This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.

This model is a replication of that described by Peterson (2002) and illustrates the ‘spread’ feedback loop type described in Millington (2013).

This model illustrates a positive ‘transport’ feedback loop in which lines with different resistance to flows of material result in variation in rates of change in linked entities.

This model illustrates a positive ‘growth’ feedback loop in which the areal extent of an entity increases through time.

This is a replication of the altruistic trait selection model described in Pepper & Smuts (2000, 2002).

This model extends the bounded confidence model of Deffuant and Weisbuch. It introduces online contexts in which a person can deliver his or her opinion to several other persons. There are 2 additional parameters accessibility and connectivity.

This is an agent-based model that allows to test alternative designs for three model components. The model was built using the LUDAS design strategy, while each alternative is in line with the strategy. Using the model, it can be shown that alternative designs, though built on the same strategy, lead to different land-use patterns over time.

How can species evolve a cooperative network to keep the environment suitable for life?

Agents are linked in a social-network and make decisions on which of 2 types of behavior to adopt. We explore consequences of different information feedback and providing targeted feedback to individuals.

This model explores the effects of agent interaction, information feedback, and adaptive learning in repeated auctions for farmland. It gathers information for three types of sealed-bid auctions, and one English auction and compares the auctions on the basis of several measures, including efficiency, price information revelation, and ability to handle repeated bidding and agent learning.