Computational Model Library

Displaying 10 of 922 results for "Chantal van Esch" clear search

DIAL1.0

P Dykstra | Published Wednesday, November 28, 2012 | Last modified Saturday, April 27, 2013

DIAL is a model of group dynamics and opinion dynamics. It features dialogues, in which agents put their reputation at stake. Intra-group radicalisation of opinions appears to be an emergent phenomenon.

Simulating the evolution of the human family

Paul Smaldino | Published Wednesday, November 29, 2017

The (cultural) evolution of cooperative breeding in harsh environments.

We construct an agent-based model to investigate and understand the roles of green attachment, engagement in local ecological investment (i.e., greening), and social feedback.

Peer reviewed A model of environmental awareness spread and its effect in resource consumption reduction

Giovanna Sissa | Published Sunday, June 21, 2015 | Last modified Monday, August 17, 2015

The model reproduces the spread of environmental awareness among agents and the impact of awareness level of the agents on the consumption of a resource, like energy. An agent is a household with a set of available advanced smart metering functions.

The model combines the two elements of disorganization and motivation to explore their impact on teams. Effects of disorganization on team task performance (problem solving)

Residents planned behaviour of waste sorting to explore urban situations

Jonathan Edgardo Cohen | Published Wednesday, June 07, 2023 | Last modified Thursday, March 14, 2024

Municipal waste management (MWM) is essential for urban development. Efficient waste management is essential for providing a healthy and clean environment, for reducing GHGs and for increasing the amount of material recycled. Waste separation at source is perceived as an effective MWM strategy that relays on the behaviour of citizens to separate their waste in different fractions. The strategy is straightforward, and many cities have adopted the strategy or are working to implement it. However, the success of such strategy depends on adequate understanding of the drivers of the behaviour of proper waste sorting. The Theory of Planned Behaviour (TPB) has been extensively applied to explain the behaviour of waste sorting and contributes to determining the importance of different psychological constructs. Although, evidence shows its validity in different contexts, without exploring how urban policies and the built environment affect the TPB, its application to urban challenges remains unlocked. To date, limited research has focused in exposing how different urban situations such as: distance to waste bins, conditions of recycling facilities or information campaigns affect the planned behaviour of waste separation. To fill this gap, an agent-based model (ABM) of residents capable of planning the behaviour of waste separation is developed. The study is a proof of concept that shows how the TPB can be combined with simulations to provide useful insights to evaluate different urban planning situations. In this paper we depart from a survey to capture TPB constructs, then Structural Equation Modelling (SEM) is used to validate the TPB hypothesis and extract the drivers of the behaviour of waste sorting. Finally, the development of the ABM is detailed and the drivers of the TPB are used to determine how the residents behave. A low-density and a high-density urban scenario are used to extract policy insights. In conclusion, the integration between the TPB into ABMs can help to bridge the knowledge gap between can provide a useful insight to analysing and evaluating waste management scenarios in urban areas. By better understanding individual waste sorting behaviour, we can develop more effective policies and interventions to promote sustainable waste management practices.

The purpose of this model is to understand the role of trade networks and their interaction with different fish resources, for fish provision. The model is developed based on a multi-methods approach, combining agent-based modeling, network analysis and qualitative data based on a small-scale fisheries study case. The model can be used to investigate both how trade network structures are embedded in a social-ecological context and the trade processes that occur within them, to analyze how they lead to emergent outcomes related to the resilience of fish provision. The model processes are informed by qualitative data analysis, and the social network analysis of an empirical fish trade network. The network analysis can be used to investigate diverse network structures to perform model experiments, and their influence on model outcomes.

The main outcomes we study are 1) the overexploitation of fish resources and 2) the availability and variability of fish provision to satisfy different market demands, and 3) individual traders’ fish supply at the micro-level. The model has two types of trader agents, seller and dealer. The model reveals that the characteristics of the trade networks, linked to different trader types (that have different roles in those networks), can affect the resilience of fish provision.

This purpose of this model is to understand how the coupled demographic dynamics of herds and households constrain the growth of livestock populations in pastoral systems.

Inquisitiveness in ad hoc teams

Davide Secchi | Published Sunday, October 18, 2015 | Last modified Thursday, June 11, 2020

This model builds on inquisitiveness as a key individual disposition to expand the bounds of their rationality. It represents a system where teams are formed around problems and inquisitive agents integrate competencies to find ‘emergent’ solutions.

Perceived Scientific Value and Impact Factor

Davide Secchi Stephen J Cowley | Published Wednesday, April 12, 2017 | Last modified Monday, January 29, 2018

The model explores the impact of journal metrics (e.g., the notorious impact factor) on the perception that academics have of an article’s scientific value.

Displaying 10 of 922 results for "Chantal van Esch" clear search

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.
Accept