Combining morphological analysis and Bayesian Networks for strategic decision support

  title={Combining morphological analysis and Bayesian Networks for strategic decision support},
  author={Aj De Waal and Tom Ritchey},
Morphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative and quantitative part. The qualitative part is a cause-and-effect, or causal graph. The quantitative part depicts the strength of the causal relationships between variables. Combining… 

Multilevel Probabilistic Morphological Analysis for Facilitating Modeling and Simulation of Notional Scenarios

This research introduces probabilistic cross‐consistency assessments instead of traditional binary assessments to account for uncertain future states of the world and describes degrees of likelihood that two elements may coexist in a given scenario.

Integrated Bayesian network framework for modeling complex ecological issues

The IBNDC approach facilitates object‐oriented BN (OOBN) modeling, arguably viewed as the next logical step in adaptive management modeling, and that embraces iterative development.

Combining Morphological Analysis and Bayesian Belief Networks: A DSS for Safer Construction of a Smart City

The proposed DSS serves as a case exemplar for building a planning tool for scenario analysis and impact assessment, and as an incident response mechanism for safer construction of a smart city.

Construction and evaluation of Bayesian networks with expert-defined latent variables

This paper encourages the inclusion of abstract latent variables in BN fusion systems by listing the considerations for evaluating the uncertainties of such variables, illustrating a novel elicitation technique for parameterisation of large conditional probability tables and framing the uncertainty evaluation in a systems engineering validation and verification process.

Principles of Cross-Consistency Assessment in General Morphological Modelling

The methodological principles and practical procedural issues involved in the Cross-Consistency Assessment process are examined, and examples from a number of client-based projects are presented.

A framework for inferring predictive distributions of rhino poaching events through causal modelling

The developed Bayesian network based model is an initial attempt at proposing a sensible modelling approach for this problem and some of the complexities of the approach are discussed, before considering how the model may be validated at a later stage.

Futures Studies using Morphological Analysis Adapted from an Article for the UN University Millennium Project : Futures Research Methodology Series

Morphological analysis is a method for rigorously structuring and investigating the total set of relationships in inherently non-quantifiable socio-technical problem complexes (variously called

A Case Study of Complex Policy Design: The Systems Engineering Approach

The results show that it is possible to generate a solution space that highlights the best possible combinations of the given alternatives while also providing an optimal sequence and grouping for an optimized implementation.



Bayesian Networks and Decision Graphs

  • F. V. Jensen
  • Computer Science
    Statistics for Engineering and Information Science
  • 2001
The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.

Scenario Development and Force Requirements using Morphological Analysis

Abstract : Morphological analysis (MA) is a non-quantified modelling method for structuring and analyzing technical, organizational and social problem complexes. It is well suited for developing

Nuclear Facilities and Sabotage : Using Morphological Analysis as a Scenario and Strategy Development Laboratory

Modelling complex socio-technical systems and threat scenarios presents us with a number of difficult methodological problems. Most of the parameters involved are not meaningfully quantifiable, and

Building Probabilistic Networks: "Where Do the Numbers Come From?" Guest Editors Introduction

In collaboration with Finn V. Jensen and Max Henrion, a workshop was held in 1995 a workshop devoted to the theme of obtaining the numbers, the most daunting task in building probabilistic networks, and the result is the current issue of IEEE Transactions on Data and Knowledge Engineering.

Probabilistic Networks and Expert Systems

This book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms of probabilistic expert systems, emphasizing those cases in which exact answers are obtainable.

Bayesian Artificial Intelligence

This book would have been more useful had some more detailed discussion on the choices of ranked set size k and cycle number m been added, however, overall I would highly recommend this well-written and reasonably priced book to researchers and practitioners.

Dynamic bayesian networks: representation, inference and learning

This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.

Fritz Zwicky, Morphologie and Policy Analysis

Fritz Zwicky pioneered the development of morphological analysis (MA) as a method for investigating the totality of relationships contained in multi-dimensional, usually nonquantifiable problem

Whole-pattern futures projection, using field anomaly relaxation