Public goods game serves as a valuable paradigm for studying the challenges of collective cooperation in human and natural societies. Peer punishment is often considered an effective incentive for promoting cooperation in such contexts. However, previous related studies have mostly ignored the positive feedback effect of collective contributions on individual payoffs. In this work, we explore global and local state-feedback, where the multiplication factor is positively correlated with the frequency of contributors in the entire population or within the game group, respectively. By using replicator dynamics in an infinite well-mixed population, we reveal that state-based feedback plays a crucial role in alleviating the cooperative dilemma by enhancing and sustaining cooperation compared to the feedback-free case. Moreover, when the feedback strength is sufficiently strong or the baseline multiplication factor is sufficiently high, the system with local state-feedback provides full cooperation, hence supporting the "think globally, act locally" principle. Besides, we show that the second-order free-rider problem can be partially mitigated under certain conditions when the state-feedback is employed. Importantly, these results remain robust with respect to variations in punishment cost and fine.
Alexis D Earl, G. G. Carter, Arden G. Berlinger
et al.
Identifying the mechanisms that underlie cooperation is fundamental to biology1. The most complex form of cooperation in vertebrates occurs in cooperative breeders, in which helpers forego reproduction and assist in raising the young of others, typically relatives2. Not all cooperative societies, however, are kin-based—nearly half of all avian3 and mammalian4 cooperative breeders form mixed-kin societies, much like those of humans5. Kin selection in mixed-kin societies occurs when individuals gain indirect fitness from the preferential helping of relatives6, but helpers also frequently assist non-kin7, highlighting a potential role for direct fitness in stabilizing cooperative societies7,8. Here, using a 20-year study of superb starlings (Lamprotornis superbus), we examined how direct and indirect fitness jointly influence helping behaviour. Although we detected kin-biased helping (demonstrating kin selection), non-kin helping was common despite opportunities to aid kin. Unexpectedly, specific pairs maintained long-term reciprocal helping relationships by swapping social roles across their lifetimes—a subtle pattern of reciprocity requiring decades of observation to detect. Given the frequency of non-kin helping and the occurrence of reciprocal helping among both kin and non-kin, helping behaviour in superb starlings seems to be greatly influenced by direct fitness. However, the relative importance of direct and indirect fitness varied with helpers’ sex and dispersal history. By uncovering a cryptic yet crucial role of long-term reciprocal helping, we suggest that reciprocity may be an underappreciated mechanism promoting the stability of cooperatively breeding societies. A study of the cooperative breeding behaviour of superb starlings during 40 consecutive breeding seasons over 20 years reveals long-term reciprocal helping between both related and unrelated individuals.
Future mobile networks in the sixth generation (6G) are poised for a paradigm shift from conventional communication services toward comprehensive information services, driving the evolution of radio access network (RAN) architectures toward enhanced cooperation, intelligence, and service orientation. Building upon the concept of centralized, collaborative, cloud, and clean RAN (C-RAN), this article proposes a novel cooperative, intelligent, and service-based RAN (CIS-RAN) architecture. Focusing on cooperation, CIS-RAN extends the traditional cooperative communication paradigm by further integrating cooperative sensing and cooperative artificial intelligence (AI). To improve both performance and effectiveness across diverse application scenarios, CIS-RAN enhances network cooperation throughout the entire process of acquisition, transmission, and processing, thereby enabling efficient information acquisition, diverse cooperative interactions, and intelligent fusion decision-making. Key technologies are discussed, with network cooperative multiple-input multiple-output (MIMO) examined as a case study, demonstrating superior performance over traditional architectures, as demonstrated by numerical results. Future research directions are outlined, emphasizing the continued exploration and advancement of the CIS-RAN architecture, particularly in enhancing network cooperation.
Cooperation is fundamental to human societies. While several basic theoretical mechanisms underlying its evolution have been established, research addressing more realistic settings remains underdeveloped. Drawing on the hypothesis that intensified environmental fluctuations drove early behavioral evolution in humans during the Middle Stone Age in Africa, we examine the effects of environmental variability and human mobility on the evolution of cooperation. In our model, the variability is represented by randomly moving resource-rich spots across a two-dimensional space, and the mobility is represented by resource-seeking migration of agents. These agents interact cooperatively or competitively for resources while adopting behavioral strategies from more successful neighbors. Through extensive simulations of this model, we reveal three key findings: (i) with sufficient agent mobility, even modest environmental variability promotes cooperation, but further variability does not enhance cooperation; (ii) with any level of environmental variability, agent mobility promotes cooperation; and (iii) these effects occur because the joint effect of environmental variability and agent mobility disrupts defector groups in resource-rich areas, forming cooperator groups on those sites. Although previous studies examine environmental variability and mobility separately, to our knowledge this is the first study to analyze their joint effects on the evolution of cooperation. These findings suggest that environmental variability can promote cooperative group formation without enhanced cognitive abilities, providing new insights into the evolution of human cooperation and, by extension, sociality.
Understanding the emergence of cooperation in systems of computational agents is crucial for the development of effective cooperative AI. Interaction among individuals in real-world settings are often sparse and occur within a broad spectrum of incentives, which often are only partially known. In this work, we explore how cooperation can arise among reinforcement learning agents in scenarios characterised by infrequent encounters, and where agents face uncertainty about the alignment of their incentives with those of others. To do so, we train the agents under a wide spectrum of environments ranging from fully competitive, to fully cooperative, to mixed-motives. Under this type of uncertainty we study the effects of mechanisms, such as reputation and intrinsic rewards, that have been proposed in the literature to foster cooperation in mixed-motives environments. Our findings show that uncertainty substantially lowers the agents' ability to engage in cooperative behaviour, when that would be the best course of action. In this scenario, the use of effective reputation mechanisms and intrinsic rewards boosts the agents' capability to act nearly-optimally in cooperative environments, while greatly enhancing cooperation in mixed-motive environments as well.
Hamid Amirnejad, Sareh Hosseini, Sayed Mohsen Hosseini
et al.
Extended Abstract
Background: Forests are one of the complex ecosystems of natural resources with an important role in the environment and multiple production capacities and functions. The benefit of different human societies from these resources in various forms has various effects and consequences. Without accurate and complete identification of all its stakeholders, logical and acceptable results cannot be achieved in decision-making and policy-making. Therefore, it is essential to fully understand the stakeholders (actors) and their identification criteria in line with basic policies for planning, decision-making, and success in this field. Because decision-making in the management of forest resources always faces challenges and is often associated with complexity, immutability, and uncertainty due to the multi-purpose nature of the benefits and services of these resources, the difficulty of monetary valuation of ecological services, and the great diversity of the stakeholders of their services. Therefore, the stakeholders in the fields of natural resources and forests are considered vital components, and their presence and activity in these fields are undeniable and provide a guarantee of success in cooperative management for the protection of forests. The advancement of the sustainable management goals of natural resources and forests is realized by identifying an active presence of key actors and the most important and effective groups involved, i.e. forest producers and users, along with other identified stakeholders. The primary purpose of identifying those involved is the names of all those who can and should have a role in the planning and management process, and their identification is an important part of the participatory planning process because it is considered a part of the prerequisite for participation. Therefore, identifying and determining the stakeholders make it possible to take advantage of their cooperation in a planned way in the implementation and management of forestry projects and to facilitate the implementation process of the programs.
Methods: To identify, weight, and prioritize the stakeholders involved in the Hyrcanian forest ecosystem services using multi-criteria decision-making models, the stakeholders involved in the Hyrcanian forest ecosystem services were first identified and gathered based on the review of various studies. Then, the research questionnaire was designed to answer two important questions in this study: a) who are the people involved in Hyrcanian forest areas? and b) what is their priority? Therefore, the beneficiaries of the economy of Hyrcanian forests were identified by designing a questionnaire scored with a Likert scale, designing items with five priority options, and determining the individuals or groups of stakeholders. In this research, the opinions of 50 experts, academics, and experts of the Natural Resources and Watershed Management Organization at the provincial and regional levels were used to identify the beneficiaries of the ecosystem services of Hyrcanian forests. The questionnaire was validated and confirmed by experts, and its reliability was confirmed with a Cronbach's alpha statistic of α = 0.97. The stakeholders of the forest ecosystem services were weighted with the Step-Wise Weight Assessment Ratio Analysis (SWARA) method, the Simple Additive Weighting (SAW), and Additive Ratio Assessment (ARAS), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to prioritize them.
Results: By analyzing the findings of the questionnaire using experts' opinions, the Hyrcanian forest ecosystem goods and services stakeholders were identified in 19 groups and five levels, such as international, national, regional, stakeholders outside the forest, and stakeholders inside the forest. Based on the results of the weighting of the stakeholders using the SWARA technique, the forest dwellers and communities downstream of the forest edge are among the most important Hyrcanian forest ecosystem goods and services stakeholders with the highest weight, and other stakeholders were placed in the next priorities based on the received weight. The prioritization of the stakeholders of the goods and services of the Hyrcanian forest ecosystem in the TOPSIS, ARAS, and SAW models showed similar results in terms of the prioritization of the stakeholders of the surveyed services.
Conclusion: The results of stakeholder prioritization models were compared using the slope of the service weight curve (R2) in three models. The slope of the relative closeness curve of the weights in the ARAS model was a descending exponential function with an explanatory degree of 0.93, which indicated an obvious difference between the stakeholders of Hyrcanian forest ecosystem services. The slope of the curve of the relative closeness of the weights is higher and closer to one in the ARAS model than in the other two models from different points of view. Based on this result and the consensus of some experts, the prioritization of stakeholders in the ARAS model is closer to reality. Therefore, the ARAS model was proposed as a suitable model for prioritizing the stakeholders of Hyrcanian forest ecosystem services. According to the results of this model, forest dwellers, downstream communities on the edge of the forest, and the off-site users of the forests are considered the major stakeholders of Hyrcanian forest services. Therefore, the role, presence, and influence of these stakeholders cannot be ignored in the management of natural resources and the environment, especially in forestry projects, and it is recommended to optimally use the presence of these communities in cooperative forest management. Furthermore, the human relationship with the natural ecosystem should be at the top of policies and comprehensive management of forest resources. It is necessary to support wood planting projects through subsidy policies and to grant facilities and low-interest loans to forest dwellers and communities downstream of the forest edge. Other necessary measures include efforts to reduce deprivations and increase the socioeconomic development of the villages on the edge of the forest, holding training entrepreneurship courses and classes, introducing handicrafts and non-wood products, identifying target markets, and strengthening the marketing network for the export of non-wood products to increase the share of the forest in the growth and economic development of rural communities on the edge of the forest. Employment programs should also be developed for local communities in the forest protection sectors.
The most successful human societies are those that have found better ways to promote cooperative behaviour. Yet, cooperation is individually costly and, therefore, it often breaks down, leading to enormous social costs. In this article, I review the literature on the mechanisms and interventions that are known to promote cooperative behaviour in social dilemmas. In iterated or non-anonymous interactions, I focus on the five rules of cooperation, as well as on structural changes, involving the cost or the benefit of cooperation, or the size of the interacting group. In one-shot and anonymous interactions, I focus on the role of internalised social heuristics as well as moral preferences for doing the right thing. For each account, I summarize the available experimental evidence. I hope that this review can be helpful for social scientists working on cooperation and for leaders and policy makers who aim at promoting social cooperation or teamwork.
Cooperative perception can effectively enhance individual perception performance by providing additional viewpoint and expanding the sensing field. Existing cooperation paradigms are either interpretable (result cooperation) or flexible (feature cooperation). In this paper, we propose the concept of query cooperation to enable interpretable instance-level flexible feature interaction. To specifically explain the concept, we propose a cooperative perception framework, termed QUEST, which let query stream flow among agents. The cross-agent queries are interacted via fusion for co-aware instances and complementation for individual unaware instances. Taking camera-based vehicle-infrastructure perception as a typical practical application scene, the experimental results on the real-world dataset, DAIR-V2X-Seq, demonstrate the effectiveness of QUEST and further reveal the advantage of the query cooperation paradigm on transmission flexibility and robustness to packet dropout. We hope our work can further facilitate the cross-agent representation interaction for better cooperative perception in practice.
Genki Ichinose, Daiki Miyagawa, Erika Chiba
et al.
Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a Lévy flight. A Lévy flight is a kind of random walk but it is composed of many small movements with a few big movements. The role of Lévy flights for cooperation has been studied by Antonioni and Tomassini. They showed that Lévy flights promoted cooperation combined with conditional movements triggered by neighboring defectors. However, the optimal condition for neighboring defectors and how the condition changes by the intensity of Lévy flights are still unclear. Here, we developed an agent-based model in a square lattice where agents perform Lévy flights depending on the fraction of neighboring defectors. We systematically studied the relationships among three factors for cooperation: sensitivity to defectors, the intensity of Lévy flights, and population density. Results of evolutionary simulations showed that moderate sensitivity most promoted cooperation. Then, we found that the shortest movements were best for cooperation when the sensitivity to defectors was high. In contrast, when the sensitivity was low, longer movements were best for cooperation. Thus, Lévy flights, the balance between short and long jumps, promoted cooperation in any sensitivity, which was confirmed by evolutionary simulations. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve. Our study highlights that Lévy flights are an optimal searching strategy not only for foraging but also for constructing cooperative relationships with others.
Hoseyn Vafaeibasir, Alireza Eslambolchi, Mansour Esmaeilpour
et al.
Investing in industry cooperatives, especially small and medium industries, has always been very important. Because in addition to playing a key role in a country's employment, it will also lead to economic growth and increase GDP. The purpose of this study is to investigate the factors that can lead to successful investment in small and medium industrial cooperatives and show a way for industrial entrepreneurs to enter this important economic field with more awareness. This research is a qualitative research based on the collected data of interviews with 15 industrial experts using the method of sensitive event technique and content analysis using MAX QDA 18 software. The last step of the final research model categories was obtained. The results showed that 5 general groups of factors including: managerial factors, entrepreneur related factors, production related factors, market factors and environmental factors can play an important role in the success of this type of investment. And private sector investors, including managers of production cooperatives, will help reduce investment risk by paving the way for better measures and paving the way for industrial growth and employment growth in the country.
Vahid Kashafinia, Mahdi Yakhchali, Behzad Minaei
et al.
Although transparency literature is mainly concentrated on government transparency, but it has gone far beyond it and has been extended to private companies, cooperatives, NGOs and charities. The field of cooperatives is one of the fields in which transparency has entered in recent decades in the form of concepts such as "corporate governance", "corporate transparency" and "open corporate data". The aim of this research is to design an index to measure transparency in cooperatives. This qualitative research has been conducted using documentary studies, interviews with 45 experts and focus group. The statistical population of the study includes all experts and activists in the field of cooperation, including managers and government experts, members of unions and chambers, and members of cooperatives. Cooperative Transparency Index (CTI) consists of 6 dimensions and 39 items. In addition to measuring the level of transparency in cooperatives (in the range of 0 to 100), this index determines the optimum or desired level of transparency, separately for "each user in each item", "each item", "each dimension" and "final amount of Index". Finally, some policy recommendations have been proposed to promote transparency in the cooperative sector, including participation of major actors in cooperative sector, reduction of bureaucratic complexities and education of cooperatives.
Cooperation is a universal phenomenon, it is present in all human cultures from hunter–gatherers to industrialised societies, and it constitutes a fundamental aspect of social relationships. There is, however, variability in the amount of resources people invest in cooperative activities. Recent findings indicate that this variability may be partly explained as a contextually appropriate response to environmental conditions. Specifically, adverse environments seem to be associated with less cooperation and recent findings suggest that this effect is partly mediated by differences in individuals’ life-history strategy. In this paper, we set out to replicate and extend these findings by measuring actual cooperative behaviour in three economic games – a Dictator game, a Trust game and a Public Goods game – on a nationally representative sample of 612 people. Although we found that the cooperation and life-history strategy latent variables were adequately captured by the models, the hypothesised relationship between childhood environmental adversity and adult cooperation and the mediation effect by life-history strategy were not found.
With artificial intelligence systems becoming ubiquitous in our society, its designers will soon have to start to consider its social dimension, as many of these systems will have to interact among them to work efficiently. With this in mind, we propose a decentralized deep reinforcement learning algorithm for the design of cooperative multi-agent systems. The algorithm is based on the hypothesis that highly correlated actions are a feature of cooperative systems, and hence, we propose the insertion of an auxiliary objective of maximization of the mutual information between the actions of agents in the learning problem. Our system is applied to a social dilemma, a problem whose optimal solution requires that agents cooperate to maximize a macroscopic performance function despite the divergent individual objectives of each agent. By comparing the performance of the proposed system to a system without the auxiliary objective, we conclude that the maximization of mutual information among agents promotes the emergence of cooperation in social dilemmas.
Recent studies suggest that the emergence of cooperative behavior can be explained by generalized reciprocity, a behavioral mechanism based on the principle of "help anyone if helped by someone". In complex systems, the cooperative dynamics is largely determined by the network structure which dictates the interactions among neighboring individuals. Despite an abundance of studies, the role of the network structure in in promoting cooperation through generalized reciprocity remains an under-explored phenomenon. In this doctoral thesis, we utilize basic tools from the dynamical systems theory, and develop a unifying framework for investigating the cooperation dynamics of generalized reciprocity on complex networks. We use this framework to present a theoretical overview on the role of generalized reciprocity in promoting cooperation in three distinct interaction structures: i) social dilemmas, ii) multidimensional networks, and iii) fluctuating environments. The results suggest that cooperation through generalized reciprocity always emerges as the unique attractor in which the overall level of cooperation is maximized, while simultaneously exploitation of the participating individuals is prevented. The effect of the network structure is captured by a local centrality measure which uniquely quantifies the propensity of the network structure to cooperation, by dictating the degree of cooperation displayed both at microscopic and macroscopic level. As a consequence, the implementation of our results may go beyond explaining the evolution of cooperation. In particular, they can be directly applied in domains that deal with the development of artificial systems able to adequately mimic reality, such as reinforcement learning.
The maintenance of cooperative behavior is fundamental for the prosperity of human societies. Empirical studies show that high cooperation is frequently associated with the presence of strong social ties, but they are silent on whether a causal mechanism exists, how it operates, and what features of the social environment are conducive to its emergence. Here we show experimentally that strong ties increase cooperation and welfare by enabling the emergence of a close-knit and strongly bound cooperative elite. Crucially, this cooperative elite is more prevalent in social environments characterized by a large payoff difference between weak and strong ties, and no gradation in the process of strengthening a tie. These features allow cooperative individuals to adopt an all or nothing strategy to tie strengthening based on the well-known mechanism of direct reciprocity: participants become very selective by forming strong ties only with other cooperative individuals and severing ties with everyone else. Once formed, these strong ties are persistent and enhance cooperation. A dichotomous society emerges with cooperators prospering in a close-knit, strongly bound elite, and defectors earning low payoffs in a weakly connected periphery. Methodologically, our set-up provides a framework to investigate the role of the strength of ties in an experimental setting.
The history of humanity is a story of cooperation. Issues pertaining to the origin of human cooperation have, however, been characterized by a forager bias, the assumption being that they have a close link to our evolutionary past. In contrast little effort has been spent on documenting and explaining cooperative herding among nomadic pastoralists. As the Mongolian empire attest to nomadic pastoralists—in contrast to foragers—can form large-scale empires. In combination with the prevalence of small-scale cooperative herding groups, nomadic pastoral societies thus provide a fertile ground for expanding our understanding of the evolution of cooperation. In this paper I aim to extend our understanding of human cooperation through a comparison of the most well-known cooperative herding groups—namely the Mongolian khot ail and the Saami siida—and the most well-studied forager bands, namely the Hadza camp and Ache band.