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Sensemaking and Analytics for Organizations

Archive for February 2010

Communicating risks using the Improbability Scale

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It can be hard to develop an intutitive feel for a probability  that is expressed in terms of a single number. The main reason for this is that a numerical probability, without anything to compare it to, may not convey a sense of how likely (or unlikely) an event is. For example, the NSW Road Transport Authority tells us that  0.97% of the registered vehicles on the road in NSW in 2008 were involved in at least one accident.  Based on this, the probability that a randomly chosen vehicle  will be involved in an accident over a period of one year is 0.0097. Although this number suggests the risk is small, it begs the question: how small? How does it compare to the probability of other, known events?   In a short paper entitled, The Improbability Scale,  David Ritchie outlines how to make this comparison in an inituitively appealing way.

Ritchie defines the Improbability Scale, I, as:

I = - \log (p)

where p is the probability of the event.

By definition, I is 0 for absolutely certain events (p=1), and increases as p decreases. The advantage of using I (as opposed to p) is that, in most case, I, will be a number between 0 and 10.  An I of 10 corresponds to a probability of 0.0000000001, which is so small that the event it refers to is practically impossible.

Let’s look at the improbability of some events expressed in terms of  I.

  1. Rolling a six on the throw of a die. p= 1/6;  I= 0.8.
  2. Picking a specific card (say the 10 of diamonds) from a pack (wildcards excluded). p= 1/52;  I= 1.7.
  3. A (particular) vehicle being involved in at least one accident in the Australian state of NSW over a period of one year (the example quoted in the in the first paragraph). p= .0097;  I =  2.0.
  4. One’s birthday occurring on a randomly picked day of the year. p= 1/365;  I =  2.6.
  5. Probability of getting 10 heads in 10 consecutive coin tosses. p(0.5)^{10}  (or 0.00098 ); I = 3
  6. Drawing 5 sequential cards of the same suit from a complete deck (a straight flush). p= 0.0000139;  I=  4.9 (Note: This can be calculated by dividing the total number of sequential 5 card hands and dividing it by the total number of 5 card hands from a deck of 52. I’m too lazy to do the calculation myself, but it’s explained in this Wikipedia article if you’re interested. )
  7. Being struck by lightning in Australia. p= 1/1600000; I =  6.2. (source: this article from Australian Geographic – the article doesn’t say over what period, but I reckon it’s per year)
  8. Winning the Oz Lotto Jackpot. p2.204 \times 10^{-8}; I =  7.7 (based on odds from NSW lotteries for a single game)

Apart from clarifying the risk of a traffic accident, this tells me  (quite unambiguously!)  that I must stop buying lottery tickets.

A side benefit of the improbability scale is that it eases the tasks of calculating the probability of combined events. If two events are independent, the probability that they will occur together is given by the product of their individual probabilities of occurrence. Since the logarithm of a product of two number equals the sum of the numbers, I for the combined event is obtained by adding their individual I values. So the I  for throwing a six and drawing a specific card from a deck is 2.5 (that is, 0.8+1.7), making it more unlikely than being involved in a vehicle accident. That certainly puts both probabilities in perspective.

In short: the improbability scale offers a nice way to understand the likelihood of an event occuring  in comparison to other events. In particular, the examples discussed above show how it can be used to illustrate and communicate the likelihood of  risks in a vivid and intuitive  manner.

Written by K

February 23, 2010 at 10:15 pm

The failure of risk management: a book review

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Introduction

Any future-directed activity has a degree of uncertainty, and uncertainty implies risk. Bad stuff happens – anticipated events don’t unfold as planned and unanticipated events occur.  The main function of risk management is to deal with this negative aspect of uncertainty.  The events of the last few years suggest that risk management as practiced in many organisations isn’t working.  A book by Douglas Hubbard entitled, The Failure of Risk Management – Why it’s Broken and How to Fix It, discusses why many commonly used risk management practices are flawed and what needs to be done to fix them. This post is a summary and review of the book.

Interestingly, Hubbard began writing the book well before the financial crisis of 2008 began to unfold.  So although he discusses matters pertaining to risk management in finance, the book has a much broader scope. For instance, it will be of interest to project and  program/portfolio management professionals because many of the flawed risk management practices that Hubbard mentions are often used in project risk management.

The book is divided into three parts: the first part introduces the crisis in risk management; the second deals with why some popular risk management practices are flawed; the third discusses what needs to be done to fix these.  My review covers the main points of each section in roughly the same order as they appear in the book.

The crisis in risk management

There are several risk management methodologies and techniques in use ;  a quick search will reveal some of them. Hubbard begins his book by asking the following simple questions about these:

  1. Do these risk management methods work?
  2. Would any organisation that uses these techniques know if they didn’t work?
  3. What would be the consequences if they didn’t work

His contention is that for most organisations the answers to the first two questions are negative.  To answer the third question, he gives the example of the crash of United Flight 232 in 1989. The crash was attributed to the simultaneous failure of three independent (and redundant) hydraulic systems. This happened because the systems were located at the rear of the plane and debris from a damaged turbine cut lines to all them.  This is an example of common mode failure – a single event causing multiple systems to fail.  The probability of such an event occurring was estimated to be less than one in a billion. However, the reason the turbine broke up was that it hadn’t been inspected properly (i.e. human error).  The probability estimate hadn’t considered human oversight, which is way more likely than one-in-billion.  Hubbard uses this example to make the point that a weak risk management methodology can have huge consequences.

Following a very brief history of risk management from historical times to the present, Hubbard presents a list of common methods of risk management. These are:

  1. Expert intuition – essentially based on “gut feeling”
  2. Expert audit – based on expert intuition of independent consultants.  Typically involves the development  of checklists and also uses stratification methods (see next point)
  3. Simple stratification methodsrisk matrices are the canonical example of stratification methods.
  4. Weighted scores – assigned scores for different criteria (scores usually assigned by expert intuition), followed by weighting based on perceived importance of each criterion.
  5. Non-probabilistic financial analysis –techniques such as computing the financial consequences of best and worst case scenarios
  6. Calculus of preferences – structured decision analysis techniques such as multi-attribute utility theory and analytic hierarchy process. These techniques are based on expert judgements. However, in cases where multiple judgements are involved these techniques ensure that the judgements are logically consistent  (i.e. do not contradict the principles of logic).
  7. Probabilistic models – involves building probabilistic models of risk events.  Probabilities can be based on historical data, empirical observation or even intuition.  The book essentially builds a case for evaluating risks using probabilistic models, and provides advice on how these should be built

The book also discusses the state of risk management practice (at the end of 2008) as assessed by surveys carried out by The Economist, Protiviti and Aon Corporation. Hubbard notes that the surveys are based  largely on self-assessments of risk management effectiveness. One cannot place much confidence in these because self-assessments of risk are subject to well known psychological effects such as cognitive biases (tendencies to base judgements on flawed perceptions) and the Dunning-Kruger effect (overconfidence in one’s abilities).   The acid test for any assessment  is whether or not it use sound quantitative measures.  Many of the firms surveyed fail on this count: they do not quantify risks as well as they claim they do. Assigning weighted scores to qualitative judgements does not count as a sound quantitative technique – more on this later.

So, what are some good ways of measuring the effectiveness of risk management? Hubbard lists the following:

  1. Statistics based on large samples – the use of this depends on the availability of historical or other data that is similar to the situation at hand.
  2. Direct evidence – this is where the risk management technique actually finds some problem that would not have been found otherwise. For example, an audit that unearths dubious financial practices
  3. Component testing – even if one isn’t able to test the method end-to-end, it may be possible to test specific components that make up the method. For example, if the method uses computer simulations, it may be possible to validate the simulations by applying them to known situations.
  4. Check of completeness – organisations need to ensure that their risk management methods cover the entire spectrum of risks, else there’s a danger that mitigating one risk may increase the probability of another.  Further, as Hubbard states, “A risk that’s not even on the radar cannot be managed at all.” As far as completeness is concerned, there are four perspectives that need to be taken into account. These are:
    1. Internal completeness – covering all parts of the organisation
    2. External completeness – covering all external entities that the organisation interacts with.
    3. Historical completeness – this involves covering worst case scenarios and historical data.
    4. Combinatorial completeness – this involves considering combinations of events that may occur together; those that may lead to common-mode failure discussed earlier.

Finally, Hubbard closes the first section with the observation that it is better not to use any formal methodology than to use one that is flawed. Why? Because a flawed methodology can lead to an incorrect decision being made  with high confidence.

Why it’s broken

Hubbard begins this section by identifying the four major players in the risk management game. These are:

  1. Actuaries:  These are perhaps the first modern professional risk managers.  They use quantitative methods to manage risks in the insurance and pension industry.  Although the methods actuaries use are generally sound, the profession is slow to pick up new techniques. Further, many investment decisions that insurance companies do not come under the purview of actuaries. So, actuaries typically do not cover the entire spectrum of organizational risks.
  2. Physicists and mathematicians: Many rigorous risk management techniques came out of statistical research done during the second world war. Hubbard therefore calls this group War Quants. One of the notable techniques to come out of this effort is the Monte Carlo Method – originally proposed by Nick Metropolis, John Neumann and Stanislaw Ulam as a technique to calculate the averaged trajectories of neutrons in fissile material  (see this article by Nick Metropolis for a first-person account of how the method was developed). Hubbard believes that Monte Carlo simulations offer a sound, general technique for quantitative risk analysis. Consequently he spends a fair few pages discussing these methods, albeit at a very basic level. More about this later.
  3. Economists:  Risk analysts in investment firms often use quantitative techniques from economics.  Popular techniques include modern portfolio theory and models from options theory (such as the Black-Scholes model) . The problem is that these models are often based on questionable assumptions. For example, the Black-Scholes model assumes that the rate of return on a stock is normally distributed (i.e.  its value is lognormally distributed) – an assumption that’s demonstrably incorrect as witnessed by the events of the last few years .  Another way in which economics plays a role in risk management is through behavioural studies,  in particular the recognition that decisions regarding future events (be they risks or stock prices) are subject to cognitive biases. Hubbard suggests that the role of cognitive biases in risk management has been consistently overlooked. See my post entitled Cognitive biases as meta-risks and its follow-up for more on this point.
  4. Management consultants: In Hubbard’s view, management consultants and standards institutes are largely responsible for many of the ad-hoc approaches  to risk management. A particular favourite of these folks are ad-hoc scoring methods that involve ordering of risks based on subjective criteria. The scores assigned to risks are thus subject to cognitive bias. Even worse, some of the tools used in scoring can end up ordering risks incorrectly.  Bottom line: many of the risk analysis techniques used by consultants and standards have no justification.

Following the discussion of the main players in the risk arena, Hubbard discusses the confusion associated with the definition of risk. There are a plethora of definitions of risk, most of which originated in academia. Hubbard shows how some of these contradict each other while others are downright non-intuitive and incorrect. In doing so, he clarifies some of the academic and professional terminology around risk. As an example, he takes exception to the notion of risk as a “good thing” – as in the PMI definition, which views risk as  “an uncertain event or condition that, if it occurs, has a positive or negative effect on a project objective.”  This definition contradicts common (dictionary) usage of the term risk (which generally includes only bad stuff).  Hubbard’s opinion on this may raise a few eyebrows (and hackles!) in project management circles, but I reckon he has a point.

In my opinion, the most important sections of the book are chapters 6 and 7, where Hubbard discusses why “expert knowledge and opinions” (favoured by standards and methodologies are flawed) and why a very popular scoring method (risk matrices) is “worse than useless.”  See my posts on the  limitations of scoring techniques and Cox’s risk matrix theorem for detailed discussions of these points.

A major problem with expert estimates is overconfidence. To overcome this, Hubbard advocates using calibrated probability assessments to quantify analysts’ abilities to make estimates. Calibration assessments involve getting analysts to answer trivia questions and eliciting confidence intervals for each answer. The confidence intervals are then checked against the proportion of correct answers.  Essentially, this assesses experts’ abilities to estimates by tracking how often they are right. It has been found that  people can improve their ability to make subjective estimates through calibration training – i.e. repeated calibration testing followed by feedback. See this site for more on probability calibration.

Next Hubbard tackles several “red herring” arguments that are commonly offered as reasons not to manage risks using rigorous quantitative methods.  Among these are arguments that quantitative risk analysis is impossible because:

  1. Unexpected events cannot be predicted.
  2. Risks cannot be measured accurately.

Hubbard states that the first objection is invalid because although some events (such as spectacular stockmarket crashes) may have been overlooked by models, it doesn’t prove that quantitative risk as a whole is flawed. As he discusses later in the book, many models go wrong by assuming Gaussian probability distributions where fat-tailed ones would be more appropriate. Of course, given limited data it is difficult to figure out which distribution’s the right one. So, although Hubbard’s argument is correct, it offers little comfort to the analyst who has to model events before they occur.

As far as the second is concerned, Hubbard has written another book on how just about any business variable (even intangible ones) can be measured. The book makes a persuasive case that most quantities of interest can be measured, but there are difficulties.  First, figuring out the factors that affect a variable  is not a straightforward task.  It depends, among other things,  on the availability of reliable data, the analyst’s experience etc. Second, much depends on the judgement of the analyst, and such judgements are subject to bias. Although calibration may help reduce certain biases such as overconfidence, it is by no means a panacea for all biases.  Third, risk-related measurements generally  involve events that are yet to occur.  Consequently, such measurements are  based on  incomplete information.  To make progress one often has to make additional assumptions which may not justifiable a priori.

Hubbard is a strong advocate for quantitative techniques such as Monte Carlo simulations in managing risks. However,  he believes that they are often used incorrectly.  Specifically:

  1. They are often used without empirical data or validation – i.e. their inputs and results are not tested through observation.
  2. Are generally used piecemeal – i.e. used in some parts of an organisation only, and often to manage low-level, operational risks.
  3. They frequently focus on variables that are not important (because these are easier to measure) rather than those that are important. Hubbard calls this perverse occurrence measurement inversion. He contends that analysts often exclude the most important variables because these are considered to be “too uncertain.”
  4. They use inappropriate probability distributions. The Normal distribution (or bell curve) is not always appropriate. For example, see my posts on the inherent uncertainty of project task estimates for an intuitive discussion of the form of the probability distribution for project task durations.
  5. They do not account for correlations between variables. Hubbard contends that many analysts simply ignore correlations between risk variables (i.e. they treat variables as independent when they actually aren’t). This almost always leads to an underestimation of risk because correlations can cause feedback effects and common mode failures.

Hubbard dismisses the argument that rigorous quantitative methods such as Monte Carlo are “too hard.” I  agree, the principles behind Monte Carlo techniques aren’t hard to follow – and I take the opportunity to plug my article entitled  An introduction to Monte Carlo simulations of project tasks 🙂 .  As far as practice is concerned,  there are several commercially available tools that automate much of the mathematical heavy-lifting. I won’t recommend any, but a search using the key phrase monte carlo simulation tool will reveal many.

How to Fix it

The last part of the book outlines Hubbard’s recommendations for improving the practice of risk management. Most of the material presented here draws on the previous section of the book. His main suggestions are to:

  1. Adopt the language, tools and philosophy of uncertain systems. To do this he recommends:
    • Using calibrated probabilities to express uncertainties. Hubbard believes that any person who makes estimates that will be used in models should be calibrated. He offers some suggestions on people can improve their ability to estimate through calibration – discussed earlier and on this web site.
    • Employing quantitative modelling techniques to model risks. In particular, he advocates the use of Monte Carlo methods to model risks. He also provides a list of commercially available PC-based Monte Carlo tools. Hubbard makes the point that modelling forces analysts to decompose the systems  of interest and understand the relationships between their components (see point 2 below).
    • Developing an understanding of the basic rules of probability including independent events, conditional probabilities and Bayes’ Theorem. He gives examples of situations in which these rules can help analysts extrapolate

    To this, I would also add that it is important to understand the idea that an estimate isn’t a number, but a  probability distribution – i.e. a range of numbers, each with a probability attached to it.

  2. Build, validate and test models using reality as the ultimate arbiter. Models should be built iteratively, testing each assumption against observation. Further, models need to incorporate mechanisms (i.e. how and why the observations are what they are), not just raw observations. This is often hard to do, but at the very least models should incorporate correlations between variables.  Note that correlations are often (but not always!) indicative of an underlying mechanism. See this post for an introductory example of Monte Carlo simulation involving correlated variables.
  3. Lobbying for risk management to be given appropriate visibility in organisation.s

In the penultimate chapter of the book, Hubbard fleshes out the characteristics or traits of good risk analysts. As he mentions several times in the book, risk analysis is an empirical science – it arises from experience. So, although the analytical and mathematical  (modelling) aspects of risk are important,  a good analyst must, above all, be an empiricist – i.e. believe that knowledge about risks can only come from observation of reality. In particular, tesing models by seeing how well they match historical data and tracking model predictions are absolutely critical aspects of a risk analysts job. Unfortunately, many analysts do not measure the performance of their risk models. Hubbard offers some excellent suggestions on how analysts can refine and improve their models via observation.

Finally, Hubbard emphasises the importance of creating an organisation-wide approach to managing risks. This ensures that organisations will tackle the most important risks first, and that its risk management budgets  will be spent in the most effective way. Many of the tools and approaches that he suggests in the book are most effective if they are used in a consistent way across the entire organisation. In reality, though,  risk management languishes way down in the priorities of senior executives. Even those who profess to understanding the  importance of managing risks in a rigorous way, rarely offer risk managers the organisational visibility and support they need to do their jobs.

Conclusion

Whew, that was quite a bit to go through, but for me it was was worth it.  Hubbard’s views impelled me to take a closer look at the foundations of project risk management and  I learnt a great deal from doing so.  Regular readers of this blog would have noticed that I have referenced the book (and some of the references therein)  in a few of my articles on risk analysis.

I should  add that I’ve never felt entirely comfortable with the risk management approaches advocated by project management methodologies.  Hubbard’s book articulates these shortcomings and offers solutions to fix them. Moreover, he does so in a way that is entertaining and accessible.  If there is a gap, it is that he does does not delve into the details of model building, but then his other book deals with this in some detail.

To summarise:  the book is a must read for anyone interested in risk management. It is  especially recommended for project professionals who manage risks using methods that  are advocated by project management standards and methodologies.

Written by K

February 11, 2010 at 10:11 pm

Fooled by conditionality

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Conditional probability refers to the chance that an event will occur given that another (possibly related) event has occurred. Understanding how conditional probability works is important –  and occasionally even a matter of life or death. For instance,  a person may want to know the chance that she has a life-threatening disease given that she has tested positive for it.

Unfortunately, there is a good deal of confusion about conditional probability. I’m amongst the confused, so I thought I’d do some reading on the topic. I began with the Wikipedia article (which wasn’t too helpful) and then went on to other references. My search lead me to some interesting research papers on the confusion surrounding conditional probabilities.   This post is inspired by a couple of papers I came across.

In a paper on the use and misuse of conditional probabilities,  Walter Kramer and Gerd Gigerenzer point out that many doctors cannot answer the “life or death” question that I posed in the first paragraph of this post. Here are a few pertinent lines from from their article:

German medical doctors with an average of 14 years of professional experience were asked to imagine using a certain test to screen for colorectal cancer. The prevalence of this type of cancer was 0.3%, the sensitivity of the test (the conditional probability of detecting cancer when there is one) was 50% and the false positive rate was 3%. The doctors were asked: “What is the probability that someone who tests positive actually has colorectal cancer?

Kramer and Gigerenzer found that the doctors’ answers ranged from 1% to 99% with about half of them answering 50% (the sensitivity) or 47% (the sensitivity minus that false positive rate).

You may want to have a try at answering the question before proceeding further.

The question can be answered quite easily using Bayes’ rule, which tells us how to calculate the conditional probability of an event  given that another (possibly related) event has occurred.  If the two events are denoted by A and B, the conditional probability that A will occur given that B has occurred,  denoted by   P(A|B),  is:

P(A|B) =\displaystyle \frac{P(B|A) \times P(A)}{P(B)}

Where  P(B|A) is the conditional probability that B will occur given that A has occurred and P(A) and P(B) are the probabilities of A and B occurring respectively. See the appendix at the end of this post for more on Bayes rule.

In terms of the problem stated above, Bayes rule is:

P(Has cancer|Tests positive) = P(Tests positive|Has cancer) * P(Has cancer) / P(Tests positive)

From the problem statement we have:

P(Tests positive|Has cancer) =0.5

P(Has cancer) =  0.003

P(Tests positive) = (1-0.003)*0.03 + 0.003*0.5

Note that  P(Tests positive)  is obtained by noting that a person can test positive in two ways:

  1. Not having the disease and testing positive.
  2. Having the disease and testing positive.

Plugging the numbers in, we get:

P(Has cancer|Tests positive) = 0.5 * 0.003  / (0.997*0.03 + 0.003*0.5) = 0.047755

Or about 5%.

Kramer and Gigerenzer contend that the root of the confusion lies in the problem statement: people find it unnatural to reason in terms of probabilities because the terminology of conditional probability is confusing (A given B, B given A  – it’s enough to make one’s head spin). To resolve this they recommend stating the problem in terms of frequencies – i.e. number of instances –  rather than ratios.

OK, so let’s restate the problem in terms of frequencies (note this is my restatement, not Gigerenzer’s):

Statistically, 3 people in every 1000 have colorectal cancer.  We have a test that is 50% accurate. So, out of the 3 people who have the disease, 1.5 of them will test positive for it. The test has a false positive rate of 3%: so about 30 (29.91 actually) of the remaining 997 people  who don’t have the disease will test positive. What is the probability that someone who tests positive has the disease?

From the problem restatement we have:

Total number of people who have cancer and test positive in every 1000 = 1.5

Total number of people who test positive in every 1000 = 1.5+30=31.5

P(Have cancer|Test positive) = 1.5/31.5=0.047619

The small difference between the two numbers is due to  rounding error (I’ve rounded 29.91 up to 30)

There’s no question that this is much more  straightforward.

But the story doesn’t end there. In a paper entitled the Non-use of Bayes Rule, Thomas Dohmen and his colleagues Armin Falk,David Huffman, Felix Marklein and Uwe Sunde measured the ability to use Bayesian reasoning (which is academese for “reasoning using Bayes rule”)  in a representative sample of the German population.  They did so by asking those sampled to answer a question that involved conditional probability. Being aware of Gigerenzer’s work, they stated their question in frequencies rather than probabilities. Here is the question they posed, taken directly from their paper:

Imagine you are on vacation in an area where the weather is mostly sunny and you ask yourself how tomorrow’s weather will be. Suppose that, in the area you are in, on average 90 out of 100 days are sunny, while it rains on 10 out of 100 days. The weather forecast for tomorrow predicts rain. On average, the weather forecast is correct on 80 out of 100 days. What do you think is the probability, in percent, that it is going to rain tomorrow?

Again, you may want to have a go at the problem before proceeding further.

The solution is obtained by a straightforward application of Bayes rule which, for the problem above, reads:

P(Rain|Rain forecast)=P(Rain forecast|Rain)* P(Rain) / P(Rain Forecast)

P(Rain forecast|Rain)=0.8  (since there’s an 80% probability of correct forecast)

P(Rain) = 0.1

P(Rain forecast) = P(Rain forecast|Rain)* P(Rain) +  P(Rain forecast|Sun)* P(Sun) = 0.8*0.1+0.2*0.9

So, plugging the numbers In , we get P(Rain|Rain forecast)=0.08 / (0.08 + 0.18) = 0.3077 – or approximately 31%.

The surprising thing is that in the study no one got this right, and only 6% of those who were surveyed gave answers within 10% of the correct one.

Dohmen et. al. go on to point out that those with higher education levels – in particular, those with higher degrees were more likely to get the problem wrong!  (So it is true: education causes more confusion than clarity.)

Anyway, it appears that stating the problem in terms of frequencies doesn’t help as much as Kramer and Gigerenzer suggest.

In my opinion, whether the problem is stated in terms of  frequency or ratio is neither here nor there. The key is to state the problem clearly. In the restatement of the cancer test problem, it isn’t so much the use of frequencies that helps, but that the relevant numbers are presented unambiguously. There is little interpretation required on the problem solver’s part. It is very clear as to what needs to be done;  so clear that one does not need to use Bayes rule.  In contrast, in the second problem the respondent still has to figure out the individual probabilities that need to be plugged into Bayes’ formula. This requires some interpretation and thought, which doesn’t always come easy.  In fact, such reasoning seems to be harder for those with higher degrees than those without. The last paragraph of Dohmen’s paper states:

In a cognitive task as complex as the one we use in this paper, one would expect deliberation cost to be relatively high for people with less formal education. In contrast, for highly educated people deliberation cost should be relatively low. Other things equal, this reasoning would imply that more educated people perform better in assessing conditional probabilities. Our results indicate the contrary, as education, in particular university education, increases the likelihood that respondents are led astray in the probability judgment task. An identification of the exact channels which are responsible for the detrimental effect of education and cognitive ability on Bayesian judgment constitutes a fascinating area for future research.

Fascinating or not, I now have a scapegoat to blame for my being fooled by conditionality.

—-

Appendix:  A “derivation” of Bayes Rule

Here’s a quick “derivation” of Bayes rule (the quotes denote that some of the steps in the derivation are a consequence of definitions rather than inferences).

To keep the discussion concrete, we’ll assume that A is the event that a patient has cancer and B the event that a patient tests positive.

The left hand side of  Bayes rule, in terms of these events, is:

P(Has cancer|Test positive) = P(Has Cancer & Tests Positive|Tests Positive)

= P(Has cancer & Tests Positive)/P(Tests positive)   …..(1)

The second expression in (1)  above is merely a restatement of the first.  The third is obtained by noting that

P(Has Cancer & Tests positive|Tests positive)= (Number of people who have cancer & test positive)/(Number of people who test positive)   …..(2)

and that the probabilities in the numerator and denominator of the third statement in (1) are:

P(Has cancer & Tests positive) = (Number of people who have cancer & test positive)/(Total population sampled)  …..(3)

P(Tests positive) = (Number of people who test positive)/(Total population sampled)   …..(4)

The third expression in (1) follows from the fact the that the denominators (3) and (4) are identical.

We further note that

P(Has cancer &Tests positive) = [P(Test positive & Has cancer)/P(Has cancer)] * P(Has cancer)

=P(Tests positive|Has Cancer) * P(Has cancer) …..(5)

Here the numerator and denominator have been multiplied by the same factor –  P(Has cancer).  We have also used the fact that P(Tests positive & Has Cancer)/P(Has cancer) is the same as P(Tests positive|Has cancer).

Substituting (5) in the right hand side of (1), we get:

P(Has cancer|Tests positive) = P(Tests positive|Has cancer) *  P(Has cancer) / P(Tests positive)

Which is Bayes rule for this case.

As further reading, I recommend Eliezer Yudkowsky’s brilliant essay,  An intuitive explanation of Bayes Theorem.

Written by K

February 1, 2010 at 10:05 pm

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