Load shedding enjoys increasing popularity as a way to reduce power consumption in buildings during hours of peak demand on the electricity grid. This practice has well known cost saving and reliability benefits for the grid, and the contracts utilities sign with their “interruptible” customers often pass on substantial electricity cost savings to participants. Less well-studied are the impacts of load shedding on building occupants, hence this study investigates those impacts on occupant comfort and adaptive behaviors. It documents experience in two office buildings located near Philadelphia (USA) that vary in terms of controllability and the set of adaptive actions available to occupants. An agent-based model (ABM) framework generalizes the case-study insights in a “what-if” format to support operational decision making by building managers and tenants. The framework, implemented in EnergyPlus and NetLogo, simulates occupants that have heterogeneous
thermal and lighting preferences. The simulated occupants pursue local adaptive actions such as adjusting clothing or using portable fans when central building controls are not responsive, and experience organizational constraints, including a corporate dress code and miscommunication with building managers. The model predicts occupant decisions to act fairly well but has limited ability to predict which specific adaptive actions occupants will select.

Juan Castilla-Rho et al. (2015) developed a platform, named FLowLogo, which integrates a 2D, finite-difference solution of the governing equations of groundwater flow with agent-based simulation. We used this model for Rafsanjan Aquifer, which is located in an arid region in Iran. To use FLowLogo for a real case study, one needs to add GIS shapefiles of boundary conditions and modify the code written in NetLogo a little bit. The FlowLogo model used in our research is presented here.

In an associated paper which focuses on analyzing the structure of several egocentric networks of collective awareness platforms for sustainable innovation (CAPS), this model is developed. It answers the question whether the network structure is determinative for the sustainability of the created awareness. Based on a thorough literature review a model is developed to explain and operationalize the concept of sustainability of a social network in terms of importance, effectiveness and robustness. By developing this agent-based model, the expected outcomes after the dissolution of the CAPS are predicted and compared with the results of a network with the same participants but with different ties. Twitter data from different CAPS is collected and used to feed the simulation. The results show that the structure of the network is of key importance for its sustainability. With this knowledge and the ability to simulate the results after network changes have taken place, CAPS can assess the sustainability of their legacy and actively steer towards a longer lasting potential for social innovation. The retrieved knowledge urges organizations like the European Commission to adopt a more blended approach focusing not only on solving societal issues but on building a community to sustain the initiated development.

This ABM re-implements and extends the simulation model of peer review described in Squazzoni & Gandelli (Squazzoni & Gandelli, 2013 - doi:10.18564/jasss.2128) (hereafter: ‘SG’). The SG model was originally developed for NetLogo and is also available in CoMSES at this link.
The purpose of the original SG model was to explore how different author and reviewer strategies would impact the outcome of a journal peer review system on an array of dimensions including peer review efficacy, efficiency and equality. In SG, reviewer evaluation consists of a continuous variable in the range [0,1], and this evaluation scale is the same for all reviewers. Our present extension to the SG model allows to explore the consequences of two more realistic assumptions on reviewer evaluation: (1) that the evaluation scale is discrete (e.g. like in a Likert scale); (2) that there may be differences among their interpretation of the grades of the evaluation scale (i.e. that the grade language is heterogeneous).

This model extends the original Artifical Anasazi (AA) model to include individual agents, who vary in age and sex, and are aggregated into households. This allows more realistic simulations of population dynamics within the Long House Valley of Arizona from AD 800 to 1350 than are possible in the original model. The parts of this model that are directly derived from the AA model are based on Janssen’s 1999 Netlogo implementation of the model; the code for all extensions and adaptations in the model described here (the Artificial Long House Valley (ALHV) model) have been written by the authors. The AA model included only ideal and homogeneous “individuals” who do not participate in the population processes (e.g., birth and death)–these processes were assumed to act on entire households only. The ALHV model incorporates actual individual agents and all demographic processes affect these individuals. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. Thus, the ALHV model is a combination of individual processes (birth and death) and household-level processes (e.g., finding suitable agriculture plots).

As is the case for the AA model, the ALHV model makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original model (from Janssen’s Netlogo implementation) to estimate annual maize productivity of various agricultural zones within the valley. These estimates are used to determine suitable locations for households and farms during each year of the simulation.

A simple model that aims to demonstrate the influence of agri-environmental payments on land-use patterns in a virtual landscape. The landscape consists of grassland (which can be managed extensively or intensively) and a river. Agri-environmental payments are provided for extensive management of grassland. Additionally, there are boni for (a) extensive grassland in proximity of the river; and (b) clusters (“agglomerations”) of extensive grassland. The farmers, who own randomly distributed grassland patches, make decisions either on the basis of simple income maximization or they maximize only up to an income threshold beyond which they seize making changes in management. The resulting landscape pattern is evaluated by means of three simple models for (a) agricultural yield, (b) habitat/biodiversity and (c) water quality. The latter two correspond to the two boni. The model has been developed within a small project called Aligning Agent-Based Modelling with Multi-Objective Land-Use Allocation (ALABAMA).

A demonstration model showing how modellers can create a multi regional tram network with commuters, destinations and houses. The model offers options to create a random tram network made from modeller input or to load shapefiles for the Greater Manchester Metrolink.

The model uses NetLogo with gis, nw an csv extensions.

The model simulates the decisions of residents and a water authority to respond to socio-hydrological hazards. Residents from neighborhoods are located in a landscape with topographic complexity and two problems: water scarcity in the peripheral neighborhoods at high altitude and high risk of flooding in the lowlands, at the core of the city. The role of the water authority is to decide where investments in infrastructure should be allocated to reduce the risk to water scarcity and flooding events in the city, and these decisions are made via a multi-objective site selection procedure. This procedure accounts for the interdependencies and feedback between the urban landscape and a policy scenario that defines the importance, or priorities, that the authority places on four criteria.
Neighborhoods respond to the water authority decisions by protesting against the lack of investment and the level of exposure to water scarcity and flooding. Protests thus simulate a form of feedback between local-level outcomes (flooding and water scarcity) and higher-level decision-making. Neighborhoods at high altitude are more likely to be exposed to water scarcity and lack infrastructure, whereas neighborhoods in the lowlands tend to suffer from recurrent flooding. The frequency of flooding is also a function of spatially uniform rainfall events. Likewise, neighborhoods at the periphery of the urban landscape lack infrastructure and suffer from chronic risk of water scarcity.
The model simulates the coupling between the decision-making processes of institutional actors, socio-political processes and infrastructure-related hazards. In the documentation, we describe details of the implementation in NetLogo, the description of the procedures, scheduling, and the initial conditions of the landscape and the neighborhoods.
This work was supported by the National Science Foundation under Grant No. 1414052, CNH: The Dynamics of Multi-Scalar Adaptation in Megacities (PI Hallie Eakin).

The model demonstrates how non-instantaneous sampling techniques produce bias by overestimating the number of counted animals, when they move relative to the person counting them.

This model grows land use patterns that emerge as a result of land-use compatibilities stablished in urban development plans, land topography, and street networks. It contains urban brushes to paint streets and land uses as a way to learn about urban pattern emergence through free experimentation.