Transhumants move their herds based on strategies simultaneously considering several environmental and socio-economic factors. There is no agreement on the influence of each factor in these strategies. In addition, there is a discussion about the social aspect of transhumance and how to manage pastoral space. In this context, agent-based modeling can analyze herd movements according to the strategy based on factors favored by the transhumant. This article presents a reductionist agent-based model that simulates herd movements based on a single factor. Model simulations based on algorithms to formalize the behavioral dynamics of transhumants through their strategies. The model results establish that vegetation, water outlets and the socio-economic network of transhumants have a significant temporal impact on transhumance. Water outlets and the socio-economic network have a significant spatial impact. The significant impact of the socio-economic factor demonstrates the social dimension of Sahelian transhumance. Veterinarians and markets have an insignificant spatio-temporal impact. To manage pastoral space, water outlets should be at least 15 km
from each other. The construction of veterinary centers, markets and the securitization of transhumance should be carried out close to villages and rangelands.

This model is supporting the serious game RÁC (“waste” in Vietnamese), dedicated to foster discussion about solid waste management in a Vietnamese commune in the Bắc Hưng Hải irrigation system.
The model is replicating waste circulation and environmental impact in four fictive villages. During the game, the players take actions and see how their decisions have an impact on the model.
This model was implemented using the GAMA platform, using gaml language.

Implementation of Milbrath’s (1965) model of political participation. Individual participation is determined by stimuli from the political environment, interpersonal interaction, as well as individual characteristics.

The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. This agent-based model in Netlogo simulates a group discussion among myside-biased agents, within a Bayesian setting. This model is designed to investigate the effects of the myside bias on the ability of groups to reach a consensus or collectively track the correct answer to a given binary issue.

This version 2.1.0 of the uFunk model is about setting a business strategy (the S in the name) for an organization. A team of managers (or executives) meet and discuss various options on the strategy for the firm. There are three aspects that they have to agree on to set the strategic positioning of the organization.
The discussion is on market, stakeholders, and resources. The team (it could be a business strategy task force) considers various aspects of these three elements. The resources they use to develop the discussion can come from a traditional approach to strategy or from non-traditional means (e.g., so-called serious play, creativity and imagination techniques).
The S-uFunk 2.1.0 Model wants to understand to which extent cognitive means triggered by traditional and non-traditional resources affect the making of the strategy process.

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 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.

The model measures drivers of effectiveness of risk assessments in risk workshops where a calculative culture of quantitative skepticism is present. We model the limits to information transfer, incomplete discussions, group characteristics, and interaction patterns and investigate their effect on risk assessment in risk workshops, in order to contrast results to a previous model focused on a calculative culture of quantitative enthusiasm.

The model simulates a discussion in the context of a risk workshop with 9 participants. The participants use constraint satisfaction networks to assess a given risk individually and as a group.

The model measures drivers of effectiveness of risk assessments in risk workshops regarding the correctness and required time. Specifically, we model the limits to information transfer, incomplete discussions, group characteristics, and interaction patterns and investigate their effect on risk assessment in risk workshops.

The model simulates a discussion in the context of a risk workshop with 9 participants. The participants use Bayesian networks to assess a given risk individually and as a group.

In macroeconomics, an emerging discussion of alternative monetary systems addresses the dimensions of systemic risk in advanced financial systems. Monetary regime changes with the aim of achieving a more sustainable financial system have already been discussed in several European parliaments and were the subject of a referendum in Switzerland. However, their effectiveness and efficacy concerning macro-financial stability are not well-known. This paper introduces a macroeconomic agent-based model (MABM) in a novel simulation environment to simulate the current monetary system, which may serve as a basis to implement and analyze monetary regime shifts. In this context, the monetary system affects the lending potential of banks and might impact the dynamics of financial crises. MABMs are predestined to replicate emergent financial crisis dynamics, analyze institutional changes within a financial system, and thus measure macro-financial stability. The used simulation environment makes the model more accessible and facilitates exploring the impact of different hypotheses and mechanisms in a less complex way. The model replicates a wide range of stylized economic facts, including simplifying assumptions to reduce model complexity.