Aims and scope

Journal of Intelligent Socio-Ecological Systems (JISES) aims to advance interdisciplinary research on complex systems in which environmental, social, technological, economic, and organizational processes interact. The journal provides an international platform for theoretical, methodological, computational, and applied research combining intelligent and data-driven methods with systems thinking to address contemporary sustainability and socio-ecological challenges.

The journal bridges traditionally separated research domains by integrating artificial intelligence, computational intelligence, data science, decision science, operations research, optimization, modelling, and simulation with environmental science, sustainability, management, economics, engineering, and the social sciences. Particular emphasis is placed on research that advances the understanding, prediction, assessment, optimization, governance, resilience, adaptation, and transformation of interconnected systems.

A central aim of JISES is to foster methodological innovation for the analysis and management of complex and uncertain environments. The journal welcomes the development, integration, and rigorous application of artificial intelligence, machine learning, computational intelligence, optimization, multi-criteria decision analysis, simulation, system dynamics, agent-based modelling, spatial analytics, statistical methods, and other advanced analytical approaches.

The journal equally encourages domain-driven research in which methodological advances address substantive environmental, social, technological, organizational, and sustainability challenges. Relevant areas include climate change, energy transitions, natural resource management, biodiversity, circular economy, sustainable agriculture and food systems, smart and sustainable cities, resilient infrastructure, environmental governance, sustainable operations and supply chains, organizational resilience, technology adoption, digital transformation, entrepreneurship, and innovation ecosystems.

A distinguishing feature of JISES is its integration of intelligent methodologies with systems-oriented perspectives. Rather than treating technological, environmental, social, and managerial phenomena as isolated domains, the journal encourages research that examines their interactions, dependencies, feedback mechanisms, uncertainties, trade-offs, and implications for sustainable and resilient development.

Core Areas of Scope

The journal welcomes submissions within, but not limited to, the following interconnected areas:

AI, Data Science and Intelligent Systems

Artificial intelligence; machine learning; deep learning; computational intelligence; explainable and responsible AI; soft computing; fuzzy systems; data science; big data analytics; predictive and prescriptive analytics; intelligent decision support systems; knowledge-based and expert systems; digital twins; hybrid intelligence; and AI-enabled sustainability analytics.

Modeling, Simulation and Optimization

Agent-based modelling; system dynamics; complex systems modelling and simulation; multi-criteria decision analysis and decision-making (MCDA/MCDM); mathematical and computational optimization; evolutionary computation; metaheuristic algorithms; operations research; spatial analytics; geographic information systems (GIS); multivariate and statistical analysis; scenario analysis and forecasting; network and complexity analysis; uncertainty and sensitivity analysis; and robust and adaptive decision-making.

Sustainability, Climate and Natural Resource Systems

Climate change adaptation and mitigation; climate risk and resilience; circular economy; biodiversity and ecosystem conservation; ecological modelling; sustainable agriculture and food systems; water resource management; energy transitions; renewable and sustainable energy systems; pollution prevention and control; environmental impact and sustainability assessment; natural resource management; ecosystem services; land-use change; and sustainable production and consumption.

Smart, Resilient and Socio-Ecological Systems

Socio-ecological systems; coupled human–environment systems; human–environment interactions; socio-ecological resilience and adaptation; smart and sustainable cities; smart communities; resilient infrastructure; urban sustainability; environmental policy and governance; public policy analytics; socio-technical transitions; digital sociology; human–computer interaction; technology-enabled environmental management; systemic risk; adaptive systems; and resilience modelling.

Organizational Systems, Management and Innovation Ecosystems

Sustainable operations and supply chains; sustainable enterprise management; environmental and sustainability management; technology and innovation management; technology adoption and diffusion; digital transformation; sustainable entrepreneurship; innovation ecosystems; organizational resilience; sustainability-oriented decision-making; green and circular business models; sustainable business models; and organizational and socio-technical transformation.

Interdisciplinary and Methodological Orientation

JISES particularly encourages research that connects methodological innovation with substantive environmental, social, technological, managerial, or policy challenges. Contributions that bridge two or more disciplinary or application domains within the journal's scope are especially welcome.

Recognizing the complexity of sustainability and socio-ecological challenges, the journal welcomes methodological pluralism. Contributions may employ quantitative, computational, mathematical, simulation-based, empirical, spatial, experimental, or mixed-method research designs where these approaches provide rigorous and meaningful scientific insights.

Methodological papers should introduce a novel method, substantially advance an existing approach, or integrate established methodologies in a scientifically meaningful way. Appropriate theoretical analysis, benchmarking, validation, experimentation, or application should be provided to demonstrate the contribution.

Applied studies should provide scientific insight beyond the routine implementation of established methods. The application of an existing algorithm, model, or decision-making technique to a new dataset or case study, without sufficient methodological, theoretical, empirical, or domain-specific contribution, is generally not sufficient for publication.

Research Relevance

Submissions to JISES should demonstrate a meaningful connection to one or more of the journal's principal dimensions:

  • intelligent, computational, analytical, or decision-support methodologies;
  • complex, interconnected, or adaptive systems;
  • sustainability and environmental challenges;
  • socio-ecological and human–environment interactions;
  • resilient and smart systems;
  • organizational, managerial, or innovation systems related to sustainable transformation.

Research does not need to explicitly use the term “socio-ecological system” to fall within the journal's scope. However, manuscripts should demonstrate a clear connection to the journal's broader mission of advancing intelligent and systems-oriented approaches to complex environmental, social, technological, organizational, or sustainability challenges.

Out of Scope

To maintain a coherent scientific identity, JISES generally does not consider manuscripts that:

  • present purely technical AI, machine learning, optimization, or computational developments without a meaningful connection to complex systems, sustainability, environmental, social, organizational, or decision-making problems;
  • address environmental or sustainability topics descriptively without an adequate analytical, methodological, empirical, or systems-oriented contribution;
  • apply established methods mechanically to a new case or dataset without sufficient scientific novelty or substantive new insight;
  • focus exclusively on conventional business or management problems without a meaningful connection to sustainability, resilience, innovation, technological transformation, or interconnected systems;
  • present purely local case descriptions with limited methodological, theoretical, or transferable scientific value.