This model is programmed in Python 3.6. We model how different consensus protocols and trade network topologies affect the performance of a blockchain system. The model consists of multiple trader and miner agents (Trader.py and Tx.py), and one system agent (System.py). We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS). We also examined three common trade network topologies: random, small-world, and scale-free. To reproduce our results, you may need to create some databases using, e.g., MySQL; or read and write some CSV files as model configurations.

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 [email protected]

This model is intended to explore the effectiveness of different courses of interventions on an abstract population of infections. Illustrative findings highlight the importance of the mechanisms for variability and mutation on the effectiveness of different interventions.

The ABM model is designed to model the adaptability of farmers in DTIM. This model includes two groups of farmers and local government admins agents. Farmers with different levels, with low WP of DTIM, are looking for economic benefits and reduced irrigation and production costs. Meanwhile, the government is looking for strategic goals to maintain water resources’ sustainability. The local government admins employ incentives (subsidies in this study) to encourage farmers to DTIM. In addition, it is used as a tool for supervision and training farmers’ performance. Farmers are currently harvesting water resources with irrigation systems and different levels of technology, and they intend to provide short-term benefits. Farmers adjust the existing approach based on their knowledge of the importance of DTIM and propensity to increase WP and cost-benefit evaluation. DTIM has an initial implementation fee. Every farmer can increase WP by using government subsidies. If none of the farmers create optimal use of water resources, access to water resources will be threatened in the long term. This is considered a hypothetical cost for farmers who do not participate in DTIM. With DTIM, considering that local government admins’ facilities cover an essential part of implementation costs, farmers may think of profiting from local government admins’ facilities by selling that equipment, especially if the farmers in the following conditions may consider selling their developed irrigation equipment. In this case, the technology of their irrigation system will return to the state before development.
- When the threshold of farmers’ propensity to DTIM is low (for example, in the conditions of scarcity of access to sufficient training about the new irrigation system or its role in reducing the cost and sustainability of water resources)
- When the share of government subsidy is high, and as a result, the profit from the sale of equipment is attractive, especially in conditions of inflation.
- Finally, farmers’ honesty threshold should be reduced based on the positive experience of profit-seeking and deception among neighbors.
Increasing the share of government subsidies can encourage farmers to earn profits. Therefore, the government can help increase farmers’ profits by considering the assessment teams at different levels with DTIM training . local government admins evaluations monitor the behavior of farmers. If farmers sell their improved irrigation system for profit, they may be deprived of some local government admins’ services and the possibility of receiving subsidies again. Assessments The local government admins can increase farmers’ honesty. Next, the ABM model evaluates local government admins policies to achieve a suitable framework for water resources management in the Miandoab region.

An Agent-based model simulates consumer demand for Smart Metering tariffs. It utilizes the Bass Diffusion Model and Rogers´s adopter categories. Integration of empirical census microdata enables a validated socio-economic background for each consumer.

Demand planning requires processing of distributed information. In this process, individuals, their properties and interactions play a crucial role. This model is a computational testbed to investigate these aspects with respect to forecast accuracy.

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 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

The purpose of this model is to explore the influence of integrating individuals’ behavioral dynamics in an agent-based model of COVID-19, on the dynamics of disease transmission. The model is an agent-based extention of an established large-scale Individual-based model called STRIDE. Four risk factors determine the individual’s perception of the risk and how they behave accordingly. It is assumed that individuals with higher levels of risk perception adopt higher levels of contact reduction in their daily routines. Individuals can assign different weights to any of the four different risk factors, i.e., the modeler can model different populations and explore how the transmission dynamics vary among them.

This is a replication of the SequiaBasalto model, originally built in Cormas by Dieguez Cameroni et al. (2012, 2014, Bommel et al. 2014 and Morales et al. 2015). The model aimed to test various adaptations of livestock producers to the drought phenomenon provoked by climate change. For that purpose, it simulates the behavior of one livestock farm in the Basaltic Region of Uruguay. The model incorporates the price of livestock, fodder and paddocks, as well as the growth of grass as a function of climate and seasons (environmental submodel), the life cycle of animals feeding on the pasture (livestock submodel), and the different strategies used by farmers to manage their livestock (management submodel). The purpose of the model is to analyze to what degree the common management practices used by farmers (i.e., proactive and reactive) to cope with seasonal and interannual climate variations allow to maintain a sustainable livestock production without depleting the natural resources (i.e., pasture). Here, we replicate the environmental and livestock submodel using NetLogo.

One year is 368 days. Seasons change every 92 days. Each day begins with the growth of grass as a function of climate and season. This is followed by updating the live weight of cows according to the grass height of their patch, and grass consumption, which is determined based on the updated live weight. After consumption, cows grow and reproduce, and a new grass height is calculated. Cows then move to the patch with less cows and with the highest grass height. This updated grass height value will be the initial grass height for the next day.