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.

Presented here is a socioeconomic agent-based model (ABM) to examine the Hollywood labor system as a network within a simulated movie labor market based on preferential attachment and compare the findings with 50 co-production ego networks during the 2015 movie year. Using the ABM, I test the role slight individual preference for racial and ethnic similarity within one’s own network at the microlevel and find that it is insufficient to explain the phenomena of racial and ethnic underrepresentation at the macrolevel. The ABM also includes the ability to test alternative explanations, such as overt opportunity loss as a possible explanation.

This project was developed during the Santa Fe course Introduction to Agent-Based Modeling 2022. The origin is a Cellular Automata (CA) model to simulate human interactions that happen in the real world, from Rubens and Oliveira (2009). These authors used a market research with real people in two different times: one at time zero and the second at time zero plus 4 months (longitudinal market research). They developed an agent-based model whose initial condition was inherited from the results of the first market research response values and evolve it to simulate human interactions with Agent-Based Modeling that led to the values of the second market research, without explicitly imposing rules. Then, compared results of the model with the second market research. The model reached 73.80% accuracy.
In the same way, this project is an Exploratory ABM project that models individuals in a closed society whose behavior depends upon the result of interaction with two neighbors within a radius of interaction, one on the relative “right” and other one on the relative “left”. According to the states (colors) of neighbors, a given cellular automata rule is applied, according to the value set in Chooser. Five states were used here and are defined as levels of quality perception, where red (states 0 and 1) means unhappy, state 3 is neutral and green (states 3 and 4) means happy.
There is also a message passing algorithm in the social network, to analyze the flow and spread of information among nodes. Both the cellular automaton and the message passing algorithms were developed using the Python extension. The model also uses extensions csv and arduino.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

The NIER model is intended to add qualitative variables of building owner types and peer group scales to existing energy efficiency retrofit adoption models. The model was developed through a combined methodology with qualitative research, which included interviews with key stakeholders in Cleveland, Ohio and Detroit and Grand Rapids, Michigan. The concepts that the NIER model adds to traditional economic feasibility studies of energy retrofit decision-making are differences in building owner types (reflecting strategies for managing buildings) and peer group scale (neighborhoods of various sizes and large-scale Districts). Insights from the NIER model include: large peer group comparisons can quickly raise the average energy efficiency values of Leader and Conformist building owner types, but leave Stigma-avoider owner types as unmotivated to retrofit; policy interventions such as upgrading buildings to energy-related codes at the point of sale can motivate retrofits among the lowest efficient buildings, which are predominantly represented by the Stigma-avoider type of owner; small neighborhood peer groups can successfully amplify normal retrofit incentives.

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.

ViSA simulates the decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. The lack of sufficient supply of ESSs triggers stakeholders to apply different management options to increase their supply. However, while attempting to reduce the supply-demand gap, conflicts arise among stakeholders due to the tradeoff nature of some ESS. ViSA investigates conditions and scenarios that can minimize such supply-demand gap while reducing the risk of conflicts by suggesting different mixes of management options and decision rules.

This is a replication of the SequiaBasalto model, originally built in Cormas by Dieguez Cameroni et al. (2012, 2014, Bommel et al. 2014 and Morales et al. 2015). The model aimed to test various adaptations of livestock producers to the drought phenomenon provoked by climate change. For that purpose, it simulates the behavior of one livestock farm in the Basaltic Region of Uruguay. The model incorporates the price of livestock, fodder and paddocks, as well as the growth of grass as a function of climate and seasons (environmental submodel), the life cycle of animals feeding on the pasture (livestock submodel), and the different strategies used by farmers to manage their livestock (management submodel). The purpose of the model is to analyze to what degree the common management practices used by farmers (i.e., proactive and reactive) to cope with seasonal and interannual climate variations allow to maintain a sustainable livestock production without depleting the natural resources (i.e., pasture). Here, we replicate the environmental and livestock submodel using NetLogo.

One year is 368 days. Seasons change every 92 days. Each day begins with the growth of grass as a function of climate and season. This is followed by updating the live weight of cows according to the grass height of their patch, and grass consumption, which is determined based on the updated live weight. After consumption, cows grow and reproduce, and a new grass height is calculated. Cows then move to the patch with less cows and with the highest grass height. This updated grass height value will be the initial grass height for the next day.

ViSA 2.0.0 is an updated version of ViSA 1.0.0 aiming at integrating empirical data of a new use case that is much smaller than in the first version to include field scale analysis. Further, the code of the model is simplified to make the model easier and faster. Some features from the previous version have been removed.
It simulates decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. It investigates conditions and scenarios that can increase the supply of ecosystem services while keeping the viability of the social system by suggesting different mixes of initial unit utilities and decision rules.

The various technologies used inside a Dutch greenhouse interact in combination with an external climate, resulting in an emergent internal climate, which contributes to the final productivity of the greenhouse. This model examines how differing technology development styles affects the overall ability of a community of growers to approach the theoretical maximum yield.