The document discusses how mineral exploration faces high risk and uncertainty due to the low probability of success in finding deposits. While some companies have found lucrative deposits, on average exploration returns are barely above breakeven. This is due to the low base rate of deposits and the large number of unsuccessful projects. The document also examines how human psychology and cognitive biases can negatively impact decision-making under such uncertain conditions. Heuristics like representativeness, anchoring and framing can cause errors. Looking at how the petroleum industry improved through formal risk management, 3D modeling and probabilistic approaches provides lessons for better managing risk and uncertainty in mineral exploration.

1. Risk, Uncertainty and Bias: Rulers over ExplorationSuccess and Failure Oliver Kreuzer Centre for Exploration Targeting The University of Western Australia

2. Acknowledgements Mike Etheridge, Maureen McMahon GEMOC Key Centre, Macquarie University Colin Wastell, Gillian Lucas Department of Psychology, Macquarie University

3. 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?

4. Mineral explorationBusiness aspects Randolph (2002)

5. Mineral explorationBusiness aspects Bosma (2003)

6. 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

7. 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

8. 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!

9. 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

10. Mineral explorationBusiness aspects Lord et al. (2001)

11. Mineral exploration Business aspects Parry (2001)

12. Mineral explorationBusiness aspects Parry (2001)

13. 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.”

14. 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)

15. 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)

16. Uncertainty Most decisions we make in mineral exploration are made under conditions of significant uncertainty

17. 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)

18. Decision analysis e.g. Newendorp & Schuyler (2000)

19. 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)

20. 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

21. 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

22. 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)

23. Psychology of decision-making The Two-Systems View Recognizes that we use 2 main types of cogitive process e.g. Kahneman (2003)

24. 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)

25. 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)

26. 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)

27. Heuristics Common types of heuristics Representiveness Framing Anchoring and adjustment Availability e.g. Kahneman (2003)

28. 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)

29. 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

30. Heuristics Representativeness Answer: 8.3% True Positives : Total Positives = 9 : 108 = 0.083 Example based on Nick Hayward (BHP Billiton), 2003 AIG Symposium

31. 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)

32. 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)

33. 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)

34. 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)

35. 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)

36. 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)

37. 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

38. 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)

39. 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

40. 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

41. 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)

42. 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

43. 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

44. “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