Abstract. Understanding coupled World-Earth system dynamics is central to navigating the Anthropocene, yet global social and environmental systems are still predominantly modelled separately. Bidirectionally coupled models of global scale that represent both subsystems as endogenous components are increasingly developed, but the field remains fragmented across disciplines, modelling traditions, and epistemological assumptions. This study presents a systematic review of global social-environmental modelling approaches, drawing on 314 peer-reviewed studies identified through a comprehensive database search and the usage of a newly developed, Large-Language Model assisted approach. We propose a typology of three model groups – Goal-Oriented models, Structural Feedback models, and Dynamic Co-evolutionary approaches – that can help to navigate the diverse landscape of this research field and assess them against the design principles for World-Earth Models proposed by Donges/Heitzig et al. (2020). Our analysis shows characteristic strengths and limitations tied to each typical model group. Across all, socio-cultural processes beyond economic optimization, diverse bidirectional feedback, endogenous tipping dynamics, and cross-scale interactions remain systematically underrepresented. A collaboration analysis further confirms the conceptional difference between the proposed groups and highlights the strong fragmentation of the field along the model groups. These findings suggest that future modelling efforts towards a better understanding of Anthropocene dynamics and World-Earth System resilience should prioritize broader actor representation, more diverse feedback structures, and explicit coupling of social and biophysical tipping processes, ideally through hybrid modelling formats – while remaining grounded in the specific research question rather than aiming for universal coverage.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026