方向一：大语言模型与多主体建模——跨学科集体协作研究

Direction 1: LLM-Enabled Agent-Based Modelling — Transdisciplinary Collective Collaboration

研究内容：围绕科学协作、集体协作行为与知识整合机制，探索大语言模型（LLM）与多主体建模（ABM）的结合，研究不同知识背景、认知方式和决策规则的主体如何互动，以及协作、知识整合和协同创新如何形成与演化。

Research focus: This project investigates scientific collaboration, collective cooperative behaviour, and knowledge integration by combining large language models (LLMs) with agent-based modelling (ABM). It examines interactions among actors with different knowledge backgrounds, cognitive approaches, and decision rules, and how collaboration, integration, and collective innovation emerge and evolve.

适合的申请者：

Suitable applicants:

• 对科学协作行为、集体行动、社会互动、知识整合及复杂社会系统的动态演化感兴趣；

Interested in scientific collaboration, collective action, social interaction, knowledge integration, and the dynamics of complex social systems;

• 欢迎具有社会学、公共管理、组织科学、心理学、计算社会科学、人工智能、复杂系统或其他相关背景的学生；

Students from sociology, public administration, organisation studies, psychology, computational social science, AI, complex systems, and related fields are welcome;

• 对将社会行为理论与计算模型相结合，研究个体决策、群体互动及集体协作机制感兴趣；

Interested in integrating theories of social behaviour with computational models to investigate individual decisions, group interactions, and mechanisms of collective collaboration;

• 具有多主体建模（ABM）、大语言模型与智能体（LLM-based Agents）、博弈论、复杂网络、计算认知建模或相关计算与定量研究基础者优先。

Prior experience in ABM, LLM-based agents, game theory, complex networks, computational cognitive modelling, or related computational and quantitative methods is an advantage.

方向二：可持续发展贡献与影响力评价——概念框架、指标体系与语义分析

Direction 2: Sustainability Contribution and Impact Assessment — Conceptual Frameworks, Metrics, and Semantic Analysis

研究内容：围绕可持续发展贡献与影响力的概念界定、理论框架和评价指标体系创新，研究如何重新认识、定义和测量不同学科、机构及社会行动的贡献。相关工作包括SDG研究资源图谱、知识关联识别、综合评价框架，以及定性与定量证据迭代整合的多维度测量。

Research focus: This research rethinks how sustainability contributions and impacts are conceptualised, defined, and measured across disciplines, institutions, and societal activities. It includes SDG research mapping, identifying connections across knowledge domains, developing assessment frameworks, and iteratively integrating qualitative and quantitative evidence into multidimensional measurement.

适合的申请者：

Suitable applicants:

• 对重新定义和构建可持续发展贡献与影响力的概念框架、测量体系及评价方法感兴趣，尤其欢迎希望发展现有评价范式的学生；

Interested in reconceptualising sustainability contribution and impact, and developing their conceptual frameworks, measurement systems, and evaluation methods, including reconsidering existing assessment paradigms;

• 欢迎具有可持续发展科学、统计学、测量科学、经济学、科学计量、信息科学、计算语言学、自然语言处理及相关学科背景的学生；

Students from sustainability science, statistics, measurement science, economics, scientometrics, information science, computational linguistics, NLP, and related fields are welcome;

• 对科研评价、可持续发展评价、综合指标体系、元分析、知识关联及跨学科与超学科研究评价感兴趣；

Interested in research evaluation, sustainability assessment, composite indicator systems, meta-analysis, knowledge relationships, and interdisciplinary or transdisciplinary research evaluation;

• 具有统计建模、测量模型、计算文本分析、语义建模、知识挖掘、主题识别、聚类分析或多准则综合评价等相关研究基础者优先；

Experience in statistical modelling, measurement models, computational text analysis, semantic modelling, knowledge mining, topic identification, clustering, or multi-criteria assessment is an advantage;

• 对融合多源、异构及不同尺度的定性与定量证据，通过迭代建模、交叉验证和方法整合，建立具有测量有效性、可解释性及跨情境可比性的综合评价体系感兴趣。

Interested in integrating qualitative and quantitative evidence from multiple heterogeneous sources and scales, using iterative modelling, cross-validation, and methodological integration to develop assessments with measurement validity, interpretability, and cross-context comparability.

方向三：可持续发展的本地化与全球南方治理研究

Direction 3: Localising Sustainability and Governance in the Global South

研究内容：围绕可持续发展的本地化实践、全球南方发展议题与多层级治理，开展深度定性研究及相关定量分析，探索社会文化情境、不同主体、知识体系和治理实践之间的关系，并发展跨情境的系统性分析方法。

Research focus: This research examines the localisation of sustainability, development challenges in the Global South, and multi-level governance. It combines in-depth qualitative inquiry with relevant quantitative analysis to examine relationships among sociocultural contexts, actors, knowledge systems, and governance practices, and to develop systematic approaches to cross-context comparison.

适合的申请者：

Suitable applicants:

• 对可持续发展本地化、全球南方发展议题、多层级治理及不同社会文化情境下的治理机制感兴趣；

Interested in sustainability localisation, development issues in the Global South, multi-level governance, and governance mechanisms across sociocultural contexts;

• 欢迎具有发展研究、政治学、社会学、人类学、公共管理、环境社会科学、地理学或相关背景的学生；

Students from development studies, political science, sociology, anthropology, public administration, environmental social science, geography, and related fields are welcome;

• 对深度定性研究与定量研究的结合，以及跨情境、跨群体、跨尺度的比较研究感兴趣；

Interested in integrating in-depth qualitative and quantitative research and in comparative analysis across contexts, groups, and scales;

• 具有比较研究、混合研究方法（Mixed Methods）、社会网络分析、多层级统计分析、空间分析、语义分析或相关定量研究基础者优先。

Experience in comparative research, mixed methods, social network analysis, multilevel statistical analysis, spatial analysis, semantic analysis, or related quantitative approaches is an advantage.

Direction 4: Philosophy of Science and Methodological Foundations of Knowledge Integration

研究内容：围绕跨学科与超学科研究中的知识整合，探讨不同知识体系的关系、知识有效性的判断依据，以及科学知识与实践知识如何整合。重点开展科学哲学、认识论、逻辑学及应用哲学视角下的概念分析、理论论证与方法论研究，并连接具体的可持续发展研究实践。

Research focus: This research examines the philosophical foundations of knowledge integration in interdisciplinary and transdisciplinary inquiry: relationships among knowledge systems, criteria for epistemic validity, and ways of integrating scientific and practice-based knowledge. It emphasises conceptual analysis, philosophical argumentation, and methodological inquiry grounded in philosophy of science, epistemology, logic, and applied philosophy, in dialogue with sustainability research practice.

适合的申请者：

Suitable applicants:

• 对知识如何形成、理解、表达与整合，以及不同知识体系之间的关系、知识有效性和跨学科知识整合的基本科学问题感兴趣；

Interested in fundamental questions about how knowledge is formed, understood, articulated, and integrated, relationships between knowledge systems, epistemic validity, and interdisciplinary knowledge integration;

• 欢迎具有科学哲学、认识论、逻辑学、应用哲学、认知科学、心理学、认知神经科学、脑科学、人工智能或相关学科背景的学生；

Students from philosophy of science, epistemology, logic, applied philosophy, cognitive science, psychology, cognitive neuroscience, brain science, AI, and related fields are welcome;

• 对将哲学问题与认知机制、计算模型或实证科学研究相结合，探索知识整合、意义形成与理解过程感兴趣；

Interested in connecting philosophical questions with cognitive mechanisms, computational models, or empirical research to explore knowledge integration, meaning-making, and understanding;

• 具有哲学概念分析、形式逻辑、形式认识论、认知实验、计算认知建模、心理测量或神经科学相关研究基础者优先；

Experience in philosophical conceptual analysis, formal logic, formal epistemology, cognitive experiments, computational cognitive modelling, psychometrics, or neuroscience is an advantage;

• 尤其欢迎对跨越哲学、认知与行为等不同分析层次，探索可检验的理论模型与知识整合有效性判据感兴趣的学生。

Particularly welcome are students interested in linking philosophical, cognitive, and behavioural levels of analysis to develop testable theoretical models and criteria for valid knowledge integration.