Leveraging LLMs for predictive insights in food policy and behavioral interventions
Abstract Food consumption and production significantly contribute to global greenhouse gas emissions, making them key targets for climate change mitigation. Over the past two decades, food policy initiatives have focused on reshaping production and consumption patterns by reducing food waste and cur...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Food |
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| Online Access: | https://doi.org/10.1007/s44187-025-00552-x |
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| _version_ | 1849333215509086208 |
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| author | Micha Kaiser Paul M. Lohmann Peter Ochieng Billy Shi Cass R. Sunstein Lucia A. Reisch |
| author_facet | Micha Kaiser Paul M. Lohmann Peter Ochieng Billy Shi Cass R. Sunstein Lucia A. Reisch |
| author_sort | Micha Kaiser |
| collection | DOAJ |
| description | Abstract Food consumption and production significantly contribute to global greenhouse gas emissions, making them key targets for climate change mitigation. Over the past two decades, food policy initiatives have focused on reshaping production and consumption patterns by reducing food waste and curbing ruminant meat consumption. While evidence on effective interventions is improving, assessing appropriate and context-specific policies remains difficult due to external validity concerns. This paper demonstrates that a fine-tuned large language model (LLM) can accurately predict outcome directions in approximately 80% of empirical studies evaluating dietary interventions. Predictive accuracy improves with richer input detail, peaking at around 75 prompts before declining due to overfitting or saturation. To contextualize these results, we benchmark the LLM against both classical random-effects meta-regression and a prompt-based variant executed entirely within the model. Although traditional approaches yield reasonable magnitude estimates, they lag behind LLMs in directional accuracy and adaptability to diverse intervention formats. Together, our findings suggest that LLMs-especially when fine-tuned on curated evidence-offer a scalable pathway for data-driven, context-sensitive food policy modeling. |
| format | Article |
| id | doaj-art-74eac22d44254ce18b6fa040cbab3b4c |
| institution | Kabale University |
| issn | 2731-4286 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Food |
| spelling | doaj-art-74eac22d44254ce18b6fa040cbab3b4c2025-08-20T03:45:56ZengSpringerDiscover Food2731-42862025-08-015112510.1007/s44187-025-00552-xLeveraging LLMs for predictive insights in food policy and behavioral interventionsMicha Kaiser0Paul M. Lohmann1Peter Ochieng2Billy Shi3Cass R. Sunstein4Lucia A. Reisch5El-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of CambridgeEl-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of CambridgeYNOT Institute, Queens’ College, Cambridge, University of CambridgeYNOT Institute, Queens’ College, Cambridge, University of CambridgeHarvard Law School, Harvard UniversityEl-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of CambridgeAbstract Food consumption and production significantly contribute to global greenhouse gas emissions, making them key targets for climate change mitigation. Over the past two decades, food policy initiatives have focused on reshaping production and consumption patterns by reducing food waste and curbing ruminant meat consumption. While evidence on effective interventions is improving, assessing appropriate and context-specific policies remains difficult due to external validity concerns. This paper demonstrates that a fine-tuned large language model (LLM) can accurately predict outcome directions in approximately 80% of empirical studies evaluating dietary interventions. Predictive accuracy improves with richer input detail, peaking at around 75 prompts before declining due to overfitting or saturation. To contextualize these results, we benchmark the LLM against both classical random-effects meta-regression and a prompt-based variant executed entirely within the model. Although traditional approaches yield reasonable magnitude estimates, they lag behind LLMs in directional accuracy and adaptability to diverse intervention formats. Together, our findings suggest that LLMs-especially when fine-tuned on curated evidence-offer a scalable pathway for data-driven, context-sensitive food policy modeling.https://doi.org/10.1007/s44187-025-00552-xBehavioral public policyFood policyFood wasteLarge language models |
| spellingShingle | Micha Kaiser Paul M. Lohmann Peter Ochieng Billy Shi Cass R. Sunstein Lucia A. Reisch Leveraging LLMs for predictive insights in food policy and behavioral interventions Discover Food Behavioral public policy Food policy Food waste Large language models |
| title | Leveraging LLMs for predictive insights in food policy and behavioral interventions |
| title_full | Leveraging LLMs for predictive insights in food policy and behavioral interventions |
| title_fullStr | Leveraging LLMs for predictive insights in food policy and behavioral interventions |
| title_full_unstemmed | Leveraging LLMs for predictive insights in food policy and behavioral interventions |
| title_short | Leveraging LLMs for predictive insights in food policy and behavioral interventions |
| title_sort | leveraging llms for predictive insights in food policy and behavioral interventions |
| topic | Behavioral public policy Food policy Food waste Large language models |
| url | https://doi.org/10.1007/s44187-025-00552-x |
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