PyIBL is a Python implementation of a subset of Instance Based Learning Theory (IBLT) [1]. It is made and distributed by the Dynamic Decision Making Laboratory of Carnegie Mellon University for making computational cognitive models supporting research in how people make decisions in dynamic environments. Here is documented version 5.1.6 of PyIBL.
Typically PyIBL is used by creating an experimental framework in the Python programming language, which
uses one or more PyIBL Agent objects. The framework
then asks these agents to make decisions, and informs the agents of the results of
those decisions. The framework, for example, may be strictly algorithmic, may interact with human
subjects, or may be embedded in a web site.
PyIBL is a library, or module, of Python code, useful for creating Python programs; it is not a stand alone application. Some knowledge of Python programming is essential for using it.
Contents¶
- Introduction
- Installation
- Tutorial
- Reference
AgentAgent.nameAgent.attributesAgent.choose()Agent.respond()Agent.populate()Agent.default_utilityAgent.default_utility_populatesAgent.reset()Agent.timeAgent.advance()Agent.noiseAgent.decayAgent.temperatureAgent.mismatch_penaltyAgent.similarity()Agent.optimized_learningAgent.discrete_blend()Agent.instances()Agent.detailsAgent.traceAgent.aggregate_detailsAgent.plot()Agent.noise_distributionAgent.fixed_noise
DelayedResponsepositive_linear_similarity()positive_quadratic_similarity()bounded_linear_similarity()bounded_quadratic_similarity()
- Internals
- Examples
- Changes