Computational Model Library

This model implements a classic scenario used in Reinforcement Learning problem, the “Cliff Walking Problem”. Consider the gridworld shown below (SUTTON; BARTO, 2018). This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff.” Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start (SUTTON; BARTO, 2018).

CliffWalking

The problem is solved in this model using the Q-Learning algorithm. The algorithm is implemented with the support of the NetLogo Q-Learning Extension

The model reflects the predator-prey mustelid-vole population dynamics, typically observed in boreal systems. The goal of the model is to assess which intrinsic and extrinsic factors (or factor combinations) are needed for the generation of the cyclic pattern typically observed in natural vole populations. This goal is achieved by contrasting the alternative model versions by “switching off” some of the submodels in order to reflect the four combinations of the factors hypothesized to be driving vole cycles.

The model is a combination of a spatially explicit, stochastic, agent-based model for wild boars (Sus scrofa L.) and an epidemiological model for the Classical Swine Fever (CSF) virus infecting the wild boars.

The original model (Kramer-Schadt et al. 2009) was used to assess intrinsic (system immanent host-pathogen interaction and host life-history) and extrinsic (spatial extent and density) factors contributing to the long-term persistence of the disease and has further been used to assess the effects of intrinsic dynamics (Lange et al. 2012a) and indirect transmission (Lange et al. 2016) on the disease course. In an applied context, the model was used to test the efficiency of spatiotemporal vaccination regimes (Lange et al. 2012b) as well as the risk of disease spread in the country of Denmark (Alban et al. 2005).

References: See ODD model description.

Spatial model of the noisy Prisoner's Dilemma with reward shift

Matus Halas | Published Thu Mar 5 16:17:54 2015 | Last modified Tue May 29 09:09:01 2018

Interactions of players embedded in a closed square lattice are determined by distance and overall gains and they lead to shifts of reward payoff between temptation and punishment. A new winner balancing against threats is ultimately discovered.

The model represents empirically observed recycling behaviour of Chinese citizens, based on the theory of reasoned action (TRA), the theory of planned behaviour (TPB) and the theory of planned behaviour extended with situational factors (TPB+).

This is an adaptation and extension of Robert Axtell’s model (2013) of endogenous firms, in Python 3.4

The Travel-tour case study

Christophe Sibertin-Blanc Françoise Adreit Joseph El Gemayel | Published Sun May 19 17:52:35 2013 | Last modified Fri Jun 14 08:56:29 2013

This model describes and analyses the Travel-Tour Case study.

Equity Constrained Dispatching Model of Emergency Medical Services

Sreekanth V K Ram Babu Roy | Published Thu Sep 8 20:18:19 2016 | Last modified Mon May 1 11:39:19 2017

Model for evaluating various ambulance dispatching policies of an equity constrained emergency medical services under bounded rationality.

This agent-based model represents a stylized inter-organizational innovation network where firms collaborate with each other in order to generate novel organizational knowledge.

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