# Value of Information for Argumentation based Intelligence Analysis

arXiv: https://arxiv.org/abs/2102.08180 · HTML: https://mlsanity.com/paper/2102.08180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Optimal Value of Information in Graphical Models](https://mlsanity.com/paper/1401.3474/) (2014)
- [Myopic Value of Information in Influence Diagrams](https://mlsanity.com/paper/1302.1535/) (2013)
- [Formulating Semantics of Probabilistic Argumentation by Characterizing Subgraphs: Theory and Empirical Results](https://mlsanity.com/paper/1608.00302/) (2016)
- [Value of Information in Probabilistic Logic Programs](https://mlsanity.com/paper/1909.08234/) (2019)

## Built on by (most cited first, 1 of 1)

- [Online Handbook of Argumentation for AI: Volume 3](https://mlsanity.com/paper/2212.07996/) (2022)

## Abstract (arXiv)

Argumentation provides a representation of arguments and attacks between these arguments. Argumentation can be used to represent a reasoning process over evidence to reach conclusions. Within such a reasoning process, understanding the value of information can improve the quality of decision making based on the output of the reasoning process. The value of an item of information is inherently dependent on the available evidence and the question being answered by the reasoning. In this paper we introduce a value of information on argument frameworks to identify the most valuable arguments within the finite set of arguments in the framework, and the arguments and attacks which could be added to change the output of an evaluation. We demonstrate the value of information within an argument framework representing an intelligence analysis in the maritime domain. Understanding the value of information in an intelligence analysis will allow analysts to balance the value against the costs and risks of collection, to effectively request further collection of intelligence to increase the confidence in the analysis of hypotheses.