A Graph-based Similarity Function for CBDT: Acquiring and Using New Information

TL;DR

This paper introduces a graph-based similarity function for CBDT that models decision-making under extreme uncertainty by measuring distances in a feature space, accommodating new information dynamically.

Contribution

It proposes a novel variant of Case-Based Decision Theory utilizing a feature-based space and a graph approach to incorporate new data for decision evaluation.

Findings

Formalizes a feature-based space for problem comparison

Demonstrates how new information updates decision models

Provides a framework for decision-making with incomplete data

Abstract

One of the consequences of persistent technological change is that it force individuals to make decisions under extreme uncertainty. This means that traditional decision-making frameworks cannot be applied. To address this issue we introduce a variant of Case-Based Decision Theory, in which the solution to a problem obtains in terms of the distance to previous problems. We formalize this by defining a space based on an orthogonal basis of features of problems. We show how this framework evolves upon the acquisition of new information, namely features or values of them arising in new problems. We discuss how this can be useful to evaluate decisions based on not yet existing data.