Strategy, Enterprise and Innovation

Our PhD students

Photo of Darren Simpson

Darren Simpson

  • Qualifications: TBC
  • Role Title: PhD student
  • Address: Richmond Building Portland Street Portsmouth PO1 3DE
  • Telephone: TBA
  • Email: darren.simpson@port.ac.uk
  • Department: Strategy, Enterprise and Innovation
  • Faculty: Portsmouth Business School

Biography

Nationality:                          First supervisor: Dr Sarah Thorne, Second supervisor: Dr Andreas Hoecht

Graphical Modelling and Pattern Based Analytics: An Antecedent or Replacement for Quantitative Risk Assessments?

Thesis summary

Basic methodologies such as risk matrices, fault tree analysis (FTA), failure mode and effect analysis (FMEA), together with computational models   such as Monte Carlo and Bayesian Networks, have been designed to evaluate risks in order to promote effective management. However, popularity of these methodologies coupled with the associated failures of well-documented, high-risk environments that employed these techniques, would suggest either underlying causal failure of risk management techniques, erroneous modeling inputs, or potential cognitive  behavioural deficiencies when implementing such systems may be responsible. 

Previous research undertaken by the author further demonstrated the accuracy of QRA and associated qualitative data was questionable. Whilst many issues were raised regarding the overall value of current risk management standards, the subjective nature of the informing parameters were deemed to be influential factors, with the complicated nature of  assessing risks significantly compounding the problem.

Therefore, the objective of this research is to investigate whether graphical modeling and pattern-based analytics can reduce the potential bias and interpretive behavior.  Attention will primarily focus on the following subject matter:   

  1. The possible reduction of qualitative influences
  2. Pattern based analytics vs. quantitative risk analysis
  3. The value of graphical based modeling within risk management   
  4. Establishing an alternative methodology