Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
All users of models published in the library must cite model authors when they use and benefit from their code.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with additional detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
Displaying 10 of 51 results Social Networks clear
Non-traditional tools and mediums can provide unique methodological and interpretive opportunities for archaeologists. In this case, the Unreal Engine (UE), which is typically used for games and media, has provided a powerful tool for non-programmers to engage with 3D visualization and programming as never before. UE has a low cost of entry for researchers as it is free to download and has user-friendly “blueprint” tools that are visual and easily extendable. Traditional maritime mobility in the Salish Sea is examined using an agent-based model developed in blueprints. Focusing on the sea canoe travel of the Straits Salish northwestern Washington State and southwest British Columbia. This simulation integrates GIS data to assess travel time between Coast Salish archaeological village locations and archaeologically represented resource gathering areas. Transportation speeds informed by ethnographic data were used to examine travel times for short forays and longer inter-village journeys. The results found that short forays tended to half day to full day trips when accounting for resource gathering activities. Similarly, many locations in the Salish Sea were accessible in long journeys within two to three days, assuming fair travel conditions. While overall transportation costs to reach sites may be low, models such as these highlight the variability in transport risk and cost. The integration of these types of tools, traditionally used for entertainment, can increase the accessibility of modeling approaches to researchers, be expanded to digital storytelling, including aiding in the teaching of traditional ecological knowledge and placenames, and can have wide applications beyond maritime archaeology.
This is v0.01 of a UE5.2.1 agent based model.
Viable North Sea (ViNoS) is an Agent-based Model of the German North Sea Small-scale Fisheries in a Social-Ecological Systems framework focussing on the adaptive behaviour of fishers facing regulatory, economic, and resource changes. Small-scale fisheries are an important part both of the cultural perception of the German North Sea coast and of its fishing industry. These fisheries are typically family-run operations that use smaller boats and traditional fishing methods to catch a variety of bottom-dwelling species, including plaice, sole, and brown shrimp. Fisheries in the North Sea face area competition with other uses of the sea – long practiced ones like shipping, gas exploration and sand extractions, and currently increasing ones like marine protection and offshore wind farming. German authorities have just released a new maritime spatial plan implementing the need for 30% of protection areas demanded by the United Nations High Seas Treaty and aiming at up to 70 GW of offshore wind power generation by 2045. Fisheries in the North Sea also have to adjust to the northward migration of their established resources following the climate heating of the water. And they have to re-evaluate their economic balance by figuring in the foreseeable rise in oil price and the need for re-investing into their aged fleet.
The purpose of this model is to explore the impact of combining archaeological palimpsests with different methods of cultural transmission on the visibility of prehistoric social networks. Up until recently, Paleolithic archaeologists have relied on stylistic similarities of artifacts to reconstruct social networks. However, this method - which is successfully applied to more recent ceramic assemblages - may not be applicable to Paleolithic assemblages, as several of those consist of palimpsests of occupations. Therefore, this model was created to study how palimpsests of occupation affect our social network reconstructions.
The model simplifies inter-groups interactions between populations who share cultural traits as they produce artifacts. It creates a proxy archaeological record of artifacts with stylistic traits that can then be used to reconstruct interactions. One can thus use this model to compare the networks reconstructed through stylistic similarities with direct contact.
An agent-based framework to simulate the diffusion process of a piece of misinformation according to the SBFC model in which the fake news and its debunking compete in a social network. Considering new classes of agents, this model is closer to reality and proposed different strategies how to mitigate and control misinformation.
The Communication-Based Model of Perceived Descriptive Norm Dynamics in Digital Networks (COMM-PDND) is an agent-based model specifically created to examine the dynamics of perceived descriptive norms in the context of digital network structures. The model, developed as part of a master’s thesis titled “The Dynamics of Perceived Descriptive Norms in Digital Network Publics: An Agent-Based Simulation,” emphasizes the critical role of communication processes in norm formation. It focuses on the role of communicative interactions in shaping perceived descriptive norms.
The COMM-PDND is tuned to explore the effects of normative deviance in digital social networks. It provides functionalities for manipulating agents according to their network position, and has a versatile set of customizable parameters, making it adaptable to a wide range of research contexts.
This model takes concepts from a JASSS paper this is accepted for the October, 2023 edition and applies the concepts to empirical data from counties surrounding and including Cleveland Ohio. The agent-based model has a proportional number of agents in each of the counties to represent the correct proportions of adults in these counties. The adoption decision probability uses the equations from Bass (1969) as adapted by Rand & Rust (2011). It also includes the Outgroup aversion factor from Smaldino, who initially had used a different imitation model on line grid. This model uses preferential attachment network as a metaphor for social networks influencing adoption. The preferential network can be adjusted in the model to be created based on both nodes preferred due to higher rank as well as a mild preference for nodes of a like group.
We present a socio-epistemic model of science inspired by the existing literature on opinion dynamics. In this model, we embed the agents (or scientists) into social networks - e.g., we link those who work in the same institutions. And we place them into a regular lattice - each representing a unique mental model. Thus, the global environment describes networks of concepts connected based on their similarity. For instance, we may interpret the neighbor lattices as two equivalent models, except one does not include a causal path between two variables.
Agents interact with one another and move across the epistemic lattices. In other words, we allow the agents to explore or travel across the mental models. However, we constrain their movements based on absorptive capacity and cognitive coherence. Namely, in each round, an agent picks a focal point - e.g., one of their colleagues - and will move towards it. But the agents’ ability to move and speed depends on how far apart they are from the focal point - and if their new position is cognitive/logic consistent.
Therefore, we propose an analytical model that examines the connection between agents’ accumulated knowledge, social learning, and the span of attitudes towards mental models in an artificial society. While we rely on the example from the General Theory of Relativity renaissance, our goal is to observe what determines the creation and diffusion of mental models. We offer quantitative and inductive research, which collects data from an artificial environment to elaborate generalized theories about the evolution of science.
An Agent-Based Model to simulate agent reactions to threatening information based on the anxiety-to-approach framework of Jonas et al. (2014).
The model showcases the framework of BIS/BAS (inhibitory and approach motivated behavior) for the case of climate information, including parameters for anxiety, environmental awareness, climate scepticism and pro-environmental behavior intention.
Agents receive external information according to threat-level and information frequency. The population dynamic is based on the learning from that information as well as social contagion mechanisms through a scale-free network topology.
The model uses Netlogo 6.2 and the network extension.
The purpose of the model is to investigate how different factors affect the ability of researchers to reconstruct prehistoric social networks from artifact stylistic similarities, as well as the overall diversity of cultural traits observed in archaeological assemblages. Given that cultural transmission and evolution is affected by multiple interacting phenomena, our model allows to simultaneously explore six sets of factors that may condition how social networks relate to shared culture between individuals and groups:
The Social Identity Model of Protest Emergence (SIMPE), an agent-based model of national identity and protest mobilisations.
I developed this model for my PhD project, “Polarisation and Protest Mobilisation Around Secessionist Movements: an Agent-Based Model of Online and Offline Social Networks”, at the University of Glasgow (2019-2023).
The purpose of this model is to simulate protest emergence in a given country where there is an independence movement, fostering the self-categorisation process of national identification. In order to contextualised SIMPE, I have used Catalonia, where an ongoing secessionist movement since 2011 has been present, national identity has shown signs of polarisation, and where numerous mobilisations have taken place over the last decade. Data from the Catalan Centre of Opinion Studies (CEO) has been used to inform some of the model parameters.
Displaying 10 of 51 results Social Networks clear