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With research staff from more than 70 countries, and offices across the globe, IFPRI provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries.

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Kinya Kaibung’a

Kinya Kaibung’a is a Research Officer with the Development Strategies and Governance Unit, based in Nairobi, Kenya. She has a keen interest in leveraging machine learning, AI, and other cutting-edge technologies to boost climate resilience and food security in smart agriculture systems.

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Since 1975, IFPRI’s research has been informing policies and development programs to improve food security, nutrition, and livelihoods around the world.

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Foresight and Policy Modeling

James Thurlow

Director, Foresight and Policy Modeling (FPM)

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James Thurlow is Director of the Foresight and Policy Modeling Unit, where he oversees the institute’s research program on the major drivers of inclusive agricultural transformation and sustainable development. James’ own research uses economywide models to study the interactions between policies and agrifood systems; structural, climate, and demographic change; and livelihoods, poverty, and food security. He has worked with researchers and governments across Africa and Asia to strengthen foresight capabilities and enhance the coherence of development strategies.

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Publications

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Working Paper

Applying foresight analysis in national agrifood system investment planning

2026Lapidus, Daniel; Rose, Amanda; Aragie, Emerta A.; Jones, Eleanor; Pauw, Karl; Thurlow, James; Cox, Micaela; Vasquez-Caballero, Smit
Details

Applying foresight analysis in national agrifood system investment planning

National governments, particularly in low- and middle-income countries, face considerable complexity in long- and midrange agrifood system development planning. These processes must translate political priorities and commitments into credible investment strategies, while coordinating across ministries, engaging diverse stakeholders, and mobilizing domestic and external financing. At the same time, planners must contend with deep uncertainty around climate change, technological change, market dynamics, and demographic and nutrition transitions, often under conditions of constrained analytical capacity and limited resources.

Year published

2026

Authors

Lapidus, Daniel; Rose, Amanda; Aragie, Emerta A.; Jones, Eleanor; Pauw, Karl; Thurlow, James; Cox, Micaela; Vasquez-Caballero, Smit

Citation

Lapidus, Daniel; Rose, Amanda; Aragie, Emerta A.; Jones, Eleanor; Pauw, Karl; et al. 2026. Applying foresight analysis in national agrifood system investment planning. IFPRI Modeling Systems Technical Paper 6. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/184056

Keywords

Agrifood Systems; Modelling; Investment; Foresight

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Record type

Working Paper

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Working Paper

An agentic AI assistant for country-level economic modeling: Methods, data, and expert evaluation

2026Mukashov, Askar; Kim, Soonho; Fang, Peixun; Diao, Xinshen; Thurlow, James; Proctor, Joshua; Rennison, Alan
Details

An agentic AI assistant for country-level economic modeling: Methods, data, and expert evaluation

The demand for high-quality, rapid economic analysis to navigate complex issues faced by many low- and middle-income countries has led to the development of detailed structural simulation models, such as Computable General Equilibrium (CGE) models. Policy analysis with such models requires deep knowledge of their structure and applicability to the policy issues at hand. Policymakers in these settings often lack access to the expertise required for articulating, analyzing, and interpreting the relevant causal chains captured by the models. Attempting to circumvent these barriers by submitting complex economic questions directly to off-the-shelf large language models (LLMs) introduces severe analytical risks, including hallucinations and insufficient expert guidance. To resolve this limitation, we developed and empirically evaluated an agentic AI assistant called RIAPA-AI that integrates LLMs with a CGE model. We evaluated the performance of RIAPA-AI against expert human CGE modelers and a general-purpose plain LLM baseline across samples of complex economic scenarios, utilizing an independent panel of senior economists to grade the outputs. Our statistical analysis reveals no statistically significant difference in analytical accuracy between RIAPA-AI and human experts, while the AI accelerates reproducible policy analysis from weeks to minutes. Furthermore, by operating without manual processing limits, RIAPA-AI eliminates the 6.7% error rate observed among human modelers. Conversely, the general-purpose plain LLM exhibits profound failure rates, failing to achieve policy-ready scores in over 60% of depth evaluations. Without an underlying CGE model acting as a bounding force to reflect economic structural constraints, the general-purpose plain LLM defaults to linear economic assumptions and inserts unmodeled socio-political narratives. Crucially, by explicitly restricting the AI’s narrative interpretation solely to deterministic numerical outputs, RIAPA-AI mitigates the risk of unverified assumptions and logic hallucinations. We conclude that by deploying an agentic AI assistant that layers a generative AI over a formal CGE model, RIAPA-AI successfully delivers sensible, rigorous, and rapid policy analysis.

Year published

2026

Authors

Mukashov, Askar; Kim, Soonho; Fang, Peixun; Diao, Xinshen; Thurlow, James; Proctor, Joshua; Rennison, Alan

Citation

Mukashov, Askar; Kim, Soonho; Diao, Xinshen; Thurlow, James; Proctor, Joshua; and Rennison, Alan. 2026. An agentic AI assistant for country-level economic modeling: Methods, data, and expert evaluation. IFPRI Discussion Paper 2424. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/183521

Keywords

Artificial Intelligence; Machine Learning; Modelling; Computable General Equilibrium Models; Large Language Models; Agent-based Models; Econometric Models

Language

English

Access/Licence

Open Access

Source

Source record

Record type

Working Paper

Resource thumbnail

Conference Paper

Key agricultural supply risks for global poverty and undernourishment: A systematic risk profiling approach

2026Mukashov, Askar; Thierfelder, Karen; Robinson, Sherman; Thurlow, James
Details

Key agricultural supply risks for global poverty and undernourishment: A systematic risk profiling approach

The intersection of agricultural production shocks—ranging from climate-induced yield failures to conflicts and policy responses—can trigger systemic volatility similar to the 2008 agrifood crisis. Standard Computable General Equilibrium (CGE) assessments typically rely on discrete deterministic counterfactuals that fail to capture the complex, correlated nature of real-world risk factors. This paper addresses a critical gap: How does the complex landscape of agricultural shocks shape global poverty and undernourishment? We propose a framework to move from isolated scenario analysis to a probabilistic “risk map” of global poverty using the Systematic Risk Profiling (SRP) method (Mukashov et al., 2026). Our approach relies on three pillars. Pillar 1: Monte Carlo Simulations. Utilizing historical data, we generate 10,000 large-scale Monte Carlo simulation scenarios of global agricultural production shocks across key global agricultural producers and vulnerable Global South regions. Pillar 2: Microsimulation Integration. We link the global CGE model with continuous household poverty and undernourishment microsimulation modules to dynamically update regional estimates. Pillar 3: Machine Learning for Risk Attribution. We employ Random Forest regression (Permutation Importance) to estimate the relative importance of sampled agricultural shocks on household welfare outcomes worldwide.

Year published

2026

Authors

Mukashov, Askar; Thierfelder, Karen; Robinson, Sherman; Thurlow, James

Citation

Mukashov, Askar; Thierfelder, Karen; Robinson, Sherman; and Thurlow, James. 2026. Key agricultural supply risks for global poverty and undernourishment: A systematic risk profiling approach. Presented during the 29th Annual Conference on Global Economic Analysis in Kyoto, Japan June 2026. https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=7902

Keywords

Modelling; Supply; Poverty; Undernourishment; Computable General Equilibrium Models; Food Security; Risk

Language

English

Access/Licence

Open Access

Source

Source record

Project

Foresight

Record type

Conference Paper

Events

Global Agrifood Systems Outlook to 2050: Informing Decisions for an Uncertain Future
IFPRI Policy Seminar

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September 10, 2026, 9:30 – 11:00 am

Global Agrifood Systems Outlook to 2050: Informing Decisions for an Uncertain Future

Also streaming on Please type your questions into the chat box with name, affiliation, and country. The event video, presenter slides, and podcast will be available in the days following the event. Around the world, agrifood systems are changing rapidly as populations grow, incomes rise, technological innovations emerge, and the climate shifts. But how will […]

Africa Food Systems Forum (AFSF) 2026
IFPRI at External Event

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August 31 to September 4, 2026

Africa Food Systems Forum (AFSF) 2026

The Africa Food Systems Forum (AFSF) will host its 20th Annual Summit in Kigali, Rwanda from 31 August to 4 September 2026, under the theme “Investing in Africa’s Agri-Food Systems: Nourishing Nations, Growing Jobs, Building Resilience.” The Summit will bring together stakeholders from across the agri-food sector for a programme of plenary sessions, technical discussions, […]

IFPRI @ 29th Annual Conference on Global Economic Analysis
IFPRI at External Event

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June 17 to 19, 2026

IFPRI @ 29th Annual Conference on Global Economic Analysis

The 29th Annual Conference on Global Economic Analysis will promote the exchange of ideas among economists conducting quantitative analysis of global economic issues. The conference will feature specially organized and contributed paper breakout sessions. The organized sessions have been developed by members of the GTAP Network to highlight a specific area of research and/or policy […]

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News

June 23, 2026 | In the News

Iran-US war in numbers (The Independent)

The Independent reports on the global economic fallout from the ongoing US‑Israel war on Iran, highlighting significant implications for food security and poverty, with reference to IFPRI analysis: “The International Food Policy Research Institute (IFPRI) notes how higher fuel and fertiliser prices will dent the national GDP of reliant countries, in particular Vietnam (-0.8%), Mozambique […]

February 09, 2026 | Press Release

New IFPRI study introduces breakthrough framework to assess economic risks in developing countries

A groundbreaking approach to understanding how developing economies are affected by multiple, simultaneous shocks.

June 23, 2025 | Press Release

New study proposes smarter cropland allocation to support sustainable agricultural intensification in Senegal

The study explores how farmers’ land-use decisions can be guided by the relative risks and returns of different crops—similar to how financial investments are managed.

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