Considering that two of the three avoider species could not reach the target area in the inittial scenario, five alternative corridor scenarios were created. In all cases, we generated a greater amount of cover area under ‘Urban forest’, including elements such as scattered trees, woody plants, wooded areas, and rows of trees. This covered type was selected since all three species use it as a regular habitat. That is the second sceneario where those ecological parks and other areas inside the capital city were boostered into “urban forest patches” or buffer points, with the idea of improving the survive of the three bird species and their movement. However one of the most restrictive specie was still having movement and survival issues.

ABMIND, the Agent-Based Model of Individual Psychological Distance, is a modeling framework developed to examine how psychological distance influences environmental protection behavior in coastal farming communities in southern China. Using household survey data and empirically estimated behavioral pathways, the model represents how uncertainty shapes four dimensions of psychological distance, namely temporal, spatial, social and hypothetical distance, and how these dimensions guide protection and degradation decisions. Agents include households, government actors and mangrove ecosystem patches, connected through social networks and ecological feedbacks that affect learning, expectations and perceived benefits. Policy interventions such as rewards, penalties and publicity guidance efforts work by modifying uncertainty and psychological distance rather than directly controlling behavior. ABMIND is implemented as a spatially explicit model following the ODD protocol, and a concise user guide is provided. In developing ABMIND we introduce a structured validation workflow that links statistical mediation analysis with simulation-based diagnostics, allowing empirical cognitive mechanisms to be systematically embedded and tested within the ABM. This integrated approach strengthens the credibility of psychological-mechanism models and supports their use in policy evaluation. The framework offers a methodological platform for integrating cognitive mechanisms into agent-based environmental behavior modeling and for evaluating policy strategies that support ecosystem protection.
Model paper:
ABMIND: An empirically informed agent-based model of psychological distance and environmental protection behaviour
Ecological Modelling
https://doi.org/10.1016/j.ecolmodel.2026.111700

This model is an agent-based simulation designed to explore how climate-induced environmental degradation can contribute to the emergence of social violence in coastal communities that depend heavily on ecosystem services for their livelihoods. The model represents a coupled social–ecological system in which environmental shocks—such as sea level rise and marine ecosystem decline—affect local economic conditions, food security, and community stability.

Agents in the model represent individuals whose livelihoods depend on coastal ecosystems. Environmental degradation reduces ecosystem productivity and increases economic hardship, which can lead to the formation of grievances among agents. The model incorporates behavioral thresholds that determine how individuals respond to hardship and perceived injustice. Under certain conditions—particularly when institutional capacity and law enforcement effectiveness are limited—these grievances may escalate into violent behavior.

The simulation allows users to explore how different climate scenarios, levels of ecosystem degradation, livelihood dependence, and institutional responses influence the probability of social instability and violence. By modeling the interactions between environmental stress, socio-economic vulnerability, and governance capacity, the model provides a computational framework for examining potential pathways linking climate change and conflict in coastal social–ecological systems.

…

This is a generic sub-model of animal territory formation. It is meant to be a reusable building block, but not in the plug-and-play sense, as amendments are likely to be needed depending on the species and region. The sub-model comprises a grid of cells, reprenting the landscape. Each cell has a “quality” value, which quantifies the amount of resources provided for a territory owner, for example a tiger. “Quality” could be prey density, shelter, or just space. Animals are located randomly in the landscape and add grid cells to their intial cell until the sum of the quality of all their cells meets their needs. If a potential new cell to be added is owned by another animal, competition takes place. The quality values are static, and the model does not include demography, i.e. mortality, mating, reproduction. Also, movement within a territory is not represented.

This package implements a simplified artificial agent-based demographic model of the UK. Individuals of an initial population are subject to ageing, deaths, births, divorces and marriages. A specific case-study simulation is progressed with a user-defined simulation fixed step size on a hourly, daily, weekly, monthly basis or even an arbitrary user-defined clock rate. While the model can serve as a base model to be adjusted to realistic large-scale socio-economics, pandemics or social interactions-based studies mainly within a demographic context, the main purpose of the model is to explore and exploit capabilities of the state-of-the-art Agents.jl Julia package as well as other ecosystem of Julia packages like GlobalSensitivity.jl. Code includes examples for evaluating global sensitivity analysis using Morris and Sobol methods and local sensitivity analysis using OFAT and OAT methods. Multi-threaded parallelization is enabled for improved runtime performance.

The intention of this model is to create an universal basis on how to model change in value prioritizations within social simulation. This model illustrates the designing of heterogeneous populations within agent-based social simulations by equipping agents with Dynamic Value-based Cognitive Architectures (DVCA-model). The DVCA-model uses the psychological theories on values by Schwartz (2012) and character traits by McCrae and Costa (2008) to create an unique trait- and value prioritization system for each individual. Furthermore, the DVCA-model simulates the impact of both social persuasion and life-events (e.g. information, experience) on the value systems of individuals by introducing the innovative concept of perception thermometers. Perception thermometers, controlled by the character traits, operate as buffers between the internal value prioritizations of agents and their external interactions. By introducing the concept of perception thermometers, the DVCA-model allows to study the dynamics of individual value prioritizations under a variety of internal and external perturbations over extensive time periods. Possible applications are the use of the DVCA-model within artificial sociality, opinion dynamics, social learning modelling, behavior selection algorithms and social-economic modelling.

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.

Our aim is to show effects of group living when only low-level cognition is assumed, such as pattern recognition needed for normal functioning, without assuming individuals have knowledge about others around them or warn them actively.
The model is of a group of vigilant foragers staying within a patch, under attack by a predator. The foragers use attentional scanning for predator detection, and flee after detection. This fleeing action constitutes a visual cue to danger, and can be received non-attentionally by others if it occurs within their limited visual field. The focus of this model is on the effectiveness of this non-attentional visual information reception.
A blind angle obstructing cue reception caused by behaviour can exist in front, morphology causes a blind angle in the back. These limitations are represented by two visual field shapes. The scan for predators is all-around, with distance-dependent detection; reception of flight cues is limited by visual field shape.
Initial parameters for instance: group sizes, movement, vision characteristics for predator detection and for cue reception. Captures (failure), number of times the information reached all individuals at the same time (All-fled, success), and several other effects of the visual settings are recorded.

Provided is a landscape of properties where pastoralists make decisions how much livestock they put on their property and how much to suppress fire from occuring. Rangelands can be grass dominated, or unproductive shrubb dominated. Overgrazing and fire suppresion lead to shrub dominated landscapes. What management strategies evolve, and how is this impacted by policies?
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.

The model is designed to analyse the effects of mitigation measures on the European brown hare (Lepus europaeus), which is directly affected by ongoing land use change and has experienced widespread decline throughout Europe since the 1960s. As an input, we use two 4×4 km large model landscapes, which were generated by a landscape generator based on real field sizes and crop proportions and differed in average field size and crop composition. The crops grown annually are evaluated in terms of forage suitability, breeding suitability and crop richness for the hare. Six mitigation scenarios are implemented, defined by a 10 % increase in: (1) mixed silphie, (2) miscanthus, (3) grass-clover ley, (4) alfalfa, (5) set-aside, and (6) general crop richness. The model shows that that both landscape configuration and composition have a significant effect on hare population development, which responds particularly strongly to compositional changes.