In society, institutions are the foundation that governs human behaviour through rules, norms, and regulations. The actions and interactions of individuals are shaped by these institutions, forming a cyclic system with numerous parameters and factors. Altering any of these factors, triggers the entire system to transition into a new state that comprises new emergent institutions. This process can take anywhere from days to thousands of years.

Employing agent-based models and simulation techniques enables the study of the emergence and transformation of institutions in a shorter timeframe, with reasonable cost, and under diverse parameters and conditions.

The purpose of this dissertation is to enhance institutional theories by generating new insights, testing hypotheses, and offering support to researchers, historians, policymakers, and social scientists who are studying institutional dynamics. The outcomes of this research may assist in the identification of successful institutions and the comprehension of the factors that contribute to their success....@en

Social disruptions caused by the COVID-19 pandemic challenged existing institutional arrangements that govern the society. During that time, nation-states had to prevent the collapse of society and rapidly establish new institutions and adapt existing ones to address public health, job security, and freedom-of-movement concerns. At the same time, institutional developments are explicitly or implicitly related to the cultural and moral values relevant to societal well-being. Values hold a significant role in governing society during crises, guiding states' institutional response to unforeseen challenges. However, values themselves are not static: research has shown that values may change rapidly during crises. This paper studies the relationship between value change and institutional change in times of crisis using agent-based modelling and machine learning techniques. In our model, we represent countries as agents who define institutional strategies to control disease spread and subsequently protect the well-being of their citizens. Institutional change and value change are modelled as two independent processes. Yet, the model confirms the seemingly trivial inverse correlation between them: when the value of openness-to-change increases in a society, the institutional strategies also become less strict. Conversely, when conservatism increases, the strategies become stricter on average. However, there is no direct causal relationship between the two changes: being open to change does not necessarily make a government select more relaxed rules, but this correlation is rather an emergent consequence of being more flexible in changing rules, whether the new ones are stricter or more relaxed.@en

Agent-based modelling and simulation (ABMS), whether simple toy models or complex data-driven ones, is regularly applied in various domains to study the system-level patterns arising from individual behaviour and interactions. However, ABMS still faces diverse challenges such as modelling more representative agents or improving computational efficiency. Research shows that machine learning (ML) techniques, when used in ABMS can address such challenges. Yet, the ABMS literature is still marginally leveraging the benefits of ML. One reason is the vastness of the ML domain, which makes it difficult to choose the appropriate ML technique to overcome a specific modelling challenge. This paper aims to bring ML more within reach of the ABMS community. We first conduct a structured literature review to investigate how the ABMS process uses ML techniques. We focus specifically on articles where ML is applied for the structural specifications of models such as agent decision-making and behaviour, rather than just for analysing output data. Given that modelling challenges are mainly linked to the purpose a model aims to serve (e.g., behavioural accuracy is required for predictive models), we frame our analysis within different modelling purposes. Our results show that Reinforcement Learning algorithms may increase the accuracy of behavioural modelling. Moreover, Decision Trees, and Bayesian Networks are common techniques for data pre-processing of agent behaviour. Based on the literature review results, we propose guidelines for purposefully integrating ML in ABMS. We conclude that ML techniques are specifically fit for currently underrepresented modelling purposes of social learning and illustration; they can be used in a transparent and interpretable manner. @en

Historical data are valuable resources for providing insights into general sociological patterns in the past. How-ever, these data often inform us at the macro-level of analysis but not about the role of individuals’ behaviours in the emergence of long-term patterns. Therefore, it is difficult to infer ‘how’ and ‘why’ certain patterns emerged in the past. Historians use various methods to draw hypotheses about the underlying reasons for emerging patterns and trends, but since the patterns are the results of hundreds if not thousands of years of human behaviour, these hypotheses can never be tested in reality. Our proposition is that simulation models and specifically, agent-based models (ABMs) can be used as complementary tools in historical studies to support hypothesis building. The approach that we propose and test in this paper is to design and configure models in such a way as to generate historical patterns, consequently aiming to find individual-level explanations for the emerging pattern. In this work, we use an existing, empirically validated, agent-based model of common pool resource management to test hypotheses formulated based on a historical dataset. We first investigate whether the model can replicate various patterns observed in the dataset, and second, whether it can contribute to a better understanding of the underlying mechanism that led to the observed empirical trends. We showcase how ABM can be used as a complementary tool to support theory development in historical studies. Finally, we provide some guidelines for using ABM as a tool to test historical hypotheses.@en

In their studies of collective exploitation of common-pool resources, Ostrom and other scholars have stressed the importance of sanctioning as an essential method for preventing overuse and, eventually, the collapse of commons. However, most of the available evidence is based on data covering a relatively small period in history, and thus does not inform us about the evolution of rules, including sanctions, over time. In this article, we demonstrate, based on historical sources covering several centuries, that sanctioning was not always the preferred way of preventing or dealing with free-riding in institutions for collective action, but that the legal context is decisive to understand why commoners in some countries were using more sanctions than those in others to regulate commoners' behavior. Commoners that could self-govern their resources used fewer sanctions, and when they did, it was mainly to avoid overuse of their most vulnerable resources. Moreover, graduated sanctioning seems to be less important than suggested in Ostrom's famous Design Principles, and was reserved primarily for immediate threats to the commons' resources. We also show the importance of other types of rules, such as differentiated rules, which have hardly been taken into account in literature to date.

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We present an analysis of regulatory activities in historical commons offering a unique picture of their long-term institutional dynamics. The analysis took into account almost 3,800 regulatory activities in eighteen European commons in two countries across seven centuries. Despite differences in time and space, we found a shared pattern where an initial, highly-dynamic institutional-definition phase was followed by a relatively long period of stability and a final burst of activities, possibly in an attempt to respond to new challenges. In addition, most of the initial regulatory activities focused on resource use, while towards the end other activities prevailed. Our approach allows for a better understanding of institutional dynamics and our findings also provide important insights about how to regulate the use of current natural resources.

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Historical commons represent self-governed governance regimes that regulate the use and management of natural and man-made shared resources. Despite growing scientific interests, analyses of commons evolution and temporal dynamics are rare and drivers of change (birth, adaptation, dissolution) remain obscure. We apply an interdisciplinary approach and address these issues from an eco-evolutionary perspective. Analyses of > 400 Dutch commons over more than a millennium (between the 9th and the 20th century) uncovered that most commons originated between 1200 and 1700, and that there was a particularly high rate of evolution during 1300-1550, a pattern intermediate to gradualism and punctuated equilibrium in biological evolution. Dissolutions of commons were rare prior to 1800 and peaked around 1850, comparable to a mass extinction in biology. Temporal trends in number, spatial distribution, density, and dispersion of historical commons were distinctive and resembled developments seen at the levels of species and individuals in the growth of biological communities and populations, in that they showed signs of saturation determined by the abundance and distribution of resources. The spatiotemporal dynamics of commons also pointed to important roles of social, economic and political factors, such as new reclamations of resources and pressure on resources due to population growth. Despite internal and external pressures, the self-governing commons studied here were very successful, in the sense that they persisted for on average >350 years. There was a weak positive relationship between the use of multiple resources and the lifespan of commons, resembling associations between diversity and persistence seen in biological systems. It is argued that eco-evolutionary perspectives can further the understanding of the long-term dynamics of commons as institutions for collective action, vitalize future research, improve management of shared goods, and advise about sustainable utilization of finite resources.

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