Computational Model Library

Digital social networks facilitate the opinion dynamics and idea flow and also provide reliable data to understand these dynamics. Public opinion and cooperation behavior are the key factors to determine the capacity of a successful and effective public policy. In particular, during the crises, such as the Corona virus pandemic, it is necessary to understand the people’s opinion toward a policy and the performance of the governance institutions. The problem of the mathematical explanation of the human behaviors is to simplify and bypass some of the essential process. To tackle this problem, we adopted a data-driven strategy to extract opinion and behavioral patterns from social media content to reflect the dynamics of society’s average beliefs toward different topics. We extracted important subtopics from social media contents and analyze the sentiments of users at each subtopic. Subsequently, we structured a Bayesian belief network to demonstrate the macro patters of the beliefs, opinions, information and emotions which trigger the response toward a prospective policy. We aim to understand the factors and latent factors which influence the opinion formation in the society. Our goal is to enhance the reality of the simulations. To capture the dynamics of opinions at an artificial society we apply agent-based opinion dynamics modeling. We intended to investigate practical implementation scenarios of this framework for policy analysis during Corona Virus Pandemic Crisis. The implemented modular modeling approach could be used as a flexible data-driven policy making tools to investigate public opinion in social media. The core idea is to put the opinion dynamics in the wider contexts of the collective decision-making, data-driven policy-modeling and digital democracy. We intended to use data-driven agent-based modeling as a comprehensive analysis tools to understand the collective opinion dynamics and decision making process on the social networks and uses this knowledge to utilize network-enabled policy modeling and collective intelligence platforms.

CINCH1 (Covid-19 INfection Control in Hospitals)

N Gotts | Published Sun Aug 29 13:13:03 2021

CINCH1 (Covid-19 INfection Control in Hospitals), is a prototype model of physical distancing for infection control among staff in University College London Hospital during the Covid-19 pandemic, developed at the University of Leeds, School of Geography. It models the movement of collections of agents in simple spaces under conflicting motivations of reaching their destination, maintaining physical distance from each other, and walking together with a companion. The model incorporates aspects of the Capability, Opportunity and Motivation of Behaviour (COM-B) Behaviour Change Framework developed at University College London Centre for Behaviour Change, and is aimed at informing decisions about behavioural interventions in hospital and other workplace settings during this and possible future outbreaks of highly contagious diseases. CINCH1 was developed as part of the SAFER (SARS-CoV-2 Acquisition in Frontline Health Care Workers – Evaluation to Inform Response) project
(https://www.ucl.ac.uk/behaviour-change/research/safer-sars-cov-2-acquisition-frontline-health-care-workers-evaluation-inform-response), funded by the UK Medical Research Council. It is written in Python 3.8, and built upon Mesa version 0.8.7 (copyright 2020 Project Mesa Team).

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.

This is model that explores how a few farmers in a Chinese village, where all farmers are smallholders originally, reach optimal farming scale by transferring in farmland from other farmers in the context of urbanization and aging.

Peer reviewed AMRO_CULEX_WNV

Aniruddha Belsare Jennifer Owen | Published Sat Feb 27 00:21:08 2021 | Last modified Thu Mar 11 23:13:31 2021

An agent-based model simulating West Nile Virus dynamics in a one host (American robin)-one vector (Culex spp. mosquito) system. ODD improved and code cleaned.

This software simulates cars and bicycles as traffic participants while crossing different crossroad designs such as roundabouts, protected crossroads and standard crossroads. It is written in Netlogo 6.2 and aims to identify safety characteristics of these layouts using agent-based modeling. Participants track the line of sight to each other and print them as an output alongside with the adjacent destination, used layout, count of collisions/cars/bicycles and time.

Detailed information can be found within the info tab of the program itself.

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.

Style_Net_01

Andrew White | Published Tue Aug 3 16:06:06 2021

Style_Net_01 is a spatial agent-based model designed to serve as a platform for exploring geographic patterns of tool transport and discard among seasonally mobile hunter-gatherer populations. The model has four main levels: artifact, person, group, and system. Persons make, use, and discard artifacts. Persons travel in groups within the geographic space of the model. The movements of groups represent a seasonal pattern of aggregation and dispersal, with all groups coalescing at an aggregation site during one point of the yearly cycle. The scale of group mobility is controlled by a parameter. The creation, use, and discard of artifacts is controlled by several parameters that specify how many tools each person carries in a personal inventory, how many times each tool can be used before it is discarded, and the frequency of tool usage. A lithic source (representing a geographically-specific, recognizable source of stone for tools) can be placed anywhere in the geographic space of the model.

SiFlo is an ABM dedicated to simulate flood events in urban areas. It considers the water flowing and the reaction of the inhabitants. The inhabitants would be able to perform different actions regarding the flood: protection (protect their house, their equipment and furniture…), evacuation (considering traffic model), get and give information (considering imperfect knowledge), etc. A special care was taken to model the inhabitant behavior: the inhabitants should be able to build complex reasoning, to have emotions, to follow or not instructions, to have incomplete knowledge about the flood, to interfere with other inhabitants, to find their way on the road network. The model integrates the closure of roads and the danger a flooded road can represent. Furthermore, it considers the state of the infrastructures and notably protection infrastructures as dyke. Then, it allows to simulate a dyke breaking.
The model intends to be generic and flexible whereas provide a fine geographic description of the case study. In this perspective, the model is able to directly import GIS data to reproduce any territory. The following sections expose the main elements of the model.

Modeling Personal Carbon Trading with ABM

Roman Seidl | Published Fri Dec 7 13:35:10 2018 | Last modified Thu Jul 29 07:52:21 2021

A simulated approach for Personal Carbon Trading, for figuring out what effects it might have if it will be implemented in the real world. We use an artificial population with some empirical data from international literature and basic assumptions about heterogeneous energy demand. The model is not to be used as simulating the actual behavior of real populations, but a toy model to test the effects of differences in various factors such as number of agents, energy price, price of allowances, etc. It is important to adapt the model for specific countries as carbon footprint and energy demand determines the relative success of PCT.

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