Computational Model Library

Network Behaviour Diffusion

Jennifer Badham | Published Sat Oct 2 22:44:08 2021

This model implements two types of network diffusion from an initial group of activated nodes. In complex contagion, a node is activated if the proportion of neighbour nodes that are already activated exceeds a given threshold. This is intended to represented the spread of health behaviours. In simple contagion, an activated node has a given probability of activating its inactive neighbours and re-tests each time step until all of the neighbours are activated. This is intended to represent information spread.

A range of networks are included with the model from secondary school friendship networks. The proportion of nodes initially activated and the method of selecting those nodes are controlled by the user.

This agent-based simulation model for group interaction is rooted in social psychological theory. The
model integrates affect control theory with networked interaction structures and sequential behavior protocols as they are often encountered in task groups. By expressing status hierarchy through network structure we build a bridge between expectation states theory and affect control theory, and are able to reproduce central results from the expectation states research program in sociological social psychology. Furthermore, we demonstrate how the model can be applied to analyze specialized task groups or sub-cultural domains by combining it with empirical data sources. As an example, we simulate groups of open-source software developers and analyze how cultural expectations influence the occupancy of high status positions in these groups.

MHCABM is an agent-based, multi-hazard risk interaction model with an integrated applied dynamic adaptive pathways planning component. It is designed to explore the impacts of climate change adaptation decisions on the form and function of a coastal human-environment system, using as a case study an idealised patch based representation of the Mount North-Omanu area of Tauranga city, New Zealand. The interacting hazards represented are erosion, inundation, groundwater intrusion driven by intermittent heavy rainfall / inundations (storm) impacts, and sea level rise.

The SMASH model is an agent-based model of rural smallholder households. It models households’ evolving income and wealth, which they earn through crop sales. Wealth is carried in the form of livestock, which are grazed on an external rangeland (exogenous) and can be bought/sold as investment/coping mechanisms. The model includes a stylized representation of soil nutrient dynamics, modeling the inflows and outflows of organic and inorganic nitrogen from each household’s field.

The model has been applied to assess the resilience-enhancing effects of two different farm-level adaptation strategies: legume cover cropping and crop insurance. These two strategies interact with the model through different mechanims - legume cover cropping through ecological mechanisms and crop insurance through financial mechanisms. The model can be used to investigate the short- and long-term effects of these strategies, as well as how they may differently benefit different types of household.

MELBIS-V1 is a spatially explicit agent-based model that allows the geospatial simulation of the decision-making process of newcomers arriving in the bilingual cities and boroughs of the island of Montreal, Quebec in CANADA, and the resulting urban segregation spatial patterns. The model was implemented in NetLogo, using geospatial raster datasets of 120m spatial resolution.

MELBIS-V2 enhances MELBIS-V1 to implement and simulate the decision-making processes of incoming immigrants, and to analyze the resulting spatial patterns of segregation as immigrants arrive and settle in various cities in Canada. The arrival and segregation of immigrants is modeled with MELBIS-V2 and compared for three major Canadian immigration gateways, including the City of Toronto, Metro Vancouver, and the City of Calgary.

The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).

The data the model is currently able to output are the following:

Policymakers decide on alternative policies facing restricted budgets and uncertain, ever-changing future. Designing housing policies is further difficult giving the heterogeneous characteristics of properties themselves and the intricacy of housing markets and the spatial context of cities. We propose PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks to integrate economic, spatial and transport literature. PS2 is applied to a comparison among three competing municipal housing policies aimed at alleviating poverty: (a) property acquisition and distribution, (b) rental vouchers and (c) monetary aid. Within the model context, the monetary aid, that is, a smaller amounts of help for a larger number of households, makes the economy perform better in terms of production, consumption, reduction of inequality and maintenance of financial duties. PS2 as such is also a framework that may be further adapted to a number of related research questions.

The agent-based perspective allows insights on how behaviour of firms, guided by simple economic rules on the micro-level, is dynamically influenced by a complex environment in regard to the assumed relocation, decision-making hypotheses. Testing various variables sensitive to initial conditions, increased environmental regulations targeting global trade and upward shifting wage levels in formerly offshore production locations have shown to be driving and inhibiting mechanisms of this socio-technical system. The dynamic demonstrates a shift from predominantly cited economic reasoning for relocation strategies towards sustainability aspects, pressingly changing these realities on an environmental and social dimension. The popular debate is driven by increased environmental awareness and the proclaimed fear of robots killing jobs. In view of reshoring shaping the political agenda, interest in the phenomenon has recently been fuelled by the rise of populism and protectionism.

This model was developed to test the usability of evolutionary computing and reinforcement learning by extending a well known agent-based model. Sugarscape (Epstein & Axtell, 1996) has been used to demonstrate migration, trade, wealth inequality, disease processes, sex, culture, and conflict. It is on conflict that this model is focused to demonstrate how machine learning methodologies could be applied.

The code is based on the Sugarscape 2 Constant Growback model, availble in the NetLogo models library. New code was added into the existing model while removing code that was not needed and modifying existing code to support the changes. Support for the original movement rule was retained while evolutionary computing, Q-Learning, and SARSA Learning were added.

This repository contains: (1) a model calibration procedure that identifies a set of diverse, plausible models; and (2) an ABM of smallholder agriculture, which is used as a case study application for the calibration method. By identifying a set of diverse models, the calibration method attends to the issue of “equifinality” prevalent in complex systems, which is a situation where multiple plausible process descriptions exist for a single outcome.

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