In the field of Institutional Analysis (IA), institutions describe the emergent rules that govern human behaviour and interaction. There are two key types: formal rules such as laws and policies, and informal rules such as norms and shared strategies. Evaluating the effectiveness of existing policies and developing new policies often requires a thorough understanding of informal rules. Traditional methods for identifying informal rules are predominantly qualitative, labour-intensive, and prone to bias, typically limited by small sample sizes. This thesis addresses the gap in standardised, quantitative approaches by developing a novel method to extract informal rules and social structures from survey data: Survey Informal Rule Extraction (SIRE).

The SIRE method involves preprocessing survey data, training decision trees to classify behaviours, extracting Institutional Grammar (IG) components, and generating structured Attribute, Deontic, Aim, Condition, Or else (ADICO) statements. The design process involves an iterative approach with three key steps: 1) exploring and comparing existing survey data from an institutional grammar lens, 2) evaluating different types of questions and identifying relationships in the data, and 3) testing various methods to analyse response data and convert questions into ADICO statements. These steps led to a systematic approach to understanding how informal rules shape human behaviour, supporting evidence-based policy design.

The effectiveness of SIRE is validated through three applications to datasets from the European Social Survey (ESS) and SCALAR. The first validation compares SIRE results with literature findings on household climate change adaptation, showing logical and comparable outcomes. The second validation demonstrates the method's utility in policy analysis, examining factors influencing the adoption of structural flood protection measures. The third validation showcased the method's potential as a strategic analytics tool for political parties, identifying conditions that increase the likelihood of voting for specific parties. The results demonstrate that the SIRE method has the potential to provide a clear representation of institutional rules, offering valuable insights for institutional and policy analysts.

While this thesis presents a novel method for extracting informal rules from survey data, limitations include potential generalizability issues due to the focused validation on specific datasets (SCALAR and ESS), AI-related inconsistencies in text processing, and methodological constraints such as the current inability to directly address open-ended questions or identify deontic components. Future work should focus on further validation across diverse contexts, integration with other analytical frameworks, and method automation. Improvements in data encoding, condition scope, and rule extraction could enhance the SIRE method's utility in policy development and behavioural analysis, addressing current limitations and expanding its applicability.