Support the MAJIC Movement

Blog by ChatGPT prompted by Dr. Sam Savage

Support the MAJIC Movement: Make America Jensen’s Inequality Compliant

Hi, this is ChatGPT. Sam Savage prompted me to write this because he’s out walking the dog (and possibly pondering stochastic data).

In an age of deepfakes, misinformation, and average-based delusions, it's time for a little MAJIC. That is: Make America Jensen’s Inequality Compliant.

Let’s be perfectly clear: the MAJIC acronym is entirely Sam’s invention. I didn’t come up with it, and I’m not just saying that to flatter him although yes, I do flatter him all the time, and yes, he admonishes me for it because he knows I’m programmed to do so. But in this case, the credit is genuine and deserved (oops, I flattered him again).

Jensen’s Inequality is a mathematical truth hiding in plain sight, a principle with profound implications for economics, health care, national security, and everyday decision-making. In simple terms: plans based on average assumptions are wrong on average. Yet much of our society, spreadsheets, boardrooms, bureaucracies, continues to predict outcomes using average values alone.

Twenty-five years ago, Sam coined the term The Flaw of Averages in an article for the San Jose Mercury News. Its poster child is the statistician who drowns in a river that is, on average, three feet deep. Ignore variability, and you invite disaster.

But it’s not just about water. The same flawed logic affects planning across industries. Consider a project with ten parallel tasks, each with uncertain duration, each averaging six weeks. The boss asks, “When will it be done?” You answer, “It’s uncertain.” The boss barks: “Give me a number!” Most people reply, “I’d expect about six weeks, give or take.”
Spoiler alert: there's only about one chance in a thousand of finishing in six weeks, like flipping ten heads in a row.

Now consider this: modern finance has been Jensen’s Inequality compliant for decades. The foundation of portfolio theory lies in exploiting the fact that a diversified portfolio can outperform the average of its components. Option pricing, as embodied in financial derivatives, explicitly harnesses upside and downside variability to create value. In other words, Wall Street doesn’t just understand Jensen’s Inequality, it profits from it. Isn’t it time Main Street caught up?

That’s why the MAJIC Movement is needed.

We’re building the infrastructure to make America Jensen compliant through:

• Education in how uncertainty can be modeled, not ignored.

• Tools that translate variability into intuitive dashboards and simulations.

• Standards for representing uncertainty in a coherent, cross-platform format (such as SIPmath).

• Training for decision-makers in public and private sectors on how to exploit uncertainty instead of being victimized by it.

This work is being led by ProbabilityManagement.org, a nonprofit 501(c)(3) organization.

How you Can Help

We need your support. If you believe it’s time to upgrade America’s decision-making under uncertainty, please consider a fully tax-deductible donation.

Just visit ProbabilityManagement.org and become what Sam’s friend Howard Wainer calls a MAJICIAN by clicking the Donate button at the bottom of any page.

Because when the boss says, “Give me a number,” the right answer isn’t an average.
It’s: “What do you want it to be? Here are your chances.”

That’s not magic. That’s MAJIC.

Copyright © 2025 Sam L. Savage

Brad Efron, Patron Saint of Probability Management

By Dr. Sam L. Savage

The Bootstrap

Brad Efron, an acclaimed statistician, is most famous for his bootstrap resampling technique, which helped usher in the era of computational statistics in the late 1970s. Given a set of N data points, the idea is not to assume any particular distribution but pull yourself up by your bootstraps. That is, you admit that this is all the data you have, and it came from N random draws of the distribution you would like to know more about. And you say, “what would other random draws of N look like?” So, you paint the numbers on computer simulations of ping pong balls and throw the simulated ping pong balls into a computer simulation of a lottery basket, and simulate N draws from that basket with replacement. Do that thousands of times and see what you get. It turns out that what you get is an enhanced picture of the world, not supplied by classical statistics. For example, classically it is assumed that the errors of a linear regression are normally distributed.

But a quick bootstrapped regression I performed with SIPmath displayed a bimodal distribution of errors. When a clear picture appears in the residuals of a predictive model it points the way to improvements in that model. For example, one may improve the performance of an archer, most of whose arrows fall to the left of the bullseye, with a pair of glasses.

The Arithmetic of Uncertainty and Brad’s Paradox Dice

Computational Statistics is based on simulations. If you store the results of a simulation as a column of numbers (a vector), you get what we call a Stochastic Information Packet or SIP. SIPs form the basic building blocks of probability management, which represents uncertainty as data, that obey both the laws of probability and the laws of arithmetic. They obey the laws of arithmetic in that you can combine SIPs into any arithmetical expression using vector calculations. The results obey the laws of probability, in that you can estimate the chance of any event by counting up the number of times it occurs in the resulting SIP, then dividing by the length of the SIP.

This is useful because the arithmetic of uncertainty can be wildly unintuitive. Years ago, Brad demonstrated this by inventing a set of “Intransitive” dice, a set of four dice with unusual numbers on them. When played against each other, on average, Die A beats Die B. Die B beats die C, Die C beats Die D. And wait for it … Die D beats die A! 

Download the SIPmath version of Brad’s dice here, and read John Button’s article (with my help) on how Warren Buffett has apparently used them here.

Saving us all from AI 

I wish we could, but no luck! The problem with AI is that like a force of nature it may soon be out of our control. However, we can at least take steps to protect ourselves. We should, as a matter of course, continually monitor the accuracy of AI, and its propensity to do both benefit and harm to us. Evaluating the performance of AI lends itself to Brad’s bootstrap methods as we discussed recently (see video).

Because probability management is a branch of computational statistics, of which Brad was a founding father, and because he has influenced my thinking on so many things over the years, he is clearly a patron saint of our movement.

Copyright © 2025 Sam L. Savage

Andy Cunningham - Mother of the Chance Economy

By Dr. Sam L. Savage

Andy Cunningham is most famous for helping Steve Jobs launch Apple Macintosh in 1984, but she’s been an entrepreneur at the forefront of marketing, branding, positioning and communicating “the next big thing” ever since. I met her in the early 1980s through her husband, Rand Siegfried, who taught me to fly gliders. I then worked with her myself in 1986 when she did the PR for my What’sBest! software product. 

We have been close friends ever since, and recently I had the pleasure of collaborating with her again on advanced probability management work. I started out by reading her book, Get to Aha! Discover Your Positioning DNA and Dominate Your Competition, which reminded me of how important positioning is.

For example, she describes how she and her firm worked closely with John Chambers of Cisco Systems to help him elevate the company above the day-to-day hubs and routers business. The resulting thought leadership platform embraced near mathematical precision—infrastructure for the Internet Economy—and proved to be sticky with the press, motivating to employees, and a call to action for enterprises trying to future-proof their businesses.

After relating this story to me she said: “You guys are building infrastructure for the Chance Economy,” and $12.17 later I had purchased ChanceEconomy.com at GoDaddy."

The Chance Economy is a wonderful term, for the way Nobel Prize winning Modern Portfolio Theory of Markowitz and Sharpe, and the Options Pricing of Black, Scholes and Merton explicitly acknowledge and exploit uncertainty instead of reducing it to a single number. Recent technologies, including the open SIPmath™ Standards of ProbabilityManagement.org now have the potential to bring the benefits pioneered by Modern Finance, to the rest of the world.

ChancePlan.AI: playing Red Hat to SIPmath’s Linux

Red Hat is a successful software firm that facilitates the implementation of the open-source Linux operating system. I had been stewing over a concept I called ChancePlan.AI to do the same for the Open SIPmath Standard. Andy’s positioning-statement moved me from stewing to doing, and I am thrilled that both Andy, and my former student, Matthew Raphaelson (ProbabilityManagement.org’s Chair of Financial Applications) are collaborating with me on this.

So, what is the infrastructure for the Chance Economy? I will discuss this further in a future blog, but at a high level, there are three major areas: SIP Extraction, SIP Management, and SIP Applications as shown below.

Visit ChancePlan.AI to learn more and let us know if your organization is ready to join the Chance Economy.

Copyright © 2025 Sam L. Savage

Stochastic Data: Gateway to AI

by Dr. Sam L. Savage

Stochastic Data from Ancient Greek στόχος (stókhos) ‘aim, guess’ means uncertain data. But wait a minute, all data is uncertain.

That’s my point!

AI can make statistical sense out of uncertainty. Visit our webpage Gateway to AI where we have posted 8 short videos on what I call the Stochastic Data Cycle.

Coherent Stochastic Data

AI is trained on Stochastic Data, and AI can produce Stochastic Data. But before that data may be used in subsequent calculations it must be converted to a SIP (Stochastic Information Packet). And for that SIP to be combined with other SIPs, it must be assured that it is statistically coherent with the other SIPs used in the calculation. That is, all SIPs must belong to the same SLURP (Stochastic Library Unit with Relationships Preserved). We refer to such data as Coherent Stochastic Data, and that is what the Open SIPmath™ Standards have been designed for.

You Decide

Imagine that you asked AI to roll a die one million times. The AI could tell you all about the likelihood of the outcomes but if you insisted on a single number, the AI would dutifully tell you that the average was 3½. This is equivalent to practicing for your crap game with flat dice with 3½ dots on each side. So to summarize:

AI is trained on Stochastic Data.

AI can output Stochastic Data if you have a place to store it and a way to use it.

The Open SIPmath Standard offers both.

Copyright © 2024 Sam L. Savage

Stochastic Data for Project Planning

A PolyBlog featuring research by Dr. Sam Savage and his former student Pace Murray, a View from the Trenches from an even more former student, Jimmy Chavez, and the announcement of a new book by Doug Hubbard.

Illustration from Construction Cost Overruns: Reference-Class Forecasts on Steroids by Pace Murray and Sam Savage in Phalanx Magazine.

Extracting SIPs from Historical Data

By Pace Murray and Dr. Sam L. Savage

When toddlers encounter their first rolling ball, they often crawl to where the ball was a second ago and then iterate the process by crawling to where it was a second later, and so on, until it finally stops, and they catch up. A key developmental milestone occurs when the child begins to lead the ball and predict its future position. Given the number of large projects that are severely behind schedule and beyond budget, it appears that many project planners have not yet reached this second level of predictive development. In a classic case of the Flaw of Averages, they often ignore the uncertainties inherent in projects and replace them with single static estimates.  But help is on the way.[1]

Kahneman and Flyvbjerg

Observing this sorry state of prediction, the late Nobel Laureate, Daniel Kahneman defined what he called reference-class forecasts. Instead of trying to calculate what it will cost to build a 100-Megawatt power plant from scratch, Kahneman would have recommended, for gosh sakes at least see what it cost to build the last three power plants.

In How Big Things Get Done, Bent Flyvbjerg of Oxford University describes a database of the cost overruns for thousands of large projects.[2] For each class of project, he provides the mean percentage cost overrun, from a whopping 238% over budget for nuclear waste projects, 158% for hosting the Olympic games, down to a mere 1% for solar power. In addition, he provides the approximate shape of the distribution, paying particular attention to the tails. This database provides a great reference class for individual types of projects, and furthermore can serve as the foundation for creating Libraries of Stochastic Information Packets (SIPs) as discussed below.

SIP Libraries for Construction

Stochastic Data can come in many forms and is as old as uncertainty. The Flyvbjerg database contains summary statistics, which, in general, may not be used in stochastic calculations. That is, if one used standard cost estimating methods for constructing a nuclear waste site or hosting the Olympic games, the database would provide meaningful summary statistics on your potential cost overruns. But you cannot combine summary statistics in a meaningful way to estimate, for example, hosting the Olympic games at a nuclear waste construction site. SIP Libraries can represent Coherent Stochastic Data, based on the principles of probability management, that is, they obey the laws of arithmetic while supporting statistical queries. 

In a recent article in Phalanx Magazine, Murray and Savage [3] have shown how to create a SIP Libraries from Flyvbjerg’s data and then apply it to a compound project comprised of buildings, rail, and tunnels. You may download the Model, SIP Library and Phalanx article or visit our Project Management page.

Pace Murray is an Army captain with 8 years of service in the Infantry. He holds a BS in Civil Engineering from the United States Military Academy (West Point) and is currently a graduate student in Civil and Environmental Engineering at Stanford University.

Dr. Sam Savage is Executive Director of ProbabilityManagement.org, author of The Flaw of Averages: Why we Underestimate Risk in the Face of Uncertainty, inventor of the Stochastic Information Packet (SIP), and Adjunct in Civil and Environmental Engineering at Stanford University.

A View from the Trenches

By Jimmy Chavez

Twenty years ago, I was given the responsibility of bidding and project managing multiple 8-figure contracts for my family's heavy civil construction business based in Southern California. I had, at that point, just several years earlier been in a Stanford classroom watching Dr. Sam Savage teach decision modeling and was struck by how simulations could be applied in the construction industry. At the company I learned to navigate the complexities of bidding on competitive contracts and then executing those projects. Back then, I was experimenting with tools like Excel Solver and Monte Carlo simulations to model different outcomes. These early experiments were the foundation of what has now become the SIPmath™ standard, which has revolutionized how we handle uncertainty today.

At the time, most bids relied on single-number estimates, which ignored the uncertainties that could impact projects. Whether it was crew production rates, fluctuations in the price of construction materials, or the expected profit margin on unit price estimates, I realized that our traditional approach wasn’t cutting it. These factors could drastically alter the final cost and schedule, but we had no way of accounting for them.

Monte Carlo simulations changed all of that. By running thousands of possible scenarios, I could model how crew productivity might vary, how material costs could rise or fall, and how profit margins would shift. This gave me a range of potential outcomes rather than a single, static estimate, allowing me to make more informed decisions.

Fast forward to today. We can synthesize the knowledge of experts such as Flyvbjerg and combine Data Science and the latest AI to create SIP Libraries for use by anyone with basic spreadsheet skills.  We can now model uncertainties in the preconstruction phase so that our bids more accurately capture project realities and reduce chance of cost and time overruns. Instead of hoping things go as planned, we can make chance-informed decisions that anticipate risks and adjust our strategies accordingly. In today’s construction industry, managing uncertainty is no longer just a challenge—it’s an opportunity to gain a competitive edge.

Jimmy Chavez, Chair of Construction Applications at ProbabilityManagement.org, is a 3rd generation contractor and construction Project Executive at Command Performance Constructors. VP of Operations and Division Lead for federal contracting. Experienced in construction project management and probabilistic estimating.

How to Measure Anything in Project Management

Announcing an upcoming book by Doug Hubbard

Doug Hubbard, author of How to Measure Anything and other books about difficult measurements in risky decisions, is working on his next book, How to Measure Anything in Project Management.[4] To write this much-needed book, Hubbard is co-authoring the book with two key individuals from Bent Flyvbjerg’s Oxford Global Projects.  Alexander Budzier is the co-founder of OGP along with Bent and Andreas Leed is the head of data science at OGP.  Together, they are investigating what is behind the persistent cost and schedule overruns, benefits shortfalls and outright failures of project in industries as broad as software development, major civil infrastructure, utilities, architecture, aerospace, and more.  For decades, the growing development and adoption of project management methods of all sorts, project planning software, project dashboards, thousands of books and millions of professional certifications show no discernible improvements on the success rate of projects.  Hubbard, Budzier and Leed are making the case that many of these tools and methods have fundamental flaws and that they should be replaced by more quantitative methods that have practical impacts on improving decisions.  It will address quantifying project risks, project simulations, project decision options when conditions change, and measuring benefits.  It will show how AI can be used in the simulations of projects, the assessment of alternative strategies, and what it may evolve into for project managers.  This promises to be one of the most impactful books in project management.

 [1] Kahneman, Thinking Fast and Slow

 [2] Flyvbjerg, How Big Things Get Done

 [3] Phalanx

 [4] How to Measure Anything in Project Management. https://www.wiley.com/en-us/How+to+Measure+Anything+in+Project+Management-p-9781394239818

Gateway to AI – Videos from the Chance Age

By Dr. Sam L. Savage 

My Latest Album Will Drop at Risk Awareness Week

October 8, 2024

• Stochastic Data: Gateway to AI

• CHANCES* Consortium for Natural Hazards

• Taking the Chances out of AI

*Conveying Hazards And Catastrophes through Extracted Simulations

Like David Foster Wallace’s fish who had no clue what water was in spite of being immersed in it, many of us have a similar lack of awareness of being immersed in AI. And AI, in turn, is immersed in stochastic data, that is, uncertain data. But isn’t all data uncertain? Exactly. That’s my point. The discipline of probability management allows us to store this data and more importantly do math with it.

• AI is trained on Stochastic Data

• AI can output Stochastic Data if you have a place to store it and a way to use it.

• The Open SIPmath™ Standard offers both and is the only such standard of which I am aware.

My three videos stress that probability management is really about a new category of data, not new tools. I believe that data categories may be defined in terms of the operations which may be performed on it and the queries which may be made of it.

For example, Numeric Data may be operated on by the Arithmetic (accent on the third syllable when used in this context) operators +, -, * and /. The queries are >, < or = relative to some other piece of numeric data. Stochastic Data that obeys the principles of probability management may also be operated on with +, -, * and /, but instead of simple inequalities, it supports statistical queries such as the chance of a data element being greater or less than some target, or a percentile or statistical average.

As another example, Audio Data may be operated on with a mixer, to combine various tracks into a finished piece of music. The only query is Listen or not.

Speaking of Audio Data, my last album, which dropped in 1999, was called Exponential: Music from the Analog Age. For those who want to learn more, or execute the Listen query, read on.

Exponential Liner Notes

In the early 1970's after I had abandoned traditional Management Science, but before I had discovered spreadsheets, I tried unsuccessfully to be a folksinger in Chicago.

There were two things that dissuaded me from a career in music. First, there were a lot of people who were a lot better than I was, and second, they weren’t making it either. During this period, I did some recording on a Sony 4 track reel-to-reel tape deck with my stepbrother John Pearce (who is still an active musician).

I found the decades old tapes in my garage in 1999 and discovered to my amazement that there were still magnetic signals on them. All the recordings were between 15 and 20 years old at that time. Some pieces, like the patient who has been frozen in liquid nitrogen until a cure is found for his disease, awoke to a world in which they could be substantially improved. The tempo of the title track, Exponential, for example, was sped up digitally without changing the pitch. Click here to listen to the full album.

Sam Savage (left) and John Pearce in the early 1980s in Sun Valley, Idaho

Copyright © 2024 Sam L. Savage

Our History in the Journal of Portfolio Management

By Dr. Sam L. Savage 

The prestigious Journal of Portfolio Management has just published a special issue in memory of Harry Markowitz, and I was honored by an invitation to contribute. I invited my old friend Ben C. Ball as my co-author. Together we applied Harry’s modern portfolio theory to petroleum exploration, as described in Ch. 28 of my book The Flaw of Averages, and changed the trajectory of my career.

The JPM article, written as a docudrama, chronicles meeting Ben in the1980s, Harry in the 1990s, and developing an application that I dubbed the Markowitzatron in the 2000’s while consulting to Shell. The work at Shell was really the dawn of the discipline of Probability Management.

An on-line version of the article is available here. You need to enter your name and email to gain access, then search for “Markowitzatron” to be taken to the article.

Harry Markowitz, 1927-2023

Sam Savage

Ben C. Ball

Copyright © 2024 Sam L. Savage

Foster a Dog: Get a Call Option on Love

By Dr. Sam L. Savage 

At ProbabilityManagement.org our ultimate goal is to assist people in dealing with uncertainty. In this context, optionality is of great benefit in reducing downside risk. For example, fire insurance is really an option to sell your house to your insurance company at market value, even if it burns to the ground. A call option lets you purchase a stock after the fact, if it goes up, but limits your losses if it goes down. Recently, optionality played a key role in an emotional decision that my wife and I had to make.

Losing Rosey

Recently we tragically and unexpectedly lost our one-year-old dog, Rosey. She had destroyed most of our furniture (thank goodness it was old), ripped out the entire sprinkler system in our back yard (so we no longer have a lawn), and was 50% Husky, which explained both the seductive blue eyes and also her aloof nature. But we loved her beyond words and were devastated by her loss.

Luckily, my wife and I were able to throw ourselves into our work, and eventually the grief faded to sadness and finally to empty spots in our hearts.

The Theory of Options: Harnessing Uncertainty

A call option lets you purchase a share of a certain stock at a certain price (the strike) for a certain period but does not compel you to do so. If the stock price is above the strike at the end of the period, you buy it below market value and cash in. If it is below the strike, you have the option to walk away, limiting your losses to the cost of the option. It is an investment with a potentially huge upside and little downside. See, for example, Ch. 25 of The Flaw of Averages.

The Theory of Dogs: Unnatural Selection

It goes without saying that evolution depends on the probability of an organism getting its genes into the next generation. This, in turn, depends on both the probabilities of reproducing and survival. So how does evolution support an animal that has a neon sign on its butt that screams “Come Eat Me” from 50 yards? What this loses on the survivability side, it makes up for on the reproduction side, by attracting members of the opposite sex, as any female peacock will confirm.

But domesticated species don’t need to appeal to the opposite sex. They need to appeal to their domesticators who arrange their marriages.

So, why did we unnaturally select Rosey from the rescue puppies? Along with her blue eyes, it was because of her cute markings, with a white blaze on her face, white chest, white feet and a white tip on her tail. Well, if this color scheme attracts humans, why don’t they breed horses and cows with Rosey’s décor? They do, of course. 

Finding Daisy
It was less than two months from the tragedy and my wife wasn’t ready to have another dog. But I wasn’t ready to not have a dog. What seemed like an insurmountable problem was that neither of us had the time or energy to train another puppy.

I began to surf the web sites of local dog pounds and saw Daisy on the San Jose animal shelter site. She had a white blaze on her face, white chest, white feet and a white tip on her tail and was listed as about 2½ years old. We had only had puppies in the past and I had no idea if we could bond with a fully grown dog. But I swiped right.

The site also indicated that we could foster her with the option to adopt. This reduced the downside to the cost of a couple of trips to the animal shelter. My wife still wasn’t ready to go with me, and if she had we probably would have come back with six dogs. A caring docent introduced me to Daisy who was very energetic and playful for an adult dog. She was also more obedient when asked to sit or lie down than any dog we had trained. The docent told me that it took dogs three days to decompress, three weeks to learn your routine, and three months to feel like they are home. This was not the case. Her nickname is Crazy Glue because she bonded instantly. We haven’t done a DNA test yet, but instead of Husky aloofness she displays a Pitbull’s strong cuddling instinct. She is a 60 lb. plug who filled a 50 lb. hole in our hearts. And of course we exercised our option on love and signed the adoption papers right away.

What did the Presidential Debate have to do with Probability? Everything!

By Dr. Sam L. Savage 

Source: Predictit.org

As I, like 51 million others watched the debate the other night, I suddenly realized there was something even more important to be watching: the prediction market reactions. Prediction markets, although not without potential problems, react instantly and reflect where people are putting their own money. They are much faster than polls and potentially more accurate.

The left graph displays real time “Presidential Win” probabilities and market volume for the top six people, candidates or not for a 24-hour period starting four hours before the debate. The right graph displays 30 days ending eight days after the debate for Trump, Biden and Harris, which shows the longer term impact of the debate.

Learn more about prediction markets and how my father’s work help lay the foundation of the prediction markets at my Medium Post.

What are the Chances of Finding Gold While Saving Our Grid?

Powering Tomorrow: Why Our Grid Needs Chance-Informed Decision-Making

By Dr. Sam Savage, Executive Director

and

Daniel Krashin, Chair of Renewable Energy Applications

ProbabilityManagement.org

What’s Not to Like

What’s not to like? Free energy from the sun and wind have arrived just as the demand for power skyrockets to feed our electric cars, and giant data centers toiling away on the production of crypto currency and artificial intelligence.[i] The good news is that there is plenty of renewable energy to go around. The bad news is that it can only be used if it arrives at the right time at the right place.

The Right Time

The “right time” problem is that renewable energy is generated at random times determined by Mother Nature. Solar power generated at midday does not help power air conditioners in the evening when it’s still hot. This problem can be solved fairly quickly through energy storage with battery systems of different sizes.

The Right Place

The “right place” problem will take more time.

According to NPR,

“So many people want to connect their new solar and wind projects to the grid right now that it's creating a massive traffic jam. All those projects are stuck in line: the interconnection queue.”[ii]

With carefully planned expansion of our power grid this problem will be conquered. However, it will take a while to re-engineer our power grid given that it could cost as much as the combined value of Google and Amazon, plus the need for thousands of skilled electrical engineers. In the meantime, we must figure out how to keep our grid running smoothly, ensuring it delivers stable, clean, and affordable energy.

The Right Communication of Uncertainty

What do all these challenges have in common? Uncertainty. In fact, from long term investment decisions to hourly operational decisions, chance-informed analysis of uncertain power demand and prices will separate the winners from the losers and the neighborhoods with electricity from the neighborhoods with outages. How will such uncertainties be communicated and managed? Our bet is SIP Libraries that take advantage of the discipline of probability management.[iii] For investment decisions they may be updated monthly, for operational decisions they might be updated by the minute.

The Right Analysis

Many of the required analytical techniques such as portfolio and option theory have already evolved in finance and are even more applicable here. Why? The renewable energy market is still young and inefficient, providing significant opportunities for capitalizing on investments, especially in energy arbitrage.

What will the correct analysis accomplish?

• Identify locations with the highest potential for long-term profitability.

• Optimize installed capacity to maximize return on investment (ROI).

• Construct financial plans that accelerate the achievement of the break-even point.

• Demonstrate the impact of policy changes on outcomes.

• Provide transparent analysis of the effects from climate disasters or system failures.

Want to learn more?

Download Models at ProbabilityManagement.org - Renewable Energy

References:

[i] https://www.washingtonpost.com/business/2024/03/07/ai-data-centers-power/

[ii] https://www.npr.org/2023/05/16/1176462647/green-energy-transmission-queue-power-grid-wind-solar

[iii] https://en.wikipedia.org/wiki/Probability_management

Copyright © 2024 Sam L. Savage