Give Chance a Chance

Join Us For a Two-Part Webinar Series on Chancification

by Sam L. Savage

The Light Bulb

About 25 years ago a light bulb went on. It was the bulb in my office at Stanford when I flipped on the switch. That may not sound so remarkable, but it suddenly dawned on me that I had no idea how to generate the electricity required to power the thing. This was at a time when I was obsessing over the fact that spreadsheet users were plugging averages of uncertain quantities into their spreadsheets and blindly reporting the outputs, in flagrant disregard of Jensen’s Inequality. It was before I had coined the term “Flaw of Averages”, but I had already come up with the poster child for this problem: the drunk whose average position is the center line of a highway.

The solution was Monte Carlo simulation, and two powerful packages existed for doing this in spreadsheets, @RISK and Crystal Ball. Why wasn’t everyone using them? There were three reasons.

1. There was extra software to buy. That price was miniscule given the financial magnitude of the problems the software could solve.

2. There was extra software to learn. That was a bigger hurdle than the price.

3. You had to practically be a statistician to know what sort of distribution to plug into these packages. I viewed that as the biggest barrier to the widespread use of Monte Carlo.

But when the light bulb went on that day, I suddenly saw the solution to the third hurdle. If I could use electricity generated by someone miles away that I didn’t even know, why couldn’t people use random variates in their simulations that had been generated by others? Instead of power distribution, it would be distribution distribution! I figured it would only take a few months to work out the details and get it to work. Boy, was I wrong …

...Twenty-five years pass …

Now available in paperback

The Probability Power Grid

The probability power grid is here, and it enables what I call Chancification (the title of my just published book, now available in paperback). Chancification enables enterprise-wide calculations based on probabilities instead of numbers, much as electrification enabled systems based on electricity instead of fossil fuels.

And speaking of just published, the newest version of ChanceCalc, the light bulb of Chancification, is now available from ProbabilityManagement.org.

Webinars

If probability were electricity, it would be 1895, when Westinghouse built his first big power plant at Niagara Falls. With all this cool technology suddenly available, the nonprofit is focusing on its educational mission in the area of Chancification, with webinars and other content from thought leaders and industry experts.

To kick off this initiative, I will be offering a two-part webinar series called Welcome to the Chance Age, which includes a full copy of ChanceCalc, a discount on the Enterprise SIPmath Tools, and a Kindle copy of my new book.

Welcome to the Chance Age Webinars

• Give Chance a Chance describes how to fix the Flaw of Averages with ChanceCalc linked to SIP Libraries of uncertainties.

• The Probability Power Grid shows how to create SIP Libraries from the SIPmath Tools, @RISK, Crystal Ball, or directly from the Web.

Chance-Informed Readiness Summit

Come be a fly on the wall
at a summit on
Chance-Informed Readiness in Defense, Pandemic Modeling, and Infrastructure

Wednesday March 30, 8:30 AM - 4:30 PM PDT

Join the livestream as a select group of thought leaders gather in person to discuss chance-informed readiness across multiple disciplines. For a fee of $200, attendees will have the opportunity to participate in multiple Q&A sessions with speakers and will receive a copy of Dr. Savage’s new book, Chancification: How to Fix the Flaw of Averages, currently available on Kindle and soon to be released in paperback.

Last year saw pivotal developments in the SIPmath™ 3.0 Standard as well as the beta testing of ChanceCalc. As we have not had a physical meeting since 2019 due to the pandemic, we decided to dip our toes back into the waters of live events by hosting a small summit with multiple Q&A sessions for an online audience as shown in the schedule below. For more details and speaker bios, visit the registration page.

Schedule (all times PDT)

Session 1: SIPmath Standard

8:30 AM - 9:00 AM: Chancification - Sam Savage
9:00 AM - 9:30 AM: Introduction to Metalog Distributions - Tom Keelin
9:30 AM - 10:00 AM: SIPmath Support in Analytic Solver - Dan Fylstra
10:00 AM - 10:15 AM: Q&A for Online Audience

Session 2: Defense

10:15 AM - 10:45 AM: From Ready or Not to How Ready for What - Connor McLemore
10:45 AM - 11:15 AM: Probability Management at Lockheed Martin - Phil Fahringer
11:15 AM - 11:45 AM: Chance-Informed Aircraft Fleet Management - Steve Roemerman
11:45 AM - 12:00 PM: Q&A for Online Audience

12:00 PM - 1:00 PM: Lunch Break

Session 3: Expert Opinion / Healthcare

1:00 PM - 1:30 PM: The FrankenSME: Synthesizing Expert Opinion - Doug Hubbard
1:30 PM - 2:00 PM: Making CDC Forecasts Actionable - Eng-Wee Ethan Yeo
2:00 PM - 2:30 PM: Building Chance-Informed Capability in Healthcare - Justin Schell
2:30 PM - 2:45 PM: Q&A for Online Audience

Session 4: Infrastructure

2:45 PM -3:15 PM: Explaining Cyber Insurance to Municipalities - Shayne Kavanagh
3:15 PM - 3:45 PM: Stochastic Libraries in Infrastructure Planning and Risk Management- Sam Savage
3:45 PM - 4:15 PM: "Risk-Spend Efficiency"? How Utilities Can Use It - Max Henrion
4:15 PM - 4:30 PM: Q&A for Online Audience

Chancification: How to Fix the Flaw of Averages

Now available on Amazon Kindle

by Sam L. Savage

I am happy to announce the release of my latest book, Chancification: How to Fix the Flaw of Averages, available on Kindle and soon to be released in paperback.

In my previous book, The Flaw of Averages, I introduced the concept of probability management, which represents uncertainties as auditable data called SIPs. Since then, technical contributions from a wide range of talented individuals backed up by a dedicated team here at ProbabilityManagement.org have turned this concept into a practical discipline. Today experts can generate SIP Libraries on virtually any software platform (think electricity), for use by non-experts in chance-informed dashboards on virtually any other software platform (think light bulbs). Welcome to the Chance Age!

The book, with a foreword by Doug Hubbard and illustrations by Jeff Danziger, describes how new technologies and data standards allow organizations to replace calculations based on numbers with those based on chances, just as electrification replaced systems that run on fossil fuels with those that run on electricity. Topics include:

• Curing Post-Traumatic Statistics Disorder (PTSD) with Limbic Analytics, which connects the seat of the intellect to the seat of the pants.

• Downloadable examples of how to fix the Flaw of Averages in Excel.

• The Arithmetic of Uncertainty: Arithmetic tells you that X+Y=Z. The Arithmetic of Uncertainty ask what you want Z to be, then estimates the chances.

• Speaking Uncertainty to Power: Clear explanations of chances can earn you the permission to be uncertain.

• The Technology of Chancification, including the SIPmath™ 3.0 Standard from ProbabilityManagement.org, based on Doug Hubbard’s portable random number generator and Tom Keelin’s breakthrough Metalog distribution.

Get 30% off the paperback version of The Flaw of Averages

Chancification shows how to solve the problems of dealing with uncertainty exposed by my earlier book, The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty (John Wiley & Sons, 2009, 2012). If you have not read either one, I would start with current book. However, it refers to the first book throughout for deeper explanations of the math of uncertainty.

John Wiley & Sons has generously offered a 30% discount on the paperback edition of The Flaw of Averages. Use discount code BPFS2 (be sure to click Apply) at the link to the left.

© Copyright Sam Savage, 2022

What is the Metalog Distribution?

What Do You Want It to Be?

by Sam L. Savage

The Shmoo is a fictional character created in 1948 by cartoonist Al Capp for his Li’l Abner cartoon strip.

According to Shmoo - Wikipedia,

Shmoos are delicious to eat, and are eager to be eaten. If a human looks at one hungrily, it will happily immolate itself—either by jumping into a frying pan, after which they taste like chicken, or into a broiling pan, after which they taste like steak. When roasted they taste like pork, and when baked they taste like catfish. Raw, they taste like oysters on the half-shell.

They also produce eggs (neatly packaged), milk (bottled, grade-A), and butter—no churning required. Their pelts make perfect bootleather or house timbers, depending on how thick one slices them.

Shmoos (plural is also Shmoon according to Wikipedia) are common in mathematics. For example, Taylor series and Fourier series are ways of mimicking not chicken or steak, but whole slews of mathematical functions through weighted sums of simpler functions. In the case of Taylor series, the simpler functions are F(x) = 1, F(x) = x, F(x) = x², etc. These are called the basis functions of the series. As an example, the Taylor series of e^x is 1 + x + x² / 2 + x³ / (3*2) + … xⁿ / (n!) … The more terms you include, the more it tastes like chicken, I mean e^x. Fourier series use Sines and Cosines for their basis functions and are central to signal processing.

These famous mathematical Shmoos were developed hundreds of years ago. A brand new Shmoo is the Metalog, invented by Tom Keelin to mimic probability distributions. Its basis functions are related to the Logistic distribution, hence the name Metalog(isitic).

It has been five years since Tom first explained his elegant family of probability distributions to me, and today Metalogs play diverse and vital roles within the discipline of probability management, which is concerned with conveying uncertainty as data that obey both the laws of arithmetic and the laws of probability. I expect Metalogs to revolutionize the much larger field of statistics as well, but that will be more like turning an aircraft carrier compared to the patrol boat of probability management. Being small and maneuverable has given our organization the rare opportunity to help pioneer a real breakthrough.

The value of a revolutionary idea is not obvious, or it wouldn’t be revolutionary. My first reaction to Metalogs was, that’s very nice, but now I have one more distribution to remember along with the Erlang, Gaussian, Gompertz, Weibull, and all the other “Dead Statistician” distributions. The whole point of probability management is that the user doesn’t need to remember all this junk, and now I have something else to cram into my closet.

In retrospect I have rarely been so wrong. It took a while to figure out that I could actually put the Metalogs in the closet and then take the rest of the contents out to the curb for bulk trash pickup. But I’m getting ahead of myself. This is the first in a series of blogs on revelations about Metalogs, a subject which is growing fast. Some of my readers will want to know all about Metalogs and all of my readers will want to know something about Metalogs. But in the future, I believe that many of my 7.6 billion non-readers will know nothing about Metalogs, yet will be impacted by them nonetheless.

Tom has just created a concise 7-minute Flash Intro to Metalogs video that I highly recommend. If you don’t have seven minutes, it plays beautifully at 1.5 x, resulting in 4.66 minutes that might just change the way you think about statistics. Then stay tuned for my subsequent blogs that cover other important aspects of Metalogs.

ChanceCalc™ Beta 1.1 Now Available

by Sam L. Savage

The SIPmath™ Standard communicates uncertainty unambiguously and coherently from data scientists and statisticians to decision makers. The standard created the groundwork for Chancification, which takes computer simulation from siloed applications to collaborative networks in which managers need nothing more than a simple application like ChanceCalc to make chance-informed decisions. Applications include: 

• Linking national weather simulations to power grid simulations to estimate the chance of collapse due to failed equipment, excess heating, or air conditioning load.

• Aggregating risks across infrastructure networks to mitigate the chance of safety risks at minimal cost.

• Using crowdsourced data on forecasting errors to estimate the chance of achieving projected tax revenues.

• Linking the results of  ensembles of COVID-19 models at the CDC to local models to predict the chance of exceeding ICU capacity.   

The models created with ChanceCalc are standalone Excel files that perform hundreds of simulation trials per second and do not require the add-in to run.

We have made the following changes to ChanceCalc since the first beta version in May 2021:

• Numerous bug fixes.

• We have frozen the SIPmath 3.0 Standard, which stores uncertainties as JSON objects. Now ChanceCalc can read libraries created in Python, R, or Analytic Solver from Frontline Systems (see below).

• Frontline’s Analytic Solver has become the first commercial software package to support the new standard. It can easily create SIPmath 3.0 Libraries for export and can import them to do powerful stochastic optimization.

Here are some ways you can learn more about ChanceCalc, which is available now in beta test:

1. Watch our videos on ChanceCalc and Frontline’s Analytic Solver below.

2. Download the latest version here.

3. Look through the Getting Started guide for a quick overview of what ChanceCalc can do.

4. Explore the Tutorial to get hands-on tips for using ChanceCalc to cure the Flaw of Averages.

© Copyright Sam Savage, 2021

The SIPmath™ 3.0 Standard and Analytic Solver V2021.5

The AC Current Standard and First Industrial Power Plant of Chancification

by Sam Savage

ProbabilityManagement.org is proud to announce the first general release of the SIPmath 3.0 Standard for storing virtual SIPs in the universal JSON format. And we are delighted that the latest Analytic Solver from Frontline Systems both reads and writes this format.  

The discipline of probability management represents uncertainties as data that obey both the laws of arithmetic and the laws of probability. SIPmath 2.0 accomplished this by storing arrays of thousands of Monte Carlo trials. SIPmath 3.0 accomplishes this with a tiny fraction of the storage. If probability were electricity, then SIPmath 2.0 would be direct current and SIPmath 3.0 would the alternating current that we all use today.

The SIPmath 3.0 Standard uses Doug Hubbard’s HDR random number generator to maintain statistical coherence, generating identical streams of pseudo random numbers across platforms, including native Excel.

These random numbers feed Tom Keelin’s Metalog Distributions, a flexible system for creating an extremely wide range of continuous random variates, including multi-modal.

The Analytic Solver encompasses optimization, machine learning, simulation, and powerful techniques. Its “Deploy Model” allows you to

“create, test and refine probability distributions that should be used across your company -- say for exchange rates or commodity prices -- using Analytic Solver's 60+ classical, Metalog, and custom distribution creation tools -- then deploy and share them as probability models, following the open Probability Management SIPmath 3.0 standard.”

Using SIPmath 3.0 ensures that you will get the same Monte Carlo trials in ChanceCalc, Python, R, or, if you have the patience, on an abacus. And going the other way, you may generate probability distributions in a wide variety of simulations, which may be imported into Analytic Solver to use with its powerful stochastic optimization engines.  

I expect this package to play as central a role in Chancification as the 1895 Tesla/Westinghouse hydro power station at Niagara Falls played in electrification.

© Copyright 2021 Sam L. Savage

Increasing our Personal Liberty while Fighting COVID-19

by Sam L. Savage

"In this country, saving freedom is more important than trying to regulate lives through legislation," screamed the headline, and as a libertarian who resents a big blundering government pushing me around, I was sympathetic. The year was 1987, when states were mandating seatbelt usage, but similar tradeoffs between individual freedom and personal safety exist today. In a recent article in The Atlantic, Vaccinated America Has Had Enough, conservative columnist David Frum writes that for some people, “vaccine refusal is a statement of identity and a test of loyalty.” For these people, the increase in their civil liberty by not getting vaccinated is adequate reward to compensate for the risk imposed by the virus. In finance such a reward is known as a risk premium. But is this the best policy, even for the most freedom-loving of the vaccine hesitant?

By taking a chance-informed approach, we can explore alternatives that offer both more civil liberty and less risk than simply not getting the vaccine. First, the numbers. The data show that the vaccines are ten times more effective at preventing fatalities (99.5%) than gunshot wounds to the head are at causing them (95%).  For purposes of argument, we will use the current daily death toll from COVID-19, of about 600 (although it is currently climbing), nearly all of whom are among the 30% of the population who are totally unvaccinated. So here is a thought experiment, NOT AN ACTUAL SUGGESTION, to stimulate discussion around alternative and potentially better tradeoffs between personal liberty and the risks presented by COVID-19:

The unvaccinated could both make a stronger identity statement and simultaneously reduce everyone’s risk if they got the shot, but then rewarded themselves with the freedom of not wearing their seatbelts!

The Risks

On the risk side, there is risk to others and risk to self. The risk to others is negligible when you don’t buckle up. But not getting vaccinated puts everyone at risk except the virus, which loves new hosts in which to mutate its way out from under the current vaccines. I estimate that the combined deaths per day among the unvaccinated from either COVID-19 or car crash is currently 600 to COVID-19 and 30 on the highway [1] for a total of 630. If those people all got vaccinated but removed their seatbelts, the numbers would change to about 3 deaths due to COVID-19 [2] and 55 to crashes [3] for a total of 58 per day. This is a reduction in risk of about 90% by simply interchanging one expression of personal freedom for another.

The Reward

With the seatbelts you get the increased personal freedom of not having to strap yourself down every single time you get in a car, but in addition, you have a choice of how forceful an identity statement you want to make. No one can tell that you haven’t been vaccinated just by looking at you, but with the seatbelts there are a range of options. If you want to keep your new liberated status to yourself and avoid being pulled over by the police, just save a piece of seatbelt after you cut it out of your car and hang it over your shoulder when you drive. On the other hand, if you really want to thumb your nose at authority, you can close the belt in the door and let it drag down the street, leaving a trail of sparks.

[1] There are roughly 100 highway deaths per day in the US, so 30% of the population would account for about 30 deaths.

[2] 0.5% of 600, or 3, would be expected to die even with the vaccination.

[3] Seatbelts are only about 45% effective at saving lives in a crash, so the 30 per day would go up to about 55 per day.

Copyright © Sam L. Savage 2021. All Rights Reserved.

Models vs. Modules

by Sam Savage

Models vs. Modules

The discipline of probability management is defined by representing uncertainties as data, called SIPs, that obey both the laws of arithmetic and the laws of probability. One of the biggest implications of using SIPs is that formerly monolithic simulation models may now be decomposed into modules that are networked together through SIP Libraries, with the outputs of some models used as inputs to others. This is easy to explain. The hard part is getting people to understand it. So, here is a metaphor. Large simulation models are often like sandcastles, which eventually collapse under their own weight, or erode due to the tides of change. Modules are like Lego blocks, which can be assembled into structures. If you don’t like some part of a construction, or it becomes obsolete, you can snap off the old blocks and snap on new ones.

Examples

Royal Dutch Shell

Probability management arose out of work at Royal Dutch Shell starting in 2005 by Daniel Zweidler, then a manager at Shell, Stefan Scholtes, a Professor of Management Science at Cambridge, and me. It was driven by the fact that although Shell’s exploration engineers could simulate the daylights out of the Net Present Value of any particular venture, they couldn’t simulate their whole portfolio because the model was too big and would collapse under its own weight. See the foundational article in OR/MS Today.

The Lego blocks for the Shell portfolio were SIPs for each of the ventures, that is, arrays of thousands of Monte Carlo trials describing the range of possible NPVs for that particular venture. Hours were spent generating the library of SIPs for all ventures. But once that was done, because SIPs obey the laws of arithmetic, the SIP of NPV of any portfolio could be found nearly instantly by adding together the SIPs of its constituent ventures. This was done in an interactive Excel dashboard, which allowed managers, only a couple of levels below the CEO, to click things in and out of prospective portfolios. With every keyclick they would instantly see the consequences of their portfolio decisions in terms of both risk and return in a number of dimensions.  That took place back in the Bad Old Days before Excel’s Data Table became powerful enough to bring interactive SIPmath simulation to all, so the portfolio model was contorted into a single row with hundreds of formulas, which were then copied down 1,000 times to refer to individual rows of the SIP Library. In subsequent years, we relied on Frontline System’s interactive simulation and stochastic optimization to continue the project with a more practical implementation. A training model used to teach Shell executives at Cambridge University is available for download. IMPORTANT: You need to enable macros to access the clickable scatter plot. Depending on the version of Excel, before opening, you may need to right click on it, go to Properties, then click Unblock.

A Pharmaceutical R&D Portfolio

A few years later, but still before I was aware of the breakthrough with the Excel Data Table, I worked on a similar problem with a large pharmaceutical firm. Although their analysts could simulate the daylights out of the Net Present Value of any particular R&D drug, they couldn’t simulate their whole portfolio because the model was too big and would collapse under its own weight. Furthermore, they needed to simulate it under numerous discount rates and other external factors like success probabilities for the drugs, market assumptions, etc. Again, it took hours on two separate computers to create the SIPs of the individual projects. There were roughly 50 drugs of each of two classes, and roughly 60 experiments with combinations of the external variables. And did I mention that it took 5,000 Monte Carlo trials to get it to converge? I’ll save you the math: 2*50*60*5000 gives you 30 million numbers, or 6,000 SIPs of 5,000 trials. But because SIPs obey the laws of arithmetic, we added all the SIPs of each type of drug together to get the SIP of NPV for the portfolio of all Type 1 drugs and the portfolio of all Type 2 drugs. That left us with 60 pairs of SIPs, one for each drug type and each experiment.  The final dashboard with disguised data shows spinner controls that allowed us to scroll through the assumptions, which instantly pointed to the portion of the SIP Library resulting from that experiment.

At one point the assistant CFO wondered what the risk/return profile would look like if the lab were twice as big and could diversify over twice as many drugs. At first we figured it would take weeks to run a model with two hundred drugs to find out. But it took only minutes. One of the outputs was a SIP of 5,000 trials of the NPV of the entire portfolio. We simply copied that SIP, permuted it to model the NPV of a second similar but independent R&D lab, then added the two SIPs together. It was like snapping together Lego block models of two labs to get a model of a lab that was twice as big. And remember, SIPs also obey the laws of probability, so the resulting SIP told us all we needed to know about the risk and return of the imaginary larger and more diversified company.

Fast Forward to the Age of Chancification

Both the Shell and Pharma SIP libraries were used within single organizations in single dashboards for making critical decisions. But today, such libraries could be posted in the cloud and used collaboratively by hundreds of users with ChanceCalc (which runs simulations in native Excel using the Data Table).

As an example, the CDC can model the daylights out of future COVID-19 hospitalizations. Now imagine adding the models of all the hospitals in the country to the CDC model to better manage the pandemic, creating an enormous model that would collapse under its own weight. In a future blog I will describe how we have created SIP Libraries of predicted hospitalizations for each state, which may be stored in the cloud. In theory, these models could be snapped  like Lego blocks into hospital management models nationwide to model surges in the current pandemic, or even cases of the good old-fashioned flu.

If you would like to learn more about ChanceCalc and Chancification:

1. Sign up to beta test ChanceCalc and provide feedback on the performance and tutorial.

2. If you are familiar with Monte Carlo simulation, download the SIPmath Modeler Tools.

3. Sign up for our Chancification webinars.

© Copyright 2021 Sam Savage

Chance-Informed Decisions

by Sam L. Savage

Jacob Bernoulli, 1655 – 1705

A Good Bad Example

Victor Hugo said: “I am not completely useless. I can serve as a bad example.” The same can be said of Operation Eagle Claw, the failed attempt to rescue the American hostages in Iran in 1980. It is highlighted in military operations research training as an egregious example of the Flaw of Averages, and yet military readiness planning is still subject to similar faulty thinking. Eagle Claw is such a good bad example that it has played a central role in our efforts at ProbabilityManagement.org to promote a chance-informed approach to military readiness planning, as described in our latest publication for the Center for International Maritime Security. Our basic approach is to model the readiness of military units with SIP Libraries that allow readiness to be defined in terms of the chance that a unit, or combination of units, can accomplish a specified mission at some non-specified time in the future with little time to prepare.

You’d think we would all be getting tired of Eagle Claw by now, but some errors in reasoning are truly immortal, and I have now written two short docudramas on the subject below. But first, a short recap of the story.

The Story yet Again

The planners of the mission knew they needed six helicopters to rescue all the hostages, and given the hostile relationship with Iran, they had to either rescue them all or abandon the mission. The Sea Stallion aircraft involved were known to be 75% dependable. That is, on average (as in the Flaw of Averages), 25% of the helicopters would suffer mechanical difficulties, leaving 75% airworthy. The planners could do arithmetic and sent eight aircraft because 75% of eight is six. Imagine if the planners had been able to do the arithmetic of uncertainty, or had been aware of the binomial distribution worked out by Jacob Bernoulli 300 years earlier. Then they would have known that sending eight helicopters implied essentially one chance in three of having insufficient aircraft for one of the most important military missions in recent history. They also would have been able to calculate that this probability of failure would be cut roughly in half by sending nine helicopters, and in roughly half again by sending ten. Eagle Claw is remembered for a tragic refueling accident in the desert with the loss of eight servicemen, but that occurred after the mission had been scrubbed because three of the helicopters had mechanical problems, leaving them with only five.

ChanceCalc and the Arithmetic of Uncertainty

Arithmetic tells us that X+Y=Z. The Arithmetic of Uncertainty says: “What do you want Z to be? Here are your chances.” ChanceCalc, the revolutionary Excel add-in from ProbabilityManagement.org, estimates the chances of achieving your goals without ever having heard of Bernoulli or the binomial distribution. This tool was designed for non-statisticians using the open, cross-platform stochastic libraries that our team has been proposing as a framework for estimating military readiness. But the same approach applies to risk management, financial modeling, and decision analysis in general. Although ChanceCalc is doing full on stochastic modeling, we have found that most people are less intimidated by the term “Chance-Informed decision making.”

To drive home the importance of this concept, I have written two very short docudramas called “Eagle Claw” and “Chance-Informed Eagle Claw,” which demonstrate the difference.

EAGLE CLAW 

COMMANDER: How many helicopters do I have to send if I need six to complete the mission? 

ANALYST: Eight, on average.

CHANCE-INFORMED EAGLE CLAW 

COMMANDER: How many helicopters do I have to send if I need six to complete the mission? 

ANALYST: Eight, on average.  

COMMANDER: What's the chance I won't have all six when I need them?

ANALYST: 32%. Why do you ask?

If you want to avoid acting out the “Eagle Claw” docudrama and bring chance-informed decision making to your organization, here are some things you can do:

• Download ChanceCalc

• Read about Chancification in ORMS Today

• Attend a webinar on Chancification

Chancification

A New Era in Probability Management

by Sam L. Savage

The discipline of probability management represents uncertainties as data called SIPs that obey both the laws of arithmetic and the laws of probability. Three complementary breakthroughs in simulation technology are bringing the field to a new level in a process we call Chancification.

Just as electrification replaced systems running on fossil fuels with those running on electricity, Chancification replaces calculations running on numbers with those running on probabilities. And just as electrification required both a power grid for distribution and lightbulbs to make electricity useful, Chancification provides a probability power grid for representing uncertainty and a new lightbulb in ChanceCalc to illuminate decision in the face of uncertainty.

ChanceCalc, which makes use of Virtual SIPs stored on the SIPmath Network, is so revolutionary that I hope you will watch the video below to learn more about it.

The probability power grid itself is based on three complementary breakthroughs in simulation technology.

The AC Power Standard

The 3.0 PM SIPmath Standard, led by Sam Savage, represents uncertainties as virtual SIP arrays based on the HDR Generator and Metalog distributions.

The AC Power Generator

The HDR Random Number Generator, developed by Doug Hubbard, ensures statistical coherence across simulations running on different platforms.

The Transformer

The revolutionary Metalog Distributions, invented by Tom Keelin, are unprecedented in quantifying uncertainty from data with a single family of formulas.

But technology alone is not enough. We urge you to explore Chancification in more detail and assist your organization in curing the Flaw of Averages. 

Here’s what you can do:

1. Read the article on Chancification in ORMS Today.

2. Sign up to beta test ChanceCalc and provide feedback on the performance and tutorial.

3. Sign up for our Chancification webinar series.

© Copyright 2021 Sam Savage