This NetLogo model illustrates the cultural evolution of pro-environmental behaviour patterns. It illustrates how collective behaviour patterns evolve from interactions between agents and agents (in a social network) as well as agents and the affordances (action opportunities provided by the environment) within a niche. More specifically, the cultural evolution of behaviour patterns is understood in this model as a product of:

1. The landscape of affordances provided by the material environment,
2. Individual learning and habituation,
3. Social learning and network structure,
4. Personal states (such as habits and attitudes), and
   …

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.

DARTS simulates food systems in which agents produce, consume and trade food. Here, food is a summary item that roughly corresponds to commodity food types (e.g. rice). No other food types are taken into account. Each food system (World) consists of its own distribution of agents, regions and connections between agents. Agents differ in their ability to produce food, earn off-farm income and trade food. The agents aim to satisfy their food requirements (which are fixed and equal across agents) by either their own food production or by food purchases. Each simulation step represents one month, in which agents can produce (if they have productive capacity and it is a harvest month for their region), earn off-farm income, trade food (both buy and sell) and consume food. We evaluate the performance of the food system by averaging the agents’ food satisfaction, which is defined as the ratio of the food consumed by each agent at the end of each month divided by her food requirement. At each step, any of the abovementioned attributes related to the agents’ ability to satisfy their food requirement can (temporarily) be shocked. These shocks include reducing the amount of food they produce, removing their ability to trade locally or internationally and reducing their cash savings. Food satisfaction is quantified (both immediately after the shock and in the year following the shock) to evaluate food security of a particular food system, both at the level of agent types (e.g. the urban poor and the rural poor) and at the systems level. Thus, the effects of shocks on food security can be related to the food system’s structure.

The model presented here is extensively described in the paper ‘Talk less to strangers: How homophily can improve collective decision-making in diverse teams’ (forthcoming at JASSS). A full replication package reproducing all results presented in the paper is accessible at https://osf.io/76hfm/.

Narrative documentation includes a detailed description of the model, including a schematic figure and an extensive representation of the model in pseudocode.

The model develops a formal representation of a diverse work team facing a decision problem as implemented in the experimental setup of the hidden-profile paradigm. We implement a setup where a group seeks to identify the best out of a set of possible decision options. Individuals are equipped with different pieces of information that need to be combined to identify the best option. To this end, we assume a team of N agents. Each agent belongs to one of M groups where each group consists of agents who share a common identity.
The virtual teams in our model face a decision problem, in that the best option out of a set of J discrete options needs to be identified. Every team member forms her own belief about which decision option is best but is open to influence by other team members. Influence is implemented as a sequence of communication events. Agents choose an interaction partner according to homophily h and take turns in sharing an argument with an interaction partner. Every time an argument is emitted, the recipient updates her beliefs and tells her team what option she currently believes to be best. This influence process continues until all agents prefer the same option. This option is the team’s decision.

Non-traditional tools and mediums can provide unique methodological and interpretive opportunities for archaeologists. In this case, the Unreal Engine (UE), which is typically used for games and media, has provided a powerful tool for non-programmers to engage with 3D visualization and programming as never before. UE has a low cost of entry for researchers as it is free to download and has user-friendly “blueprint” tools that are visual and easily extendable. Traditional maritime mobility in the Salish Sea is examined using an agent-based model developed in blueprints. Focusing on the sea canoe travel of the Straits Salish northwestern Washington State and southwest British Columbia. This simulation integrates GIS data to assess travel time between Coast Salish archaeological village locations and archaeologically represented resource gathering areas. Transportation speeds informed by ethnographic data were used to examine travel times for short forays and longer inter-village journeys. The results found that short forays tended to half day to full day trips when accounting for resource gathering activities. Similarly, many locations in the Salish Sea were accessible in long journeys within two to three days, assuming fair travel conditions. While overall transportation costs to reach sites may be low, models such as these highlight the variability in transport risk and cost. The integration of these types of tools, traditionally used for entertainment, can increase the accessibility of modeling approaches to researchers, be expanded to digital storytelling, including aiding in the teaching of traditional ecological knowledge and placenames, and can have wide applications beyond maritime archaeology.

This is v0.01 of a UE5.2.1 agent based model.

A general model of human mate choice in which agents are localized in space, interact with close neighbors, and tend to range either near or far. At the individual level, our model uses two oft-used but incompletely understood decision rules: one based on preferences for similar partners, the other for maximally attractive partners.

ForagerNet3_Demography is a non-spatial ABM for exploring hunter-gatherer demography. Key methods represent birth, death, and marriage. The dependency ratio is an imporant variable in many economic decisions embedded in the methods.

MUSA is an ABM that simulates the commuting sector in USA. A multilevel validation was implemented. Social network with a social-circle structure included. Two types of policies have been tested: market-based and preference-change.

ForagerNet3_Demography_V2 is a non-spatial ABM for exploring hunter-gatherer demography. This version (developed from FN3D_V1) contains code for calculating the ratio of old to young adults (the “OY ratio”) in the living and dead populations.

This is a multi-patch meta-population ecological model. It intended as a test-bed in which to test the impact of humans with different kinds of social structure.