This model analyzes two investors forming their expectations with heterogeneous strategies in order to optimize their portfolios by means of a Sharpe ratio maximization. Traders are distinguished according to their methodology used in forecasting. Two acknowledged algorithms of technical analysis have been implemented to compare portfolios performances and assess profitability of each technique.

The Rigor and Transparency Reporting Standard (RAT-RS) is a tool to improve the documentation of data use in Agent-Based Modelling. Following the development of reporting standards for models themselves, attention to empirical models has now reached a stage where these standards need to take equally effective account of data use (which until now has tended to be an afterthought to model description). It is particularly important that a standard should allow the reporting of the different uses to which data may be put (specification, calibration and validation), but also that it should be compatible with the integration of different kinds of data (for example statistical, qualitative, ethnographic and experimental) sometimes known as mixed methods research.

For the full details on the RAT-RS, please refer to the related publication “RAT-RS: A Reporting Standard for Improving the Documentation of Data Use in Agent-Based Modelling” (http://dx.doi.org/10.1080/13645579.2022.2049511).

Here we provide supplementary material for this article, consisting of a RAT-RS user guide and RAT-RS templates.

Chicago’s demographic, neighborhood, sex risk behaviors, sexual network data, and HIV prevention and treatment cascade information from 2015 were integrated as input to a new agent-based model (ABM) called the Levers-of-HIV-Model (LHM). This LHM, written in NetLogo, forms patterns of sexual relations among Men who have Sex with Men (MSM) based on static traits (race/ethnicity, and age) and dynamic states (sexual relations and practices) that are found in Chicago. LHM’s five modules simulate and count new infections at the two marker years of 2023 and 2030 for a wide range of distinct scenarios or levers, in which the levels of PrEP and ART linkage to care, retention, and adherence or viral load are increased over time from the 2015 baseline levels.

This is an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network.

The ABM contains two types of agents: insurers and customers. These act within the environment of a motor insurance market. At each simulation, the model undergoes the following steps:

1. Network generation: At the start of the simulation, the model generates a small world network of social links between the customers, and randomly assigns each customer to an initial insurer
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Motivated by the emergence of new Peer-to-Peer insurance organizations that rethink how insurance is organized, we propose a theoretical model of decision-making in risk-sharing arrangements with risk heterogeneity and incomplete information about the risk distribution as core features. For these new, informal organisations, the available institutional solutions to heterogeneity (e.g., mandatory participation or price differentiation) are either impossible or undesirable. Hence, we need to understand the scope conditions under which individuals are motivated to participate in a bottom-up risk-sharing setting. The model puts forward participation as a utility maximizing alternative for agents with higher risk levels, who are more risk averse, are driven more by solidarity motives, and less susceptible to cost fluctuations. This basic micro-level model is used to simulate decision-making for agent populations in a dynamic, interdependent setting. Simulation results show that successful risk-sharing arrangements may work if participants are driven by motivations of solidarity or risk aversion, but this is less likely in populations more heterogeneous in risk, as the individual motivations can less often make up for the larger cost deficiencies. At the same time, more heterogeneous groups deal better with uncertainty and temporary cost fluctuations than more homogeneous populations do. In the latter, cascades following temporary peaks in support requests more often result in complete failure, while under full information about the risk distribution this would not have happened.

An agent-based model for the diffusion of innovations with multiple characteristics and price-premiums

AMIRIS is the Agent-based Market model for the Investigation of Renewable and Integrated energy Systems.

It is an agent-based simulation of electricity markets and their actors.
AMIRIS enables researches to analyse and evaluate energy policy instruments and their impact on the actors involved in the simulation context.
Different prototypical agents on the electricity market interact with each other, each employing complex decision strategies.
AMIRIS allows to calculate the impact of policy instruments on economic performance of power plant operators and marketers.
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The purpose of this curricular model is to teach students the basics of modeling complex systems using agent-based modeling. It is a simple SIR model that simulates how a disease spreads through a population as its members change from susceptible to infected to recovered and then back to susceptible. The dynamics of the model are such that there are multiple emergent outcomes depending on the parameter settings, initial conditions, and chance.

The curricular model can be used with the chapter Agent-Based Modeling in Mixed Methods Research (Moritz et al. 2022) in the Handbook of Teaching Qualitative & Mixed Methods (Ruth et al. 2022).

The instructional videos can be accessed on YouTube: Video 1 (https://youtu.be/32_JIfBodWs); Video 2 (https://youtu.be/0PK_zVKNcp8); and Video 3 (https://youtu.be/0bT0_mYSAJ8).

This is a ridesharing model (Uber/Lyft) of the larger Washington DC metro area. The model can be modified (Netlogo 6.x) relatively easily and be adapted to any metro area. Please cite generously (this was a lot of work) and please cite the paper, not the comses model.

Link to the paper published in “Complex Adaptive Systems” here: https://link.springer.com/chapter/10.1007/978-3-030-20309-2_7

Citation: Shaheen J.A.E. (2019) Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM). In: Carmichael T., Collins A., Hadžikadić M. (eds) Complex Adaptive Systems. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-20309-2_7

In this model, the spread of a virus disease in a network consisting of school pupils, employed, and umemployed people is simulated. The special feature in this model is the distinction between different types of links: family-, friends-, school-, or work-links. In this way, different governmental measures can be implemented in order to decelerate or stop the transmission.