The School Enrollment Model is a spatially-explicit computational model that depicts a city, with schools and students located within the space. The model represents the Chilean school system, a market-based educational system, where people are free to choose among public, private voucher, or private fee-paying schools. In the model, students become aware of some schools, apply to schools, switch schools, pass or fail grade levels, and eventually either graduate or dropout. Schools select students, update their tuition, test scores, and other characteristics.

The purpose of the model is to represent the Chilean school system and analyze the different mechanisms that affected the enrollment distribution between public, private voucher, and private fee-paying school sectors during the period 2004-2016.

Simulations based on the Axelrod model and extensions to inspect the volatility of the features over time (AXELROD MODEL & Agreement threshold & two model variations based on the Social identity approach)
The Axelrod model is used to predict the number of changes per feature in comparison to the datasets and is used to compare different model variations and their performance.

Input: Real data

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Overview

Purpose

Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..
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Inspired by the European project called GLODERS that thoroughly analyzed the dynamics of extortive systems, Bottom-up Adaptive Macroeconomics with Extortion (BAMERS) is a model to study the effect of extortion on macroeconomic aggregates through simulation. This methodology is adequate to cope with the scarce data associated to the hidden nature of extortion, which difficults analytical approaches. As a first approximation, a generic economy with healthy macroeconomics signals is modeled and validated, i.e., moderate inflation, as well as a reasonable unemployment rate are warranteed. Such economy is used to study the effect of extortion in such signals. It is worth mentioning that, as far as is known, there is no work that analyzes the effects of extortion on macroeconomic indicators from an agent-based perspective. Our results show that there is significant effects on some macroeconomics indicators, in particular, propensity to consume has a direct linear relationship with extortion, indicating that people become poorer, which impacts both the Gini Index and inflation. The GDP shows a marked contraction with the slightest presence of extortion in the economic system.

Considering that two of the three avoider species could not reach the target area in the inittial scenario, five alternative corridor scenarios were created. In all cases, we generated a greater amount of cover area under ‘Urban forest’, including elements such as scattered trees, woody plants, wooded areas, and rows of trees. This covered type was selected since all three species use it as a regular habitat. That is the second sceneario where those ecological parks and other areas inside the capital city were boostered into “urban forest patches” or buffer points, with the idea of improving the survive of the three bird species and their movement. However one of the most restrictive specie was still having movement and survival issues.

NOMAD is an agent-based model of firm location choice between two aggregate regions (“near” and “off”) under logistics uncertainty. Firms occupy sites characterised by attractiveness and logistics risk, earn a risk-adjusted payoff that depends on regional costs (wages plus congestion) and an individual risk-tolerance trait, and update location choices using aspiration-based satisficing rules with switching frictions. Logistics risk evolves endogenously on occupied sites through a region-specific absorption mechanism (good/bad events that reduce/increase risk), while congestion feeds back into regional costs via regional shares and local crowding. Runs stop endogenously once the near-region share becomes quasi-stable after burn-in, and the model records time series and quasi-stable outcomes such as near/off composition, switching intensity, costs, average risk, and average risk tolerance.

An agent-based model of saving and dissaving behaviour under quasi-hyperbolic (β–δ) discounting. Building on the individual decision problem of Cao and Werning (2018), the model embeds present-biased agents in a Watts–Strogatz small-world network and adds three configurable mechanisms of social influence — information diffusion, peer comparison, and social-norm conformity — across five heterogeneous behavioural profiles (Planners, Moderates, Procrastinators, Inverse Procrastinators, and Impulsive agents).
Each profile’s saving policy is approximated by value-function iteration over a discretised wealth grid; the solved policies are cached and applied as agents interact over their network neighbourhoods. The model tests whether each social mechanism can alter the saving and wealth trajectories that present-biased agents would otherwise follow in isolation, and characterises the direction and size of each effect on median wealth, wealth inequality (Gini), and the incidence of severely depleted agents.
The deposit includes the core model (Model.py), an analysis and visualisation pipeline (analyze_results.py), a standalone ODD description (ODD.md), and pinned dependencies.

The model aims to investigate the role of Microfinance Institutes (MFIs) in strengthening the coping capacity of slum-dwellers (residents) in case of frequent disasters. The main purpose of the model is system understanding. It aids in understanding the following research question: Are the microcredits provided by MFI to start a small business helpful in increasing coping capacity of a slum dweller for recovering from frequent and intense disasters?

The purpose of this model is to analyze different configurations and scenarios of ecological corridors to simulate the movement of three avoider bird species at a local scale: Chondrohierax uncinatus (Accipitridae), a large carnivorous bird; Ampelion rubrocristatus (Cotingidae), a species that seeks areas with substantial land cover for refuge and rest; and Coeligena bonapartei (Trochilidae), a large hummingbird that prefers areas with a rich and diverse food supply. The model focusses on juvenile bird individuals seeking refuge and food, taking into account the mobility parameters of each species and the existing land cover types within the study area.
Specifically, the model aims to:
• Simulate the movement of 45 avoiders birds which are considered umbrella species sensitive to urban changes (which were chosen based on their specific biological and ecological requirements and parameters relevant to urban conservation efforts), 15 avoiders birds per specie to cross a two-dimensional world predominant urban.
• To be able to select which corridor scenario would be the most beneficial, in order to help the mobility of other species affected by urban fragmentation.
• Contribute to urban ecology research and support decision-making processes by relevant stakeholders.

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.