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.

An agent-based model that simulates urban neighbourhoods. The model has been designed to simulate perceived livability and safety (PLS) of citizens. The score attached to perceived livability and safety, PLS, is the main output of the model and is the average of each individual’s PLS. These PLS scores, in turn, are specific to each citizen and highly dependent on their individual experiences. PLS is impacted by several different social factors: interactions with fellow citizens, police officers, and community workers; visiting or starting a neighbourhood initiative; experiencing a burglary; seeing a youth gang; or hearing from friends (of friends) about these events. On top of this, the model allows to set various types of social networks which also influence the PLS.

This is an interdisciplinary agent-based model with Monte Carlo simulations to assess the relative effects of broadcast and contagion processes in a multiplex social network. This multiplex approach models multiple channels of informal communication - phone, word-of-mouth, and social media - that vary in their attribute values. Each agent is an individual in a threatened community who, once warned, has a probability of warning others in their social network using one of these channels. The probability of an individual warning others is based on their warning source and the time remaining until disaster impact, among other variables. Default parameter values were chosen from empirical studies of disaster warnings along with the spatial aspects of Coos Bay, OR, USA and Seaside, OR, USA communities.

Organizations are complex systems comprised of many dynamic and evolving interaction patterns among individuals and groups. Understanding these interactions and how patterns, such as informal structures and knowledge sharing behavior, emerge are crucial to creating effective and efficient organizations. To explore such organizational dynamics, the agent-based model integrates a cognitive model, dynamic social networks, and a physical environment.

The model explores food distribution patterns that emerge in artificial small-scale human groups when agents follow a set of spatially explicit sharing interaction rules derived from a theory on the evolution of the egalitarian social instinct.

This program was developed to simulate monogamous reproduction in small populations (and the enforcement of the incest taboo).

Every tick is a year. Adults can look for a mate and enter a relationship. Adult females in a Relationship (under the age of 52) have a chance to become pregnant. Everyone becomes not alive at 77 (at which point people are instead displayed as flowers).

User can select a starting-population. The starting population will be adults between the ages of 18 and 42.

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Policymakers decide on alternative policies facing restricted budgets and uncertain future. Designing public policies is further difficult due to the need to decide on priorities and handle effects across policies. Housing policies, specifically, involve heterogeneous characteristics of properties themselves and the intricacy of housing markets and the spatial context of cities. We propose PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit, and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks to integrate economic, spatial and transport scholarship. PS2 is applied to a comparison among three competing public policies aimed at reducing inequality and alleviating poverty: (a) house acquisition by the government and distribution to lower income households, (b) rental vouchers, and (c) monetary aid. Within the model context, the monetary aid, that is, smaller amounts of help for a larger number of households, makes the economy perform better in terms of production, consumption, reduction of inequality, and maintenance of financial duties. PS2 as such is also a framework that may be further adapted to a number of related research questions.

The Mobility Transition Model (MoTMo) is a large scale agent-based model to simulate the private mobility demand in Germany until 2035. Here, we publish a very much reduced version of this model (R-MoTMo) which is designed to demonstrate the basic modelling ideas; the aim is by abstracting from the (empirical, technological, geographical, etc.) details to examine the feed-backs of individual decisions on the socio-technical system.

In the “World of Cows”, dairy farmers run their farms and interact with each other, the surrounding agricultural landscape, and the economic and political framework. The model serves as an exemplary case of an interdependent human-environment system.

With the model, users can analyze the influence of policies and markets on land use decisions of dairy farms. The land use decisions taken by farms determine the delivered ecosystem services on the landscape level. Users can choose a combination of five policy options and how strongly market prices fluctuate. Ideally, the choice of policy options fulfills the following three “political goals” 1) dairy farming stays economically viable, 2) the provision of ecosystem services is secured, and 3) government spending on subsidies is as low as possible.

The model has been designed for students to practice agent-based modeling and analyze the impacts of land use policies.

This NetLogo model is an implementation of the mostly verbal (and graphic) model in Jarret Walker’s Human Transit: How Clearer Thinking about Public Transit Can Enrich Our Communities and Our Lives (2011). Walker’s discussion is in the chapter “Connections or Complexity?”. See especially figure 12-2, which is on page 151.

In “Connections or Complexity?”, Walker frames the matter as involving a choice between two conflicting goals. The first goal is to minimize connections, the need to make transfers, in a transit system. People naturally prefer direct routes. The second goal is to minimize complexity. Why? Well, read the chapter, but as a general proposition we want to avoid unnecessary complexity with its attendant operating characteristics (confusing route plans in the case of transit) and management and maintenance challenges. With complexity general comes degraded robustness and resilience.

How do we, how can we, choose between these conflicting goals? The grand suggestion here is that we only choose indirectly, implicitly. In the present example of connections versus complexity we model various alternatives and compare them on measures of performance (MoP) other than complexity or connections per se. The suggestion is that connections and complexity are indicators of, heuristics for, other MoPs that are more fundamental, such as cost, robustness, energy use, etc., and it is these that we at bottom care most about. (Alternatively, and not inconsistently, we can view connections and complexity as two of many MoPs, with the larger issue to be resolve in light of many MoPs, including but not limited to complexity and connections.) We employ modeling to get a handle on these MoPs. Typically, there will be several, taking us thus to a multiple criteria decision making (MCDM) situation. That’s the big picture.