This model compares rules for allocating a fixed budget of opportunity (places, training, funding) among people who differ in potential and will. Each agent’s effective potential, its denatotita, is potential multiplied by will. Realisation is denatotita multiplied by the opportunity received, and an agent succeeds when realisation reaches a threshold. Nine allocation regimes are compared: equal shares; shares proportional to denatotita; three threshold rules that reclaim and redistribute the allocations of those who would fail; an efficient benchmark that maximises successes; and three forward-looking rules (foresight, will-aware foresight and hindsight). A mixed regime blends targeting with equal shares. Over time, opportunity develops potential through a skill-formation technology, and will rises with success and falls when an agent is passed over. The model reproduces the article’s results: a closed-form crossover between equal and proportional allocation; a reversal in favour of broad allocation when development is productive; a hollowing-out of potential under success-maximising allocation; and the failure of mixtures of targeting and equal shares. Written in Python (NumPy, pandas, Matplotlib), with fixed random seeds so results are exactly reproducible.

We develop an agent-based model (U-TRANS) to simulate the transition of an abstract city under an industrial revolution. By coupling the labour and housing markets, we propose a holistic framework that incorporates the key interacting factors and micro processes during the transition. Using U-TRANS, we look at five urban transition scenarios: collapse, weak recovery, transition, enhanced training and global recruit, and find the model is able to generate patterns observed in the real world. For example, We find that poor neighbourhoods benefit the most from growth in the new industry, whereas the rich neighbourhoods do better than the rest when the growth is slow or the situation deteriorates. We also find a (subtle) trade-off between growth and equality. The strategy to recruit a large number of skilled workers globally will lead to higher growth in GDP, population and human capital, but it will also entail higher inequality and market volatility, and potentially create a divide between the local and international workers. The holistic framework developed in this paper will help us better understand urban transition and detect early signals in the process. It can also be used as a test-bed for policy and growth strategies to help a city during a major economic and technological revolution.

This model, realized on the NetLogo platform, compares utility levels at home and abroad to simulate agents’ migration and their eventual return. Our model is based on two fundamental individual features, i.e. risk aversion and initial expectation, which characterize the dynamics of different agents according to the evolution of their social contacts.