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Underpinned by systemic thinking, social-ecological systems (SES) research has emerged as a critical field for addressing the challenges of the Anthropocene, marked by a cross-scale focus, inter- and transdisciplinary approaches, and a strong emphasis on place-based work. Thanks to the efforts of many networks and institutes, the field has advanced new theoretical and methodological approaches, fostered dedicated journals, and spurred educational programs. It has also significantly influenced sustainability initiatives and policy from local to global scales, and has richly informed place-based efforts. Despite this progress, SES research faces persistent challenges, including conceptual and methodological fragmentation, difficulty in scaling localized insights to global frameworks (and vice versa), and capturing cross-scale connections and processes while retaining contextual relevance. Inclusivity also remains a critical issue, with regional, Indigenous, and local contributions often underrepresented, as there is still a reliance on short-term, inequitably distributed grant funding for much of the research in the field. This paper introduces the Society for Social-Ecological Systems (SocSES), a global platform designed to build on and connect to the rich legacy of SES networks. SocSES aims to advance and support SES–based research, practice, and action toward a just and sustainable future. We outline how SocSES will provide a home for SES institutes, networks, researchers, and practitioners working at the science-practice-policy interface to connect and amplify existing efforts through thematic streams, regional hubs, an institutional hub, an early-career professionals hub, and synthesis groups. The society will provide a stable infrastructure to foster interdisciplinary and transdisciplinary collaboration, enhance the generalizability and policy relevance of SES research, bolster education, research, and knowledge co-production, and support the next generation of SES professionals. By addressing the persistent challenges facing the field and fostering transformative spaces and communities for innovation and action, SocSES aspires to support and leverage SES knowledge as a cornerstone of global sustainability science.In line with the society’s commitment to linguistic diversity and equitable access, this abstract has been translated into 12 languages by authors of this paper and additional contributors. These translations are available in Appendix 2 and at https://socses.org/about/paper.

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management.

Fleshy fruits have evolved multiple times and display a tremendous diversity of colours, shapes, aromas and textures. For over a century this was attributed, at least in part, to frugivore‐driven selection. The dispersal syndrome hypothesis posits that fruits and frugivores co‐evolved, each exerting sufficient selective pressure on one another, and resulting to in suites of fruit traits that match frugivore behaviour, morphology and sensory capacities. In the last two decades of the past century, the dispersal syndrome hypothesis has been deemed overly adaptationist. Challenges are based on a variety of arguments, primarily that previous studies did not sufficiently incorporate a phylogenetic framework, and that non‐adaptive factors can explain a great deal of extant fruit trait variation. In recent years, many studies have addressed these issues and found support for the dispersal syndrome hypothesis. As empirical evidence mounts, it's become increasingly clear that many fruit traits—primarily size, colour and scent—are strongly affected by frugivore trait preference. At the same time, many studies do not sufficiently consider the many confounding factors involved in fruit trait evolution. We review the evidence supporting the dispersal syndrome hypothesis and highlight the existing gaps in knowledge and the factors that are currently still not fully incorporated into the study of the evolution of fruit traits. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article

Purpose This study aims to answer the question of how business models (BMs) maintain stability while coping with environmental uncertainties. This study proposes a dynamic co-evolution of knowledge management and business model transformation based on a comparative analysis of the focal firms’ BMs and their main partners in two e-commerce ecosystems in China. Design/methodology/approach The open data of listed companies regarding the introduction of emerging topics on the transformation tendency of BMs in the post-COVID-19 business world is qualitatively analysed. The theoretical foundation is based on a critical review of the literature. Findings Three aspects of the co-evolution between knowledge management and business model transformation are introduced. These three aspects are as follows: knowledge integration helps with multi-system business integration and decision-making collaborations; knowledge sharing helps to enhance cognitive ability and network value based on businesses; and the creation of new knowledge helps enrich the knowledge base and promote the transformation of BMs. Research limitations/implications Solely attributing a firm’s ability to cope with environmental uncertainties to its business model weakens the importance of its knowledge management. This study argues that the co-evolution between knowledge management and business model transformation also plays a key role in a firm’s response to issues post-COVID-19. Originality/value This study calls for the development of a normative theory of co-evolution between knowledge management and business model transformation, implying uncharted territories of knowledge management based on interaction with business model designs in e-business ecosystems.

Many retailers invest in artificial intelligence (AI) to improve operational efficiency or enhance customer experience. However, AI often disrupts employees’ ways of working causing them to resist change, thus threatening the successful embedding and sustained usage of the technology. Using a longitudinal, multi-site ethnographic approach combining 74 stakeholder interviews and 14 on-site retail observations over a 5-year period, this article examines how employees’ practices change when retailers invest in AI. Practice co-evolution is identified as the process that undergirds successful AI integration and enables retail employees’ sustained usage of AI. Unlike product or practice diffusion, which may be organic or fortuitous, practice co-evolution is an orchestrated, collaborative process in which a practice is co-envisioned, co-adapted, and co-(re)aligned. To be sustained, practice co-evolution must be recursive and enabled via intentional knowledge transfers. This empirically-derived recursive phasic model provides a roadmap for successful retail AI embedding, and fruitful future research avenues.

As a foundation of large language models, fine-tuning drives rapid progress, broad applicability, and profound impacts on human–AI collaboration, surpassing earlier technological advancements. This paper provides a comprehensive overview of large language model (LLM) fine-tuning by integrating hermeneutic theories of human comprehension, with a focus on the essential cognitive conditions that underpin this process. Drawing on Gadamer’s concepts of Vorverständnis, Distanciation, and the Hermeneutic Circle, the paper explores how LLM fine-tuning evolves from initial learning to deeper comprehension, ultimately advancing toward self-awareness. It examines the core principles, development, and applications of fine-tuning techniques, emphasizing its growing significance across diverse field and industries. The paper introduces a new term, “Tutorial Fine-Tuning (TFT)”, which annotates a process of intensive tuition given by a “tutor” to a small number of “students”, to define the latest round of LLM fine-tuning advancements. By addressing key challenges associated with fine-tuning, including ensuring adaptability, precision, credibility and reliability, this paper explores potential future directions for the co-evolution of humans and AI. By bridging theoretical perspectives with practical implications, this work provides valuable insights into the ongoing development of LLMs, emphasizing their potential to achieve higher levels of cognitive and operational intelligence.

In this paper, we use an agent-based simulation model to investigate how coordinated exploration by multiple specialists, as in new product development, is different from individual search. We find that coordinated exploration is subject to two pathologies not present in unitary search: mutual confusion and joint myopia. In joint search, feedback to one agent's actions is confounded by the actions of the other agent. Search therefore leads to increasing mutual confusion because agents are unable to learn from feedback to correct their faulty mental models of the search space. Incorrect beliefs held by one agent lead to mistakes, and because it is unclear which agent was wrong, this confuses the other agent, either into revising (correct) beliefs or holding on to (incorrect) beliefs. Sharing knowledge aligns specialists' mental models and counters mutual confusion by inducing coordination around particular search regions. Yet that very effort increases joint myopia, as agents prematurely reinforce each other into choosing from an increasingly narrow portion of the search space. In the extreme, high levels of shared knowledge induce agents to abandon their distinct search approach in favor of a lower common denominator. In coordinated exploration, increasing coordination efforts (such as by increasing communication) reduces mutual confusion but simultaneously increases joint myopia. Efforts to reduce joint myopia, such as by slow learning or lower levels of knowledge transfer, however, automatically increase mutual confusion. As modeled in our simulation, successful joint search needs to balance these two effects. Our results suggest that because unitary-searcher models abstract from epistemic interdependence, their predictions are potentially misleading for coordinated exploration.

Purpose How does the interaction between time and knowledge affect the evolution of organizations? Past research in organizational evolution has mostly investigated time and knowledge as two separate variables. In contrast, theoretical perspectives integrating these variables are still seemingly scant. The authors believe that filling this literature gap needs attention. Thus, this study aims to contribute by developing a conceptual framework. Design/methodology/approach This is a conceptual study. The framework is centred on the concept of “co-evolutionary time”, which the authors explain through a business example from the tourism industry. Supported by a narrative-based style, from a methodological point of view the framework is featured by the attempt to synthesize specific, extant literature into new theoretical development. Findings As its main theoretical contribution, the co-evolutionary time suggests how firms can adapt in a way that, from an evolutionary perspective, proves fitting both in terms of contents and methods, thus opening possibilities for new long-term social construction and reconstruction. As its main practical contribution, co-evolutionary time can constitute not only a temporary source of organizational success and competitive advantage but also an agent of enduring change and long-term business survival. Originality/value As its main novelty, the framework is developed through merging two literature streams. In particular, the authors first consider the literature about time, with a focus on its objective and subjective dimensions. The authors then consider the literature about organizational evolution, with a focus on the co-evolutionary nature of the firm/environment relationship.

While sustainable design practice is working to reduce the ecological impacts of development, many of the earth’s already damaged life support systems require repair and regeneration. Regenerative design theory embraces this challenge using an ecological worldview that recognizes all life as intertwined and interdependent to deliver restorative outcomes that heal. Central to regenerative design theory is the mutually beneficial and coevolving ‘stewardship’ relationship between community and place, the success of which requires local ecological knowledge. However, there is a lack of understanding about how—within the design process—practitioners are integrating ‘innate knowledge’ of place held by local people. This rapid practice review sought to collate and evaluate current ‘regenerative design practice’ methods towards ensuring good practice in the integration of place-based ecological knowledge. A comprehensive online search retrieved 345 related articles from the grey literature, academic book chapters, and government reports, from which 83 articles were analyzed. The authors conclude that regenerative design practice is emergent, with the design practice of including community knowledge of ecological systems of place remaining ad hoc, highly variable, and champion-based. The findings have immediate implications for regenerative design practitioners, researchers, and developers, documenting the state of progress in methods that explore innate ecological knowledge and foster co-evolving ecological stewardship.

Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to simulate co-evolution in a complex social network. A case study is proposed for the modelling of contractual relationships between road freight transport companies. The model employs empirical data from a survey of transport companies located in the Basque Country (Spain) and utilises the DBSCAN community detection algorithm to simulate the effect of cluster size in the network. Additionally, a local spatial association indicator is employed to identify potentially favourable environments. The model enables the evolution of the network, leading to more complex collaborative structures. By means of iterative simulations, the study demonstrates how collaborative networks self-organise by distributing activity and knowledge and evolving into complex polarised systems. Furthermore, the simulations with different minimum cluster sizes indicate that clusters benefit the agents that are part of them, although they are not a determining factor in the network participation of other non-clustered agents.