Decision making in stochastic extensive form I: Stochastic decision forests
Abstract
A general theory of stochastic decision forests is developed to bridge two concepts of information flow: decision trees and refined partitions on the one side, filtrations from probability theory on the other. Instead of the traditional "nature" agent, this framework uses a single lottery draw to select a tree of a given decision forest. Each "personal" agent receives dynamic updates from an own oracle on the lottery outcome and makes partition-refining choices adapted to this information. This theory addresses a key limitation of existing approaches in extensive form theory, which struggle to model continuous-time stochastic processes, such as Brownian motion, as outcomes of "nature" decision making. Additionally, a class of stochastic decision forests based on time-indexed action paths is constructed, encompassing a wide range of models from the literature and laying the groundwork for an approximation theory for stochastic differential games in extensive form.
Cite
@article{arxiv.2404.12332,
title = {Decision making in stochastic extensive form I: Stochastic decision forests},
author = {E. Emanuel Rapsch},
journal= {arXiv preprint arXiv:2404.12332},
year = {2024}
}
Comments
35 pages (51 pages with appendix), 5 figures, first part of a three-paper series; several generalisations and additions (order consistent SDF, EIS with/without recall, larger class of action path choices, absent-minded driver SDF), typos corrected