Applying the same question to theft produces the result that if I steal your car and I get more utility out of having your car than you lose by not having it + the utility that you lose from psychological harm due to theft, insurance premiums rising, etc., I can internalize the cost and still come out ahead, so this sort of theft is not in oversupply. That seems to be a better fit for the impression of progress. You wouldn't tend, in retrospect, to call it progress if you realised you'd been going in completely the wrong direction. It's not that they're measuring the wrong variables, it's most likely that those organizations have already made the decisions based on variables they already measure. In the "Function Points" example, I would bet there were a few obvious learnings early on that spread throughout the organizations, and once the culture had changed any further effort didn't help at all. Can the thing be forced to occur under new conditions which allow you to observe it more easily? E.g. you could implement a proposed returned-items policy in some stores but not others and compare the outcomes.

Measuring - BBC Teach Measuring - BBC Teach

Hubbard says a few things in support of this approach. First, he points to some studies (e.g. El-Gamal & Grether (1995)) showing that people often reason in roughly-Bayesian ways. Next, he says that in his experience, people become better intuitive Bayesians when they (1) are made aware of the base rate fallacy, and when they (2) are better calibrated. We must also distinguish precision and accuracy. A “precise” measurement tool has low random error. E.g. if a bathroom scale gives the exact same displayed weight every time we set a particular book on it, then the scale has high precision. An “accurate” measurement tool has low systemic error. The bathroom scale, while precise, might be inaccurate if the weight displayed is systemically biased in one direction – say, eight pounds too heavy. A measurement tool can also have low precision but good accuracy, if it gives inconsistent measurements but they average to the true value. As far as the propositions of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality. —Albert Einstein (1879–1955)”Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction Observer bias: the very act of observation can affect what you observe. E.g. in one study, researchers found that worker productivity improved no matter what they changed about the workplace. The workers seem to have been responding merely to the fact that they were being observed in some way. Why do we care about measurements at all? There are just three reasons. The first reason—and the focus of this book—is that we should care about a measurement because it informs key decisions. Second, a measurement might also be taken because it has its own market value (e.g., results of a consumer survey) and could be sold to other parties for a profit. Third, perhaps a measurement is simply meant to entertain or satisfy a curiosity (e.g., academic research about the evolution of clay pottery). But the methods we discuss in this decision-focused approach to measurement should be useful on those occasions, too. If a measurement is not informing your decisions, it could still be informing the decisions of others who are willing to pay for the information.” However, it personally inspired me to measure the height of a tower by the length and arc of its shadow just for fun. The book frees my mind about what we can measure and how to do it - or even how companies might do it to collect data about their customers. The Rule of Five has another advantage over the t-statistic: it works for any distribution of values in the population, including ones with slow convergence or no convergence at all! It can do this because it gives us a confidence interval for the median rather than the mean, and it’s the mean that is far more affected by outliers.

7 Simple Principles for Measuring Anything - Hubbard Decision

No matter how “fuzzy” the measurement is, it’s still a measurement if it tells you more than you knew before. Value of information analysis: Using Excel macros, the AIE analyst runs a value of information analysis on every variable in the model. Unfortunately, in real life underlying processes tend to be unstable. For a trivial example of a known-to-not-be-stable process consider weather. Let's say I live outside of tropics and I measure air temperature over, say, 60 days. Will my temperature estimates provide a good forecast for the next month? No, they won't because the year has seasons and my "population" of days changes with time.

The fact is that the preference for ignorance over even marginal reductions in ignorance is never the moral high ground.”

How to Measure Anything in Cybersecurity Risk How to Measure Anything in Cybersecurity Risk

One possibility is that there are a very large number of things they could measure, most of which have low information value. If they chose randomly we might expect to see an effect like this, and never notice all the low information possibilities they chose not to measure. Another example: I took statistics on how my friends played games that involved bidding, such as Liar's Poker. I found that they typically would bid too much. Therefore a measurement of how many times someone had the winning bid was a high predictor of how they would perform in the game-people who bid high would typically lose. Or, even easier, make use of the Rule of FIve: “There is a 93.75% chance that the median of a population is between the smallest and largest values in any random sample of five from that population.” I think what must be intended is: your definition is for the EOL of an option. Now the EOL of a choice is the EOL of the option we choose given current beliefs. Then EVI is the expected reduction in EOL upon measurement. Updated decision model: The AIE analyst updates the decision model based on the results of the measurements.Suppose you enter this formula on cell A1 in Excel. To generate (say) 10,000 values for the maintenance savings value, just (1) copy the contents of cell A1, (2) enter “A1:A10000” in the cell range field to select cells A1 through A10000, and (3) paste the formula into all those cells.