Reusing existing material stocks in developed built environments can significantly reduce the environmental footprint of the construction and demolition sector. However, material reuse in urban areas presents technical, temporal, and geographical challenges. Although a better understanding of spatial and temporal changes in material stocks could improve city resource management, limited scientific contributions have addressed this challenge.
This study details the steps followed in developing a spatially explicit rule-based simulation of materials stock. The simulation provides a proof of concept by incorporating the spatial and temporal dimensions of construction and demolition activities to analyse how various urban parameters determine material flows and embodied carbon in urban areas. The model explores the effects of 1) re-using recycled materials, 2) demolitions, 3) renovations and 4) various building typologies.
To showcase the model’s capabilities, the residential building stock of Gothenburg City is used as a case study, and eight building materials are tracked. Environmental impacts (A1-A3) are calculated with embodied carbon factors. The main parameters are explored in a baseline scenario. Then, a second scenario focuses on a hypothetical policy that promotes improvements in building energy performance.
The simulation can be expanded to include more materials and built environment assets and allows for future explorations on, for example, the role of logistics, the implementation of recycling or reuse stations, and, in general, supporting sustainable and circular strategies from the construction sector.

The O.R.E. (Opinions on Risky Events) model describes how a population of interacting individuals process information about a risk of natural catastrophe. The institutional information gives the official evaluation of the risk; the agents receive this communication, process it and also speak to each other processing further the information. The description of the algorithm (as it appears also in the paper) can be found in the attached file OREmodel_description.pdf.
The code (ORE_model.c), written in C, is commented. Also the datasets (inputFACEBOOK.txt and inputEMAILs.txt) of the real networks utilized with this model are available.

For any questions/requests, please write me at daniele.vilone@gmail.com

This is a stylized model based on Alonso’s model investigating the relationship between urban sprawl and income segregation.

Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk. This agent-based model (ABM) explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors, and their environments have likelihoods of presenting prosocial or antisocial opportunities.

Using data from the British Social Attitude Survey, we develop an agent-based model to study the effect of social influence on the spread of meat-eating behaviour in the British population.

The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. This agent-based model in Netlogo simulates a group discussion among myside-biased agents, within a Bayesian setting. This model is designed to investigate the effects of the myside bias on the ability of groups to reach a consensus or collectively track the correct answer to a given binary issue.

An agent-based model of individual consumers making choices between five possible diets: omnivore, flexitarian, pescatarian, vegetarian, or vegan. Each consumer makes decisions based on personal constraints and values, and their perceptions of how well each diet matches with those values. Consumers can also be influenced by each other’s perceptions via interaction across three social networks: household members, friends, and acquaintances.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

The “Descriptive Norm and Fraud Dynamics” model demonstrates how fraudulent behavior can either proliferate or be contained within non-hierarchical organizations, such as peer networks, through social influence taking the form of a descriptive norm. This model expands on the fraud triangle theory, which posits that an individual must concurrently possess a financial motive, perceive an opportunity, and hold a pro-fraud attitude to engage in fraudulent activities (red agent). In the absence of any of these elements, the individual will act honestly (green agent).

The model explores variations in a descriptive norm mechanism, ranging from local distorted knowledge to global perfect knowledge. In the case of local distorted knowledge, agents primarily rely on information from their first-degree colleagues. This knowledge is often distorted because agents are slow to update their empirical expectations, which are only partially revised after one-to-one interactions. On the other end of the spectrum, local perfect knowledge is achieved by incorporating a secondary source of information into the agents’ decision-making process. Here, accurate information provided by an observer is used to update empirical expectations.

The model shows that the same variation of the descriptive norm mechanism could lead to varying aggregate fraud levels across different fraud categories. Two empirically measured norm sensitivity distributions associated with different fraud categories can be selected into the model to see the different aggregate outcomes.

This agent-based model simulates the lifecycle, movement, and satisfaction of teachers within an urban educational system composed of multiple universities and schools. Each teacher agent transitions through several possible roles: newcomer, university student, unemployed graduate, and employed teacher. Teachers’ pathways are shaped by spatial configuration, institutional capacities, individual characteristics, and dynamic interactions with schools and universities. Universities are assigned spatial locations with a controllable level of centralization and are characterized by academic ratings, capacity, and alumni records. Schools are distributed throughout the city, each with a limited number of vacancies, hiring requirements, and offered salaries. Teachers apply to universities based on the alignment of their personal academic profiles with institutional ratings, pursue studies, and upon graduation become candidates for employment at schools.
The employment process is driven by a decentralized matching of teacher expectations and school offers, taking into account factors such as salary, proximity, and peer similarity. Teachers’ satisfaction evolves over time, reflecting both institutional characteristics and the composition of their colleagues; low satisfaction may prompt teachers to transfer between schools within their mobility radius. Mortality and teacher attrition further shape workforce dynamics, leading to continuous recruitment of newcomers to maintain a stable population. The model tracks university reputation through the academic performance and number of alumni, and visualizes key metrics including teacher status distribution, school staffing, university alumni counts, and overall satisfaction. This structure enables the exploration of policy interventions, hiring and training strategies, and the impact of spatial and institutional design on the allocation, retention, and happiness of urban educational staff.