Using chains of replicas of Atwood’s Machine, this model explores implications of the Maximum Power Principle. It is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, EiLab.

Using webs of replicas of Atwood’s Machine, we explore implications of the Maximum Power Principle. This is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, CmLab.

The ABM model is designed to model the adaptability of farmers in DTIM. This model includes two groups of farmers and local government admins agents. Farmers with different levels, with low WP of DTIM, are looking for economic benefits and reduced irrigation and production costs. Meanwhile, the government is looking for strategic goals to maintain water resources’ sustainability. The local government admins employ incentives (subsidies in this study) to encourage farmers to DTIM. In addition, it is used as a tool for supervision and training farmers’ performance. Farmers are currently harvesting water resources with irrigation systems and different levels of technology, and they intend to provide short-term benefits. Farmers adjust the existing approach based on their knowledge of the importance of DTIM and propensity to increase WP and cost-benefit evaluation. DTIM has an initial implementation fee. Every farmer can increase WP by using government subsidies. If none of the farmers create optimal use of water resources, access to water resources will be threatened in the long term. This is considered a hypothetical cost for farmers who do not participate in DTIM. With DTIM, considering that local government admins’ facilities cover an essential part of implementation costs, farmers may think of profiting from local government admins’ facilities by selling that equipment, especially if the farmers in the following conditions may consider selling their developed irrigation equipment. In this case, the technology of their irrigation system will return to the state before development.
- When the threshold of farmers’ propensity to DTIM is low (for example, in the conditions of scarcity of access to sufficient training about the new irrigation system or its role in reducing the cost and sustainability of water resources)
- When the share of government subsidy is high, and as a result, the profit from the sale of equipment is attractive, especially in conditions of inflation.
- Finally, farmers’ honesty threshold should be reduced based on the positive experience of profit-seeking and deception among neighbors.
Increasing the share of government subsidies can encourage farmers to earn profits. Therefore, the government can help increase farmers’ profits by considering the assessment teams at different levels with DTIM training . local government admins evaluations monitor the behavior of farmers. If farmers sell their improved irrigation system for profit, they may be deprived of some local government admins’ services and the possibility of receiving subsidies again. Assessments The local government admins can increase farmers’ honesty. Next, the ABM model evaluates local government admins policies to achieve a suitable framework for water resources management in the Miandoab region.

This is an extension of the original RAGE model (Dressler et al. 2018), where we add learning capabilities to agents, specifically learning-by-doing and social learning (two processes central to adaptive (co-)management).

The extension module is applied to smallholder farmers’ decision-making - here, a pasture (patch) is the private property of the household (agent) placed on it and there is no movement of the households. Households observe the state of the pasture and their neighrbours to make decisions on how many livestock to place on their pasture every year. Three new behavioural types are created (which cannot be combined with the original ones): E-RO (baseline behaviour), E-LBD (learning-by-doing) and E-RO-SL1 (social learning). Similarly to the original model, these three types can be compared regarding long-term social-ecological performance. In addition, a global strategy switching option (corresponding to double-loop learning) allows users to study how behavioural strategies diffuse in a heterogeneous population of learning and non-learning agents.

An important modification of the original model is that extension agents are heterogeneous in how they deal with uncertainty. This is represented by an agent property, called the r-parameter (household-risk-att in the code). The r-parameter is catch-all for various factors that form an agent’s disposition to act in a certain way, such as: uncertainty in the sensing (partial observability of the resource system), noise in the information received, or an inherent characteristic of the agent, for instance, their risk attitude.

Default Initial skill, read ODD for more info. The purpose of the model presented by Salau is to study the ’player profit vs. club benefit’ dilemma present in professional soccer organizations.

This agent-based model simulates the implementation of a Transfer of Development Rights (TDR) mechanism in a stylized urban environment inspired by Dublin. It explores how developer agents interact with land parcels under spatial zoning, conservation protections, and incentive-based policy rules. The model captures emergent outcomes such as compact growth, green and heritage zone preservation, and public cost-efficiency. Built in NetLogo, the model enables experimentation with variable FSI bonuses, developer behavior, and spatial alignment of sending/receiving zones. It is intended as a policy sandbox to test market-aligned planning tools under behavioral and spatial uncertainty.

This agent-based model simulates the interactions between smallholder farming households, land-use dynamics, and ecosystem services in a rural landscape of Eastern Madagascar. It explores how alternative agricultural practices —shifting agriculture, rice cultivation, and agroforestry—combined with varying levels of forest protection, influence food production, food security, dietary diversity, and forest biodiversity over time. The landscape is represented as a grid of spatially explicit patches characterized by land use, ecological attributes, and regeneration dynamics. Agents make yearly decisions on land management based on demographic pressures, agricultural returns, and institutional constraints. Crop yields are affected by stochastic biotic and abiotic disruptions, modulated by local ecosystem regulation functions. The model additionally represents foraging as a secondary food source and pressure on biodiversity. The model supports the analysis of long-term trade-offs between agricultural productivity, human nutrition, and conservation under different policy and land-use scenarios.

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.

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An agent-based model of how public space conditions shape the collective formation of local sports communities in high-mobility cities. A small gathering is already exercising in an abstract, systematically configurable public space; the model asks how strangers join them and whether the loose gathering consolidates into a stable community. Three conditions are manipulated, each acting through exactly one channel: spatial legibility governs whether a passer-by notices the activity, routine compatibility governs whether a member attends in a given week, and social anchor availability governs how much one episode of co-presence builds a tie. Ties carry a continuous strength with two thresholds, so that relationships of differing strength cross their thresholds at different condition levels. Because the model does not represent the origin of an activity, dissolution is irreversible and survival is a clean binary outcome. Across 32,790 runs in eight experiments the model yields two well separated thresholds along routine compatibility — one for survival, one for consolidation — with a wide band between them in which a space is used continuously but no community forms. The tie submodel is adapted from Jin, Girvan and Newman (2001) and verified against it by docking. Robustness is established against the seeded gathering, the pre-existing social network, a joint ±20 per cent perturbation of the behavioural parameters, and the parameters of the tie machinery.

This is NetLogo code that presents two alternative implementations of Correlated Random Walk (CRW):
- 1. drawing the turning angles from the uniform distribution, i.e. drawing the angle with the same probability from a certain given range;
- 2. drawing the turning angles from von Mises distribution.
The move lengths are drawn from the lognormal distribution with the specified parameters.

Correlated Random Walk is used to represent the movement of animal individuals in two-dimensional space. When modeled as CRW, the direction of movement at any time step is correlated with the direction of movement at the previous time step. Although originally used to describe the movement of insects, CRW was later shown to sufficiently well describe the empirical movement data of other animals, such as wild boars, caribous, sea stars.
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