Living on the Real World

My dad, the statistician, used to lecture my brother and me on the importance of population size. He would say “in a town of 800 people, serious crime is a rare thing, almost unheard of. But in a population of 800,000, serious crime is commonplace, daily, inevitable.”

Today we’re going to talk in that same spirit about eight billion people – the world’s population. That’s a very big number. To see this, consider a subject or an issue that only one person in a million might explore.

That means some 8,000 people are thinking about it, obsessing about it, studying/writing about it, taking a suite of actions.

Eight thousand. Wow. Look at this in dollar terms. Suppose that they’re each being paid an average of $60,000 annually to work on it; that means society as a whole is plunking down almost 0.5 billion dollars on the topic each year. Of course “huge” number of dollars is the merest hiccup compared with the world’s $85 trillion GDP.

This background partially explains today’s discovery. I was idly wondering how opinions and concerns, hopes and fears about artificial intelligence might break depending on a person’s faith or spirituality. I googled something to along the lines of “faith and artificial intelligence.” I thought (naively; my dad notwithstanding; I never seem to learn!) that I wouldn’t find much. Within half a second, was confronted with myriad posts, articles, commentaries on the subject, the tip of an iceberg of 200 million (!) “results.” (A lot of these, of course, are duplicates, but 200 million?)[1] Everything is in there, ranging from “Can religion and AI work together?” to “Is AI a threat to Christianity,” to “Is AI a new religion,” to “Use AI responsibly and ethically.” And that’s before getting to the cringeworthy titles like “AI will be the political left’s ‘single greatest weapon’ against religious faith and truth,” to “I convinced ChatGPT that God exists.”

The noise is deafening. But among the myriad links, I came across this one: “Thinking about God increases acceptance of artificial intelligence in decision-making.”

Hmm.

The language of the title is a bit tame for internetspeak, suggesting something scholarly – evidence based. Sure enough, the source was Proceedings of the National Academy of Sciences (PNAS). The paper was published August 7 of last year; the authors are Mustafa Karatas (Nazarbayev University, Graduate School of Business, Astana, Kazakhstan) and Keisha M. Cutright (Duke University, Fuqua School of Business).

There’s a case to be made for reading the article in its entirety. ICYMI (like me, along with nearly eight billion others, to emphasize my earlier point), here are a few excerpts, to give the flavor and perhaps whet your appetite for a deeper dive.

The authors first argue the topic’s significance:

As AI recommendations become increasingly prevalent and the world grapples with its benefits and costs, it is important to understand the factors that shape whether people accept or reject AI-based recommendations. We focus on one factor that is prevalent across nearly every society: religion. Research has not yet systematically examined how religion affects decision-making in light of emerging AI technologies, which inherently raise questions on the role and value of humans. In introducing this discussion, we find that God salience heightens AI acceptance.

 The abstract that follows contains material like this:

Thinking about God promotes greater acceptance of Artificial intelligence (AI)-based recommendations. Eight preregistered experiments (n = 2,462) reveal that when God is salient, people are more willing to consider AI-based recommendations than when God is not salient. Studies 1 and 2a to 2d demonstrate across a wide variety of contexts, from choosing entertainment and food to mutual funds and dental procedures, that God salience reduces reliance on human recommenders and heightens willingness to consider AI recommendations. Studies 3 and 4 demonstrate that the reduced reliance on humans is driven by a heightened feeling of smallness when God is salient, followed by a recognition of human fallibility. Study 5 addresses the similarity in mysteriousness between God and AI as an alternative, but unsupported, explanation. Finally, study 6 (n = 53,563) corroborates the experimental results with data from 21 countries on the usage of robo-advisors in financial decision-making…

… Despite AI’s ability to outperform humans in many contexts, people often exhibit a biased preference for human recommendations, a phenomenon known as algorithm aversion…

… Having permeated the existence of nearly every known society, religion has been a persistent and powerful influence in people’s lives throughout history and continues to shape the lives of billions of people around the world…

… it affects decision-making in important ways, particularly in social and moral domains…

Importantly, a relatively nascent body of research shows that religion also influences how humans behave and make decisions in more mundane aspects of everyday life. For instance, there is growing evidence that religious reminders lower interest in self-improvement products (30), lessen reliance on brand name products (31), and decrease impulse grocery spending (32). These findings suggest that the impact of religion on human behavior is broad and that more research is needed to understand how religion influences decision-making, especially in light of massive advances in technology that have become integral to modern decision-contexts. The question of how religion affects decision-making in the face of AI is particularly interesting when considering that such technologies evoke fundamental questions about the value and role of humans (33); religion has faced such questions since its beginnings (34, 35).

To begin addressing the intersection of religion and AI, we investigate how the salience of God affects people’s propensity to rely on AI. We theorize that God salience—the extent to which individuals are actively thinking about God—is one important factor that may attenuate AI aversion. In broaching a relationship between religion and AI, we focus specifically on the salience of God for two main reasons. First, the centrality of God(s) or other supernatural deities is what is common across all large-scale religions (20, 36), as opposed to any specific set of beliefs or practices. Indeed, among all words that relate to religion, “God” is the most commonly used in the English language (37). Second, people are frequently exposed to reminders of God in their daily lives, even if they are not religious, suggesting that an effect of mere God salience may be relevant to more of the world’s population than a narrower focus on specific religious beliefs or activities.

We predict that God salience will dampen AI aversion in decision-making. That is, individuals will be less reliant on humans and more open to recommendations from AI systems when God is salient. This is because when God is salient, people feel smaller and are thus more likely to recognize themselves, and mankind more generally, as limited and fallible.

And that is what the authors find. Here’s an excerpt from their concluding discussion:

AI is now a ubiquitous part of everyday life for much of the world—perhaps even akin to the pervasiveness of God. Given the diminished role of humans when viewed in relation to God and within AI operations, might there be a relationship between how thoughts of God affect people’s reactions to AI? Across several studies, our research demonstrates that thinking about God leads people to be more willing to accept recommendations from AI systems than they otherwise would. The results hold across a variety of recommendation contexts (financial, health, entertainment decisions), religious beliefs, and research methodologies (field and lab experiments, global survey). Thoughts of God lead individuals to feel smaller, rendering them more likely to recognize the fallibility of humans. They therefore find it less essential to rely on humans when making decisions and are more accepting of AI-based recommendations.

Importantly, these results extend prior research on the rol e of religion in decision-making. Prior research has largely focused on how religion affects social and moral decision-making (54, 55). The present findings suggest that religion has important implications for a wide swath of decisions, particularly as it relates to how decisions are made in the face of new technologies that mimic the traditional role of humans. [Emphasis added.] By drawing a connection between how people view humans in relation to God (i.e., as smaller and flawed) and the decreased role that humans embody in AI, our work has broad implications for understanding the acceptance of AI as a decision-making tool. We also acknowledge the counterintuitiveness of the findings at first glance. Based on popular assumptions, one might assume that God salience leads to greater conservatism, less openness to new experiences, and decreased risk-taking, suggesting that people might be less open to the novel technology that drives AI when God is salient. However, empirical evidence provides a more complex picture. For example, prior research suggests that God salience may not necessarily lead to greater conservatism. While religious identification is positively associated with conservatism, spiritual identification is negatively associated with conservatism (56). Moreover, research suggests that there is no conclusive evidence that thoughts of God lead people to be more close-minded (57). Finally, God salience often leads to greater risk-taking, as long as one’s morals are not implicated (54, 55).

(Note that the authors are careful to speak in terms of God salience and spiritual identification in contrast to religion.)

Food for thought! Worth a more thorough, complete read – and some further reflection.

[1]In case you’re interested, my narrower search of Hindu faith and AI yielded a mere 13 million results.  

Happy Earth Day 2024.

Ask people worldwide “is climate change a problem?” and the answer is usually a clear “yes.”  But ask a follow-up, “what is the real nature of that problem?” and the picture is murkier. Answers might range from “we’re burning too much fossil fuel,” to “there are simply too many people,” to “global warming is destroying crops,” to “rainfall patterns are shifting – look at the record floods in the UAE and drought and wildfire in Hawaii,” to “we’re too consumer-oriented,” to “it’s the mis-directed policies of those rascals in that other political party.” The causes identified and the villains vilified would vary from country to country, from demographic to demographic – and from person to person. Eight billion people see the problem eight billion different ways – not just with respect to its seriousness, but with respect to its inherent features and its origins.

Whew! The good news is that the population includes professionals who make their living in the study and practice of (big-picture) problem formulation in general.

The previous LOTRW post looked at the thoughts of one such expert, Oguz Acar, on the particular challenge of harnessing artificial intelligence to problem solving.  Along the way, he observed that  “…to identify, analyze, and delineate problems… necessitates a comprehensive understanding of the problem domain and ability to distill real-world issues.”

Mr. Acar went on to identify four key elements to the process. His description of these is best read verbatim, but when condensed a bit, as, they boil down to something like:

Problem diagnosis – identifying the core problem to be solved. Typically, this involves looking deeper than the mere symptoms to discern the underlying problems.

Deconstruction – breaking down complex problems into simpler subproblems.

Reframing – changing the perspective from which the problem is viewed.

Constraint design – bounding the problem.

Hmm. Is analysis of this type being applied to the challenge of climate change? What fruit does it yield?

Start with the problem diagnosis. Eunice Foote got the ball rolling. In the mid-nineteenth century she concluded that water vapor and CO2 play a role in heating the atmosphere. Tyndall, Arrhenius, and others who followed refined the picture. Decades of measurement since document growth trends in atmospheric concentrations of greenhouse gases. Models of increasing diagnostic power predict the atmospheric temperature changes likely to result over the coming century or two based on different scenarios for future fossil-fuel use, the transition from fossil fuels to renewable energy sources, etc. Those models also reveal the changes in the hydrologic cycle likely to accompany the temperature changes. Other models reveal accompanying sea level rise and changes in ocean acidification. Research in all these areas and more is ongoing, and the findings documented every four years in voluminous  United Nations IPCC climate change assessments.

The problem has also been reframed. It is today often seen as one wedge of global change – reflecting corresponding declines in natural habitat, biomass and biodiversity, and environmental quality, for example.

In past decades the problem has also been reframed more radically – in terms of human choice. In such a social-science perspective the underlying problems look quite different. Here is an example of ten recommendations for policymakers:

1. View the issue of climate change holistically, not just as the problem of emissions reductions.

2. Recognize that, for climate policymaking, institutional limits to global sustainability are at least as important as environmental limits.

3. Prepare for the likelihood that social, economic, and technological change will be more rapid and have greater direct impacts on human populations than climate change.

4. Recognize the limits of rational planning.

5. Employ the full range of analytical perspectives and decision aids from natural and social sciences and the humanities in climate change policymaking.

6. Design policy instruments for real world conditions rather than try to make the world conform to a particular policy model.

7. Incorporate climate change into other more immediate issues, such as employment, defense, economic development, and public health.

8. Take a regional and local approach to climate policymaking and implementation.

9. Direct resources into identifying vulnerability and promoting resilience, especially where the impacts will be largest.

10. Use a pluralistic approach to decision-making.

The authors contributing to this framing recognized that the social challenges here belong to a class of so-called wicked problems. And that in turn has prompted others to see the deeper problems underlying climate change as not merely societal, but stemming from beliefs, values and attitudes, even bordering on the spiritual.

Now we’re getting warm.

More about this in a moment, but let’s first turn our attention to the two remaining elements of problem formulation. Unsurprisingly, it turns out that the vast scale, the stupefying complexity and multi-faceted nature of climate change finds it matched by countless deconstructions and constraint designs (these two go hand in hand). Early on, the physical problem was split into mitigation (reduction of fossil fuel use and transition to renewable forms of energy) and adaptation (building resilience to the coming changes in the hydrologic cycle, patterns and severity of extremes of heat and cold, flood, and drought that are now inevitable as the result of the greenhouse gas buildup to date). The natural and social framings of the problem are spawning as many deconstructions as there are national, regional, and local political and demographic boundaries worldwide. Deconstructions have also emerged along lines of individual professional and academic disciplines, or to reflect contributions needed from myriad communities of practice.

This gives the appearance of chaos. Pessimists might accurately note that there are overlaps, gaps, and resulting inefficiencies in all this that we can ill afford in terms of the overall cost and the slow rate of progress relative to the urgency of the problem. But such multiple, trial-and-error approaches are far more rapidly distinguishing profitable paths forward from dead ends than any kind of monolithic, top-down approach ever would. Think about it. Climate change is not slow-onset; it’s rapid onset compared with the time required for eight billion people to agree on what we should do. This multiplicity of efforts also has the merit of transforming the problem from one to be solved by a small minority of the population, surrounded by eight billion critics, to a problem being attacked by eight billion participants. We all have skin in the game; it turns out that we all have talents and skills and perspectives to offer as well. Earth is no place for spectators.

Pessimists might also note that whatever the problem reframing, the cost is stupefying. And we’re failing to pay the bill. According to a recent New York Times article:

Experts estimate that at least $1 trillion a year is needed to help developing countries adapt to hotter temperatures and rising seas, build out clean energy projects and cope with climate disasters.

Fact is, that sum pales beside the $100 trillion thought to be needed for food, water, and energy infrastructure investment and renovation over the next two decades. But this mountain of money is money we’re paying ourselves. Bottom line? All of us, in one way or another, intentionally or unconsciously, are formulating the climate change/global change problem – diagnosing it, deconstructing it, reframing it, constraining it. And even as we continually hone all that, we’re moving from problem definition into action. We’re putting people to work, locally, everywhere, re-greening our planet – and maybe correcting some long-standing inequities, building a more unified, peaceful global society – in the process.

So happy Earth Day! This is how it feels when things are going well.

As discussed in the previous LOTRW post, university computer science curricula nationwide are offering courses in AI-prompting – a new discipline, mixing the science and art required to harness the full power of artificial intelligence. A new subject for human beings to master? The prospect is daunting, in part because of the complexity of AI and the diversity of applications for it – and in part because the AI-landscape itself is mutating rapidly. It’s hard to keep up.

Take solace from a Harvard Business Review article of 2023 entitled AI Prompt Engineering Isn’t the Future. The author, Oguz A. Acar, points out that AI – the disease – will itself provide the cure. It is rapidly developing the capacity to assist in this professional niche and may in time take over the whole of it. To extend the metaphor of the earlier post, the horse will be opening a special whispering channel for us – perhaps even softly neighing sweet nothings into our ears.

But Mr. Acar doesn’t really let us off the hook entirely. He goes on replace that challenge with a different but closely related one:

So, what is a more enduring and adaptable skill that will keep enabling us to harness the potential of generative AI? It is problem formulation — the ability to identify, analyze, and delineate problems.

Problem formulation and prompt engineering differ in their focus, core tasks, and underlying abilities. Prompt engineering focuses on crafting the optimal textual input by selecting the appropriate words, phrases, sentence structures, and punctuation. In contrast, problem formulation emphasizes defining the problem by delineating its focus, scope, and boundaries. Prompt engineering requires a firm grasp of a specific AI tool and linguistic proficiency while problem formulation necessitates a comprehensive understanding of the problem domain and ability to distill real-world issues. The fact is, without a well-formulated problem, even the most sophisticated prompts will fall short. However, once a problem is clearly defined, the linguistics nuances of a prompt become tangential to the solution.

Unfortunately, problem formulation is a widely overlooked and underdeveloped skill for most of us. One reason is the disproportionate emphasis given to problem-solving at the expense of formulation. This imbalance is perhaps best illustrated by the prevalent yet misguided management adage, “don’t bring me problems, bring me solutions.” It is therefore not surprising to see a recent survey revealing that 85% of C-suite executives consider their organizations bad at diagnosing problems.

It’s hard to read this without wanting to get better at problem formulation. Happily, Mr. Acar has identified four key elements to the process, along these lines:

Problem diagnosis – identifying the core problem to be solved. Typically, this involves looking deeper than the mere symptoms to discern the underlying problems.

Deconstruction – breaking down complex problems into simpler subproblems.

Reframing – changing the perspective from which the problem is viewed.

Constraint design – bounding the problem.

This last one is a bit more complicated. He puts it this way:

Problem constraint design focuses on delineating the boundaries of a problem by defining input, process, and output restrictions of the solution search. You can use constraints to direct AI in generating solutions valuable for the task at hand. When the task is primarily productivity-oriented, employing specific and strict constraints to outline the context, boundaries, and outcome criteria is often more appropriate. In contrast, for creativity-oriented tasks, experimenting with imposing, modifying, and removing constraints allows exploring a wider solution space and discovering novel perspectives.

Mr. Acar sums up on this note:

Although prompt engineering may hold the spotlight in the short term, its lack of sustainability, versatility, and transferability limits its long-term relevance. Overemphasizing the crafting of the perfect combination of words can even be counterproductive, as it may detract from the exploration of the problem itself and diminish one’s sense of control over the creative process. Instead, mastering problem formulation could be the key to navigating the uncertain future alongside sophisticated AI systems. It might prove to be as pivotal as learning programming languages was during the early days of computing.

In other words, to someone in the business and financial world, harnessing the full power of AI for human benefit looks like a problem (as well as a wonderful curriculum development opportunity) – but for B-schools everywhere as opposed to computer sciences or data sciences departments. Somehow, to an outsider, it seems that the future will look more like “both-and” than “either-or.” Expect the courses in prompting to stick around, as well as the courses in problem formulation. The need for and offerings of university education will continue to balloon…

Did you catch Mr. Acar’s earlier phrase: problem formulation necessitates…[an] ability to distill real-world issues? The next LOTRW post looks at how this is playing out with respect to climate change.

Animal whisperers are said to:

…practice the art of telepathic animal communication. They also use other intuitive gifts to find out what is really going on with an animal at very deep energetic levels, physically, mentally, emotionally and spiritually.

This language and the concept behind it are a bit over the top, but you get the idea. The notion has been around for a long time. Think Francis of Assisi, and that’s a mere blink of an eye compared with the domestication of animals, which goes back some 20,000 years.

Some Americans of a certain age got their introduction to the term much more recently and directly – from the 1998 film the Horse Whisperer – portraying a guy with a remarkable understanding of horses. (Robert Redford starred; he was then at the peak of his powers and was really whispering throughout the film to a different species and a specific demographic.)

All this came rushing back to mind a week ago when I sat in on a talk on artificial intelligence. The speaker provided an engaging overview; the audience learned a lot. But for me the takeaway was one small piece:

Prompting is a thing:

In artificial intelligence (AI), prompting is the process of communicating with an AI system by providing specific instructions or queries to achieve a desired outcome. Prompts are the interface between human intent and machine output. For example, in Microsoft 365 Copilot, AI prompting involves communicating with the AI model to generate code, content, or responses based on user input.

Wow.

More flashback. Back in those same 1990’s I was talking with a USGS friend about search engines. I mentioned what I was using then, Ask Jeeves (now Ask.com). After a brief, polite silence, my friend gently said: “well my (ten-year-old!) son prefers this search engine called Google.”

Gave it a try – and of course never looked back.

If you started using Google about that time, or earlier, you remember that it (and other search engines) were great for addressing a certain range of questions and problems, but for others, search still required a trip to the library, burrowing into printed word, or consultation with experts. But about every six months, it was necessary to recalibrate, because the material Google had to work with was rapidly growing. Two or three decades later, for many of us, the tables are turned. If we can’t find what we need online, looking into it further isn’t worth the bother; the opportunity cost is simply too great.

Your decades of Google use also taught you something else. You became much more efficient in your queries. You learned what was essential to a good query, and what niceties and frills (such as correct spelling) could be ignored. It’s not just Google and the world’s data bases that have changed. You changed. You learned to whisper to Google.

Back to the present day and artificial intelligence. AI is a higher life form. If Google is a gerbil, AI is that horse.

If you have played around with generative AI you already know that asking the right questions in the right way is everything. And it’s tough. It’s the Google-search problem on steroids. The AI world recognizes this. They know that the productivity boosts that AI can offer improve dramatically as your prompts become more adroit. They also know that if each of us figures out what works totally on our own the process will be too slow.

Online you can find tips. The earlier link provides one such set:

• Be clear: Use plain but clear language.
• Provide context: Provide specifics about who your audience is and what sort of tone you’d like to set.
• Avoid vagueness: Being too vague or broad can lead to generic or irrelevant responses.
• Avoid over-specification: Excessively detailed prompts can confuse the model or limit its creative scope.
• Avoid literal interpretation: AI often interprets prompts literally, so figurative language can lead to unexpected results. 

Hmm. Sounds like rules for talking to another person. And such a single simple list doesn’t cut it. These very general suggestions translate into specifics that are different depending on the use of AI (much as they do depending on whether you’re talking to your life partner, or a work colleague, or a stranger). Accordingly, prompt engineering, or prompting has become a key productivity factor in the AI world:

Prompt engineering is the process of writing natural language text that guides generative AI (artificial intelligence) models to produce desired outputs. The text is called a prompt, and it describes the task the AI should perform. Prompt engineers use creativity and trial and error to create input texts that help the AI interact with users more meaningfully. The goal of prompt engineering is to ensure that AI models produce accurate and relevant outputs.

Coursera, Udemy, and others provide apps and modules on prompt engineering. University computer-science curricula now include coursework in prompt engineering. It’s essentially different depending on the stage of software development. Need AI help in coding? That’s one context for prompt engineering. Want AI help in applications to problems in health care, research, marketing, strategic planning? Effective prompting required there is quite different. And multiple levels of IT between these two ends of the spectrum each require their own prompting skills.

With AI threading through just about every aspect of knowledge work, it’s easy to be dismayed. That’s because AI is not only complicated; it’s rapidly growing in capability. As a result, the nature of all that interlaced professional activity will also be changing. The demands on and desirable attributes of prompting will be changing at the same pace. It won’t be long before AI capabilities are nearly human – or something more. How are humans to keep up? We evolved over hundreds of thousands of years of slower change; our ancestors essentially died in the same world into which they’d be born. The opposite will be true of the present and future generations.

What’s a human to do? Well – fortunately – it turns out that perhaps, instead of whispering, “you should hold your horses.” More in the next post.

A final postscript on Daniel Kahneman. Last week I had lunch with my economist friend of more than half a century (he of the  LOTRW previous post’s footnote in reference to Milton Friedman). We were discussing Kahneman and – in the same breath – our parents and grandparents, and their influence on us and the world. The juxtaposition of this renowned Nobel prizewinner and our obscure forebears prompted my friend to share, from memory, and with no little fervor, an excerpt from Ecclesiasticus (not to be confused with Ecclesiastes), also known as the Book of Sirach. The verses clearly meant a great deal to him; you’ll see why in a moment.

Ecclesiasticus? This was a new one to me. Looked into it a bit following our conversation. A Wikipedia article tells us this:

The Book of Sirach, also known as The Wisdom of Jesus the Son of Sirach or Ecclesiasticus is a Jewish work, originally written in Biblical Hebrew. The longest extant wisdom book from antiquity, it consists of ethical teachings, written approximately between 196 and 175 BCE by Yeshua ben Eleazar ben Sira (Ben Sira), a Hellenistic Jewish scribe of the Second Temple period.

Ben Sira’s grandson translated the text into Koine Greek and added a prologue sometime around 117 BCE. Although the Book of Sirach is not included in the Hebrew Bible, this prologue is generally considered to be the earliest witness to a tripartite canon of the books of the Old Testament, and thus the date of the text is the subject of intense scrutiny by biblical scholars. The ability to precisely date the composition of Sirach within a few years provides great insight into the historical development and evolution of the Jewish canon.

The passage my friend shared was from Ecclesiasticus 44: 1-15. I would like to think that Ben Sira, were he alive today, would have balanced his paean to women as well as men. Because this omission is so striking, I’ve inserted modifications to the text (with emphasis added). With those edits, the first eight verses go this way:

Praise of Famous Men and Women

Let us now praise famous men and women, and our fathers and mothers that begot us. The Lord hath wrought great glory by them through his great power from the beginning. Such as did bear rule in their kingdoms, men and women renowned for their power, giving counsel by their understanding, and declaring prophecies: leaders of the people by their counsels, and by their knowledge of learning meet for the people, wise and eloquent in their instructions: such as found out musical tunes, and recited verses in writing: rich men and women furnished with ability, living peaceably in their habitations: all these were honored in their generations, and were the glory of their times. There be of them, that have left a name behind them, that their praises might be reported. 

That’s for the Daniel Kahnemans of the world. Ben Sira (and my friend) then goes on to remind us to accord equal honor to the obscure, the unrecognized:

And some there be, which have no memorial; who are perished, as though they had never been; and are become as though they had never been born; and their children after them. But these were merciful men and women, whose righteousness hath not been forgotten. With their seed shall continually remain a good inheritance, and their children are within the covenant. Their seed standeth fast, and their children for their sakes. Their seed shall remain for ever, and their glory shall not be blotted out. Their bodies are buried in peace; but their name liveth for evermore. The people will tell of their wisdom, and the congregation will show forth their praise.

Of course, many others have taken up this notion. For example, Gray’s Elegy Written in a Country Churchyard comes to mind. The poem merits a full read (and re-readings). Here’s one famous excerpt that provides metaphors for people, who, though special, die unrecognized by history:

Full many a gem of purest ray serene
    The dark unfathom’d caves of ocean bear:
Full many a flower is born to blush unseen,
    And waste its sweetness on the desert air.

For the meteorologists who are the bulk of the LOTRW readership, the notion of big influence of the small and un-named is well known. It’s embodied by Lorenz’s butterfly, whose merest fluttering can even cause/curb hurricanes down the road. Even the history behind the origins of this image reinforce Ben Sira’s message. Read the fine print of the Lorenz Wikipedia article, and you find that the metaphor that adds luster to his famous name probably should be attributed to Phil Merilees, another remarkable meteorologist in his own right, but one who lacks Lorenz’s renown (and any corresponding Wikipedia page).

All this by way of encouragement to each of us, to see and respect and acknowledge the worth and value of our personal ancestral lines. And we should do this often! The men and women, however great, however humble, who brought us into the world also achieved much more along the way, whether recorded or not. Some played their role in the great sweep of history. Others did their part to speak out against or slow or some of the excesses of that history. (The reality? Most probably did a bit of both.) By extension, we should also reflect on, and own, our own contributions to events and trends, and to all those who follow us. Ben Sira reminds us that all of that is praiseworthy. We should all be holding our heads high, walking tall – each and every day, good or bad.

Thanks, good friend, for forcefully bringing this point home last week – this just the latest way in which you’ve enriched my life and made me a better person over a sixty-year stretch. And thank you, Mom and Dad – and happy birthday, Dad! Today is your day. A thanks as well to my grandmother, who gave birth to you 106 years ago on this day – all 13 pounds and 27 inches of you – “the biggest baby,” the doctor told my grandmother in 1918, “I ever delivered, whose mother lived.”

“I criticize; therefore I am.” – (what Descartes might have said if had lived today?)

Daniel Kahneman’s brilliant critiques of human reasoning – exposing so many of our species’ cognitive limitations – might tempt the fainthearted to clam up. In today’s sometimes polarized, irritable, quick-to-carp world, who wants to subject themselves to any more criticism than necessary?

But Kahneman’s sustained body of published work tells us to do just the opposite. He clearly didn’t allow any self-doubt about his own thought processes to hold him back. He took pains to expose his thinking to rigorous review and feedback. Unsurprisingly, he came in for his share of criticism.

Criticism? Here is one overview giving the general flavor^[1]. An excerpt:

Some have argued that his research suffers from issues such as small sample sizes, lack of reproducibility, and an overreliance on specific experimental paradigms. These criticisms have prompted scholars to reassess the value of Kahneman’s work and its implications for our understanding of human behavior.

One of the key criticisms of Kahneman’s work is that it may overstate the role of cognitive biases in decision-making. While it is undeniable that people often make irrational decisions due to biases such as anchoring or availability heuristic, critics argue that Kahneman’s emphasis on these biases may downplay the role of rational decision-making and deliberation. Furthermore, the reproducibility crisis in psychology has raised questions about the robustness of some of the key findings in “Thinking, Fast and Slow.”

Another area of criticism is Kahneman’s reliance on laboratory experiments to draw general conclusions about human behavior. Some argue that the artificial nature of these experiments may limit their external validity, and that real-world decision-making is far more complex and context-dependent than what can be captured in a controlled setting…

Which brings us to – wait for it – March Madness.

We’re currently heading into the weekend featuring the 2024 Final Four. Coincidentally in researching  Kahneman critiques, I came across this article by Joshua Miller and Adam Sanjurjo in THE CONVERSATION US & THE CONVERSATION dating back to 2017: Momentum Isn’t Magic—Vindicating the Hot Hand with the Mathematics of Streaks. This first excerpt provides some context:

Nearly every basketball player, coach or fan believes that some shooters have an uncanny tendency to experience the hot hand—also referred to as being “on fire,” “in the zone,” “in rhythm” or “unconscious.” The idea is that on occasion these players enter into a special state in which their ability to make shots is noticeably better than usual. When people see a streak, like Craig Hodges hitting 19 3-pointers in a row, or other exceptional performances, they typically attribute it to the hot hand[2].

Then the authors bring in a reference to Kahneman:

 However, if you go to the NCAA’s website, you’ll read that this intuition is incorrect—the hot hand does not exist. Belief in the hot hand is just a delusion that occurs because we as humans have a predisposition to see patterns in randomness; we see streakiness even though shooting data are essentially random. Indeed, this view has been held for the past 30 years among scientists who study judgment and decision-making. Even Nobel Prize winner Daniel Kahneman affirmed this consensus: “The hot hand is a massive and widespread cognitive illusion.”

… and go on to rope in Tversky and other Kahneman colleagues:

In the landmark 1985 paper “The hot hand in basketball: On the misperception of random sequences,” psychologists Thomas Gilovich, Robert Vallone and Amos Tversky (GVT, for short) found that when studying basketball shooting data, the sequences of makes and misses are indistinguishable from the sequences of heads and tails one would expect to see from flipping a coin repeatedly.

Then rest of the article provides a nice reappraisal, and disputes these pure-chance claims.

That led me to a 2006 article by Bar-Elia, Avugosa, and Markus Raabb: Twenty years of ‘‘hot hand’’ research: Review and critique. Was reading along, minding my own business, when I came across this passage:

The phenomenon of the hot hand is known to everyone who plays or watches the game of basketball. After the player has a run of successful baskets, people tend to believe that he will be more likely to succeed with the next shot as well. This has a plausible causal explanation: When a player feels ‘‘hot’’, his confidence in his ability increases. He becomes relaxed and focused on performing the shots accurately. So, he ‘‘gets in a groove’’, such that success in further attempts becomes more likely (Hales, 1999). As Robert Hooke (1989) expressed it so well [emphasis added]: ‘‘In almost every competitive activity in which I’ve ever engaged (baseball, basketball, golf, tennis, even duplicate bridge), a little success generates in me a feeling of confidence which, as long as it lasts, makes me do better than usual. Even more obviously, a few failures can destroy this confidence, after which for a while I can’t do anything right’’ (p. 35). The reference is to his paper in the journal Chance (1989, Vol 2, number 4) published by the American Statistical Association. This issue also juxtaposed other papers on the topic, including this one:   The “Hot Hand”: Statistical Reality or Cognitive Illusion? by Amos Tversky and Thomas Gilovich.

Robert Hooke? My father, the statistician and sports fan! I remember his discussions of this subject in dinner conversation over the years but hadn’t paid it much mind. What a wonderful thing to stumble over this small connection of his work with Kahneman’s oeuvre. (The reference is to his paper in the journal Chance (1989, Vol 2, number 4) published by the American Statistical Association. This issue also juxtaposed other papers on the topic, including this one:   The “Hot Hand”: Statistical Reality or Cognitive Illusion? by Amos Tversky and Thomas Gilovich.)

A postscript in the coming LOTRW.  

[1] Googling “critiques of Kahneman” points to copious additional material. Anyone breaking new ground pays such a price. Case in point, another thinker: Milton Friedman. Years ago, a close economist friend shared with me that while in graduate school he at first believed his distinguished faculty members who were unanimously telling him Friedman was “an idiot.” Then he asked himself, “If Friedman is so far off-base, why are so many famous economists obsessively trying to prove him wrong? There must be something there!”

[2] Want to see a “hot hand” – 2024 edition?  Consider Oakland’s journeyman ballplayer Jack Gohlke, who contributed ten three’s in Oakland’s upset of Kentucky (one short of records) in the tournament’s opening round. As you’ll find if you watch the video, only one of the ten shots is uncontested; the rest are off-balance, on-the-fly, in-your-face improvisations, some from well-behind the three-point line. In short, several of them are the type that if you take it and miss, your “hot hand” won’t save you; your “hot-under-the-collar” coach will bench you.

“Je pense, donc je suis…irrationnel” – (with apologies to both Rene Descartes and Daniel Kahneman)

According to Psychology Today,

…dreams are about events from our waking lives, including real events and modified ones. About 70% of dream content is based on events from the previous day, which are called day residue dreams. Dreams can also include events, places, and people from the past.

Good to know.

My previous LOTRW post had noted with regret the passing of Daniel Kahneman. And PBS recently aired an amazing four-hour, two-part series on Dante Alighieri’s life and the Divine Comedy that made a huge impression.

Maybe that juxtaposition accounts for my dream last night, in the early hours of April 1^st, about Daniel Kahneman’s ascent to heaven. Of course he entered at Dante’s fourth level of paradise – the Sun, which Dante tells us is the realm of the wise[1]. Where else? In my dream, Kahneman ran into Rene Descartes early on, and they naturally had a rich get-acquainted conversation. But Descartes was heard to sigh, as he walked away, head bowed, “I think, therefore I am… irrational.”

This is the nagging negative part of the extensive legacy Kahneman leaves behind. Prior to the arrival of Kahneman, Tversky, Slovik and other scholars of thought, it was possible for intellectuals of every stripe to share their wisdom and judgments with full throat and relatively free of care. But now, confronted with the extensive, unflattering litany of the logical fallacies and pitfalls that characterize all such work, it’s impossible for thinkers to proceed with even the merest shred of self-confidence. They are forced to acknowledge that most carefully crafted of their ruminations, if extensively scrutinized, can be found flawed. In this light their best thoughts do little more than add to the burgeoning mountain of defective logic and misinterpretation of data already out there.

To be clear: Everyone thinks. Indeed, everyone thinks for a living. From janitors to judges, historians to homemakers, pizza bakers to physicians, teachers to truckers, thought and action are closely linked in the job. It’s the action that earns the pay, but the thought behind the action that makes the difference.

But a few make their living by thinking in a purer form. Their thinking is less connected to doing. Their thoughts are their stock in trade – and they are correspondingly vulnerable at a deeper level. We’re talking about philosophers, writers, authors, columnists, consultants, futurists and myriad influencers.

Including, for the sake of completeness – bloggers. (Finger pointing to self. Much as “a pun is the lowest form of wit,” a blogger might be considered “the lowest form of intellect.” After all, there’s little or no peer review, there’s often an element of haste, the thoughts are popcorn-sized, offered in isolation rather than context, the prizes go to those who attract eyeballs, by whatever means. When I started LOTRW in 2010, a colleague whose opinions I respected greatly, who put most of her opinions in peer-reviewed journals, said to me, archly: “Go ahead, blog away”…

So how can a blogger – or any thinker of a higher caste – live with himself/herself/themselves? What follows are a few ideas. You can be the judge – or offer your own improvements or substitutions.

1. Clam up? That’s not the solution. Let’s start with what we shouldn’t do. We shouldn’t throw in the towel. The world needs more shared thought, not less. We all have the responsibility to put ourselves out there. Theodore Roosevelt captured this spirit with his short piece The Man in the Arena (hopefully if he were writing his piece today it would have been entitled The Person in the Arena):

“It is not the critic who counts; not the man who points out how the strong man stumbles, or where the doer of deeds could have done them better. The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat.”

Roosevelt censured those who substituted mere criticism for deeds and action, but this logic applies equally well to those who have constructive or critical thoughts but fear to expose them to the light of day.

2. self-Critical? Some point out that scientists (one flavor of the thinking class) should be the strongest critics of their own work. Unsurprisingly, it turns out human beings aren’t particularly gifted in this way (as reflected in the so-called replication crisis in science). What’s more, self-criticism may be hurting our work. The rationality sought comes too heavily laden with emotion.

2. Criticism? As for outside criticism, there may be too much of it. Since most of us are only too well aware of our shortcomings and failings, we need words of encouragement, words offering positive suggestions and ideas, if not outright affirmation. And constructive criticism is resource-intensive, and growing more so every day as eight billion people add to the world’s store of experience and insight. At the same time, criticism of the noisy, noxious sort is all-too-readily available and becoming even more prevalent in the age of social media (including blogs – sigh). Not just when it comes to blogs, but also in ordinary conversation, we might instead develop more of the spirit of improv – using our brainpower to see how far the ideas of others might carry us, and where they might take us, versus finding fault.

3. Conjecture? Maybe we could use more of it. Consider this thought from Charles Darwin (another denizen of the fourth level of paradise?) that used to be on LOTRW’s masthead: “False facts are highly injurious to the progress of science, for they often endure long; but false views, if supported by some evidence, do little harm, for everyone takes a salutary pleasure in proving their falseness.” Instead of vainly striving to upgrade our mere conjectures into solid truth we might better foster human progress by putting those partially-formed ideas out for review.

4. Collaboration (or competition)? Sharing partially-formed thoughts, inviting comments, corrections, and outright alternatives? According to Darwin, conjecture can invite collaboration. These days, such ideas have a name: open science. Instead of sharing results only at publication, scientists are exploring the need for and the means for sharing/communicating progress at every step in the research process – experimental design, data collection, etc., etc.  One small cloud in this otherwise bright picture? The rewards in science still go to those publishing novel results in high-impact publications.

Back to my April 1 dream:

As Descartes shuffles along, he shortly encounters his old friend Francis Bacon (another post-Dante arrival in the Fourth level of paradise).

Bacon says, “Cheer up, mon ami! Fais de l’amour ton objectif (make love your aim).”

(Recall that Bacon famously argued that charity (that is, love) is the most defensible motivation for pursuing natural philosophy).

If we could only find a way to monetize love in a free-market world…

Whew! What a day. Perhaps April 2 will be better!

[1]There Dante encountered Thomas Aquinas, King Solomon, Bede and others of that ilk. Descartes would eventually arrive, of course, but only two centuries later – so Dante gives him no mention. (BTW, Dante states – fittingly, given the timing of this blogpost – that he arrived in the lowest levels of paradise on Wednesday, March 30, 1300, the day after Easter of that year. A mere coincidence? Kahneman would probably say yes.

With the passing of Daniel Kahneman on March 27, the world lost a distinguished intellect. A Nobel prizewinner, his impact on economics and the social sciences more broadly has proved enduring and profound. Case in point: his ideas underlie and prompt much of the advice behavioral scientists give meteorologists seeking to improve societal response to weather warnings. For decades I’ve been “in the room,” in a variety of settings, as sociologists, psychologists, and risk communicators have patiently explained to meteorologists the rudiments of anchoring, cognitive bias, framing, heuristics, optimism bias and much, much more. Meteorologists have been a tough sell, but over time our community has come to acknowledge that communication of weather risk must take into account human behavior every bit as much as the actual or future weather itself. (That is, if we truly wish to achieve desired individual and societal uptake and outcomes – reduced fatalities, injury, property loss, business disruption, and all the rest).

Kahneman was an extraordinary scholar and a prolific writer,  but in that constellation of work one star stands out: Thinking, Fast and Slow. An excerpt from the Wikipedia article:

Thinking, Fast and Slow is a 2011 popular science book by psychologist Daniel Kahneman. The book’s main thesis is a differentiation between two modes of thought: “System 1” is fast, instinctive and emotional; “System 2” is slower, more deliberative, and more logical.

The book delineates rational and non-rational motivations or triggers associated with each type of thinking process, and how they complement each other, starting with Kahneman’s own research on loss aversion. From framing choices to people’s tendency to replace a difficult question with one which is easy to answer, the book summarizes several decades of research to suggest that people have too much confidence in human judgment.

The book, a 400+ page tome, sold over one million copies. It consists of several sections. To quote Wikipedia further:

In the book’s first section, Kahneman describes two different ways the brain forms thoughts.

…The second section offers explanations for why humans struggle to think statistically. It begins by documenting a variety of situations in which we either arrive at binary decisions or fail to associate precisely reasonable probabilities with outcomes. Kahneman explains this phenomenon using the theory of heuristics. Kahneman and Tversky originally discussed this topic in their 1974 article titled Judgment Under Uncertainty: Heuristics and Biases.

(examples of each follow)

Wikipedia goes on:

…Kahneman describes a number of experiments which purport to examine the differences between these two thought systems and how they arrive at different results even given the same inputs. Terms and concepts include coherence, attention, laziness, association, jumping to conclusions, WYSIATI (What you see is all there is), and how one forms judgments. The System 1 vs. System 2 debate includes the reasoning or lack thereof for human decision making, with big implications for many areas including law and market research.

The second section deals with heuristics and biases. Wikipedia notes:

Kahneman uses heuristics to assert that System 1 thinking involves associating new information with existing patterns, or thoughts, rather than creating new patterns for each new experience. [emphasis added; see below] For example, a child who has only seen shapes with straight edges might perceive an octagon when first viewing a circle. As a legal metaphor, a judge limited to heuristic thinking would only be able to think of similar historical cases when presented with a new dispute, rather than considering the unique aspects of that case. In addition to offering an explanation for the statistical problem, the theory also offers an explanation for human biases.

The shortcomings of human thought? These include: Anchoring. Availability. Conjunction fallacy. Optimism and loss aversion. Framing. Sunk cost. In the book, Kahneman discusses each at length. The result is an impressive indictment of human ability to think rationally.

Being merely human myself, in the face of this intimidating list of rational shortcomings I want to fall back on System 1 thinking and (as noted above) associate this new information with existing patterns, or thoughts, rather than create new patterns for each new experience.

Aha! I do indeed have such a pre-existing thought/memory to lean on.

It’s the 1960’s. I’m an undergraduate physics major at Swarthmore College, and I’m subscribing to the American Journal of Physics. In my August 1963 issue I encounter an article entitled Batting the Ball, by Paul Kirkpatrick. The abstract reads:

The velocity vector of a ball struck by a bat is a stated function of the ball and bat velocities, bat orientation, and certain constants. In the light of the equations of the collision, the operation and the consequences of swinging the bat are analyzed, and the role of the constants is discussed.

Utterly fascinating! Physics rules! I eagerly shared these insights with my mathematician/statistician father, who had played quite a bit of baseball in his younger days, and was a lifelong, ardent fan. This would be our chance to bond!

“Dad, look at this article, which lists 90+ parameters and actions a batter has to adjust to hit a baseball!”

My father’s response was swift – and dismissive.

“If I were a pitcher, I’d be sure to send copies of this article to the opposing team.”

Hmm. Not quite the response I had expected. Or hoped for. But so spot on. In the 60 years since, I’ve thought of this incident again and again.

Which brings us back to Daniel Kahneman. His body of work is extraordinarily useful for critiquing the decision making under uncertainty facing all of us when we are confronted with daily weather information, especially when that information matters most. But what about the implications of this work for anyone contemplating writing a research paper on any subject, or a thought piece of any type – whether an article or book; or a blogpost or podcast? Facing so many pitfalls of thinking to avoid, how do we actually move forward? What misplaced pride possibly allows us to imagine we’ll be making a positive contribution, instead of adding to the world’s already stupefying stock of faulty thinking and misinformation?

Whew! What’s a thinker to do? I’ll have more in the next LOTRW post, but in the meantime,

Your (fast and slow) thoughts, please.

 “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”  – Mark Twain? (No, not really[1].)

The AMS 2024 Annual Meeting reflected two major trends. The first is the rapid enlisting of new technology, especially AI, to address fundamental problems of the geosciences and related social sciences, in order to advance these disciplines. The second is the growing urgency and speed with which these advances are being harnessed for societal benefit. This latter is itself motivated and enabled by technology, again highlighting AI. These trends are combining to transform the meetings and indeed the AMS. They were everywhere evident in Baltimore this past January; they will likely be even more pronounced in coming years.

Step back a bit. A worldwide awareness and mindset is driving these trends. (To greatly oversimplify, in the interest of brevity) that worldview notes that population growth is slowing, but not everywhere. It’s growing most rapidly where the local populations remain impoverished. The worldview accepts as given: a universal, insatiable human appetite for more resulting in competition for acquisition, control and consumption of natural and financial resources. It sees economic growth, largely-but-not-entirely-free competitive markets and continuing innovation in these pursuits as the only way forward.

The world knows all this for sure.

But just maybe it ain’t so.

Worldwide, nations also see, amidst this mainstream perspective, the remnants of myriad indigenous cultures that once offered and continue to hold to different viewpoints and values. (Of particular interest here are the bits of these cultures and value systems dealing with the environment, with natural resource ownership and use, and with wealth and consumption more  generally.) Though diverse, and quasi-independent in origin, a number of these indigenous cultures have developed essentially similar ideas – for example, a degree of agreement that human beings “belong to nature” as opposed to “nature belongs to human beings” – that the human role is stewardship rather than ownership.

It’s easy to oversimplify and romanticize all this; to avoid that error here let’s limit the discussion to this restrained excerpt from a United Nations document:

Indigenous Peoples have, over the course of generations, developed rich sets of knowledge about the natural world, health, technologies and techniques, rites and rituals and other cultural expressions. Culture is… inextricably linked to Indigenous Peoples’ identity, their traditional knowledge, their experiences with the natural environment [emphasis added] and hence their territorial and cultural rights. Cultural practices, traditions and values of Indigenous Peoples – as long as they are in line with human rights principles – can play a critical and positive role in advancing and promoting gender equality and human rights.

It’s worth noting that the mainstream worldview sees indigenous perspectives as endangered – on the verge of extinction. For example, from the same UN document:

The importance of land and territories to Indigenous cultural identity cannot be stressed enough. However, Indigenous Peoples have continued to experience loss of access to lands, territories and natural resources. The result has been that Indigenous cultures today are threatened with extinction in many parts of the world. Due to the fact that they have been excluded from the decision-making and policy frameworks of nation-states in which they live and have been subjected to processes of domination and discrimination, their cultures have been viewed as being inferior, primitive, irrelevant, something to be eradicated or transformed.

This same UN document ventures to forecast that 90% of the 6000-7000 oral languages in the world today may be lost within the next one hundred years. To read some of the literature on these topics is to come away with the sense that the world’s chances of preserving any of this diversity are similar to those involved in preserving biodiversity in the face of climate change or reining in climate change itself. In other words, essentially nil.

The 2024 AMS Annual Meeting offered a window into this world. The experience led me to draw a different, more hopeful conclusion.

Start with some background. UCAR’s Rising Voices Center for Indigenous and Earth Sciences collaborated with the AMS Committee on Spirituality, Multi-faith Outreach and Science (COSMOS) to put on Convergence Science: Indigenous Weather, Water and Climate Knowledge Systems, Practices and Communities. The program comprised three Monday panel discussions, entitled respectively Rising Voices, Changing Coasts: a new/old approach to convergence science; Tribal Story Maps: Integrating and Sharing Tribal Knowledge and Science through the Visual Language of Geography; and Convergence Science in the Context of Integrating Weather and Climate Science with Studies of Marine and Coastal Resources and Geophysical Processes. The conversations (captured by video recordings on these links, ICYMI) made for a memorable day. The meeting room was one of the smallest in the venue, and yet there was ample seating, in contrast to the standing-room-only AI sessions going on at the opposite end of the Convention Center. The pace was leisurely, but the conversation animated, and yet at the same time both informal and respectful. (To gain a better feel for the day and experience, read Isabella Herrera’s truly excellent post on the AMS Front Page blog, which I discovered belatedly in the act of posting this.)

But here’s the thing. The vibe didn’t have the feel of something fragile, endangered, needing protection – about to go extinct. It would more accurately be described as nascent, full of life and rich in potential, offering messages of hope and alternative pathways forward with respect to climate change, coastal resources and more that the larger world should be hungry to hear.

Why? Because the world’s prevailing approach to climate change and related challenges – maintain enthusiasm for acquisition, control and consumption of resources, but sustainably support these through improved technology – seems to be falling short. In particular, the pace of transitioning to renewable energy seems to be lagging what’s needed to maintain climate conditions favorable to life on Earth – for technological, economic, and political reasons. The world needs some new ideas – with some new intellectual DNA. Rising Voices has these on offer.

An analogy: this is not unlike the struggle individuals and populations the world over face with obesity, highlighted in a recent WHO report (worth the read), which has received a great deal of media attention the past few days. Efforts to regulate weight through pure self-control tend to come a cropper for both biological and psychological reasons.

But new medications, including some developed originally as therapies for type-2 diabetes, have been found to help. In the same way, it might be hoped that a kind of reverse acculturation – in which the world’s dominant consumption-based cultures take up some of the desired features of the minority/indigenous cultures, rather than the other way around – would be of help.

LOTRW readers might be tempted to see this possibility as unrealistic – dismiss it out of hand. Here are three reasons to reconsider.

First, meteorologists and climatologists are engaged every day in this same process of reverse acculturation. Our urbanized, climate-controlled world tends to see weather, climate and environmental conditions more broadly as irrelevant or at most minor compared to their more pressing daily concerns. We’re constantly in the business of encouraging that world to see climate change, hazard vulnerability, pollution, and reduction and habitability as requiring more priority and urgent action. In this respect, we should see ourselves as fellow travelers with indigenous peoples.

Second, climate scientists and professionals in related disciplines have long recognized there isn’t a single silver bullet for coping with these challenges. Instead, they’ve developed wedge-based approaches, grouping individual interventions into climate stabilization wedges each incrementally reducing carbon emissions. Indigenous perspectives on natural resources and their consumption might form the basis for an additional wedge – as well as strengthening some of the others.

Third, some might argue that respect for nature and stewardship rather than ownership reflect hunter-gatherer, nomadic ways of life, where resource acquisition is difficult and by definition traveling light is not just an advantage but a necessity. But it might be that closer examination of developed-country life reveals that it often brings not satisfaction, but anxiety. For most, this means the anxious striving to meet not just needs but wants – to have not just enough, but to have abundance. For a privileged few who “have all they want,” it’s the fear of somehow losing it. When it comes to happiness, even the privileged might not be better off than the nomads. (This idea is especially poignant in light of the latest World Happiness Report which suggests that world happiness is generally down; that America’s ranking among the nations has fallen from 15^th to 23^rd, a new low; and that American youth, who used to be happier than the middle-aged, and about as happy as the elderly, are now the least happy.)

In conclusion, it was a privilege to be in the Rising Voices room at AMS – and a shame that more people weren’t. One of the more encouraging outcomes of the day was an idea tabled to raise the visibility of indigenous perspective and approach at a future AMS Annual Meeting – possibly by organizing a plenary-level session inviting tribal leaders as well as indigenous scientists. In that room, on that day, it was even possible to imagine future AMS meetings where these topics might be receiving their 10^th or 20^th annual airing, and where talks and attendance might have grown by an order of magnitude or even two.

[1] Sadly, as the link demonstrates, yet another “quote” apparently misattributed to Mark Twain. The quote’s actual origins remain unknown. Its roots might more accurately be described as the result of crowdsourcing and wordsmithing over time by several persons.

The previous LOTRW post focused on the growth and importance of AI to the meteorology and to the AMS 2024 Annual Meeting. To recap:

The rapid growth of AI’s capabilities and reach are transforming research and science-based services in meteorology, climatology, oceanography and the other geosciences. And that reworking isn’t trivial. AI is not merely augmenting the use of physics to generate weather products and services; in some cases it is supplanting them[1].

But it’s not just the supply side of meteorological sciences and services that is changing, and in this way. The demand side is being utterly transformed as well. Historically, the application of meteorological information to guide weather-sensitive economic sectors comprising agriculture, energy, environmental protection, public health and safety, transportation, water resource management and more has been limited by two factors. First, the rudimentary forecast skill of the past often failed to meet sector-by-sector requirements for useful decision-making. But second, sector-by-sector decision-making wasn’t particularly nimble. Given the weather sensitivity, and limited ability to do much about it, weather-sensitive sectors had over the years developed muddle-through approaches – especially the build-up and maintenance of the excess capacity and operating margins needed to accommodate weather variability. More recently, however, population growth and economic pressures have made such excess capacity a luxury no one could afford.

Enter technological advance. Technology generally, IT, and artificial intelligence in particular are transforming the economic world. Along the way, they’re transforming weather-sensitive sectors, making them more agile: better able to incorporate weather variability into their optimization strategies – to capitalize on opportunities offered by favorable weather, and avoid the risks posed by hazards, etc. The emergence of renewable energy sources harnessing solar, wind, and hydrologic power exemplify this trend.

[What follows is conjecture, offered in the spirit of Charles Darwin’s quote to the effect that false views cause little harm to science because everyone takes salutary pleasure in proving them wrong.]

It seems to me that these trends in supply push and demand pull have been and are continuing to transform the AMS Annual Meetings (and the AMS as a whole, and the field more generally) in a way captured by today’s Venn diagram. AMS meetings focus on the advance of “meteorological” sciences (writ large – really the whole of the geosciences) and associated technologies, and their application for societal benefit[2]. That latter bit has come to mean that the AMS and its meetings also encompass and reflect the advance of social science that both studies and enhances societal uptake of meteorological science. But these disciplines are merely the intersection of the two main elements of AMS Meetings, and the Venn diagram: S&T and application more broadly.

The entire Enterprise is growing rapidly in size and in its importance to the larger world. But in the last decades of the 20^th-century, a disproportionate amount of the growth of the field and the AMS Annual Meetings in particular came from IT. That trend is continuing now – with AI providing a huge new burst. The geosciences and related social sciences are growing as well; after all, they’re also augmented by AI and technology (as well as other trends). But they’re not growing proportionately; the intersection is a smaller part of today’s meeting.

In short, meteorology and social sciences are struggling to keep pace. This was reflected, for example, in the number of AMS talks focused on the rise of AI-enabled forecast techniques that sidestep rather than build on basic meteorological physics, and remarking that evaluation of such approaches was both lacking and sorely needed. We’re likely entering an extended period of catchup.

This is not a bad thing! It’s to be celebrated! The world urgently needs a new vision and new capacity to cope with growing demands for food, water, and energy, and the accompanying threats to the environment, habitats, and ecosystems.

But scientists of all stripes like to think that science leads the way – that scientific advance fosters technology, which in turn is then applied to meet societal needs. This happens some of the time. But science can also be a laggard.

We can take comfort from the fact that this phenomenon is not new. In the second half of the 19^th century most developed countries established weather bureaus, not because of any great new theoretical breakthrough, but because the Victorian internet – the telegraph – enabled the world to track weather in real time. The scientific insights would come later.

This reality doesn’t hold true only for the geosciences. Early in my career (and before most of you were born), the military conducted Project Hindsight, looking at the role of science and technology in contributing to the military systems and weapons developed and use to effect in World War II. Science was found to be pivotal in only a small fraction of the cases examined; 90% were technological. Scientists of the time took umbrage at this. Yet the overall conclusion was positive – that S&T were among the best investments of the war effort.

The forecast? Expect AMS meetings (and AMS journals, and AMS membership) to grow dramatically over the next 5-10 years. Expect the overall vibe to be positive and energetic. Expect rapid world uptake of our science and technology. But don’t be surprised if meteorology and the social sciences as you learned them are slowly submerged as the topic of conversation, in much the same way and for many of the same reasons that machine programming language isn’t the focus of the meetings.

The world is begging: please keep the progress coming!

[1]Some (not all) current AI approaches are exploring the feasibility of ignoring the governing mathematics and physics entirely. The attitude seems to be: if the difficulties of the N-S equations are daunting, we’ll just go around them. Years ago Laura Furgione, then NWS Deputy Assistant Administrator, raised academic hackles at a UCAR-AMS-AGU heads and chairs meeting when she suggested that operational forecasters might no longer need to know calculus; that algebra (closer to the language of digital computers) would suffice. Calculus would be necessary only for researchers. The idea, which seemed so revolutionary at the time, now might be considered mundane.

[2] Essentially the AMS Mission.