You might ask yourself: Can these parameters not be chosen more dynamically? As a developer and maintainer of an integration module, I would roughly expect that introducing such automatisms has the following consequences: You can usually tweak these parameters, but if you don’t, there need to be some default values and these default values are chosen with the above setup in mind. The step-size adaption in turn is governed by a lot of parameters like absolute tolerance, relative tolerance, minimum time step, etc. The reason for the above behaviour of integrators is that they use step-size adaption, i.e., the integration step is adjusted to keep the estimated error at a defined level. This typically fails for astronomical simulations where the orders of magnitude vary and values as well as time scales are often large in typical units. the smallest time scale of your dynamics also has the order of magnitude 1.your dynamical variables have the same order of magnitude.Most, if not all integration modules work best out of the box if:
#ANYLOGIC TUTORIAL TEXT DEST CODE#
In addition, any part of the code can be changed to introduce new features and constraints. It is our hope that these implementational principles can be applied to a broad range of phenomena. We aim for simplicity in design: agents are represented as simple Python lists whose elements represent their properties populations are represented as lists of agents evolution is implemented by looping over members of the population and modifying their properties. However, we hope that the tutorials can also be of use to those who wish to build simulations of completely different topics, by modifying the basic parameters of the model. This choice of model and questions reflect our personal theoretical interests: the cultural evolution of language through interaction in populations. We will try to answer questions like: Has one of the variants spread to the entire community? Does this depend on the community's size and initial structure? How many stubborn people must be present to prevent (or facilitate?) convergence? etc. They are intended to offer anyone with little or no prior experience with Python the ability to incrementally construct a simple simulation of sound change in a population of agents with different personalities (stubborn or flexible learners). This repository contains three Jupyter notebooks offering a short tutorial on agent-based modeling using Python.
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