A model of the emergence of intersectional life course inequalities through transitions in the workplace. It explores LGBTQ citizens’ career outcomes and trajectories in relation to several mediating factors: (i) workplace discrimination; (ii) social capital; (iii) policy interventions (i.e., workplace equality, diversity, and inclusion policies); (iv) and LGBTQ employees’ behaviours in response to discrimination (i.e., moving workplaces and/or different strategies for managing the visibility of their identity).

Flibs’NLogo is an agent-based simulation implemented in NetLogo that models the evolution of perfect predictors through a genetic algorithm. The agents, called flibs (finite living blobs), are finite‑state automata whose behaviour is encoded in circular chromosomes. They inhabit a “primordial computer soup” and are tasked with anticipating a user‑defined periodic binary sequence. Each generation consists of 100 evaluation cycles, during which a flib’s fitness is incremented each time its output correctly matches the next environmental signal.
Reproduction follows an elitist scheme: a donor (current fittest individual) replaces a randomly chosen recipient either by cloning (complete genome substitution) or by bacterial‑like conjugation (unidirectional horizontal transfer of a random chromosome segment). A stochastic mutagenesis operator introduces point mutations in genes, while the reproductive strategy gene can also switch under a mixed-reproduction regime. Population dynamics are monitored via genomic diversity indices (Shannon‑Wiener, Simpson), a phenotypic simpleness metric that distinguishes the low number of states actually used from the genomic potential.
The model serves as a digital evolutionary laboratory for exploring the interplay among bounded rationality, collective adaptation, and the emergence of anticipatory behaviour. By linking evolutionary computation with cognitive concepts, Flibs’NLogo investigates fundamental transitions from reactive to predictive systems and allows for testing whether populations evolve toward minimal necessary complexity or exhibit an intrinsic drift toward structural elaboration.

This model implements a classic scenario used in Reinforcement Learning problem, the “Cliff Walking Problem”. Consider the gridworld shown below (SUTTON; BARTO, 2018). This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff.” Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start (SUTTON; BARTO, 2018).

The problem is solved in this model using the Q-Learning algorithm. The algorithm is implemented with the support of the NetLogo Q-Learning Extension

ACT is an ABM based on an existing conceptualisation of the concept of critical transitions applied to the energy transition. With the model we departed from the mean-field approach simulated relevant actor behaviour in the energy transition.

The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.

Toolkit to specify demographic multistate model with a behavioural element linking intentions to behaviour

Aroused public opinion has led to public debates on social responsibility issues in food supply chains. This model based op opinion dynamics and the linkages between involved actors simulates the public debate leading to the transitions.

the model can be used to describe the product diffusion in an Advance Selling Strategy. this model takes into account the consumers product adoption, and describe consumer’s online behavior based on four states.

What is stable: the large but coordinated change during a diffusion or the small but constant and uncoordinated changes during a dynamic equilibrium? This agent-based model of a diffusion creates output that reveal insights for system stability.

In our model, individual agents are distributed over a two-dimensional square lattice. The agents play the prisoner’s dilemma game with their neighbors, imitate the highest strategy, and then migrate to empty sites based on their tag preference.