This is a computational model to articulate the theory and test some assumption and axioms for the trust model and its relationship to SBH.

We model the epistemic dynamics preceding political uprising. Before deciding whether to start protests, agents need to estimate the amount of discontent with the regime. This model simulates the dynamics of group knowledge about general discontent.

This is a NetLogo version of Buhl et al.’s (2005) model of self-organised digging activity in ant colonies. It was built for a master’s course on self-organisation and its intended use is still educational. The ants’ behavior can easily be changed by toggling switches on the interface, or, for more advanced students, there is R code included allowing the model to be run and analysed through RNetLogo.

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.

We build a computational model to investigate, in an evolutionary setting, a series of questions pertaining to happiness.

The code contains four experiments for well-being based IMRL reward features.

Butterflies (turtles) goes through metamorphism and moves to corresponding patches each season of the year. The number of years and seasons are monitored.

Default Initial skill, read ODD for more info. The purpose of the model presented by Salau is to study the ’player profit vs. club benefit’ dilemma present in professional soccer organizations.

The purpose of the model is to better understand, how different factors for human residential choices affect the city’s segregation pattern. Therefore, a Schelling (1971) model was extended to include ethnicity, income, and affordability and applied to the city of Salzburg. So far, only a few studies have tried to explore the effect of multiple factors on the residential pattern (Sahasranaman & Jensen, 2016, 2018; Yin, 2009). Thereby, models using multiple factors can produce more realistic results (Benenson et al., 2002). This model and the corresponding thesis aim to fill that gap.

FNNR-ABM is an agent-based model that simulates human activity, Guizhou snub-nosed monkey movement, and GTGP-enrolled land parcel conversion in the Fanjingshan National Nature Reserve in Guizhou, China.

Quick-start guide:
1. Install Python and set environmental path variables.
2. Install the mesa, matplotlib (optional), and pyshp (optional) Python libraries.
3. Configure fnnr_config_file.py.
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