Risks and Decision Analysis

Risks and Decision Analysis

Date/Time: Date(s) - 26/09/2022 - 28/09/2022( 8:00 am - 5:00 pm )
Location: Malaysia, Kuala Lumpur


Overview

This training programme teaches the skills required for a key component of an Engineer’s job – creating value by making decisions that yield optimal returns on the allocation of human and financial resources. The many uncertainties inherent to the engineering business (estimating current ‘states-of-the world/nature’ and predicting future events) create considerable uncertainty in the value that can be realised from resource-allocation decisions. Consequently, there will be a strong emphasis on evaluating the impacts of uncertainty, managing its resultant risks and planning to exploit its up-side potential. Topics to be addressed are the decision-making process, multi-objective decision making, decision-tree analysis, decision criteria, Monte Carlo Simulation and Value of Information & Flexibility. In addition, Utility Theory will be introduced as a means of rationally accounting for risk attitudes. Some of the psychological and judgemental aspects of how people respond to uncertainty will be discussed. The techniques learned in this course will also be useful in making personal decisions.

Objectives

  • Describe the elements of the decision analysis process and the respective roles of management and the analysis team
  • Express and interpret judgments about risks and uncertainties as probability distributions and popular statistics
  • Represent discrete risk events in Venn diagrams, probability trees, and joint probability tables
  • Solve for expected values with decision trees, payoff tables, and Monte Carlo simulation (hand calculations)
  • Craft and solve decision models
  • Evaluate investment and design alternatives with decision tree analysis
  • Develop and solve decision trees for value of information (VOI) problems

Outlines

  • Decision Tree Analysis: decision models, value of information (a key problem type emphasized in the course), flexibility and control, project threats and opportunities
  • Monte Carlo Simulation: Latin hypercube sampling, portfolio problems, optimization, advantages and limitations
  • Decision Criteria and Policy: value measures, multiple objectives, HSE, capital constraint, risk aversion
  • Modeling the Decision: influence diagrams, sensitivity analysis, modeling correlations
  • Basic Probability and Statistics: four fundamental rules including Bayes’ rule, calibration and eliciting judgments, choosing distribution types, common misconceptions about probability
  • Expected Value Concept: foundation for decision policy, features, pitfalls to avoid
  • Implementing Decision Analysis: problem framing, guidelines for good analysis practice, team analyses, computer tools (discussion and demonstrations), mitigating risks

Training Methodology

  • Case Studies.
  • Individual and group discussions and exercises.
  • Intensive training by using templates, diagrams, and charts.
  • Planning activities and presentations.
  • Self-assessments.
  • Action plan.

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