This is an extended replication of Abelson’s and Bernstein’s early computer simulation model of community referendum controversies which was originally published in 1963 and often cited, but seldom analysed in detail. This replication is in NetLogo 6.3.0, accompanied with an ODD+D protocol and class and sequence diagrams.

This replication replaces the original scales for attitude position and interest in the referendum issue which were distributed between 0 and 1 with values that are initialised according to a normal distribution with mean 0 and variance 1 to make simulation results easier compatible with scales derived from empirical data collected in surveys such as the European Value Study which often are derived via factor analysis or principal component analysis from the answers to sets of questions.

Another difference is that this model is not only run for Abelson’s and Bernstein’s ten week referendum campaign but for an arbitrary time in order that one can find out whether the distributions of attitude position and interest in the (still one-dimensional) issue stabilise in the long run.

This model simulates the form and function of an idealised estuary with associated barrier-spit complex on the north east coast of New Zealand’s North Island (from Bream Bay to central Bay of Plenty) during the years 2010 - 2050 CE. It combines variables from social, ecological and geomorphic systems to simulate potential directions of change in shallow coastal systems in response to external forcing from land use, climate, pollution, population density, demographics, values and beliefs. The estuary is over 1000Ha, making it a large estuary according to Hume et al. (2007) - there are 12 large estuaries in the Auckland region alone (Suyadi et al., 2019). The model was developed as part of Andrew Allison’s PhD Thesis in Geography from the School of Environment and Institute of Marine Science, University of Auckland, New Zealand. The model setup allows for alteration of geomorphic, ecological and social variables to suit the specific conditions found in various estuaries along the north east coast of New Zealand’s North Island.
This model is not a predictive or forecasting model. It is designed to investigate potential directions of change in complex shallow coastal systems. This model must not be used for any purpose other than as a heuristic to facilitate researcher and stakeholder learning and for developing system understanding (as per Allison et al., 2018).

Simulation-Framework to study the governance of complex, network-like sociotechnical systems by means of ABM. Agents’ behaviour is based on a sociological model of action. A set of basic governance mechanisms helps to conduct first experiments.

The “Urban Drought Nexus Tool” is a system dynamics model, aiming to facilitate the co-development of climate services for cities under increasing droughts. The tool integrates multiple types of information and still can be applied to other case studies with minimal adjustments on the parameters of land use, water consumption and energy use in the water sector. The tool needs hydrological projections under climate scenarios to evaluate climatic futures, and requires the co-creation of socio-economic future scenarios with local stakeholders. Thus it is possible to provide specific information about droughts taking into account future water availability and future water consumption. Ultimately, such complex system as formed by the water-energy-land nexus can be reduced to single variables of interest, e.g. the number of events with no water available in the future and their length, so that the complexities are reduced and the results can be conveyed to society in an understandable way, including the communication of uncertainties. The tool and an explanatory guide in pdf format are included. Planned further developments include calibrating the system dynamics model with the social dynamics behind each flow with agent-based models.

Agent-based model of hunting behavior of Ache hunter-gatherers from Paraguay. We evaluate the effect of group size and cooperative hunting

The original Ache model is used to explore different distributions of resources on the landscape and it’s effect on optimal strategies of the camps on hunting and camp movement.

Simulations based on the Axelrod model and extensions to inspect the volatility of the features over time (AXELROD MODEL & Agreement threshold & two model variations based on the Social identity approach)
The Axelrod model is used to predict the number of changes per feature in comparison to the datasets and is used to compare different model variations and their performance.

Input: Real data

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With this model, we investigate resource extraction and labor conditions in the Global South as well as implications for climate change originating from industry emissions in the North. The model serves as a testbed for simulation experiments with evolutionary political economic policies addressing these issues. In the model, heterogeneous agents interact in a self-organizing and endogenously developing economy. The economy contains two distinct regions – an abstract Global South and Global North. There are three interlinked sectors, the consumption good–, capital good–, and resource production sector. Each region contains an independent consumption good sector, with domestic demand for final goods. They produce a fictitious consumption good basket, and sell it to the households in the respective region. The other sectors are only present in one region. The capital good sector is only found in the Global North, meaning capital goods (i.e. machines) are exclusively produced there, but are traded to the foreign as well as the domestic market as an intermediary. For the production of machines, the capital good firms need labor, machines themselves and resources. The resource production sector, on the other hand, is only located in the Global South. Mines extract resources and export them to the capital firms in the North. For the extraction of resources, the mines need labor and machines. In all three sectors, prices, wages, number of workers and physical capital of the firms develop independently throughout the simulation. To test policies, an international institution is introduced sanctioning the polluting extractivist sector in the Global South as well as the emitting industrial capital good producers in the North with the aim of subsidizing innovation reducing environmental and social impacts.

The western honey bee Apis mellifera is the most important pollinator in the world. The biggest threat to managed honey bees is the ectoparasitic mite Varroa destructor and the viruses DWV (Deformed Wing Virus) and APV (Acute Paralysis Virus) it transmits. Untreated honey bee colonies are expected to die within one to three years. This led to the development of strategies for beekeepers to control the Varroa mite in honey bee colonies and ensure the health and survival of their bee colonies, so called Good Beekeeping Practice. The aim of the extension of BEEHAVE was to represent the Good Beekeeping Practice of Varroa control in Germany. The relevant measures within the Varroa control strategies are drone brood removal as a Varroa trap and the treatment of bee colonies with organic acaricides (formic and oxalic acid) to kill the mites. This extension improves BEEHAVE and builds a bridge between beekeepers in practice and in the modelling world. It vastly contributes to the future use of BEEHAVE in beekeeping education in Germany.

Zooarchaeological evidences indicate that rabbit hunting became prevalent during the Upper Palaeolithic in the Iberian Peninsula.

The purpose of the ABM is to test if warren hunting using nets as a collective strategy can explain the introduction of rabbits in the human diet in the Iberian Peninsula during this period. It is analyzed whether this hunting strategy has an impact on human diet breadth by affecting the relative abundance of other main taxa in the dietary spectrum.
Model validity is measured by comparing simulated diet breadth to the observed diet breadth in the zooarchaeological record.

The agent-based model is explicitly grounded on the Diet Breadth Model (DBM), from the Optimal Foraging Theory (OFT).
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