state, it can also be used to resolve intergenerational social dilemmas, by incorporating provisions that establish sustainability commitments^75,76. Some constitutions include explicit reference to the rights of future generations; others provide a general right to healthy environment; whereas others impose a duty on their citizens to safeguard the environment^46,77. Thus, for example, the constitution of South Africa enshrines in section 24 a right for both present and future generations to have the environment protected⁷⁸.

However, because constitutional codification is politically costly, we believe that policymakers should also explore non-constitutional commitment mechanisms. Such mechanisms include climate acts, which create a lock-in effect through a combination of institutional, procedural and regulatory measures (e.g., the U.K. Climate Change Act)^79,80 and contractual mechanisms such as sustainability-linked loans or bonds, which enable corporations to credibly commit themselves to long-term goals^42,65. Recent work in climate economics and climate law has similarly emphasized the importance of developing credible commitment devices to ensure the alignment of state and corporate agents with net zero policies^42,44.

Although our study offers valuable insights into decision-making in intergenerational contexts, several limitations must be acknowledged, as they may affect the interpretation and generalizability of the results. The first limitation concerns the extent to which findings from a short experimental setting involving behavioral economic games can be generalized to real-world behavior. However, it is important to emphasize that behavioral patterns observed in such experiments often mirror those found in real-world contexts⁸¹. Additionally, economic-games experiments specifically designed to test lab-field generalizability demonstrated that laboratory findings could be generalized to comparable field settings⁸². A second limitation concerns criticism that has been raised regarding the generalizability of studies conducted on platforms like MTurk, particularly concerning the behavior of public policymakers. However, a substantial body of research supports the robustness and replicability of studies conducted on such platforms^83,84. Finally, in the current design, participants representing different generations were drawn from the same participant pool, with the time gap between their participation ranging from hours to days (as is the case in most existing research on multigenerational social dilemmas). In contrast, in real-world multigenerational social dilemmas, future generations often consist of entirely different individuals, sometimes many years into the future. We acknowledge these valid concerns, which highlight the importance of further exploring the questions raised in this study, as we elaborate below. Nevertheless, laboratory settings allow for controlled investigations of human behavior, complementing other forms of research. Moreover, previous research suggests that public policy can benefit from behavioral insights derived from experimental psychological research^85,86,87,88. Our study contributes to this body of knowledge by offering insights into a particularly pressing policy dilemma.

In this study, we selected a specific type of commitment mechanism that expires after one generation, binding only the second generation and requiring reinvestment to extend its effect to the third generation. Future studies could explore other methods for the present generation to pre-commit future generations and examine how these mechanisms may impact the sustainability of the common pool across generations. One approach could be to extend the duration for which the commitment mechanism remains binding across multiple generations. Another option is to design a commitment mechanism with no expiration date, but one that can be amended or abolished at a cost, similar to constitutional entrenchment. Such a mechanism would have no fixed expiration; however, any changes would require effort or entail a cost, reflecting the greater difficulty of amending a constitution compared to ordinary laws. Future research could explore the role of different variables, such as individual differences and situational factors, in the decision to implement a costly commitment mechanism. For example, it would be interesting to examine how self-interest considerations influence pro-future behavior by varying the cost of the commitment mechanism. Finally, we also acknowledge the value of studies that utilize macro-level panel data to analyze the short- and long-term impacts of climate legislation⁸⁹. Such studies can complement lab experiments by evaluating the impact of different regulatory schemes on climate-related behavior using real-world data. However, they may encounter challenges in fully isolating the specific effects of various regulatory mechanisms within the broader landscape of climate policies (see Stechemesser et al.’s recent work for a preliminary attempt in this direction)⁹⁰.

Intergenerational cooperation, crucial for addressing some of the most pressing challenges facing humanity today, is particularly challenging to achieve. An effective approach to fostering multigenerational collaboration may involve implementing a commitment mechanism imposed by the current generation, compelling future generations to continue collaborating with their subsequent generations. Our findings tentatively suggest that such mechanisms may garner broad support, even with their associated costs, and hold the potential to enhance intergenerational sustainability. Additionally, our results indicate that once initiated, commitment mechanisms tend to persist, as subsequent generations are likely to reinvest in them. However, further research is needed to deepen our understanding of how these mechanisms function in more diverse and real-world contexts.

The study was approved by the Psychology Department Ethic Review Board at Bar Ilan University (#2022/16). We followed all relevant ethical regulations and obtained informed consent from participants.

Participants

In determining our sample size, we followed previous recommendations that highlight the importance of using adequate sample sizes to avoid inflated false positives and to ensure reliable results^91,92. Based on our prior experience with binary and categorical dependent variables^93,94,95,96, we determined that 100 participants per condition, in each generation, should provide sufficient statistical power for detecting meaningful effects. Therefore, we aimed to recruit a total of 1000 participants from Amazon’s Mechanical-Turk, with 100 participants assigned to each of the two experimental conditions in each of the five distinct sequential waves, representing five generations. Our selection criteria involved only US based MTurkers with more than 100 approved HITs and approval rates of 98% or higher. We ended with a total of 1049 responses. Of these, 27 responses were from participants who attempted to complete the study twice or more, and 32 responses were from participants who accessed the study and provided incomplete responses; these responses were excluded based on our a priori exclusion criteria. Thus, our final sample included 990 adults (43.3% female, age: M = 35.8, SD = 10.5; 100.0% U.S. citizens; Ethnicity: 84.1% Caucasian, 7.3% Asian, 4.2% Hispanic, 3.4% African American, and 1.0% Other) that we recruited in five distinct sequential waves. Participants completed the study online and received a small monetary show-up payment of $0.60. In addition to this, they could earn in the game a bonus of $1.00, $0.50, $0.40, or $0.00 (Mean = $0.34, SD = 0.36) based on their decision, and the decisions of the previous generations they were assigned to interact with, as explained in the game description.

Design and procedure

The experiment employed a 2 (experimental conditions) by 5 (generations) between-participants design. We collected the data in five distinct waves, each representing one of the five generations. Within each wave (n = 198), we randomly assigned participants to either the control (n = 101; across all five waves N = 505) or the commitment mechanism (n = 97; across all five waves N = 485) conditions. Participation lasted about 5 minutes. Participants read the instructions, made their decision, and then responded to a postdecision questionnaire followed by a short demographic questionnaire.

Materials

The Intergenerational Game

In the written instructions, we explained to participants that we would assign them to 5-person anonymous groups (chains). In each chain, each participant represented one of five generations. We further explained that each chain had a common pool of 100 units transferable from one generation to the next. This pool could be harvested to generate personal bonuses. However, if overexploited by one of the five generations, it would be destroyed, and subsequent generations could not earn any bonuses. Thus, each participant’s decision, representing a distinct generation, determined their own monetary reward and influenced the potential earnings of all subsequent participants (generations) in the chain.

In the control condition, each participant (generation) was presented with two options: (1) extract all 100 units, depleting the pool to 0 units, which prevents any future participants (generations) from earning a bonus; or (2) extract 50 units, meeting the threshold for replenishment (restoring the pool to 100 units), allowing the next participant (generation) to make their choice and also earn a bonus.

In the commitment mechanism condition, on top of the two options presented in the control condition, each participant (generation) was presented with a third option of implementing a commitment mechanism that locks in one’s next generation to cooperate with the subsequent generation. Specifically, participants could extract only 40 units, investing in a commitment mechanism that cost them 10 units, which also guaranteed the future replenishment of the resource. The commitment mechanism restricts the choices available to the subsequent generation (represented by the next participant) by removing the option to fully deplete the resource (extract all 100 units), limiting them to two choices: extract 50 units or extract 40 units and invest 10 in renewing the commitment device. Consequently, investing in the commitment mechanism ensures that the third generation retains access to the common pool by preventing the second generation from fully exploiting it.

In real-world intergenerational social dilemmas, information about previous generations and their decisions is typically available to the current generation. However, in the fixed-number-of-generations paradigm used in this study, providing such information to later generations could bias their decisions, potentially promoting more selfish behavior due to their proximity to the end of the chain. Therefore, we informed participants representing generations 1-3 of their position along the chain and provided those in generations 2 and 3 with information about the decision(s) of the previous generation(s) with whom they were assigned to interact (“You were assigned to be participant (generation) 2. The previous participant (generation 1) extracted 50 units from the common pool. Thus, the pool refilled to 100 units.”). In contrast, for participants representing generations 4-5, we presented only the decision of their immediate previous generation (rather than all prior generations), and did not provide information about their position along the chain (“The previous participant (generation) extracted 50 units from the common pool. Thus, the pool refilled to 100 units.”). Since identifying information, even if minimal⁹⁷, tends to increase prosocial behavior^{66,98,99,100,101}, we decided to focus on the behavior of the first three generations and preregistered this plan (link: https://aspredicted.org/939g-jgpc.pdf).

Among generations 2–5, we informed participants whose assigned previous participants (generations) had chosen to extract all 100 units of that decision, that the pool had been depleted (set to 0 units) as a result of this choice, meaning they could no longer earn a bonus. Following this message, these inactive participants were directed to the demographic questionnaire.

The post-decision questionnaire

Following their decision in the game we asked participants to indicate, on a 5-point Likert scale, the importance of each of the following considerations in their decision-making process: maximize their personal profit, maximize the joint profit of all participants (generations) in their chain, avoiding being a “sucker” (in case the next generation will not reciprocate), be fair, hurt the next participants (generations), behave ethically, minimize the gap between myself and the next participants (generations), maximize the gap between myself and the next participants (generations), fulfilling an obligation, and reciprocating the behavior of the previous participant (generation). The last two items were presented to participants from the second generation onward. To maintain focus on the core questions of the current work and avoid unnecessary length, the results of the post-decision questionnaire are not reported in the manuscript. However, its data are presented alongside all other data from the current study and are accessible via the Open Science Framework at: https://osf.io/6d7bw/. Additionally, a summary of the number of participants in each experimental condition by each of the five generations, active/inactive participation, and decision is presented in Table 1.