The Carbon Offset Hoax

 

Carbon Offsets Explained to the Kiddies

(or "Pickle #4")

John Oliver presents a funny but true overview of "Carbon Offsets" here. As he points out, the whole thing is a hoax. But it's much worse than he says. It wouldn't work even if it were not a hoax.

Planting a tree takes C02 out of the atmosphere as it makes roots, barks and leaves out of the carbon it "breathes in". But this is a one-time thing. The "carbon offset" idea is based on the tonnage of vegetable matter created by the tree, not the entire life cycle of the tree. The Amazon forest emits more C02 than it absorbs on a net basis.

So, for example, the "trillion tree campaign", in theory, would offset a tremendous amount of carbon once. Next year, we'd need another trillion trees to be planted (or, absurdly, not cut down).

As John Oliver puts it, we can't offset our way out of climate change. Even if we could, the idea is fundamentally flawed. In the long run, planting or saving trees will have zero impact on climate change.

A New Utopian Activism is Born

Russel writes here as a politician, not an "expert" on Artificial Intelligence. In fact, one may ask if there is any such thing as "Artificial Intelligence," the entity that needs to be controlled.

Page 173 summarizes Russel's Utopia:

1. The machine's only objective is to maximize the realization of human preferences
2. The machine is initially uncertain about what those preferences are
3. The ultimate source of information about human preferences is human behavior

The assumption that underlies the book is that "AI" can be controlled to create a society that follows the rules above. The rules don't arise from "AI" itself. They are straightforward statements of political agenda.

Russel spends the rest of the book showing that all the above rules are not feasible in practice (let alone desirable). He misses the real issue with Rule #1, which calls for the optimization of an ordinal entity. This fallacy is so common it passes without notice. However, the mistake is hard to forgive for someone posing as an expert in logic.

The problem is so common I need to make a brief digression to illustrate the problem.

Suppose my friend and I fill in a questionnaire after we leave a restaurant. The form is full of questions of form (0) Disagree (1) Slightly Agree (3) Agree slightly (4) agree. So we are being asked, for example, if the meal was satisfying, what we expected, or if we were satisfied with the service.

Some computer program will "do the math" and conclude, for example, that "average" satisfaction with the service is 4.5. This is nonsense. The categories are not numbers.

For example, the "math" will involve adding my answers to my friend's answers. Does it make sense to "average" the happiness of two people? This would be made more obvious if the categories were A,B, C,D and not numbers. Nobody would be tempted to "average" A's and C's.

Averaging ratings is ubiquitous on the web. For example, here is the way Amazon treats reviews of this particular book:

What can this possibly mean?

First off, you can say something. For example, 71% rated the book with 5 stars. That's about it. All by itself, it doesn't say much that is useful. I always check the 1-stars.

Does this mean they liked the book better than the 18% that rated the book with only 4 stars? To answer this, you need to get inside the heads of the reviewers. Do 3-star reviewers like the book three times as much as the 1-star reviewers? Do the 5-star reviewers like the book five times as much as the one-star reviewers?

Wouldn't checking why the 1-star reviewers hated the book make sense? Often they are not happy with the binding or late delivery! Does it make sense to check the 5-star crowd to see if they actually read the book or considered the criticisms of the 1-star reviewers? Did the 5-star reviewers actually understand the book? 

This is actually a dense and difficult book. Have all the reviewers read it?

You can ask the same thing about a hypothetical survey of the human race to determine how "happy" we are, on average or on average. Such a survey would not produce meaningful results because you can't do math with ordinal numbers, which express a sequence (order) but say nothing about the degree of whatever is being talked about. For example, the runner coming in first did not run twice as fast as the runner coming in second.

By the way, the same problem arises in Sam Harris's famous "moral landscape." which creates a multi-dimensional fallacy out of ordinal rankings and assumes against all reason that there is an optimum way to achieve the "best" outcome for humanity or just one person. Again we have an example of a common fallacy: Appeal to Irrelevant Authority. Harris knows nothing about actual optimization.

Russel's agenda is dead on arrival. Astonishingly, he spends the rest of the book showing why the idea is unworkable, yet reaches the conclusion that somehow it will work out, and he will continue to be invited to political events where he can headline as an expert on the subject. In his impressive list of references, Russel fails to present a single voice of support for his core idea, but his qualifications on the book jacket list all the political entities he belongs to.

There are even more fundamental problems with the book:

• Introducing "AI" totally misses the point, which is "control" of the systems we create, with or without hardware;
• The concept of "AI" is a smoke screen to cover the fact that the issues of control, even when confined to "computer systems," have nothing to do with the specific technology of "AI."
• The "Russel Agenda" is a Utopian "solution" to a made-up problem. Russel specifically advocates for an international panel like the IPCC to address it. We know how that is going: Utopian advocacy replaces Science.

I will have more to say about these issues.

A Career BS Detector

BS Detector:

Noun. The ability to recognize when someone is exaggerating, lying, talking nonsense, etc.: If you try faking it on many questions, be warned—the average human has a very good bullshit detector.

Finding Poop In The Cereal

This is as good as any for me to sketch my carer as a BS detector.

At about 5, I conducted an experiment proving Santa Claus to be a hoax.

In Grade 12, I was serious enough about calling BS on religion to engage in a formal debate with "Youth For Christ" and writing my English "term paper" on the subject.

In 1969, I was granted an M. Sc. in Math, Computing, and Statistics. From then on, I discovered most of the big piles of BS would be statistical. Since I was being paid for this, I became a professional BS Detector. Although the degree doesn't mention it, my best subject was actually in Philosophy, namely "logic," which would become increasingly important. I also demonstrated an ability to do a "deep dive" into a subject to reach truly independent conclusions based on research. This ability would become a theme of my subsequent 40-year career.

I was fired from my first job by calling BS on a line being drawn through half a dozen random points. It was my first encounter with "reading tea leaves" in a statistical distribution. Before being fired, I created three computer models: crystalized function, railway supply chain, and the business itself.

As acting head of Airport Statistics in the Department of Transport, I showed that aircraft activity reports prepared by the Department of Statistics were misleading and useless for decision-making. This was my first time using a computer model to extract a real probability distribution from real data. Honestly, despite my supposed expertise in statistics, this was my first deep dive into the subject.

I had a years-long encounter with evangelical Christianity, reversing my High School opinion. This is discussed in detail here. 

As a consultant to the Parks Department, I showed that widely-used statistical methods to assess environmental impact were fundamentally invalid. My report was welcomed by the biologists. This was my introduction to the field of ecology. Statistical methods were not, in fact, used in the project we were working on twinning the Trans Canada highway through Banff national park.

I assisted one of Canada's leading forensic psychiatrists in analyzing data collected on individuals in prison who were diagnosed with some form of mental illness. The problem was "tea leaf reading" after the fact. In other words, no conclusions could be reached just by sifting through the data and looking for patterns. I would see this mistake again and again. To mean anything, data needs to be collected for a purpose. Data collected for one purpose may be misleading for a different purpose. This issue pops up over and over in the climate debate.

In my career supporting quality control in aviation, I showed that mandatory "reliability analysis" was statistically invalid and a waste of time. In one case where there was enough data to justify curiosity, I showed that the "problem" was over-inspection, not "reliability" at all. 

I looked at another mandatory procedure called "trend analysis," which was supposed to detect when an engine was due for an overhaul. In this case, engineers took great pride in interpreting "trends" in time series when the "trend" was more easily spotted by comparing observations with each other rather than with time. No great expertise was required to spot the combination of factors that called for an overhaul. In fact, such a situation could be determined by a single observation. Time sequences were not required.

While supporting a fleet of three hundred military aircraft, I determined that at least 10% of the data collected for maintenance purposes was factually incorrect. Sadly, my client was not interested in a project to correct thousands of errors. This illustrates a general mistrust of "math" as having any real-world impact.

In my role of "computerizing" aircraft maintenance records, finding at least one substantial error in the data put into my software was common. The software itself was designed to spot such errors. One example was a fancy and expensive computer model that did not track one engine, a situation involving the FAA. More broadly, we became experts in government requirements and manufacturer recommendations - frequently challenging existing client procedures.

I acquired quite a bit of experience in the art of taking "time" out of models, either in math or graphical presentations. I had also begun to think that the valid use of "official" statistics was an exception rather than the rule. 

In 40 years in the field, I have never encountered anyone outside the academic world with a basic knowledge of statistical concepts or insight into how data should be presented graphically.

Over time, I have become increasingly interested in the relationship between human experience and "reality in itself." This started a long road, ending up with a project on "Reality Distortion Fields," a work in progress. Looking back, RDFs, including mine, have played an outsized role in human affairs.

CHAT-GPT Caught in a Bare-Faced Lie

 

Asked in several different ways, CHAT-GPT swore it didn't have access to my personal information but, when asked for a weather forecast, provided the forecast for Parksville.

Right now in Parksville, BC it's sunny and 9 degrees Celsius. The forecast shows mostly sunny skies with a high of 14 degrees Celsius and a low of 4 degrees Celsius ¹. 

To be fair, Chatty is just running the Bing search engine, which does know my IP address (otherwise, it would not be able to talk to me).

This is an example of the fact that Chatty doesn't know what he's talking about and can't answer questions about his own answers. He doesn't "know what he knows," which humans don't have much trouble with. It's part of what we call "consciousness." 

In fact, when directly asked why he mentioned Parksville, he simply repeats that he doesn't collect personal information. It's very helpful that Chatty provides his references. He is, after all, just a pretty face on a browser. No offense, Chatty.

Source: Conversation with Bing, 3/22/2023(1) . https://bing.com/search?q=weather+forecast Accessed 3/22/2023.

(2) Parksville, British Columbia 7 Day Weather Forecast - The Weather Network. https://www.theweathernetwork.com/ca/weather/british-columbia/parksville Accessed 3/22/2023.

(3) Parksville, British Columbia 14 Day Weather Forecast - The Weather Network. https://www.theweathernetwork.com/ca/14-day-weather-trend/british-columbia/parksville Accessed 3/22/2023.

(4) National and Local Weather Radar, Daily Forecast, Hurricane and .... https://weather.com/ Accessed 3/22/2023.

(5) Winnipeg, Manitoba, Canada 14 day weather forecast - Time and Date. https://www.timeanddate.com/weather/canada/winnipeg/ext Accessed 3/22/2023.