The model aims to illustrate how Earned Value Management (EVM) provides an approach to measure a project’s performance by comparing its actual progress against the planned one, allowing it to evaluate trends to formulate forecasts. The instance performs a project execution and calculates the EVM performance indexes according to a Performance Measurement Baseline (PMB), which integrates the description of the work to do (scope), the deadlines for its execution (schedule), and the calculation of its costs and the resources required for its implementation (cost).

Specifically, we are addressing the following questions: How does the risk of execution delay or advance impact cost and schedule performance? How do the players’ number or individual work capacity impact cost and schedule estimations to finish? Regardless of why workers cause delays or produce overruns in their assignments, does EVM assess delivery performance and help make objective decisions?

To consider our model realistic enough for its purpose, we use the following patterns: The model addresses classic problems of Project Management (PM). It plays the typical task board where workers are assigned to complete a task backlog in project performance. Workers could delay or advance in the task execution, and we calculate the performance using the PMI-recommended Earned Value.

This model has been created with and for the researcher-farmers of the Muonde Trust (http://www.muonde.org/), a registered Zimbabwean non-governmental organization dedicated to fostering indigenous innovation. Model behaviors and parameters (mashandiro nemisiyano nedzimwe model) derive from a combination of literature review and the collected datasets from Muonde’s long-term (over 30 years) community-based research. The goals of this model are three-fold (muzvikamu zvitatu):
A) To represent three components of a Zimbabwean agro-pastoral system (crops, woodland grazing area, and livestock) along with their key interactions and feedbacks and some of the human management decisions that may affect these components and their interactions.
B) To assess how climate variation (implemented in several different ways) and human management may affect the sustainability of the system as measured by the continued provisioning of crops, livestock, and woodland grazing area.
C) To provide a discussion tool for the community and local leaders to explore different management strategies for the agro-pastoral system (hwaro/nzira yekudyidzana kwavanhu, zvipfuo nezvirimwa), particularly in the face of climate change.

This research article presents an agent-based simulation hereinafter called COMMONSIM. It builds on COMMONISM, i.e. a large-scale commons-based vision for a utopian society. In this society, production and distribution of means are not coordinated via markets, exchange, and money, or a central polity, but via bottom-up signalling and polycentric networks, i.e. ex-ante coordination via needs. Heterogeneous agents care for each other in life groups and produce in different groups care, environmental as well as intermediate and final means to satisfy sensual-vital needs. Productive needs decide on the magnitude of activity in groups for a common interest, e.g. the production of means in a multi-sectoral artificial economy. Agents share cultural traits identified by different behaviour: a propensity for egoism, leisure, environmentalism, and productivity. The narrative of this utopian society follows principles of critical psychology and sociology, complexity and evolution, the theory of commons, and critical political economy. The article presents the utopia and an agent-based study of it, with emphasis on culture-dependent allocation mechanisms and their social and economic implications for agents and groups.

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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