Intelligent Economics: Interdisciplinary AI Research for Society and Sustainability

Dear Colleagues,

We are pleased to invite submissions to this Special Issue of AI, which explores the transformative potential of artificial intelligence when developed in deep dialogue with economic science. As AI systems increasingly shape decisions that affect livelihoods, markets, and public policy, there is a pressing need for research that bridges the methodological richness of economics with the technical capabilities of machine learning. This Special Issue seeks to cultivate precisely such an interdisciplinary space.

Rationale and Vision

The field of intelligent economics sits at the confluence of multiple disciplines: computational economics, econometrics, machine learning, behavioural science, and public policy. While economics offers rigorous frameworks for causal inference, strategic interaction, and welfare analysis, AI provides powerful tools for pattern recognition, prediction, and automated decision-making under complexity. When these strengths are combined deliberately, they can yield research that is not only intellectually innovative but also genuinely beneficial to society.

This Special Issue is motivated by a conviction that the next generation of AI research must be judged not merely by predictive accuracy or computational efficiency, but by its contribution to human welfare. We therefore encourage contributions that embed normative economic reasoning, whether welfarist, capabilities-based, or rights-based, into the design and evaluation of intelligent systems.

Interdisciplinary Scope and Topics of Interest

We welcome original research articles, reviews, and communications that advance the intersection of AI and economics. Topics of particular interest include, but are not limited to:

• AI for Development and Poverty Reduction: Machine learning applications in development economics, including satellite-based poverty mapping, mobile money analytics, precision agriculture, and automated targeting of social protection programmes.
• Causal Machine Learning for Policy Evaluation: Methodological advances at the intersection of causal inference and ML, including heterogeneous treatment effect estimation, synthetic control methods with high-dimensional data, and robust policy learning from observational studies.
• Mechanism Design and Market Algorithms: AI-powered market design for resource allocation, including matching markets, auction design, pricing algorithms, and the economics of recommender systems and digital platforms.
• Behavioural and Agent-Based Economics: Computational models of economic behaviour, multi-agent reinforcement learning for studying market dynamics, and AI systems that account for bounded rationality, social preferences, and distributional concerns.
• Environmental Economics and Climate AI: AI methods for environmental monitoring, carbon pricing optimisation, climate risk assessment, natural resource management, and the economics of the energy transition.
• Labour Economics and the Future of Work: AI-driven analysis of labour market dynamics, skill-biased technological change, gig economy platforms, and the design of adaptive social safety nets.
• Fairness, Equity, and Economic Justice in AI: Theoretical and empirical investigations of algorithmic fairness from an economic perspective, including distributional impacts of automated decisions, fairness–accuracy trade-offs, and the design of equitable AI systems.
• Computational Macroeconomics and Financial Stability: AI approaches to macroeconomic forecasting, systemic risk monitoring, central bank digital currencies, and the modelling of complex economic networks.

Contribution to the Sustainable Development Goals

Papers submitted to this Special Issue should articulate a clear connection to one or more of the United Nations Sustainable Development Goals (SDGs). We warmly encourage contributions spanning the full breadth of the 2030 Agenda, from foundational human needs to economic transformation, environmental sustainability, and institutional resilience. Research that demonstrates pathways from methodological innovation to measurable progress on any of the following goals is of interest.

SDG 1 No Poverty: AI-enhanced targeting and delivery of anti-poverty interventions; predictive analytics for social protection; satellite-based poverty mapping; and mobile money and financial inclusion.

SDG 2 Zero Hunger: Precision agriculture and crop yield forecasting; AI for food supply chain optimisation; early warning systems for food insecurity; and sustainable land use modelling.

SDG 3 Good Health and Well-being: Economic evaluation of AI in healthcare; predictive models for disease burden and resource allocation; and health insurance and AI-driven diagnostics in low-resource settings.

SDG 4 Quality Education: AI for personalised learning and educational outcomes; economic analysis of EdTech; and skill gap assessment and adaptive training systems.

SDG 5 Gender Equality: Detecting and mitigating gender bias in economic algorithms; AI for women's economic empowerment; and analysing labour market discrimination with machine learning.

SDG 6 Clean Water and Sanitation: AI for water resource management and demand forecasting; economic optimisation of utility infrastructure; and leak detection and allocation efficiency.

SDG 7 Affordable and Clean Energy: Smart grid optimisation; AI for energy demand forecasting; economic modelling of renewable energy adoption; and algorithmic energy trading.

SDG 8 Decent Work and Economic Growth: Labour market impacts of automation; AI and productivity measurement; gig economy platform analytics; and designing adaptive social safety nets.

SDG 9 Industry, Innovation and Infrastructure: AI for resilient infrastructure planning; digital industrial transformation; innovation policy evaluation with big data; and transport and logistics optimisation.

SDG 10 Reduced Inequalities: Designing AI systems that reduce economic disparities; distributional analysis of algorithmic decisions; and equitable access to digital technologies.

SDG 11 Sustainable Cities and Communities: Urban economics and smart city analytics; AI for housing market regulation; sustainable urban mobility; and congestion and emissions pricing.

SDG 12 Responsible Consumption and Production: AI for circular economy and waste reduction; sustainable supply chain analytics; and demand-side efficiency and behavioural nudges.

SDG 13 Climate Action: Machine learning for climate mitigation and adaptation; carbon pricing optimisation; climate risk assessment; and natural capital accounting.

SDG 14 Life Below Water: AI for marine resource economics; fisheries management and sustainable aquaculture; and ocean ecosystem valuation.

SDG 15 Life on Land: AI for biodiversity economics; deforestation monitoring and land-use policy; and natural resource conservation and payment for ecosystem services.

SDG 16 Peace, Justice and Strong Institutions: AI for transparent public financial management; anti-corruption analytics; economic governance and institutional design; and algorithmic accountability frameworks.

SDG 17 Partnerships for the Goals: Cross-sector AI collaborations; public–private data partnerships; international cooperation on AI standards and governance; and capacity building for AI in developing economies.

Open Science and Reproducibility

In keeping with the spirit of open science and the policies of AI, all submissions to this Special Issue are strongly encouraged to include open access to data and code. Authors should deposit their datasets, analysis scripts, and computational notebooks in reputable public repositories (e.g., Zenodo, Figshare, GitHub, Hugging Face, or domain-specific archives) and provide clear documentation enabling full reproducibility.

We particularly welcome submissions that provide comprehensive supplementary materials, including:

• Complete replication packages with version-controlled code;
• Well-documented datasets, including data dictionaries and provenance records;
• Pre-trained models and inference pipelines where applicable;
• Transparent reporting of model limitations, failure modes, and uncertainty quantification.

Papers that advance open-source tools, benchmarks, or methodological frameworks with broad applicability across the intelligent economics community are especially encouraged.

Conference Collaborations

This Special Issue actively seeks to bridge the gap between conference proceedings and archival journal publication by establishing collaborations with leading conferences across the disciplinary boundaries of AI and economics. We believe that the most impactful interdisciplinary research often emerges from vibrant conference communities, and we are committed to providing a natural pathway for exceptional work to reach a broader, permanent audience.

We are pleased to announce an existing collaboration with the

Asian Conference on Artificial Intelligence Technology (ACAIT 2026)

https://2026.acaitconf.com/

Authors of outstanding papers presented at ACAIT 2026 and related tracks are warmly invited to submit extended, thoroughly revised versions of their work to this Special Issue. Submissions originating from conference presentations should include at least 50% new material, with substantial additions in theoretical depth, empirical analysis, or methodological rigour beyond the original conference version.

Beyond our collaboration with ACAIT 2026, we are actively seeking partnerships with conferences in related fields—including, but not limited to, computational economics, machine learning, econometrics, operations research, development studies, environmental economics, and public policy. Conference organisers and programme committees interested in establishing a formal collaboration with this Special Issue are encouraged to contact the Guest Editors. We are happy to discuss arrangements for fast-track review, dedicated submission windows, or co-branded calls for papers that align with the interdisciplinary spirit of this Special Issue.

Prof. Dr. Lin William Cong
Dr. Luyao Zhang
Prof. Dr. Yunquan Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence
• economics
• sustainable development goals
• interdisciplinary research
• machine learning
• causal inference
• algorithmic fairness
• open science
• computational economics
• policy evaluation
• human welfare
• open data
• reproducibility
• conference proceedings
• Asian conference on artificial intelligence technology
• ACAIT