The Retail Competition Agent-based Model (RC-ABM) is designed to simulate the retail competition system in the Region of Waterloo, Ontario, Canada, which which explicitly represents store competition behaviour. Through the RC-ABM, we aim to answer 4 research questions: 1) What is the level of correspondence between market share and revenue acquisition for an agent-based approach compared to a traditional location-allocation-based approach? 2) To what degree can the observed store spatial pattern be reproduced by competition? 3) To what degree are their path dependent patterns of retail success? 4) What is the relationship between retail survival and the endogenous geographic characteristics of stores and consumer expenditures?

Communication processes occur in complex dynamic systems impacted by person attitudes and beliefs, environmental affordances, interpersonal interactions and other variables that all change over time. Many of the current approaches utilized by Communication researchers are unable to consider the full complexity of communication systems or the over time nature of our data. We apply agent-based modeling to the Reinforcing Spirals Model and the Spiral of Silence to better elucidate the complex and dynamic nature of this process. Our preliminary results illustrate how environmental affordances (i.e. social media), closeness of the system and probability of outspokenness may impact how attitudes change over time. Additional analyses are also proposed.

This model represnts an unique human-aquifer interactions model for the Li-extraction in Salar de Atacama, Chile. It describes the local actors’ experience of mining-induced changes in the socio-ecological system, especially on groundwater changes and social stressors. Social interactions are designed specifically according to a long-term local fieldwork by Babidge et al. (2019, 2020). The groundwater system builds on the FlowLogo model by Castilla-Rho et al. (2015), which was then parameterized and calibrated with local hydrogeological inputs in Salar de Atacama, Chile. The social system of the ABM is defined and customozied based on empirical studies to reflect three major stressors: drought stress, population stress, and mining stress. The model reports evolution of groundwater changes and associated social stress dynamics within the modeled time frame.

The model is suitable to investigate the effects of different characteristics of apprenticeship programmes both in historical and contemporary societies. The model is built considering five societies, using an agent-based simulation model, we identified six main characteristics which impact the success of an apprenticeship programme in a society, which we measured by considering three parameters, namely the number of skilled agents produced by the apprenticeships, programme completion, and the contribution of programmes in the Gross Domestic Income (GDI) of the society. We investigate different definitions for success of an apprenticeship and some hypothetical societies to test some common beliefs about apprenticeships performance. The model also shows the number of unemployed agents given their work-based skills, wages, and the number of small and large companies who participate in training agents. The model enables exploring the impact of parameters, such as initial wages and the number of training years, along with the stated policies on the system.

This model simulations social and childcare provision in the UK. Agents within simulated households can decide to provide for informal care, or pay for private care, for their loved ones after they have provided for childcare needs. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. This model extends our previous simulations of social care by simulating the impact of childcare demand on social care availability within households, which is known to be a significant constraint on informal care provision.

Results show that our model replicates realistic patterns of social and child care provision, suggesting that this framework can be a valuable aid to policy-making in this area.

The purpose of this model is to explore the dynamics of residency and eviction for households renting in the greater Phoenix (Arizona) metropolitan area. The model uses a representative population of renters modified from American Community Survey (ACS) data that includes demographic, housing and economic information. Each month, households pay their subsistence, rental and utility bills. If a household is unable to pay their monthly rent or utility bill they apply for financial assistance. This model provides a platform to understand the impact of various economic shock upon households. Also, the model includes conditions that occurred as a result of the Covid-19 pandemic which allows for the study of eviction mitigation strategies that were employed, such as the eviction moratorium and stimulus payments. The model allows us to make preliminary predictions concerning the number of households that may be evicted once the moratorium on evictions ends and the long-term effects on the number of evicted households in the greater Phoenix area going forward.

The purpose of this model is the simulation of social care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. The model simulates care provision as a negotiation process conducted between agents across their kinship networks, with agents with stronger familial relationships to the recipient being more likely to attempt to allocate time to care provision. The model also simulates demographic change, the impact of socioeconomic status, and allows agents to relocate and change jobs or reduce working hours in order to provide care.
Despite the relative lack of empirical data in this model, the model is able to reproduce plausible patterns of social care provision. The inclusion of detailed economic and behavioural mechanisms allows this model to serve as a useful policy development tool; complex behavioural interventions can be implemented in simulation and tested on a virtual population before applying them in real-world contexts.

Multi-layer network agent-based model of the progression of the COVID19 infection, digital contact tracing

Pastoralscape is a model of human agents, lifestock health and contageous disease for studying the impact of human decision making in pastoral communities within East Africa on livestock populations. It implements an event-driven agent based model in Python 3.

Plastics and the pollution caused by their waste have always been a menace to both nature and humans. With the continual increase in plastic waste, the contamination due to plastic has stretched to the oceans. Many plastics are being drained into the oceans and rose to accumulate in the oceans. These plastics have seemed to form large patches of debris that keep floating in the oceans over the years. Identification of the plastic debris in the ocean is challenging and it is essential to clean plastic debris from the ocean. We propose a simple tool built using the agent-based modeling framework NetLogo. The tool uses ocean currents data and plastic data both being loaded using GIS (Geographic Information System) to simulate and visualize the movement of floatable plastic and debris in the oceans. The tool can be used to identify the plastic debris that has been piled up in the oceans. The tool can also be used as a teaching aid in classrooms to bring awareness about the impact of plastic pollution. This tool could additionally assist people to realize how a small plastic chunk discarded can end up as large debris drifting in the oceans. The same tool might help us narrow down the search area while looking out for missing cargo and wreckage parts of ships or flights. Though the tool does not pinpoint the location, it might help in reducing the search area and might be a rudimentary alternative for more computationally expensive models.