GSA-WA Perth 2006


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Risk and Uncertainty in Mineral Exploration

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GSA-WA Perth 2006

  1. 1. Risk, Uncertainty and Bias:<br />Rulers over ExplorationSuccess and Failure<br />Oliver Kreuzer<br />Centre for Exploration Targeting<br />The University of Western Australia<br />
  2. 2. Acknowledgements<br />Mike Etheridge, Maureen McMahon<br />GEMOC Key Centre, Macquarie University<br />Colin Wastell, Gillian Lucas<br />Department of Psychology, Macquarie University<br />
  3. 3. Presentation outline<br />Aspects of our business<br />Performance, low base rate situation, low probability of success <br />Risk, uncertainty and decision analysis<br />Definitions of risk and uncertainty<br />What is decision analysis?<br />The psychology of decision-making<br />Common heuristics and biases<br />What is their impact on the process of decision-making?<br />Outlook<br />What can we learn from the petroleum industry?<br />
  4. 4. Mineral explorationBusiness aspects<br />Randolph (2002)<br />
  5. 5. Mineral explorationBusiness aspects<br />Bosma (2003)<br />
  6. 6. Mineral explorationBusiness aspects<br />Economic activity<br />As such expected to provide acceptable returns to investors<br />However, probability of success so low and geological uncertainty so high that it has proven difficult to manage for financial success<br />
  7. 7. Mineral explorationBusiness aspects<br />At best a break-even proposition<br />Schodde (2003, 2004)<br />Compiled NPVs of 109 major Australian gold projects (1985–2003)<br />NPVs = $4.74 billion; costs of finding / evaluating $4.64 billion<br />Average return of $1.02 per $1 dollar spent on exploration<br />Leveille & Doggett (in press, Economic Geology Special Publication)<br />Measured costs + returns from 65 Chilean copper projects (1950–2004)<br />Only 14 generated sufficient returns to offset their exploration costs<br />Overall return below breakeven<br />
  8. 8. Mineral explorationBusiness aspects<br />Problem: Low base rate situation<br />Exploration is an example of a low base-rate situation, i.e. there is a low rate of occurrence of ore deposits in individual targets<br />High number of drill holes per discovery<br />Based on Schodde (2003)<br />Data exclude follow-up drilling!<br />
  9. 9. Kennecott<br />Rio Tinto<br />100%<br />10%<br />10%<br />10%<br />0.3%<br />10%<br />0.06%<br />0.03%<br />Mineral explorationBusiness aspects<br />Low chance of proceeding to the next stage<br />
  10. 10. Mineral explorationBusiness aspects<br />Lord et al. (2001)<br />
  11. 11. Mineral exploration Business aspects<br />Parry (2001)<br />
  12. 12. Mineral explorationBusiness aspects<br />Parry (2001)<br />
  13. 13. Observation 1<br />For a some companies exploration has been very lucrative; huge profits were made when they reached the ultimate goal of mining success<br />However, on average, mineral exploration appears to be a break-even proposition – or worse…<br />The studies of Schodde and Leveille & Doggett illustrate that we need to measure exploration performance if we want to improve it<br />E.g. Schodde (2003): As a rule of thumb, we should aim to find gold for less than A$15/oz. This is twice as good as the current average.”<br />
  14. 14. Risk<br />Variability of possible returns<br />As measured by their standard deviation<br />Risk includes but is not limited to chance of making a loss<br />Risk equals opportunity<br />Probability of failure<br />PFailure = 1 – PSuccess<br />Risk can be estimated if we can assign a value to PSuccess<br />Risk can be reduced if we can find ways of improving our PSuccess<br />e.g. Singer & Kouda (1998), Guj (2005)<br />
  15. 15. Uncertainty<br />Definition<br />A measure of our inability to assign a single value to risk<br />Types of uncertainty<br />Inherent natural variability of geologic objects and processes<br />Conceptual and model uncertainty<br />Errors / inaccuracies / biases that occur when we sample, observe, measure or mathematically evaluate geological data<br />e.g. Bardossy & Fodor (2001), Purvis (2003)<br />
  16. 16. Uncertainty<br />Most decisions we make in mineral exploration are <br />made under conditions of significant uncertainty<br />
  17. 17. Uncertainty<br />Uncertainty has rarely been estimated or quantified for our models, maps or sections<br />In fact, many geological products imply a level of certainty that is simply unrealistic<br />This is a major impediment to mineral exploration <br />If we don’t estimate or determine uncertainty we won’t be able to quantify and evaluate exploration risk<br />Figures from Shatwell (2003)<br />
  18. 18. Decision analysis<br />e.g. Newendorp & Schuyler (2000)<br />
  19. 19. Decision analysis<br />Does not eliminate or reduce risk<br />Helps us to evaluate, quantify and understand risk<br />Helps us choose the alternative that offers the best risk / reward ratio<br />Does not replace professional judgment<br />Helps us to communicate geological risks and uncertainties <br />without ambiguity, and<br />in terms of probabilistic and monetary values <br />e.g. Newendorp & Schuyler (2000)<br />
  20. 20. Decision analysis<br />Is decision analysis only for the majors?<br />To expensive (software, consultant fees) and too time consuming (compilation of input values) to be practical for juniors?<br />In my opinion – No.<br />Juniors face the same risk and uncertainty as the majors<br />The junior business model is even more vulnerable to gambler’s ruin (limited risk capital, limited diversity of portfolio, few projects)<br />A quick and dirty analysis is still better than failure to manage risk <br />
  21. 21. Observation 2<br />Mineral exploration is a business bedeviled by uncertainty<br />Yet, many of our outputs and decision-making processes imply a level of confidence that is simply unrealistic<br />For effective, formal risk management to take place we have to estimate, measure or calculate geological uncertainties<br />Decision analysis provides us with simple, effective tools for choosing the best course of action under conditions of uncertainty <br />
  22. 22. Psychology of decision-making<br />Intuitive<br />The inherent geological complexities and uncertainties in exploration clash with rational decision-making<br />Hence, we tend to rely extensively on intuitive thinking and judgment<br />Biased<br />This Intuitive thinking is subject to a well understood set of mental short cuts (heuristics) and systematic errors (biases)<br />
  23. 23. Psychology of decision-making<br />The Two-Systems View<br />Recognizes that we use 2 main types of cogitive process<br />e.g. Kahneman (2003)<br />
  24. 24. Psychology of decision-making<br />A stamp and an envelope cost $1.10 in total.<br />The stamp costs $1 more than the envelope.<br />How much does the envelope cost?<br />e.g. Kahneman (2003)<br />
  25. 25. Psychology of decision-making<br />Most people intuitively answer 10 cents<br />$1.10 separates naturally into $1 and 10 cents<br />10 cents is about the right magnitude<br />But, envelope = 5 cents, stamp = $1.05<br />Implications of such cognitive tests<br />Monitoring of System 1 by System 2 is generally quite lax<br />We tend to offer answers without checking them<br />We are not used to thinking hard and often trust a plausible judgment that quickly comes to mind<br />e.g. Kahneman (2003)<br />
  26. 26. Heuristics<br />What are heuristics?<br />Rules of thumb or mental shortcuts<br />Pros<br />Very effective, automatic processes<br />Reduce the time and effort of decision-making<br />Lead to reasonable decisions in many situations<br />Cons<br />Frequently bias our perception  impact on System 1<br />Cause severe and systematic errors of judgment<br />Worse when we are under time pressure / multitasking<br />e.g. Kahneman (2003)<br />
  27. 27. Heuristics<br />Common types of heuristics<br />Representiveness<br />Framing<br />Anchoring and adjustment<br />Availability<br />e.g. Kahneman (2003)<br />
  28. 28. Heuristics Representativeness<br />Representativeness heuristic<br />Our tendency to overgeneralize from a few characteristics or observations<br />We often judge whether an object (X) belongs to a particular class (Y) by how representative (or similar) X is of Y<br />Source of multiple biases<br />Base rate neglect<br />Gambler’s ruin<br />Overconfidence<br />e.g. Kahneman (2003)<br />
  29. 29. Heuristics Representativeness<br />Base Rate Neglect: an example<br />We know that 1 in 100 targets delivers a gold discovery<br />A new targeting method has been developed<br />It is practical only over small areas (i.e. known targets)<br />Generates an anomaly in 90% of test cases over known deposits<br />Delivers a null result in 90% of test cases in barren areas<br />Exploration companies run it over a total of 1,000 targets<br />What is the likelihood that it will correctly identify a deposit?<br />Example based on Nick Hayward (BHP Billiton), 2003 AIG Symposium<br />
  30. 30. Heuristics Representativeness<br />Answer: 8.3%<br />True Positives : Total Positives = 9 : 108 = 0.083<br />Example based on Nick Hayward (BHP Billiton), 2003 AIG Symposium<br />
  31. 31. Heuristics Representativeness<br />Exploration: example of a low base rate situation<br />Base rates should be the main factor in our estimations<br />However, we tend to ignore prior probabilities when other targeting parameters seem more relevant<br />Consequences<br />Our targeting models need to focus on those parameters that have relatively low false positive rates<br />Wasting time and money on false positives is one of our industry’s main contributors to poor performance<br />e.g. Hronsky (2004), Etheridge (2004)<br />
  32. 32. Heuristics Representativeness<br />Gambler’s ruin (gambler’s fallacy)<br />Wins are perceived more likely after we suffered a string of losses<br />Example: tossing a fair coin<br />After H turned up 9× in a row, is it more likely that T will turn up next?<br />No, the odds are exactly the same for every single toss<br />Each toss of the coin is an independent event<br />The coin has no memory of the past 9 tosses<br />e.g. Busenitz & Barney (1997),Roney & Trick (2003)<br />
  33. 33. Heuristics Representativeness<br />Small sample of tosses  very likely for the number of H and T outcomes to be unequal<br />Only in the long run will those outcomes equalize<br />Example: probability of gambler’s ruin<br />Sufficient capital for 5 trials, each @ Psuccess = 0.1 (or 10%)<br />What is the probability of at least 1 success in 5 trials?<br />Equation:<br />e.g. Busenitz & Barney (1997),Roney & Trick (2003); Example by Guj (2005)<br />
  34. 34. Heuristics Representativeness<br />Where (Cnx) = n! / [x!× (n – x)!]<br />(P15 ) = [(5! / 1! × 4!) × 0.1 × 0.94 + … + (5! / 4! × 1!) × 0.14× 0.9 = 0.4099<br />PGambler’s ruin = 1 – 0.4099 = 0.5901 or59% chance of going bust!<br />If PSuccess = 0.01PGambler’s ruin = 0.9509 or95% chance of failure!<br />Consequences<br />Spending too much on too few prospects is extremely risky<br />A streak of bad luck does not mean that we are due for success<br />Example by Guj (2005)<br />
  35. 35. Heuristics Framing<br />Framing heuristic<br />Our tendency to process information depending on how this information is presented (or framed)<br />Consequences<br />Most judgements and decisions are guided by information derived from the rarest events in our business – discoveries<br />We should start thinking outside the box by framing decisions with information derived from the bulk of our projects – those that failed<br />e.g. Kahneman (2003)<br />
  36. 36. HeuristicsAnchoring and adjustment<br />Anchoring and adjustment heuristic<br />We tend to base our initial estimates on any value we have at hand (anchor), regardless of its relevance<br />We then adjust our estimate until we reach a final value<br />Our adjustments are typically insufficient, narrow and biased towards the value of the anchor<br />e.g. Kahneman (2003), Welsh et al. (2005)<br />
  37. 37. HeuristicsAnchoring and adjustment<br />Consequences<br />Strong anchoring to specific exploration models means we are less likely to find something that is different<br />We drill our best target in a project first; but when it fails, we often lower our standards to justify drilling lesser quality targets<br />
  38. 38. Observation 3 <br />Even after decades of cognitive research we continue to assume that our intuition, experience and intelligence will guide us toward the best possible decision under conditions of uncertainty<br />Yet, the opposite is true: we are prone to cognitive biases that frequently prevent us from choosing the optimal course of action<br />Moreover, the situations of greatest uncertainty are the ones where poor judgment is most likely to result in failure <br />Awareness of our limitations is the first critical step in developing good decision-making procedures<br />cf. Bratvold et al. (2002), Purvis (2003)<br />
  39. 39. Outlook The petroleum example<br />So, where should we go from here?<br />We could, for example, look at how our colleagues in petroleum exploration have changed the fortunes of their industry<br /> What can we learn from the petroleum example?<br />That disciplined management of risk and uncertainty can generate value and turn an industry around<br />That prediction and visualization of subsurface geology can improve success rates<br />That holistic geological models that focus on “where” rather than “how” can reduce uncertainty<br />
  40. 40. Outlook The petroleum example<br />BP exploration 1983–2002<br />After<br />Onset of formal<br />risk assessment<br />Late 90’s<br />High-risk wells ~ 10% <br />Success rate &gt; 50% <br />Economic success rate<br />High-risk wells<br />Late 80’s<br />High-risk wells &gt; 50% <br />Success rate &lt; 20% <br />Before<br />Glenn McMaster (BP), 2003 SPE Distinguished Lecturer<br />
  41. 41. Outlook The petroleum example<br />Management of<br />risk and uncertainty<br />Process-based<br />models<br />Visualization of subsurface geology<br />Figures from Jones & Hillis (2003), Etheridge (2004), Cockcroft (2005)<br />
  42. 42. OutlookProbabilistic ore systems models<br />Risk management and ore deposit modeling<br />Holistic, flexible and process-based<br />build on the petroleum and mineral systems approach (Geoscience Australia)<br />Probabilistic<br />assign probabilities to critical success factors<br />multiplication rather than addition of critical success factors to eliminate those areas where one or more of these factors are absent<br />value distributions instead of single values<br />Calibrated<br />multiple realizations<br />statistical assessment of sensitivity of outputs<br />
  43. 43. OutlookProbabilistic ore systems models<br />Link models to decision structures + GIS<br />E.g. decision trees, Monte Carlo simulation<br />EV outcome for comparison of potential project risks and rewards, regardless of project type, stage or location<br />
  44. 44. “After all, the risk in discovery is still the greatest single risk”<br />Siegfried Muessig<br />The Art of Exploration: SEG Presidential Address, 1978<br />“What we need in all our endeavors … is responsible risk taking and what we want are the rewards of such responsibility”<br />Paul Bailly<br />Risk and the Economic Geologist: SEG Presidential Address, 1982<br />“The successful explorers over the next decade will be those that embrace effective risk management”<br />Marcus Randolph<br />President Diamonds and Specialty Products, BHP-Billiton, 2003<br />