Jesse Baker - RISSB - Industry incident reporting, what’s the BCR?


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Jesse Baker delivered the presentation at 2014 Major Rail Occurrence Forum (Derailments).

The RISSB Major Rail Occurrence Forum (Derailments) has been designed to build on and continue the analysis of major occurrence reports and to seek Industry learning from them. By reviewing major occurrence reports, Rail Organisations have the opportunity to learn from the lessons without having to suffer the same occurrence.

For more information about the event, please visit:

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Jesse Baker - RISSB - Industry incident reporting, what’s the BCR?

  1. 1. Major Rail Occurrence Forum - Derailments 29/30 April 2014 Industry Incident Reporting, what’s the BCR? Jesse Baker – Manager Safety & Systems RISSB
  2. 2. Who are RISSB? •  Not for profit company •  Wholly owned by the ARA (Industry) •  Half funded by Government, half by industry •  Primary business is standards, rules, codes of practice and guidelines •  But really the name of the game is harmonisation ….. but this speech is about Industry incident reporting and we have ON-S1, OC-G1 and the CFF
  3. 3. Thankyou Questions?
  4. 4. Why do we report on occurrences? – Why do we measure things at all? •  “Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.” ― H. James Harrington •  When something goes wrong (an occurrence) we get a specific type of opportunity to understand (learn). •  But why report?
  5. 5. The evolution of our reporting regime •  1992 – Commonwealth Govt forms National Rail Corporation •  National Rail lobbies the State Regulators Panel •  Late ‘90s ON-S1 was published •  c5 years later OC-G1 was published •  2012 ON-S1 & OC-G1 •  2008 NTC National Strategy for Rail Safety Data •  CFF first published in 2009 – non mandatory •  The future …. SISAR !!!
  6. 6. ON-S1 •  Terminology and descriptors •  Reporting requirements •  Occurrence categories and definitions o  Derailment: An incident where one or more rolling stock wheels leave the rail or track during railway operations. o  General description o  Number of wagons o  Operational status o  Chain of events
  7. 7. •  Terminology and descriptors •  Guiding principles for classification •  Occurrence categories and definitions o  Running Line Derailment includes / excludes o  Yard Derailment includes / excludes OC-G1
  8. 8. The problem with ON-S1 and OC-G1
  9. 9. NTC National Strategy for Rail Safety Data •  Better focused national data •  Better data quality •  Better data consistency and comparability
  10. 10. Contributing Factors Framework
  11. 11. •  Starting to become more intelligent •  But take-up isn’t great! Contributing Factors Framework
  12. 12. Decision making more generally •  None of this data collection is useful unless it can lead to decision making •  But there is another way
  13. 13. A thought experiment •  What would you rather? $1,000 Right now Roll a dice up to 100 times 1 & 5 you win $700 2 nothing 3, 4 & 6 you lose $300 (0.333 x 700) + (0.167 x 0) – (0.5 x 300) = $83.10 x 100 = $8,310
  14. 14. The human condition •  We’re vulnerable to: o  Several types of bias: Ø  Decision making, belief, and behavioural Ø  Social Ø  Memory o  Logical fallacies
  15. 15. Decision making, belief, & behavioural biases Ambiguity  effect   Dura0on  neglect   Irra0onal  escala0on   Reac0ve  devalua0on   Anchoring  or  focalism   Empathy  gap   Just-­‐world  hypothesis   Recency  illusion   A@en0onal  bias   Endowment  effect   Less-­‐is-­‐be@er  effect   Restraint  bias   Availability  heuris0c   Essen0alism   Loss  aversion   Rhyme  as  reason  effect   Availability  cascade   Exaggerated  expecta0on   Mere  exposure  effect   Risk  compensa0on  /  Peltzman  effect   Backfire  effect   Experimenter's  or  expecta0on  bias   Money  illusion   Selec0ve  percep0on   Bandwagon  effect   Func0onal  fixedness   Moral  creden0al  effect   Semmelweis  reflex   Base  rate  fallacy  or  base  rate  neglect   Focusing  effect   Nega0vity  effect   Social  comparison  bias   Belief  bias   Forer  effect  or  Barnum  effect   Nega0vity  bias   Social  desirability  bias   Bias  blind  spot   Framing  effect   Neglect  of  probability   Status  quo  bias   Cheerleader  effect   Frequency  illusion   Normalcy  bias   Stereotyping   Choice-­‐suppor0ve  bias   Gambler's  fallacy   Observa0on  selec0on  bias   Subaddi0vity  effect   Clustering  illusion   Hard-­‐easy  effect   Observer-­‐expectancy  effect   Subjec0ve  valida0on   Comfort  zone  effect   Hindsight  bias   Omission  bias   Survivorship  bias   Confirma0on  bias   Hos0le  media  effect   Op0mism  bias   Time-­‐saving  bias   Congruence  bias   Hot-­‐hand  fallacy   Ostrich  effect   Unit  bias   Conjunc0on  fallacy   Hyperbolic  discoun0ng   Outcome  bias   Well  travelled  road  effect   Conserva0sm  or  regressive  bias   Iden0fiable  vic0m  effect   Overconfidence  effect   Whole  only  effect   Conserva0sm  (Bayesian)   IKEA  effect   Pareidolia   Zero-­‐risk  bias   Contrast  effect   Illusion  of  control   Pessimism  bias   Zero-­‐sum  heuris0c   Curse  of  knowledge   Illusion  of  validity   Planning  fallacy   Decoy  effect   Illusory  correla0on   Post-­‐purchase  ra0onaliza0on   Deja  vu  effect   Impact  bias   Pro-­‐innova0on  bias   Denomina0on  effect   Informa0on  bias   Pseudocertainty  effect   Dis0nc0on  bias   Insensi0vity  to  sample  size   Reactance  
  16. 16. Social biases Actor-­‐observer  bias   Illusion  of  asymmetric  insight   Projec0on  bias   Defensive  a@ribu0on  hypothesis   Illusion  of  external  agency   Self-­‐serving  bias   Dunning–Kruger  effect   Illusion  of  transparency   Shared  informa0on  bias   Egocentric  bias   Illusory  superiority   System  jus0fica0on   Extrinsic  incen0ves  bias   Ingroup  bias   Trait  ascrip0on  bias   False  consensus  effect   Just-­‐world  phenomenon   Ul0mate  a@ribu0on  error   Forer  effect  (aka  Barnum  effect)   Moral  luck   Worse-­‐than-­‐average  effect   Fundamental  a@ribu0on  error   Naive  cynicism   Group  a@ribu0on  error   Naïve  realism   Halo  effect   Outgroup  homogeneity  bias  
  17. 17. Memory biases Bizarreness  effect   Illusion  of  truth  effect   Processing  difficulty  effect   Choice-­‐suppor0ve  bias   Illusory  correla0on   Reminiscence  bump   Change  bias   Lag  effect   Rosy  retrospec0on   Childhood  amnesia   Leveling  and  Sharpening   Self-­‐relevance  effect   Conserva0sm  or  Regressive  Bias   Levels-­‐of-­‐processing  effect   Source  confusion   Consistency  bias   List-­‐length  effect   Spacing  effect   Context  effect   Misinforma0on  effect   Spotlight  effect   Cross-­‐race  effect   Modality  effect   Stereotypical  bias   Cryptomnesia   Mood-­‐congruent  memory  bias   Suffix  effect   Egocentric  bias   Next-­‐in-­‐line  effect   Sugges0bility   Fading  affect  bias   Part-­‐list  cueing  effect   Telescoping  effect   False  memory   Peak-­‐end  rule   Tes0ng  effect   Genera0on  effect  (Self-­‐genera0on  effect)   Persistence   Tip  of  the  tongue  phenomenon   Google  effect   Picture  superiority  effect   Verba0m  effect   Hindsight  bias   Posi0vity  effect   Von  Restorff  effect   Humor  effect   Primacy  effect,  Recency  effect  &   Serial  posi0on  effect   Zeigarnik  effect  
  18. 18. Particularly relevant biases •  Availability heuristic •  Confirmation bias •  Focussing effect •  Hindsight bias •  Illusion of control •  Normalcy bias •  Misinformation effect •  But you might think you’re immune: Naïve realism The belief that we see reality as it really is – objectively and without bias; that the facts are plain for all to see; that rational people will agree with us; and that those who don't are either uninformed, lazy, irrational, or biased.
  19. 19. Logical fallacies •  Formal fallacies o  Propositional fallacies o  Quantification fallacies o  Formal syllogistic fallacies •  Informal fallacies o  Faulty generalisations o  Red herring fallacies •  Conditional or questionable fallacies
  20. 20. Logical fallacies (shibing  the)  Burden  of  proof   Argument  to  modera0on   Fallacy  of  the  undistributed  middle   Naturalis0c  fallacy  fallacy[38]  (an0-­‐naturalis0c  fallacy[39])   Abusive  fallacy   Argumentum  ad  baculum   False  analogy   Nega0ve  conclusion  from  affirma0ve  premises  (illicit  affirma0ve)   Accident   Argumentum  ad  hominem   False  a@ribu0on   Nirvana  fallacy  (perfect  solu0on  fallacy)   Ad  hominem   Argumentum  ad  populu   False  authority  (single  authority)   No  true  Scotsman   Affirma0ve  conclusion  from  a  nega0ve  premise  (illicit  nega0ve)   Argumentum  verbosium   False  dilemma   Onus  probandi   Affirming  a  disjunct   Associa0on  fallacy  (guilt  by  associa0on)   Faulty  generaliza0ons   Overwhelming  excep0on   Affirming  the  consequent   Base  rate  fallacy   Gambler's  fallacy   Pathe0c  fallacy   Ambiguous  middle  term   Begging  the  ques0on  (pe00o  principii)   Gene0c  fallacy   Pe00o  principii   Appeal  to  accomplishment   Broken  window  fallacy   Hasty  generaliza0on   Poisoning  the  well   Appeal  to  authority  (argumentum  ab  auctoritate)   Bulverism  (Psychogene0c  Fallacy)   Hedging   Post  hoc  ergo  propter  hoc  La0n  for  "aber  this,  therefore  because  of  this"     Appeal  to  consequences  (argumentum  ad  consequen0am)   Cherry  picking  (suppressed  evidence,  incomplete  evidence)   Historian's  fallacy   Proof  by  verbosity  (argumentum  verbosium,  proof  by  in0mida0on)   Appeal  to  emo0on   Chronological  snobbery   Homunculus  fallacy   Prosecutor's  fallacy   Appeal  to  equality   Circular  cause  and  consequence   If-­‐by-­‐whiskey   Psychologist's  fallacy   Appeal  to  fear   Circular  reasoning  (circulus  in  demonstrando)   Ignora0o  elenchi  (irrelevant  conclusion,  missing  the  point)   Red  herring   Appeal  to  fla@ery   Conjunc0on  fallacy   Illicit  major   Reduc0o  ad  Hitlerum  (playing  the  Nazi  card)   Appeal  to  mo0ve   Con0nuum  fallacy   Illicit  minor   Regression  fallacy   Appeal  to  nature   Correla0on  proves  causa0on  (cum  hoc  ergo  propter  hoc)   Incomplete  comparison   Reifica0on  (hyposta0za0on)   Appeal  to  novelty  (argumentum  novita0s/an0quita0s)   Definist  fallacy   Inconsistent  comparison   Retrospec0ve  determinism   Appeal  to  pity  (argumentum  ad  misericordiam)   Denying  the  antecedent   Induc0ve  fallacy   Shotgun  argumenta0on   Appeal  to  poverty  (argumentum  ad  Lazarum)   Ecological  fallacy   Infla0on  of  conflict   Slippery  slope  (thin  edge  of  the  wedge,  camel's  nose)   Appeal  to  probability   Equivoca0on   Informal  fallacies   Special  pleading   Appeal  to  ridicule   Etymological  fallacy   Judgmental  language   Straw  man   Appeal  to  spite   Existen0al  fallacy   Ke@le  logic   Suppressed  correla0ve   Appeal  to  tradi0on  (argumentum  ad  an0quitam)   Fallacy  of  composi0on   Ludic  fallacy   Syllogis0c  fallacies   Appeal  to  wealth  (argumentum  ad  crumenam)   Fallacy  of  division   Masked  man  fallacy  (illicit  subs0tu0on  of  iden0cals)   Texas  sharpshooter  fallacy   Argument  from  (personal)  incredulity   Fallacy  of  exclusive  premises   Mind  projec0on  fallacy   Thought-­‐termina0ng  cliché   Argument  from  fallacy   Fallacy  of  four  terms  (quaternio  terminorum)   Misleading  vividness   Tu  quoque  ("you  too",  appeal  to  hypocrisy,  I'm  rubber  and  you're  glue)   Argument  from  ignorance   Fallacy  of  many  ques0ons   Moral  high  ground  fallacy   Two  wrongs  make  a  right   Argument  from  repe00on  (argumentum  ad  nauseam)   Fallacy  of  quo0ng  out  of  context  (contextomy)   Moralis0c  fallacy   Wishful  thinking   Argument  from  silence  (argumentum  e  silen0o)   Fallacy  of  rela0ve  priva0on   Moving  the  goalposts  (raising  the  bar)   Wrong  direc0on   Argument  from  silence  (argumentum  ex  silen0o)   Fallacy  of  the  single  cause  (causal  oversimplifica0on[29])   Naturalis0c  fallacy  
  21. 21. So how on earth do we ever get anything done!!! •  Debate about the irrationality of biases •  Consensus decision making can be strong •  There is such a thing as ‘the fallacy fallacy’
  22. 22. Accounting for our deficiencies •  The information lifecycle •  Good data/information, & decision tools/frameworks hugely important o  RISSB Taking Safe Decisions o  Safety Performance Indicators
  23. 23. The information age •  Information (knowledge) is power •  There is more information in one edition of the New York Times than the average person in 17th - century England would have come across in a lifetime •  Watson – IBM’s supercomputer can read 1M books per second •  Neural networks •  Turing test ….. but what’s the cost of being smarter ???
  24. 24. But we’re doing ok now aren’t we? •  Many organisations are very good at information gathering / decision making •  There is always room for improvement •  Especially at a national level (remember ON-S1 and OC-G1 ?) •  Don’t be a victim to your own bias! o  Overconfidence effect o  Or worse, the Dunning-Kruger effect!
  25. 25. So what is SISAR? •  Safety Information System for Australasian Rail •  Based on UKs SMIS and SRM
  26. 26. Why would SRM be good for us?
  27. 27. Why would SRM be good for us?
  28. 28. Why would SRM be good for us?
  29. 29. So what’s the BCR? •  Impossible to put a $ value on it •  Old school attitude = get into diminishing returns •  New attitude = safety (& reliability) pays 5 x 9s organisations (99.999 ) $,
  30. 30. Thankyou (for real this time) Questions?