This agent-based model, developed for the study “Online Protest and Repression in Authoritarian Settings,” examines how online protest and repression evolve in authoritarian contexts and how these dynamics affect ordinary users’ attitudes and behavior on social media. The model integrates key theoretical and empirical insights into social media use and core political factors that shape digital contention in authoritarian settings. The following questions are addressed: (1) how online protest–repression dynamics unfold across different levels of authoritarianism and varying compositions of committed accounts, and (2) how ordinary users’ internal propensity to protest and their perceived probability of successful repression change during online protest-repression contestation. The model is evaluated against two empirically grounded macro patterns observed in the real world. The first is enduring protest: online protest becomes dominant as vocal protesters grow to outnumber vocal repressors, shrinking the pool of silent users and stabilizing a pro-protest majority. The second is suppressed protest: online dissent is contained as vocal repression and silence expand in response to protest, yielding a sustained majority of repressive and silent accounts. Together, these dynamics demonstrate how dissenting voices are empowered and suppressed online in authoritarian settings.

The core algorithm is an agent-based model, which simulates travel patterns on a network based on microscopic decision-making by each traveler.

The tragedy of the commons in public resource governance is essentially the result of repeated interaction and adaptive learning among heterogeneous agents under dynamic resource constraints. Existing studies have generated rich insights into common-pool resource governance, institutional constraints, and cooperation, but they still rely mainly on theoretical deduction, static games, or econometric identification, making it difficult to jointly characterize resource dynamics, agent heterogeneity, behavioral learning, and policy scenarios. From the perspective of computational economics, this paper develops a multi-agent reinforcement learning simulation model for the governance of the tragedy of the commons. Specifically, the fish-pond resource system is formulated as a Markov decision process in discrete states, and adaptive decision-making under pure economic incentives, sustainability penalties, behavioral heterogeneity, and cooperation is characterized through the resource dynamics equation, harvesting equation, and differentiated reward functions. The model is further examined through sensitivity analysis, parameter calibration, and theoretical validation, and then used for policy simulation. The results show that pure economic incentives quickly induce resource collapse, sustainability penalties significantly reduce harvesting intensity and maintain a low but sustainable steady state, heterogeneous behavior parameters generate clear strategic divergence, and cooperation internalizes group harvesting constraints into individual payoffs and yields the strongest resource recovery and behavioral convergence.

Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).

This model is used to investigate the role of opinion leader. More specifically: the influence of ‘innovative behavior’, ‘weigth of normative influence’, ‘better product judgment’, ‘number of opinion

CPNorm is a model of a community of harvesters using a common pool resource where adhering to the optimal extraction level has become a social norm. The model can be used to explore the robustness of norm-driven cooperation in the commons.

The aim of the model is to define when researcher’s assumptions of dependence or independence of cases in multiple case study research affect the results — hence, the understanding of these cases.

This model was created to investigate the potential impacts of large-scale recreational and transport-related physical activity promotion strategies on six United Nations Sustainable Development Goals (SDGs) related outcomes—road traffic deaths (SDG 3), transportation mode share (SDG 9), convenient access to public transport, levels of fine particulate matter, and access to public open spaces (SDG 11), and levels of carbon dioxide emissions (SDG 13)—in three cities designed as abstract representations of common city types in high-, middle-, and low-income countries.

The model represents an archetypical fishery in a co-evolutionary social-ecological environment, capturing different dimensions of trust between fishers and fish buyers for the establishment and persistence of self-governance arrangements.

This model was build to explore the bio-cultural interaction between AMH and Neanderthals during the Middle to Upper Paleolithic Transition in the Iberian Peninsula