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.

The basic idea behind developing MIXTRUST was to represent a network of agricultural stakeholders composed of farmers and a cooperative in a mixed landscape to test its performances in response to risks. A mixed landscape here is a landscape where crop and livestock systems interact by the intermediary of material flows of agricultural products. It can be within mixed farms, or between farms, often specialized, (e.g. straw-manure).

The integrated and spatially-explicit ABM, called DIReC (Demography, Industry and Residential Choice), has been developed for Aberdeen City and the surrounding Aberdeenshire (Ge, Polhill, Craig, & Liu, 2018). The model includes demographic (individual and household) models, housing infrastructure and occupancy, neighbourhood quality and evolution, employment and labour market, business relocation, industrial structure, income distribution and macroeconomic indicators. DIReC includes a detailed spatial housing model, basing preference models on house attributes and multi-dimensional neighbourhood qualities (education, crime, employment etc.).
The dynamic ABM simulates the interactions between individuals, households, the labour market, businesses and services, neighbourhoods and economic structures. It is empirically grounded using multiple data sources, such as income and gender-age distribution across industries, neighbourhood attributes, business locations, and housing transactions. It has been used to study the impact of economic shocks and structural changes, such as the crash of oil price in 2014 (the Aberdeen economy heavily relies on the gas and oil sector) and the city’s transition from resource-based to a green economy (Ge, Polhill, Craig, & Liu, 2018).

The NIER model is intended to add qualitative variables of building owner types and peer group scales to existing energy efficiency retrofit adoption models. The model was developed through a combined methodology with qualitative research, which included interviews with key stakeholders in Cleveland, Ohio and Detroit and Grand Rapids, Michigan. The concepts that the NIER model adds to traditional economic feasibility studies of energy retrofit decision-making are differences in building owner types (reflecting strategies for managing buildings) and peer group scale (neighborhoods of various sizes and large-scale Districts). Insights from the NIER model include: large peer group comparisons can quickly raise the average energy efficiency values of Leader and Conformist building owner types, but leave Stigma-avoider owner types as unmotivated to retrofit; policy interventions such as upgrading buildings to energy-related codes at the point of sale can motivate retrofits among the lowest efficient buildings, which are predominantly represented by the Stigma-avoider type of owner; small neighborhood peer groups can successfully amplify normal retrofit incentives.

This is a variant of the PaleoscapeABM model available here written by Wren and Janssen. In this variant, we give projectile weapons to hunter and document where they discard them over time. Discard rate and location are influenced by probabilities of hitting/missing the prey, probabilities of damaging the weapon, and probabilities of carrying back embedded projectile armatures to the habitation camp with the body carcass.

IDEAL: Agent-Based Model of Residential Land Use Change where the choice of new residential development in based on the Ideal-point decision rule.

This model simulates a bank - firm credit network.

A minimal genetic algorithm was previously developed in order to solve an elementary arithmetic problem. It has been modified to explore the effect of a mutator gene and the consequent entrance into a hypermutation state. The phenomenon seems relevant in some types of tumorigenesis and in a more general way, in cells and tissues submitted to chronic sublethal environmental or genomic stress.
For a long time, some scholars suppose that organisms speed up their own evolution by varying mutation rate, but evolutionary biologists are not convinced that evolution can select a mechanism promoting more (often harmful) mutations looking forward to an environmental challenge.
The model aims to shed light on these controversial points of view and it provides also the features required to check the role of sex and genetic recombination in the mutator genes diffusion.

The model simulates the spatial patterns of secondary forest succession above the current alpine tree line in the context of land use and climate change. Three scenarios are offered: (1) climate change, (2) land use change, (3) species composition.

This model is a part of an ongoing research project on Multiagent Reinforcement Learning (MARL). The ODD protocol is included in the model. In this version of the model, Proximal Policy Optimization (PPO) is designed in the agent behaviors. It also includes a designed experiment in its Behavior Space which is used in the Response Surface Methodology and training of an Artificial Neural Network (ANN) based Recommender System.