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Risk Management Using Bayesian Networks and BayesiaLab - PowerPoint PPT Presentation

Nov 11, 2022 •488 likes •986 views

The webinar will start at: 13:00:00 The current time is: 13:00:34 Central Standard Time UTC-6 Risk Management Using Bayesian Networks and BayesiaLab Introduction Your Hosts Today Stefan Conrady stefan.conrady@bayesia.us Stacey

  1. The webinar will start at: 13:00:00 The current time is: 13:00:34 Central Standard Time UTC-6 Risk Management Using Bayesian Networks and BayesiaLab

  2. Introduction Your Hosts Today • Stefan Conrady stefan.conrady@bayesia.us • Stacey Blodgett stacey.blodgett@bayesia.us BayesiaLab.com 2

  3. Today’s Objectives Methodological Objective • Bayesian Networks for Managing Risk Substantive Research Objective • Quantifying and mitigating the risk of speeding violations in a transportation business context “TOY PROBLEM” stefan.conrady@bayesia.us 3

  4. Today’s Agenda Motivation & Background • Regulations & Risk • Qualitative Risk Assessment Risk Mitigation Proposal • Methodologies 20 min. Bayesian Networks for Reasoning Without Data • • The Delphi Method The Bayesia Expert Knowledge Elicitation Environment (BEKEE) • Software Demo Building the Qualitative Structure • 40 min. • Eliciting Probabilities with BEKEE Finding the Optimal Policy • stefan.conrady@bayesia.us 4

  5. Teaching Edition Academic Edition Desktop Bayesia Market Code Export Module BayesiaLab 6 Simulator Professional Software BayesiaLab WebSimulator Web Application Bayesia Expert Knowledge Elicitation Environment (BEKEE) Bayesia Engine API for API Bayesia Engine API for Modeling and Network Learning Inference BayesiaLab.com 5

  6. Webinar Slides & Recording Available stefan.conrady@bayesia.us 6

  7. Today’s Domain: Transport & Logistics BayesiaLab.com 7

  8. Law Business SPEED LIMIT THE NEED FOR SPEED 55 Regulation MINIMUM 55 Business Requirement BayesiaLab.com 8

  9. BayesiaLab.com RISK 9

  10. Qualitative Risk Assessment Risk $$$ $ Speed BayesiaLab.com 10

  11. BayesiaLab.com 11

  12. Risk Management Premise • Human drivers are always at a risk of violating traffic rules, including speed limits. Proposal for Risk Mitigation • Equip vehicle fleet with radar detectors to reduce the risk of speeding violations. Note • We are only considering the risks of violating the law and its consequences, such as a penalties, suspension of privileges, arrests, or vehicle seizures. • We are not looking at accident risks related to speeding, which of course exist. • We assume that radar detectors are legal for the purpose of this study, which is not the case in many jurisdictions. BayesiaLab.com 12

  13. Radar Detector BayesiaLab.com 13

  14. Note: We do not advocate speeding or the use of radar detectors. Always obey all applicable traffic laws in your jurisdiction. SPEED KILLS 14

  15. Risk Management Study Questions • What is the base risk without radar detectors? • By how much do radar detectors reduce the risk of speeding violations? • What is their expected economical value to an organization? • Do they potentially lead to unintended consequences? BayesiaLab.com 15

  16. stefan.conrady@bayesia.us 16

  17. But what if we don’t have any data… But we only have observational data stefan.conrady@bayesia.us 17

  18. No Data? “Without data, you’re just another person with an opinion.” W. Edwards Deming stefan.conrady@bayesia.us 18

  19. Argument ? BayesiaLab.com 19

  20. Bayesian Networks to the Rescue! Data Model Source Theory Description Prediction Explanation Simulation Attribution Optimization Model Purpose Association/Correlation Causation BayesiaLab.com 20

  21. Bayesian Networks to the Rescue! Data Even without data, humans do possess useful knowledge, qualitative or Model Source quantitative, tacit or explicit, about many aspects of the world. Reasoning Without Data Theory Description Prediction Explanation Simulation Attribution Optimization Model Purpose Association/Correlation Causation BayesiaLab.com 21

  22. Bayesian Networks to the Rescue! ? Data YELLOW CAB CO. Model Source WHITE CAB COMPANY Reasoning Without Data Webinar on March 2, 2018 Theory Description Prediction Explanation Simulation Attribution Optimization Model Purpose Association/Correlation Causation BayesiaLab.com 22

  23. Reasoning Without Data One Expert Last Week BayesiaLab.com 23

  24. Knowledge Elicitation — Individual Biases Examples • Overconfidence • Confirmation bias • Framing effect • Escalation of commitment • Availability bias • Illusion of control • Anchoring bias stefan.conrady@bayesia.us 24

  25. Reasoning Without Data A Group of Experts One Expert Last Week Today BayesiaLab.com 25

  26. Knowledge Elicitation — Group Biases Examples • Groupthink (“toeing the line”) • Social loafing (“hiding in the crowd”) • Group polarization (“taken to the extreme”) • Escalation of commitment (“throwing good money after bad”, “sunken costs fallacy”) stefan.conrady@bayesia.us 26

  27. The Delphi Method Origins • The original Delphi method was developed in the 1940s and 50s by Norman Dalkey of the RAND Corporation. • The Delphi method was devised in order to obtain the most reliable opinion consensus of a group of experts by subjecting them to a series of questionnaires in depth interspersed with controlled opinion feedback. stefan.conrady@bayesia.us 27

  28. The Delphi Method Elicit Knowledge from Interacting Groups • Take the positive, e.g. • Knowledge from a variety of sources • Creative synthesis • Prevent the negative, e.g. • Groupthink (“toeing the line”) • Social loafing (“hiding in the crowd”) • Group polarization (“taken to the extreme”) stefan.conrady@bayesia.us 28

  29. The Delphi Method The Classical Delphi • Interviews via questionnaires • Anonymity of participants • Iteration • Controlled feedback • Statistical aggregation stefan.conrady@bayesia.us 29

  30. First Experimental Application “ to solicit expert opinion to th e selection, from th e point of view of a Soviet strategic planner, of an optimal U. S. indu strial target system. . . ” stefan.conrady@bayesia.us 30

  31. The Delphi Method “In view of the absence of a proper theoretical foundation and the consequent inevitability of having, to some extent, to rely on intuitive expertise—a situation which is still further compounded by its multidisciplinary characteristics—we are faced with two options: we can either throw up our hands in despair and wait until we have an adequate theory enabling us to deal with socioeconomic and political problems as confidently as we do with problems in physics and chemistry, or we can make the most of an admittedly unsatisfactory situation and try to obtain the relevant intuitive insights of experts and then use their judgments as systematically as possible.” stefan.conrady@bayesia.us 31

  32. The Bayesia Expert Knowledge Elicitation Environment (BEKEE) Utilizing Bayesian Networks with the Delphi Method

  33. BEKEE Workflow 1. Brainstorming & Model Construction • Variables of interest BAYESIALAB • Causal relationships • Discretization levels 2. Knowledge Elicitation (interactive/offline) BEKEE • Facilitator posts assessment tasks • Participants submit assessments 3. Inference & Optimization BAYESIALAB stefan.conrady@bayesia.us 33

  34. 1. Brainstorming & Model Construction Variables of interest • Causal relationships • Qualitative Facilitator Network Experts stefan.conrady@bayesia.us 34

  35. 2. Knowledge Elicitation Web Client ? ? BAYESIALAB BEKEE Server ? Quantitative ? Facilitator Elicitation Experts stefan.conrady@bayesia.us 35

  36. Inference, Analysis, and Optimization Final Network BAYESIALAB stefan.conrady@bayesia.us 36

  37. Building the Bayesian Network Model 37

  38. Knowledge Modeling Creating the Qualitative Structure • Variables (Nodes) Qualitative Bayesian Network Structure from Brainstorming BayesiaLab.com 38

  39. “Parameters” from BEKEE BayesiaLab.com 39

  40. Inference & Optimization 40

  41. VR In Conclusion… 42

  42. User Forum: bayesia.com/community BayesiaLab.com 43

  43. bayesia.com/pricing-2018 BayesiaLab.com 44

  44. BayesiaLab Trial Try BayesiaLab Today! • Download Demo Version: www.bayesialab.com/trial-download • Apply for Unrestricted Evaluation Version: www.bayesialab.com/evaluation BayesiaLab.com 45

  45. Webinar Series: Friday at 1 p.m. (Central) Upcoming Webinars: • March 16 Optimizing Health Policies with Bayesian Networks • March 23 t.b.d. Register here: bayesia.com/events stefan.conrady@bayesia.us 46

  46. BayesiaLab Courses Around the World in 2018 • April 11–13 • August 29–31 Sydney, Australia London, UK • May 16–18 • September 26–28 Seattle, WA New Delhi, India • June 26–28 • October 29–31 Boston, MA Chicago, IL • July 23–25 • December 4–6 San Francisco, CA New York, NY Learn More & Register: bayesia.com/events stefan.conrady@bayesia.us 47

  47. San Francisco Introductory BayesiaLab Course in San Francisco, California July 23–25, 2018 BayesiaLab.com 48

  48. 6 th Annual BayesiaLab Conference in Chicago November 1–2, 2018 Chicago BayesiaLab.com 49

  49. Thank You! stefan.conrady@bayesia.us BayesianNetwork linkedin.com/in/stefanconrady facebook.com/bayesia BayesiaLab.com 50

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