Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation
Abstract
How can heuristic strategies emerge from smaller building blocks? We propose Approximate Bayesian Computation as a computational solution to this problem. As a first proof of concept, we demonstrate how a heuristic decision strategy such as Take The Best (TTB) can be learned from smaller, probabilistically updated building blocks. Based on a self-reinforcing sampling scheme, different building blocks are combined and, over time, tree-like non-compensatory heuristics emerge. This new algorithm, coined Approximately Bayesian Computed Take The Best (ABC-TTB), is able to recover a data set that was generated by TTB, leads to sensible inferences about cue importance and cue directions, can outperform traditional TTB, and allows to trade-off performance and computational effort explicitly.
Cite
@article{arxiv.1605.01598,
title = {Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation},
author = {Eric Schulz and Maarten Speekenbrink and Björn Meder},
journal= {arXiv preprint arXiv:1605.01598},
year = {2016}
}