We propose here a computational model of school segregation that is aligned with a corresponding Schelling-type model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents’ school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school.

This is a ridesharing model (Uber/Lyft) of the larger Washington DC metro area. The model can be modified (Netlogo 6.x) relatively easily and be adapted to any metro area. Please cite generously (this was a lot of work) and please cite the paper, not the comses model.

Link to the paper published in “Complex Adaptive Systems” here: https://link.springer.com/chapter/10.1007/978-3-030-20309-2_7

Citation: Shaheen J.A.E. (2019) Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM). In: Carmichael T., Collins A., Hadžikadić M. (eds) Complex Adaptive Systems. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-20309-2_7

A-KinGDom simulates the emergence of the social structure in a group of non-human primates. The model includes dominance and affiliative interactions which allow us to define four different attack and affiliative strategies.

This is a final project for the class AML 591 at Arizona State University. I have done a small amount of bug-checking, but overall the project represents only a half of a semester’s work, so proceed w

A very simple model elaborated to explore what may happens when buyers (travelers) have more information than sellers (tourist destinations)

This model aims to investigate how different type of learning (social system) and disturbance specific attributes (ecological system) influence adoption of treatment strategies to treat the effects of ecological disturbances.

The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure

Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).

This simulation model is associated with the journal paper “A First Approach on Modelling Staff Proactiveness in Retail Simulation Models” to appear in the Journal of Artificial Societies and Social Simulation 14 (2) 2. The authors are Peer-Olaf Siebers ([email protected]) and Uwe Aickelin ([email protected]).

An empirically validated agent-based model of circular migration