We develop an IBM that predicts how interactions between elephants, poachers, and law enforcement affect poaching levels within a virtual protected area. The model is theoretical at this stage and is not meant to provide a realistic depiction of poaching, but instead to demonstrate how IBMs can expand upon the existing modelling work done in this field, and to provide a framework for future research. The model could be further developed into a useful management support tool to predict the outcomes of various poaching mitigation strategies at real-world locations. The model was implemented in NetLogo version 6.1.0.

We first compared a scenario in which poachers have prescribed, non-adaptive decision-making and move randomly across the landscape, to one in which poachers adaptively respond to their memories of elephant locations and where other poachers have been caught by law enforcement. We then compare a situation in which ranger effort is distributed unevenly across the protected area to one in which rangers patrol by adaptively following elephant matriarchal herds.

WWHW is an agent-based model designed to allow the exploration of the emergence, resilience and evolution of cooperative behaviours in hunter-fisher-gatherer societies.

Patagonia PSMED is an agent-based model designed to study a simple case of Evolution of Ethnic Differentiation. It replicates how can hunter-gatherer societies evolve and built cultural identities as a consequence of the way they interacted.

We develop a spatial, evolutionary model of the endogenous formation and dissolution of groups using a renewable common pool resource. We use this foundation to measure the evolutionary pressures at different organizational levels.

This model builds on inquisitiveness as a key individual disposition to expand the bounds of their rationality. It represents a system where teams are formed around problems and inquisitive agents integrate competencies to find ‘emergent’ solutions.

This model explores a price Q-learning mechanism for perishable products that considers uncertain demand and customer preferences in a competitive multi-agent retailer market (a model-free environment).

BESTMAP-ABM-DE is an agent-based model to determine the adoption and spatial allocation of selected agri-environmental schemes (AES) by individual farmers in the Mulde River Basin located in Western Saxony, Germany. The selected AES are buffer areas, cover crops, maintaining permanent grassland and conversion of arable land to permanent grassland. While the first three schemes have already been offered in the case study area, the latter scheme is a hypothetical scheme designed to test the impact of potential policy changes. For the first model analyses, only the currently offered schemes are considered. With the model, the effect of different scenarios of policy design on patterns of adoption can be investigated. In particular, the model can be used to study the social-ecological consequences of agricultural policies at different spatial and temporal scales and, in combination with biophysical models, test the ecological implications of different designs of the EU’s Common Agricultural Policy. The model was developed in the BESTMAP project.

In this presentation, we use the concept of meme to explore evolution of demand.

Comparing impact of alternative behavioral theories in a simple social-ecological system.

We present here MEGADAPT_SESMO model. A hybrid, dynamic, spatially explicit, integrated model to simulate the vulnerability of urban coupled socio-ecological systems – in our case, the vulnerability of Mexico City to socio-hydrological risk.