@inproceedings{yeo-etal-2026-tigris,
title = "{TIGRIS}: Integrating Policy Knowledge Graphs and Theory of Change for Institutionally Grounded Policy Impact Simulation",
author = "Yeo, Haein and
Kim, Junghyun and
Son, Beejin and
Han, Kyungsik",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1551/",
doi = "10.18653/2026.findings-emnlp.1551",
pages = "33008--33031",
ISBN = "979-8-89176-462-0",
abstract = "Anticipating the social impacts of a policy prior to implementation requires institutional consistency: multi-agent reasoning must remain aligned with the rules specified in policy documents. However, existing LLM-based multi-agent simulations struggle to maintain such fine-grained institutional rules when policy documents are provided only as prompt context. In this paper, we propose TIGRIS, a Theory-guided framework for Institutionally Grounded policy Impact Simulation. TIGRIS (1) constructs a structured policy knowledge graph from natural-language policy documents, using the Institutional Analysis and Development framework to define institutional positions and Institutional Grammar 2.0 to encode behavioral rules; and (2) instantiates actor nodes as persona agents while anchoring them to a Theory of Change framework to map the causal pathways of policy impacts. Evaluated across four AI talent development policies and three LLMs, TIGRIS achieves the lowest macro-average MAPE, indicating that theory-guided institutional grounding supports domain-aligned reasoning for policy impact analysis."
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<abstract>Anticipating the social impacts of a policy prior to implementation requires institutional consistency: multi-agent reasoning must remain aligned with the rules specified in policy documents. However, existing LLM-based multi-agent simulations struggle to maintain such fine-grained institutional rules when policy documents are provided only as prompt context. In this paper, we propose TIGRIS, a Theory-guided framework for Institutionally Grounded policy Impact Simulation. TIGRIS (1) constructs a structured policy knowledge graph from natural-language policy documents, using the Institutional Analysis and Development framework to define institutional positions and Institutional Grammar 2.0 to encode behavioral rules; and (2) instantiates actor nodes as persona agents while anchoring them to a Theory of Change framework to map the causal pathways of policy impacts. Evaluated across four AI talent development policies and three LLMs, TIGRIS achieves the lowest macro-average MAPE, indicating that theory-guided institutional grounding supports domain-aligned reasoning for policy impact analysis.</abstract>
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%0 Conference Proceedings
%T TIGRIS: Integrating Policy Knowledge Graphs and Theory of Change for Institutionally Grounded Policy Impact Simulation
%A Yeo, Haein
%A Kim, Junghyun
%A Son, Beejin
%A Han, Kyungsik
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yeo-etal-2026-tigris
%X Anticipating the social impacts of a policy prior to implementation requires institutional consistency: multi-agent reasoning must remain aligned with the rules specified in policy documents. However, existing LLM-based multi-agent simulations struggle to maintain such fine-grained institutional rules when policy documents are provided only as prompt context. In this paper, we propose TIGRIS, a Theory-guided framework for Institutionally Grounded policy Impact Simulation. TIGRIS (1) constructs a structured policy knowledge graph from natural-language policy documents, using the Institutional Analysis and Development framework to define institutional positions and Institutional Grammar 2.0 to encode behavioral rules; and (2) instantiates actor nodes as persona agents while anchoring them to a Theory of Change framework to map the causal pathways of policy impacts. Evaluated across four AI talent development policies and three LLMs, TIGRIS achieves the lowest macro-average MAPE, indicating that theory-guided institutional grounding supports domain-aligned reasoning for policy impact analysis.
%R 10.18653/2026.findings-emnlp.1551
%U https://aclanthology.org/2026.findings-emnlp.1551/
%U https://doi.org/10.18653/2026.findings-emnlp.1551
%P 33008-33031
Markdown (Informal)
[TIGRIS: Integrating Policy Knowledge Graphs and Theory of Change for Institutionally Grounded Policy Impact Simulation](https://aclanthology.org/2026.findings-emnlp.1551/) (Yeo et al., Findings 2026)
ACL