Western Mensurationists Meeting June 24, 2013 Greg Johnson Director , Forest Research, Weyerhaeuser NR Company

1. What Thomas (with help from Richard and Pierre-Simon), Sir Ronald, Claude, and Daniel have to do with Information, Forest Biometrics, and Economics and how Jack got me to think about it. Western Mensurationists MeetingJune 24, 2013Greg Johnson Director, Forest Research, Weyerhaeuser NR Company

2. My Assignment How is information used in forest management? What are the key questions from a forest industry perspective.

3. My Objective Is to make you uncomfortable!

4. The Back-story • The fields of mensuration and forest biometrics are well-developed and mature. • Increases in analytical power (both computing and statistical techniques) over the past several decades have been staggering. • Sampling theory and its application in forest measurements is robust, sophisticated, and efficient. • The biometricians and economists are even talking to each other now! (just have to get those pesky geneticists to join the party and we’ll be set)

5. Leading to … • Better information. • More accurate and precise answers to business and social questions. • Cost-effective investments in information technology.

6. Leading to … • Better information? • More accurate and precise answers to business and social questions? • Cost-effective investments in information technology?

7. The (Rest of the) Story • We continue to see policy formulated in the absence or in spite of sound science and data. • We still see abysmal forest and resource inventories (data and methods) in the public and private sectors. • Planning tools (e.g., growth models) are unvalidated and frequently produce abiological results. • It is common to see cost cutting initiatives targeting information-gathering expenses in forestry. • A nearly complete disinvestment in commercial forest management research in the private sector and a severe retrenchment in the public sector.

8. Guess What? Our mixed-effects model accounting autocorrelation and heterogeneous variance won’t fix it.

9. Why? • Because we just don’t get it! • Despite poor practice, decisions get made, companies make profits, policy is formed. • In each case, there are expected outcomes, established norms, biological boundaries. • We don’t seem to be running amok.

10. Our first cast of characters Presbyterian minister Chaplain Attended a school run at a Benedictine priory

11. Belief versus Outcomes • Two ways of using this equation: • Bayesian (belief) • Frequentist (proportion of outcomes) Everyone Else US

12. Belief versus Outcomes • From a Bayesian viewpoint, the stronger the belief, the more difficult it is to alter that belief with new evidence (observations). • Fast versus slow thinking. • System 1:Fast, automatic, frequent, emotional, stereotypic, subconscious. • System 2:Slow, effortful, infrequent, logical, calculating, conscious. Daniel Kahneman

13. System 1 and 2 The Müller-LyerIllusion

14. Belief versus Outcomes • Combined with the Confirmation Bias managers may be reluctant to spend money on new data when their priors are strong (they’ve seen it before, aren’t looking for differences, and “know” it won’t change). • Intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution. • There is an additional bias against new data that would alter the posterior: “It is the consistency of the available information that matters for a good story, not its completeness. Indeed, you will often find thatknowing little makes it easier to fit everything you know into a coherent pattern.” Daniel Kahneman

15. So what is information anyway? • We like to distinguish between data and information. Data being the attributes/traits (stuff) you observe, and information being the what gets used. • “Information” has a more formal interpretation: Given what you know, how surprised can you be in the next outcome? • Entropy quantifies the uncertainty involved in predicting the value of a random variable. • Information is predictive. Claude Shannon (1916-2001)

16. An Example • So when we go to cruise a stand, we will often have: • An existing inventory estimate. • The time period since the last measurement. • Some expectation of stand development given it’s prior measurement, age, etc. • When combined, these data are highly predictive • An Information Scientist would say we have low entropy. • A Bayesian would say we have a strong prior. • A Manager would/should ask: will collecting new data significantly change my decision/plan/etc?

17. Here’s our problem • We don’t have a good answer for the manager. • Sir Ronald can’t help us – tests of significance and the like are not helpful. We are talking about the same questions and the same data,but weighing outcomes differently. We can test hypotheses, but we are not moving the posterior. Sir Ronald Aylmer Fisher (1890-1962)

18. Value of Information • Value of Information (VOI)*: • VOI = (Vi+ - Vi- ) – C where: Vi+ = the expected monetary value of the decision with new information Vi- = the expected monetary value of the decisionwithout new information C = cost of information acquisition • Vi+ may not be known a priori – “expected” then becomes a different statistical statement. *After Kangas 2011 and 2010

19. VOI: Expected Utility Theory *Hirshliefer and Riley 1979

20. Prospect Theory where: • ω = weighting function based on outcome probabilities pi. • ν= value function for outcomes xi. • Losses hurt more than gains feel good. • Depends on a reference point (not considered in expected utility theory where higher expected value decisions are simply (linearly) preferred).

21. Consequences • Managers and decision makers often set their reference points differently than we do. • Potential upsides for new information are discounted and downsides (costs of acquisition with little change in decision outcomes) are over-weighted.

22. Value of Information • Some economists treat the uncertainty problem as Bayesians. • The value of information is tied to beliefs. Hirshleifer and Riley, 1979 Jack Hirshleifer

23. What to do … What to do … • People (managers) preferentially use System 1 (fast) thinking, that is numerically and probabilistically inept. • Biometricians/mensurationists attempt to inform managers using highly refined analytics useful to System 2 (slow) thinking. • New information on its own will be less effective at changing decisions when it is aimed at fast thinking situations where there are strong beliefs (priors) and/or complex consequences (in danger of the substitution effect).

24. Our Challenges • Slow the decision making process down. • Recognize the inherent Bayesianness of the decision making process. • Recognize the nonlinear and over-weighted negative utility of losses. • Work to develop new approaches to couch the value of information problem in a belief-based decision process. • Talk to our economist friends more – they discovered this a while ago (went to the dark side?).

25. “A mistake is not something to be determined after the fact, but in the light of the information until that point” NassimTaleb Fooled by Randomness