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.

As climate change impacts rapidly escalate, ensuring the just adaptation of vulnerable communities has become a critical challenge. However, procedural justice—a key pillar of climate justice that focuses on the fairness of policies from a systemic standpoint—remains underexplored. Policy practitioners currently lack the tools to assess whether a policy can meaningfully engage affected groups. This research presents a novel approach to evaluating the procedural justice of climate change adaptation policies ex-ante, before their implementation.

While the impact of institutional structures on the adaptive capacity of systems is well-established, the direct relationship between these structures and the procedural justice of adaptive interventions has not been systematically studied. This research addresses this gap by applying the Institutional Analysis and Development (IAD) framework and incorporating Institutional Network Analysis to assess the procedural justice of climate adaptation interventions.

The research also demonstrates how Large Language Models support the automation of policy analysis tasks, and may be used to significantly improve the scale and timeliness of policy analysis research.

In this research we identify 4 values critical to procedurally just climate change adaptation policy institutions – namely Voice, Transparency, Accountability and Correctability. We operationalise these values in terms of institutional networks, and develop an empirical method for their identification. The work features a tool to automate extraction of policy statements from documents and features an annotation accuracy of 0.67.

Using this methodology, an empirical case of climate change policy ­— the expansion of Water and Wastewater Systems in Chennai — is investigated. The analysis shows that the institutional environment is severely lacking in procedural justice elements and that policy statements relevant to procedurally justice lack specificity.

Tipping points are an active and growing interest in both the scientific and political study of climate change: what are they, how can we identify them, and how can we avoid them (negative tipping points) or encourage them (positive tipping points). As climate change worsens, scientists and policy analysts have turned to computer models of complex, interconnected, human-Earth systems to help understand and address both its physical and social aspects. As this field has matured, so too has the complexity of both the models being developed and the questions being asked with them. Agent-based modeling (ABM) is one framework that has become popular for its ability to observe system-level behaviour without closed-form equations due to its encoding of heterogeneous individual-level behaviour.

Analyzing the data generated by ABMs is not straightforward, as they tend to have many input and output dimensions, most outputs are either temporally or spatially distributed (or both) and can be sensitive to stochastic effects. When applying a exploratory modeling or deep uncertainty lens—a philosophy that seeks to explore the effects of assumptions made in a model’s development and parametrization, understanding more about the modeled system’s behaviour as opposed to attempting to predict it—the complexity of this analysis grows further. However, this complexity should not discourage analysts from bringing existing Decision-Making under Deep Uncertainty (DMDU) methods to ABMs.

This study applies one such method (scenario discovery) to a complex ABM of household and firm climate adaptation in a coastal economy, attempting to uncover the existence of socio-environmental tipping points in the system. Based on a previously developed analogy connecting the output space of an ABM to the traditional notion of a physical phase diagram, Scenario Discovery is used to generate such a phase diagram and infer tipping points at the boundaries between distinct system states. Ultimately, a set of possible population-change tipping points are generated.

While this work demonstrates the fitness of scenario discovery as a tool for exploring the output spaces of ABMs and finding tipping points within them, it is very preliminary. The work should be repeated with several improvements. First, either the uncertain parameters varied in this study should be selected to be more policy-relevant, controllable system factors, or the system states and thus the tipping points should be expressed in terms of endogenous variables instead of input parameters. Second, this study demonstrates that the typical approach to processing stochastic replications in exploratory modeling—simply averaging all outcomes—is not fit for use with complex modeling like ABM. Despite the computational and cognitive load introduced by simultaneously handling both a wide uncertainty space and many stochastic replications, efforts must be made to ensure any dynamically distinct behaviour generated by the original model is not lost to averaging. Studies like this one that do not put in this effort risk enabling the extraction of incorrect political and policy lessons.

This Master’s thesis examines the relationship between public debt and climate-related natural disasters in Southern Europe, focusing on Greece, Italy, Spain, and Portugal. It analyzes how increased frequency and severity of climate events strain public finances, leading to higher debt levels and reduced economic stability. The research synthesizes existing literature and employs rigorous scenario analysis to project future fiscal outcomes, highlighting the unique vulnerabilities of these countries. The thesis concludes with policy recommendations to enhance fiscal resilience, advocating for tailored fiscal rules, increased investment in climate adaptation and mitigation, and the establishment of fiscal buffers to ensure long-term economic stability in the face of climate change.

Climate change is an omnipresent issue in politics, industry, science, and society. Scientist knew of its existence for several decades, but only recently has the perception of it shifted from a solely environmental problem to a matter of international security. Climate change is thought to impact the likelihood of extreme weather events, intensity of droughts, rising sea levels and more. In this way climate change contributes to ongoing conflicts around the world by destroying resources or forcing people out of habitats, creating tensions among groups of people.

As the impacts of climate change are being felt more directly, calls for bolder climate action are getting louder. At the same time, there is also resistance by climate skeptics in industry, government, and among voters. Although most people appear aware and concerned with the development of climate change, the attitude towards climate action is extremely broad. Political rhetoric and disinformation in the media compound the disagreement on climate change, causing polarization. Although some polarization is beneficial for democratic deliberation and for mobilizing voters, if polarization divides the electorate into mutually distrustful groups, it can trap societies in a stifling gridlock or gradually erode democratic institutions. These pernicious effects are what necessitates closer study to monitor societal polarization, prevent conflict and improve climate action efforts.

Social media offers huge amounts of textual data to study discourse on climate change, which this study leverages to make first steps in understanding what topics make up the public discourse, how it
varies across linguistic regions (English, German, Dutch) and how it changes considering climate-related events, namely floods and a climate protest. Using both topic modelling and sentiment analysis
on Twitter data, it could be identified that activity spiked around major events: namely the 2019 Climate Strike and the 2021 Flood in Western Europe. However, reaction to events appears to be
mainly regional with reaction to floods seen only in the Dutch and German tweets. The English Twittersphere seemed to be mainly skewed towards events in America. Polarization appeared low as
most tweets were neutral. Examining only tweets with high sentiment, a different behavior was detected in terms of news coverage as there existed more high negative sentiment tweets, but almost
no high positive sentiment tweets. During the 2019 Global Climate Strike, extreme sentiment tweets went both positive and negative, possibly indicating more intense discourse and a more polarized
atmosphere.

Overall, results show that conflict-triggering climate events are very likely to be domestic events. Thus, from a security perspective, it suggests on the short-term to focus on the monitoring of domestic
actors. On the long-term however, policies need to be implemented which address underlying factors that make society susceptible to polarization, such as socioeconomic inequalities and exclusion,
disinformation in media or corruption. Lastly, from an academic perspective, more research on other dimensions of polarization needs to be conducted. Polarization is a complex phenomenon, whose full
implications cannot be captured by the sentiment metrics. Thus, research moving forward should strive towards a more refined multi-method approach to further investigate climate change polarization and its conflict potential. A better understanding could benefit practitioners in many areas such as security and social policy, for strategic planning regarding climate change.