## 1 Introduction
ABM, however, does not have a predefined structure for model representation; models are primarily based on either highly theoretical or best-guess ad-hoc structures, which are problematic for model structural validation [16; 29]. As a consequence, social and cognitive theories [30; 31; 32; 33; 34] often form the basis for translating qualitative data to empirical ABM [35]. Since social behavior is complex and challenging to comprehend, using social and cognition theories helps determine the system's expected behavior. Moreover, using theories streamlines data management and analysis for model development. Another school of thought bases model development on stakeholder cognition. Rather than relying mainly on social theories, this approach focuses on extracting empirical information about system components and behaviors. Participatory or companion modeling [36], as well as role-playing games [11], are some of the conventional approaches to eliciting stakeholder knowledge for model development [23; 24]. Stakeholders usually develop model structures in real time, while some modelers prefer to process stakeholders' information after the discussions. For instance, [37] employs computer technologies to post-process stakeholder responses to develop a rule-induction algorithm for her ABM. Stakeholders are assumed to be the experts of their systems, and using their knowledge in model building makes the model valid and reliable. However, stakeholder involvement is not always feasible; for instance, when modeling remote places or historical events. In such cases, modelers resort to information elicitation tools for information extraction. In the context of ABM, translating empirical textual data into agent architecture is complex and requires concrete algorithms and structures [25; 38]. Therefore, modelers first explore the context of the narratives and then identify potential context-specific scopes. Determining narrative elements becomes straightforward once context and scopes are identified [38]. Many ABM modelers have formulated structures for organizing qualitative data for model development. For instance, [39] used Institutional Analysis and Development framework for managing qualitative data in their Modeling Agent system based on Institutions Analysis (MAIA). MAIA comprises five structures: collective, constitutional, physical, operational, and evaluative. Information on agents is populated in a collective structure, while behavior rules and environment go in constitutional and physical structures. Similar frameworks were introduced by [40; 41], to name but a few. However, all these structures use manual, slow, and bias-prone data processing and extraction. A potential solution presented in this paper is to employ AI tools, such as NLP, for unsupervised information extraction for model development [30]. The remainder of the paper is structured as follows: After the introduction, we briefly characterize NPL, focusing on its utility for ABM conceptualization. Next, we describe the proposed methodology. Finally, we demonstrate the framework in a case study of processing narratives from in-depth household interviews on individual food security in Mali, noting the framework's advantages and limitations.