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 is an agent-based model that captures the dynamic processes related to moving from an educational system where the school a student attends is based on assignment to a neighborhood school, to one that gives households more choice among existing and newly formed public schools.

“Food for all” (FFD) is an agent-based model designed to study the evolution of cooperation for food storage. Households face the social dilemma of whether to store food in a corporate stock or to keep it in a private stock.

It is NetLogo reconstruction of the original FORTRAN code of the classical M. Cohen, J. March, and J. Olsen “garbage can model” (GCM or CMO) of collective decision-making.

A NetLogo ABM developed to explore unarmed resistance to an active shooter. The landscape is a generalized open outdoor area. Parameters enable the user to set shooter armament and control for assumptions with regard to shooter accuracy.

Sahelian transhumance is a type of socio-economic and environmental pastoral mobility. It involves the movement of herds from their terroir of origin (i.e., their original pastures) to one or more host terroirs, followed by a return to the terroir of origin.  According to certain pastoralists, the mobility of herds is planned to prevent environmental degradation, given the continuous dependence of these herds on their environment. However, these herds emit Greenhouse Gases (GHGs) in the spaces they traverse. Given that GHGs contribute to global warming, our long-term objective is to quantify the GHGs emitted by Sahelian herds. The determination of these herds’ GHG emissions requires: (1) the artificial replication of the transhumance, and (2) precise knowledge of the space used during their transhumance.
This article presents the design of an artificial replication of the transhumance through an agent-based model named MSTRANS. MSTRANS determines the space used by transhumant herds, based on the decision-making process of Sahelian transhumants.
MSTRANS integrates a constrained multi-objective optimization problem and algorithms into an agent-based model. The constrained multi-objective optimization problem encapsulates the rationality and adaptability of pastoral strategies. Interactions between a transhumant and its socio-economic network are modeled using algorithms, diffusion processes, and within the multi-objective optimization problem. The dynamics of pastoral resources are formalized at various spatio-temporal scales using equations that are integrated into the algorithms.
The results of MSTRANS are validated using GPS data collected from transhumant herds in Senegal. MSTRANS results highlight the relevance of integrated models and constrained multi-objective optimization for modeling and monitoring the movements of transhumant herds in the Sahel. Now specialists in calculating greenhouse gas emissions have a reproducible and reusable tool for determining the space occupied by transhumant herds in a Sahelian country. In addition, decision-makers, pastoralists, veterinarians and traders have a reproducible and reusable tool to help them make environmental and socio-economic decisions.

The mode implements a variant of Ant Colony Optimization to explore routing on infrastructures through a landscape with forbidden zones, connecting multiple sinks to one source.

This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.

The purpose of the simulation was to explore and better understand the process of bridging between an analysis of qualitative data and the specification of a simulation. This may be developed for more serious processes later but at the moment it is merely an illustration.
This exercise was done by Stephanie Dornschneider (School of Politics and International Relations, University College Dublin) and Bruce Edmonds to inform the discussion at the Lorentz workshop on “Integrating Qualitative and Quantitative Data using Social Simulation” at Leiden in April 2019. The qualitative data was collected and analysed by SD. The model specification was developed as the result of discussion by BE & SD. The model was programmed by BE. This is described in a paper submitted to Social Simulation 2019 and (to some extent) in the slides presented at the workshop.

The model is a combination of a spatially explicit, stochastic, agent-based model for wild boars (Sus scrofa L.) and an epidemiological model for the Classical Swine Fever (CSF) virus infecting the wild boars.

The original model (Kramer-Schadt et al. 2009) was used to assess intrinsic (system immanent host-pathogen interaction and host life-history) and extrinsic (spatial extent and density) factors contributing to the long-term persistence of the disease and has further been used to assess the effects of intrinsic dynamics (Lange et al. 2012a) and indirect transmission (Lange et al. 2016) on the disease course. In an applied context, the model was used to test the efficiency of spatiotemporal vaccination regimes (Lange et al. 2012b) as well as the risk of disease spread in the country of Denmark (Alban et al. 2005).

References: See ODD model description.