GSA-WA Perth 2006
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GSA-WA Perth 2006



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?
    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
    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
    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
    The inherent geological complexities and uncertainties in exploration clash with rational decision-making
    Hence, we tend to rely extensively on intuitive thinking and judgment
    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
    Very effective, automatic processes
    Reduce the time and effort of decision-making
    Lead to reasonable decisions in many situations
    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
    Anchoring and adjustment
    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
    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
    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?
    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!
    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)
    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
    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
    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%
    Glenn McMaster (BP), 2003 SPE Distinguished Lecturer
  • Outlook The petroleum example
    Management of
    risk and uncertainty
    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)
    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
    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