A Bayesian network-based TOPSIS framework to dynamically control the risk of maritime piracy

Hanwen Fan, Jing Lu, Zheng Chang and Yuan Ji

Maritime Policy & Management, 2024, vol. 51, issue 7, 1582-1601

Abstract: Piracy has long plagued the maritime industry, and has led to significant losses of life and goods. The risk of maritime piracy is largely predicted at present based on static analysis. This does not suitably address practical needs because the behavior and activities of maritime pirates are dynamic. Selecting an appropriate strategy for reducing the risk of piracy under a dynamic environment featuring uncertainty thus remains a key challenge. In this study, we propose a two-stage technique for order of preference by similarity to an ideal solution (TOPSIS) model based on the Bayesian network (BN). A data-driven BN is constructed in the first stage of the proposed method to identify the causal relationships influencing the behaviors of pirates. The second stage involves calculating a decision matrix of the strategies by using TOPSIS, where this enhances the strength of risk prediction and dynamic diagnosis by the BN. The main novelty of the proposed model is that it can provide quantitative measurements of strategies for reducing the probability of piracy in a dynamic environment. It provides a decision-making tool for researchers, shipping companies, and national navies to assess the risk of piracy and select effective measures to reduce its likelihood.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03088839.2023.2193585 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:marpmg:v:51:y:2024:i:7:p:1582-1601

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TMPM20

DOI: 10.1080/03088839.2023.2193585

Access Statistics for this article

Maritime Policy & Management is currently edited by Dr Kevin Li and Heather Leggate McLaughlin

More articles in Maritime Policy & Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().