Hasil untuk "Cooperation. Cooperative societies"

Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner et al.

As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge. We introduce the Governance of the Commons Simulation (GovSim), a generative simulation platform designed to study strategic interactions and cooperative decision-making in LLMs. In GovSim, a society of AI agents must collectively balance exploiting a common resource with sustaining it for future use. This environment enables the study of how ethical considerations, strategic planning, and negotiation skills impact cooperative outcomes. We develop an LLM-based agent architecture and test it with the leading open and closed LLMs. We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. Ablations reveal that successful multi-agent communication between agents is critical for achieving cooperation in these cases. Furthermore, our analyses show that the failure to achieve sustainable cooperation in most LLMs stems from their inability to formulate and analyze hypotheses about the long-term effects of their actions on the equilibrium of the group. Finally, we show that agents that leverage"Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability. Taken together, GovSim enables us to study the mechanisms that underlie sustainable self-government with specificity and scale. We open source the full suite of our research results, including the simulation environment, agent prompts, and a comprehensive web interface.

Duc Nghia Vu, Truc Le Nguyen, Thi Hong Ngoc Nguyen et al.

Abstract This study models evolutionary game dynamics among three-generation families and family physicians (FPs) to optimize resource sharing in high-cost, increasingly individualistic societies. While FPs deliver vital preventative care and multigenerational households offer economic and social resilience, rising individualism undermines these structures by prioritizing autonomy over collective bonds, eroding intergenerational support, increasing isolation, and weakening shared legacy. In such contexts, multigenerational living becomes an active, difficult choice rather than a cultural norm in many nations. To counter this, we introduce a novel five-player evolutionary game involving government (G), grandparents (R), parents (P), children (C), and FPs (D), analyzing how policy can stabilize cooperation. Using stability analysis of evolutionarily stable strategies (ESS) and MATLAB simulations based on Hanoi, Vietnam data, we identify key drivers: government subsidies, cost-sharing mechanisms, healthcare collaboration, and mutual benefits. Results show that without government intervention, individualism destabilizes cooperation; with targeted policy support, however, a self-sustaining equilibrium emerges where resource efficiency, lower costs, and improved wellbeing arise organically from structural incentives, not continuous coordination. Government thus plays a pivotal, active role: not merely enabling, but anchoring multigenerational resilience against cultural fragmentation. When incentives align family cost-sharing with FP collaboration, all actors benefit, enhancing health outcomes, financial stability, and caregiving capacity. This framework offers policymakers a pathway to reinforce intergenerational ties while strengthening community-based healthcare. Strategic government action can transform resource-sharing from a fragile, voluntary act into a robust, systemically supported norm, countering individualism’s erosion of family and health systems, and fostering sustainable, cooperative solutions for aging populations, childcare, and rising healthcare costs.

B. Earp, Sebastian Porsdam Mann, Mateo Aboy et al.

How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being.

Dini Wang, Peng Yi, Yiguang Hong et al.

Cooperation is fundamental to human societies, and the interaction structure among individuals profoundly shapes its emergence and evolution. In real-world scenarios, cooperation prevails in multi-group (higher-order) populations, beyond just dyadic behaviors. Despite recent studies on group dilemmas in higher-order networks, the exploration of cooperation driven by higher-order strategy updates remains limited due to the intricacy and indivisibility of group-wise interactions. Here we investigate four categories of higher-order mechanisms for strategy updates in public goods games and establish their mathematical conditions for the emergence of cooperation. Such conditions uncover the impact of both higher-order strategy updates and network properties on evolutionary outcomes, notably highlighting the enhancement of cooperation by overlaps between groups. Interestingly, we discover that the group-mutual comparison update – selecting a high-fitness group and then imitating a random individual within this group – can prominently promote cooperation. Our analyses further unveil that, compared to pairwise interactions, higher-order strategy updates generally improve cooperation in most higher-order networks. These findings underscore the pivotal role of higher-order strategy updates in fostering collective cooperation in complex social systems. Author summary Human societies often organize and cooperate within social groups, where relatives, friends, neighbors, and colleagues influence behavior at both group and individual levels. Individuals may exhibit biased or neutral attitudes when selecting a neighboring group and a peer within it for imitation or comparison, a process termed as higher-order strategy update. These selection preferences originate from four personality types: aggressive, open-minded, myopic, and passive. This work demonstrates that the open-minded type – indiscriminately imitating a peer within a well-functioning group – significantly promotes cooperation. The mathematical framework proposed in this study deepens the understanding of how decision-making within higher-order structures affects the emergence and spread of cooperative behaviors.

Pedro M. P. Curvo, M. Dragomir, Salvador Torpes et al.

This study evaluates and extends the findings made by Piatti et al., who introduced GovSim, a simulation framework designed to assess the cooperative decision-making capabilities of large language models (LLMs) in resource-sharing scenarios. By replicating key experiments, we validate claims regarding the performance of large models, such as GPT-4-turbo, compared to smaller models. The impact of the universalization principle is also examined, with results showing that large models can achieve sustainable cooperation, with or without the principle, while smaller models fail without it. In addition, we provide multiple extensions to explore the applicability of the framework to new settings. We evaluate additional models, such as DeepSeek-V3 and GPT-4o-mini, to test whether cooperative behavior generalizes across different architectures and model sizes. Furthermore, we introduce new settings: we create a heterogeneous multi-agent environment, study a scenario using Japanese instructions, and explore an"inverse environment"where agents must cooperate to mitigate harmful resource distributions. Our results confirm that the benchmark can be applied to new models, scenarios, and languages, offering valuable insights into the adaptability of LLMs in complex cooperative tasks. Moreover, the experiment involving heterogeneous multi-agent systems demonstrates that high-performing models can influence lower-performing ones to adopt similar behaviors. This finding has significant implications for other agent-based applications, potentially enabling more efficient use of computational resources and contributing to the development of more effective cooperative AI systems.

Ettore Camerlenghi, Danai Papageorgiou

Multi-level vertebrate societies, characterized by nested social units, allow individuals to perform a wide range of tasks in cooperation with others beyond their core social unit. In these societies, individuals can selectively interact with specific partners from higher social levels to cooperatively perform distinct tasks. Alternatively, social units of the same level can merge to form higher-level associations, enabling individuals to benefit from large social units without always maintaining a large core social unit. The reasons why multi-level sociality evolves in some systems but not in others are not well understood. We propose that this is partly due to a lack of data, especially regarding the fitness consequences of cooperation at different social levels. First, we argue that in multi-level societies individual fitness benefits should increase when performing tasks in cooperation with associates from higher social levels. Second, as more multi-level societies are documented across taxa, we will continue to find similar cooperative tasks performed at each of the different social levels. By providing compelling species examples, from dolphins to fairy-wrens, we underscore that despite the diversity of multi-level social organization, convergence in task performance across social levels will become clearer as more data accumulates. Finally, we highlight the role of multi-level sociality in buffering fluctuating environmental conditions by enabling flexible social associations to emerge according to need. This article is part of the theme issue ‘Division of labour as key driver of social evolution’.

Kai Xie, Attila Szolnoki

Understanding and resolving cooperation dilemmas are key challenges in evolutionary game theory, which have revealed several mechanisms to address them. This paper investigates the comprehensive influence of multiple reputation-related components on public cooperation. In particular, cooperative investments in public goods game are not fixed but simultaneously depend on the reputation of group organizers and the population's cooperation willingness, hence indirectly impacting on the players' income. Additionally, individual payoff can also be directly affected by their reputation via a weighted approach which effectively evaluates the actual income of players. Unlike conventional models, the reputation change of players is non-monotonic, but may transform abruptly due to specific actions. Importantly, a theoretically supported double Q-learning algorithm is introduced to avoid overestimation bias inherent from the classical Q-learning algorithm. Our simulations reveal a significantly improved cooperation level, that is explained by a detailed Q-value analysis. We also observe the lack of massive cooperative clusters in the absence of network reciprocity. At the same time, as an intriguing phenomenon, some actors maintain moderate reputation and are continuously flipping between cooperation and defection. The robustness of our results are validated by mean-field approximation.

Zhao Song, Ndidi Bianca Ogbo, Xinyu Wang et al.

Understanding how cooperation emerges and persists is a central challenge in evolutionary game theory. Existing models often rely on restricted, hand-picked strategy sets, which can overlook critical behavioural pathways. A recent four-strategy framework showed that cheap talk can promote cooperation through local interactions, yet it remained unclear whether modelled strategies might alter these conclusions. Here, we extend this framework to the complete set of eight strategies that naturally arise from communication and decision-making rules. We show that incorporating the full strategy space dramatically changes the evolutionary landscape. Cooperation becomes both more robust and more versatile, driven by novel pathways absent in the restricted model. In particular, we uncover a previously overlooked mechanism in which suspicious cooperation catalyses a cyclic dynamic that sustains cooperation. Conversely, the assumed role of strategic defection in the biased model is fragile, acting mainly as a spoiler rather than a genuine evolutionary attractor. The complete model further reveals a rich spectrum of long-term behaviours, including stable coexistence among up to seven strategies and time-varying patterns of partial coexistence. These results demonstrate that the full strategy space unlocks hidden routes to cooperative behaviour and highlight the importance of comprehensive modelling when explaining the emergence of cooperation.

Alexandre S. Pires, Laurens Samson, Sennay Ghebreab et al.

Humans increasingly rely on large language models (LLMs) to support decisions in social settings. Previous work suggests that such tools shape people's moral and political judgements. However, the long-term implications of LLM-based social decision-making remain unknown. How will human cooperation be affected when the assessment of social interactions relies on language models? This is a pressing question, as human cooperation is often driven by indirect reciprocity, reputations, and the capacity to judge interactions of others. Here, we assess how state-of-the-art LLMs judge cooperative actions. We provide 21 different LLMs with an extensive set of examples where individuals cooperate -- or refuse cooperating -- in a range of social contexts, and ask how these interactions should be judged. Furthermore, through an evolutionary game-theoretical model, we evaluate cooperation dynamics in populations where the extracted LLM-driven judgements prevail, assessing the long-term impact of LLMs on human prosociality. We observe a remarkable agreement in evaluating cooperation against good opponents. On the other hand, we notice within- and between-model variance when judging cooperation with ill-reputed individuals. We show that the differences revealed between models can significantly impact the prevalence of cooperation. Finally, we test prompts to steer LLM norms, showing that such interventions can shape LLM judgements, particularly through goal-oriented prompts. Our research connects LLM-based advices and long-term social dynamics, and highlights the need to carefully align LLM norms in order to preserve human cooperation.

Zhao Song, The Anh Han

Commitment is a well-established mechanism for fostering cooperation in human society and multi-agent systems. However, existing research has predominantly focused on the commitment that neglects the freedom of players to abstain from an interaction, limiting their applicability to many real-world scenarios where participation is often voluntary. In this paper, we present a two-stage game model to investigate the evolution of commitment-based behaviours and cooperation within the framework of the optional Prisoner's Dilemma game. In the pre-game stage, players decide whether to accept a mutual commitment. Once in the game, they choose among cooperation, defection, or exiting, depending on the formation of a pre-game commitment. We find that optional participation boosts commitment acceptance but fails to foster cooperation, leading instead to widespread exit behaviour. To address this, we then introduce and compare two institutional incentive approaches: i) a strict one (STRICT-COM) that rewards only committed players who cooperate in the game, and ii) a flexible one (FLEXIBLE-COM) that rewards any committed players who do not defect in the game. The results reveal that, while the strict approach is demonstrably better for promoting cooperation as the flexible rule creates a loophole for an opportunistic exit after committing, the flexible rule offers an efficient alternative for enhancing social welfare when such opportunistic behaviour results in a high gain. This study highlights the limitations of relying solely on voluntary participation and commitment to resolving social dilemmas, emphasising the importance of well-designed institutional incentives to promote cooperation and social welfare effectively.

Jordi Piñero, Artemy Kolchinsky, Sidney Redner et al.

Mutualistic interactions are widespread in nature, from plant communities and microbiomes to human organizations. Along with competition for resources, cooperative interactions shape biodiversity and contribute to the robustness of complex ecosystems. We present a stochastic neutral theory of cooperator species. Our model shares with the classic neutral theory of biodiversity the assumption that all species are equivalent, but crucially differs in requiring cooperation between species for replication. With low migration, our model displays a bimodal species-abundance distribution, with a high-abundance mode associated with a core of cooperating species. This core is responsible for maintaining a diverse pool of long-lived species, which are present even at very small migration rates. We derive analytical expressions of the steady-state species abundance distribution, as well as scaling laws for diversity, number of species, and residence times. With high migration, our model recovers the results of classic neutral theory. We briefly discuss implications of our analysis for research on the microbiome, synthetic biology, and the origin of life.

Aron Vallinder, Edward Hughes

Large language models (LLMs) provide a compelling foundation for building generally-capable AI agents. These agents may soon be deployed at scale in the real world, representing the interests of individual humans (e.g., AI assistants) or groups of humans (e.g., AI-accelerated corporations). At present, relatively little is known about the dynamics of multiple LLM agents interacting over many generations of iterative deployment. In this paper, we examine whether a ''society'' of LLM agents can learn mutually beneficial social norms in the face of incentives to defect, a distinctive feature of human sociality that is arguably crucial to the success of civilization. In particular, we study the evolution of indirect reciprocity across generations of LLM agents playing a classic iterated Donor Game in which agents can observe the recent behavior of their peers. We find that the evolution of cooperation differs markedly across base models, with societies of Claude 3.5 Sonnet agents achieving significantly higher average scores than Gemini 1.5 Flash, which, in turn, outperforms GPT-4o. Further, Claude 3.5 Sonnet can make use of an additional mechanism for costly punishment to achieve yet higher scores, while Gemini 1.5 Flash and GPT-4o fail to do so. For each model class, we also observe variation in emergent behavior across random seeds, suggesting an understudied sensitive dependence on initial conditions. We suggest that our evaluation regime could inspire an inexpensive and informative new class of LLM benchmarks, focussed on the implications of LLM agent deployment for the cooperative infrastructure of society.

Qichao Qin, Y. Lei

From the characteristics and significance of school-enterprise cooperation and the important way that school-enterprise cooperation is suitable for the transformation and development of private vocational colleges and universities, this paper expounds the necessity of implementing school-enterprise cooperation in Guangdong private vocational colleges and universities. From the four aspects of schools, students, enterprises and society, this paper analyzes the problems existing in the cooperation between enterprises of private vocational colleges and universities in Guangdong. Put forward four strategies of cooperation between enterprises of private vocational colleges and universities in Guangdong: reform of talent training model based on cooperation between schools and enterprises; To construct a reasonable and effective cooperative operation mechanism for schools and enterprises; Strengthen the school connotation construction, improve talent competitiveness; To create a good social environment for cooperation between schools and enterprises.

Kazuhiro Tamura, S. Morita

Electricity competition, restrictions on carbon dioxide (CO2) emissions and arm races between nations are examples of social dilemmas within human society. In the presence of social dilemmas, rational choice in game theory leads to the avoidance of cooperative behaviour owing to its cost. However, in experiments using public goods games that simulate social dilemmas, humans have often exhibited cooperative behaviour that deviates from individual rationality. Despite extensive research, the alignment between human cooperative behaviour and game theory predictions remains inconsistent. This study proposes an alternative approach to solve this problem. We used Q-learning, a form of artificial intelligence that mimics decision-making processes of humans who do not possess the rationality assumed in game theory. This study explores the potential for cooperation by varying the number of participants in public goods games using deep Q-learning. The simulations demonstrate that agents with Q-learning can acquire cooperative behaviour similar to that of humans. Moreover, we found that cooperation is more likely to occur as the group size increases. These results support and reinforce existing experiments involving humans. In addition, they have potential applications for creating cooperation without sanctions.

Xiaochun Han, Yina Ma

Human society is organized in structured social networks upon which large-scale cooperation among genetically unrelated individuals is favored and persists. Such large-scale cooperation is crucial for the success of the human species but also one of the most puzzling challenges. Recent work in social and behavioral neuroscience has linked human cooperation to oxytocin, an evolutionarily ancient and structurally preserved hypothalamic neuropeptide. This review aims to elucidate how oxytocin promotes nonkin cooperation in social networks by reviewing its effects at three distinct levels: individual cooperation, the formation of interpersonal relationships, and the establishment of heterogeneous network structures. We propose oxytocin as a proximate mechanism for fostering large-scale cooperation in human societies. Specifically, oxytocin plays an important role in facilitating network-wide cooperation in human societies by 1) increasing individual cooperation, mitigating noncooperation motives, and facilitating the enforcement of cooperative norms; 2) fostering interpersonal bonding and synchronization; and 3) facilitating the formation of heterogeneous network structures.

Ahmed R. Sadik, Bodo Urban

Cooperative manufacturing is a new trend in industry, which depends on the existence of a collaborative robot. A collaborative robot is usually a light-weight robot which is capable of operating safely with a human co-worker in a shared work environment. During this cooperation, a vast amount of information is exchanged between the collaborative robot and the worker. This information constructs the cooperative manufacturing knowledge, which describes the production components and environment. In this research, we propose a holonic control solution, which uses the ontology concept to represent the cooperative manufacturing knowledge. The holonic control solution is implemented as an autonomous multi-agent system that exchanges the manufacturing knowledge based on an ontology model. Ultimately, the research illustrates and implements the proposed solution over a cooperative assembly scenario, which involves two workers and one collaborative robot, whom cooperate together to assemble a customized product.

Seth Z. Zhao, Hao Xiang, Chenfeng Xu et al.

Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-agent 3D annotations. Nevertheless, it is time-consuming and expensive to collect and annotate real-world data, especially for V2X systems. In this paper, we present a self-supervised learning framwork for V2X cooperative perception, which utilizes the vast amount of unlabeled 3D V2X data to enhance the perception performance. Specifically, multi-agent sensing information is aggregated to form a holistic view and a novel proxy task is formulated to reconstruct the LiDAR point clouds across multiple connected agents to better reason multi-agent spatial correlations. Besides, we develop a V2X bird-eye-view (BEV) guided masking strategy which effectively allows the model to pay attention to 3D features across heterogeneous V2X agents (i.e., vehicles and infrastructure) in the BEV space. Noticeably, such a masking strategy effectively pretrains the 3D encoder with a multi-agent LiDAR point cloud reconstruction objective and is compatible with mainstream cooperative perception backbones. Our approach, validated through extensive experiments on representative datasets (i.e., V2X-Real, V2V4Real, and OPV2V) and multiple state-of-the-art cooperative perception methods (i.e., AttFuse, F-Cooper, and V2X-ViT), leads to a performance boost across all V2X settings. Notably, CooPre achieves a 4% mAP improvement on V2X-Real dataset and surpasses baseline performance using only 50% of the training data, highlighting its data efficiency. Additionally, we demonstrate the framework's powerful performance in cross-domain transferability and robustness under challenging scenarios. The code will be made publicly available at https://github.com/ucla-mobility/CooPre.

Zhixue He, Chen Shen, Lei Shi et al.

The rise of artificial intelligence (AI) offers new opportunities to influence cooperative dynamics with greater applicability and control. In this paper, we examine the impact of third-party bots--agents that do not directly participate in games but unbiasedly modify the payoffs of normal players engaged in prisoner's dilemma interactions--on the emergence of cooperation. Using an evolutionary simulation model, we demonstrate that unbiased bots are unable to shift the defective equilibrium among normal players in well-mixed populations. However, in structured populations, despite their unbiased actions, the bots spontaneously generate distinct impacts on cooperators and defectors, leading to enhanced cooperation. Notably, bots that apply negative influences are more effective at promoting cooperation than those applying positive ones, as fewer bots are needed to catalyze cooperative behavior among normal players. However, as the number of bots increases, a trade-off emerges: while cooperation is maintained, overall social payoffs decline. These findings highlight the need for careful management of AI's role in social systems, as even well-intentioned bots can have unintended consequences on collective outcomes.

Javad Mikaniki

AbstractBackground: The sustainable security of rural settlements is a topic that has been studied from various perspectives by using different approaches. Purpose: This article explored an integration-oriented approach to reveal the capacities of the local community in achieving sustainable security in the rural border settlements of Darmiyan and Sarbisheh Counties in Southern Khorasan. Research Method: The study employed a quantitative, descriptive-analytical research method with a survey approach. The statistical population consisted of 62 rural settlements within 30 kilometers of the common border with Afghanistan, comprising 4,502 households. A non-probability method was used for sampling at the village level, while a random-stratified probability method was employed at the household level. The sample size was 354 rural households, which was calculated using Cochran's formula. The data collection tool was a researcher-made questionnaire examining variables related to participation, trust, cohesion, and social solidarity. Findings: The results indicated that the components of participation, trust, and social solidarity had a significant impact on sustainable security from the local community's perspective. Furthermore, the research confirmed a favorable mentality towards social components in rural areas, which could foster the growth and development of security and its dissemination to weaker rural areas. Keywords: Sustainable Security, Social Participation, Social Trust, Social Cohesion. IntroductionA rural community refers to a set of human behaviors and interactions that take place in villages. The fundamental characteristics of rural society can be examined from an economic and social perspective. Economically, rural areas typically rely on a subsistence-based economy dependent on the agricultural sector. Socially, they are often marked by cooperation, mutual assistance, homogeneity, and cultural unity. The settlement patterns in rural areas are influenced by natural, economic, and social factors. Social institutions are relatively stable patterns of behavior or a set of relationships, trends, and tools that are built around social interests and needs. Investigating and analyzing the capacities of rural societies, especially in border areas, based on their economic and social characteristics are of particular importance. South Khorasan Province shares a 331-kilometer border with Afghanistan, spanning the counties of Nahbandan, Sarbisheh, Darmiyan, and Zirkoh. This border region has long been considered one of the safest in the country's east and southeast. The purpose of this research was to identify the capacities of the local community in the border settlements of Sarbisheh and Darmiyan Counties, particularly in terms of social activities, such as participation and social cohesion, as well as cultural institutions. Materials & MethodsThis research employed a descriptive-analytical approach and was considered an applied research. The required data were collected using both library research and a survey method. The primary data collection tool was a researcher-made questionnaire, which was administered after establishing its validity and reliability among the research sample population. The main components of the research included collaborative capacities, social trust, cohesion, and social solidarity, which were measured through 33 Likert-scale items (5 options). The collected survey data were analyzed using both descriptive (central tendency and dispersion indices) and inferential statistics. Due to the non-normal distribution of the data, the research hypotheses were tested using the binomial test. The statistical population consisted of the villages located in the Gezik and Tabas sections of Darmiyan County (33 villages) and the villages in the Doroh and Lano sections of Sarbisheh County (65 villages). Sampling was conducted at two levels: first, the villages within 30 kilometers of the common border with Afghanistan were included, totaling 62 villages; then, at the household level, a simple random probability sampling method was utilized. Research FindingsThe area studied in this research included the border villages of Darmiyan and Sarbisheh Counties in South Khorasan Province. Darmiyan City is located between 32°33' and 33°21' north latitude and 59°28' and 60°41' east longitude. Sarbisheh City, on the other hand, is situated between 32°02' and 32°56' north latitude and 59°13' and 60°53' east longitude. Darmiyan comprises 4 districts, 4 cities, and 3 villages, covering an area of 5,816 m2. Sarbisheh, with 3 districts, 3 cities, and 6 villages, spans an area of 7,928 m2 and shares a border with Afghanistan. This research focused on the border settlements located within the border districts and villages of these two cities. The research findings indicated that the component of people's participation in solving rural problems had the highest average score of 4.29 among the variables examined. From the perspective of the majority of respondents, the cooperative spirit of villagers had a highly influential role in achieving sustainable security. Additionally, the will and collective awareness in solving problems were also considered important, ranking in the second or third highest categories. Since the average for the other variables was higher than 3 (the midpoint), it could be concluded that the majority of respondents perceived the impacts of all variables on realizing stable security to be above average. Regarding social trust, the trust of family members in each other scored the highest with an average of 4.2. This was followed by trust in relatives and friends, while trust in official and government organizations ranked the lowest. However, as the average for all social trust variables exceeded 3, it could be said that from the perspective of the local community, social trust, particularly within family, friends, and neighbors, was seen as effective in realizing sustainable security. The research examined the impact of cohesion and solidarity on the realization of sustainable security from the perspective of the local community. The findings indicated that the variables of family relationships and ties, religious and ideological convergence, and interaction with each other scored above 4 (on a high scale), suggesting a relatively high level of internal convergence within the local community in the border settlements. While political and party solidarity had the lowest average among the variables, the average for all variables was still above 3.5, indicating that the different aspects of cohesion and solidarity were seen as contributing to sustainable security in the studied area.Hypothesis 1: Collaborative capacities in the studied area have been highly effective in achieving sustainable security.The observed ratio in the second group was much higher than the first group and the p-value supported the researcher's hypothesis that the participation capacities within the rural community had been highly effective in achieving sustainable security.Hypothesis 2: Social trust in the study area has been highly effective in achieving stable security.The observed ratio in the second group was much higher than the first group and the p-value confirmed the researcher's hypothesis that the social trust component within the rural community had been highly effective in achieving stable security.Hypothesis 3: The cohesion and solidarity of the local community has been very effective in achieving sustainable security.The observed ratio in the second group was much higher than the first group and the p-value supported the researcher's hypothesis that social cohesion and solidarity within the rural community had been highly effective in achieving sustainable security. Discussion of Results & ConclusionThe results of the present study indicated that participation capacities, both in terms of mental engagement (people's mindset towards participation, cooperative thinking, and collective will to solve problems) and objective aspects (including participation among villagers and in public/social activities), were at an upper level in the studied area. Regarding the social component, the local community perceived interpersonal trust, generalized trust, and institutional trust (mostly in non-official and non-governmental institutions) to be largely present. Similarly, social cohesion and solidarity (in-group and inter-group interactions and relations, as well as out-group relations) were evaluated at a high level. The findings of this research are consistent with the results obtained by previous studies, such as those conducted by Kladivo (2012), Geertrui et al. (2013), and Bazarafshan and Tulabinejad (2015). Based on the results, it is suggested that in addition to prioritizing native and local values and strengthening effective social institutions (e.g., Dispute Resolution Council), attention should be paid to people with social influence. Furthermore, the constructive interaction of official institutions (e.g., Islamic Council, Rural municipality) – which is currently less favored by the local community – should be addressed as a potential solution.

Mostafa Ebrahimpour, Mohsen Akbari, Atefeh Abdollahi

The most basic goal of cooperative companies of any type is to improve the economic status of the members of these companies. After the victory of the Islamic Revolution, the cooperatives of the country have grown tremendously physically and have provided the necessary grounds for the participation of the general public in various economic and social matters. State economic inadequacies and harmful consequences of the private economy make it necessary to pay more attention to cooperatives. In this research, upstream document analysis using the document analysis method has been used to determine the investment priorities of the city's development and construction cooperative companies. Then, according to the interviews conducted with the members of the development and construction cooperative companies of Rasht city, the cooperative organization, work and social welfare, the management of statistics and information, and the management and planning organization of Gilan province, the governor and prefect of the city, experts A university active in the field of Gilanology, investment opportunities were identified separately and analyzed using the document analysis technique and ranked using the Delphi technique. In the following, according to the output of the Delphi technique in line with strategic analysis, using the parental strategy, the priority of each of the identified projects of Rasht city is determined and the position of each of them according to the key success factors and cooperative capabilities in the matrix. Placement parents and necessary implementation suggestions have been provided.