Computational Model Library

Displaying 10 of 54 results agent based model (abm) clear search

The model is an agent-based artificial stock market where investors connect in a dynamic network. The network is dynamic in the sense that the investors, at specified intervals, decide whether to keep their current adviser (those investors they receive trading advise from). The investors also gain information from a private source and share public information about the risky asset. Investors have different tendencies to follow the different information sources, consider differing amounts of history, and have different thresholds for investing.

We compare three model estimates for the time and treatment requirements to eliminate HCV among HIV-positive MSM in Victoria, Australia: a compartmental model; an ABM parametrized by surveillance data; and an ABM with a more heterogeneous population.

The purpose of the OMOLAND-CA is to investigate the adaptive capacity of rural households in the South Omo zone of Ethiopia with respect to variation in climate, socioeconomic factors, and land-use at the local level.

Transport simulation in a real road network

Jiaqi Ge Gary Polhill | Published Tuesday, April 17, 2018 | Last modified Tuesday, April 17, 2018

Ge, J., & Polhill, G. (2016). Exploring the Combined Impact of Factors Influencing Commuting Patterns and CO2 Emission in Aberdeen Using an Agent-Based Model. Journal of Artificial Societies and Social Simulation, 19(3). http://jasss.soc.surrey.ac.uk/19/3/11.html
We develop an agent-based transport model using a realistic GIS-enabled road network and the car following method. The model can be used to study the impact of social interventions such as flexi-time and workplace sharing, as well as large infrastructure such as the construction of a bypass or highway. The model is developed in Netlogo version 5 and requires road network data in GIS format to run.

TRUE GRASP

Marco Braasch Luis García-Barrios | Published Tuesday, April 03, 2018

TRUE GRASP (Tree Recruitment Under Exotic GRAsses in a Savanna-Pineland)
is a socio-ecological agent-based model (ABM) and role playing game (RPG) for farmers and other stakeholders involved in rural landscape planning.

The purpose of this model is to allow actors to explore the individual and combined effects - as well as tradeoffs - of three methods of controlling exotic grasses in pine savannas: fire, weeding, and grazing cattle.

Design of TRUE GRASP is based on 3 years of socio-ecological fieldwork in a human-induced pine savanna in La Sepultura Biosphere Reserve (SBR) in the Mexican state of Chiapas. In this savanna, farmers harvest resin from Pinus oocarpa, which is used to produce turpentine and other products. However, long term persistence of this activity is jeopardized by low tree recruitment due to exotic tall grass cover in the forest understory (see Braasch et al., 2017). The TRUE GRASP model provides the user with different management strategies for controlling exotic grass cover and avoiding possible regime shifts, which in the case of the SBR would jeopardize resin harvesting.

PolicySpace models public policies within an empirical, spatial environment using data from 46 metropolitan regions in Brazil. The model contains citizens, markets, residences, municipalities, commuting and a the tax scheme. In the associated publications (book in press and https://arxiv.org/abs/1801.00259) we validate the model and demonstrate an application of the fiscal analysis. Besides providing the basics of the platform, our results indicate the relevance of the rules of taxes transfer for cities’ quality of life.

This is an initial exploratory exercise done for the class @ http://thiagomarzagao.com/teaching/ipea/ Text available here: https://arxiv.org/abs/1712.04429v1
The program:
Reads output from an ABM model and its parameters’ configuration
Creates a socioeconomic optimal output based on two ABM results of the modelers choice
Organizes the data as X and Y matrices
Trains some Machine Learning algorithms

DiDIY Factory

Ruth Meyer | Published Tuesday, February 20, 2018

The DiDIY-Factory model is a model of an abstract factory. Its purpose is to investigate the impact Digital Do-It-Yourself (DiDIY) could have on the domain of work and organisation.

DiDIY can be defined as the set of all manufacturing activities (and mindsets) that are made possible by digital technologies. The availability and ease of use of digital technologies together with easily accessible shared knowledge may allow anyone to carry out activities that were previously only performed by experts and professionals. In the context of work and organisations, the DiDIY effect shakes organisational roles by such disintermediation of experts. It allows workers to overcome the traditionally strict organisational hierarchies by having direct access to relevant information, e.g. the status of machines via real-time information systems implemented in the factory.

A simulation model of this general scenario needs to represent a more or less abstract manufacturing firm with supervisors, workers, machines and tasks to be performed. Experiments with such a model can then be run to investigate the organisational structure –- changing from a strict hierarchy to a self-organised, seemingly anarchic organisation.

Mobility USA (MUSA)

Davide Natalini Giangiacomo Bravo | Published Sunday, December 08, 2013 | Last modified Monday, December 30, 2013

MUSA is an ABM that simulates the commuting sector in USA. A multilevel validation was implemented. Social network with a social-circle structure included. Two types of policies have been tested: market-based and preference-change.

Due to the large extent of the Harz National Park, an accurate measurement of visitor numbers and their spatiotemporal distribution is not feasible. This model demonstrates the possibility to simulate the streams of visitors with ABM methodology.

Displaying 10 of 54 results agent based model (abm) clear search

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