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GSA-WA Perth 2006
 

GSA-WA Perth 2006

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Risk and Uncertainty in Mineral Exploration

Risk and Uncertainty in Mineral Exploration

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    GSA-WA Perth 2006 GSA-WA Perth 2006 Presentation Transcript

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