Politics & Stuff

[Vesper 28,807]

https://www.vanityfair.com/news/story/trumps-msg-rally-was-a-preview-of-his-second-term

New York, New York - October 27: Former president Donald Trump speaks at a rally on Oct. 27 at Madison Square Garden in New York. (Photo by Peter W. Stevenson /The Washington Post via Getty Images)The Washington Post/Getty Images.

If Donald Trump wins the 2024 election and returns to the White House for a second term, one thing people will absolutely not be able to say is “I didn’t think it would be so bad.” First, because Trump has been literally telling us exactly how bad things will get should he win another round in office, and second, because it was truly all on display last night at Madison Square Garden.

Trump’s Manhattan rally, held just over a week before Election Day, can and should be viewed as his closing message to voters. The message? That he and his associates believe,* among other things, that:

This is an acceptable way to talk about Latinos, a.k.a. Americans

 

That Puerto Rico, a.k.a. home to millions of US citizens, is a “floating island of garbage”**

 

That Kamala Harris has “pimp handlers,” the takeaway seemingly being that the female nominee for president is a prostitute

 

That this is an acceptable way to talk about Palestinians and Jews

 

That Hillary Clinton is a “sick bastard”

 

That “America is for Americans and Americans only,” a line the man who uttered it on Sunday will have absolutely known had disturbing historical parallels

 

That Americans who don’t support him are the “enemy from within”—a claim he has made numerous times over the last month

 

In addition to previewing the groups he can be expected to go after in a second term—possibly with military force—Trump also suggested he’s got a plan to make it back to the White House in the event the votes don’t go his way. “We can take the Senate pretty easily, and I think with our little secret we’re gonna do really well with the House, right?” Trump said, speaking to House Speaker Mike Johnson, who was in the audience. “Our little secret is having a big impact. He and I have a secret—we’ll tell you what it is when the race is over.”

 

Anyway, yeah, this as as clear a warning as we’re going to get.

 

*It’s important to note that after he took the stage on Sunday, Trump did not disavow any of the remarks that people had made before him, which he of course could have.

As of Monday afternoon, the campaign had only spoken out against the line about Puerto Rico. A spokesperson for the Trump campaign said, of the “floating island of garbage” line, that “This joke does not reflect the views of President Trump or the campaign.”

That claim would be more believable if Trump (1) hadn’t treated Puerto Rico as an inconvenience when he was president (2) hadn’t reportedly tried to trade Puerto Rico for Greenland and (3) didn’t himself speak using the same dehumanizing language.

[Vesper 28,807]

https://www.thetimes.com/uk/environment/article/climate-change-action-worse-after-emissions-hit-record-high-n8pcprg75

The United Nations has warned that progress on tackling climate change is in a “worse position” than it was a year ago after emissions jumped to a record high in 2023.

Global carbon emissions rose faster than average last year and countries have failed to upgrade their national climate plans despite calls from campaigners, businesses and António Guterres, the UN secretary-general.

“As this report rightly puts it, people and planet cannot afford more hot air,” said Guterres. “There is a direct link between increasing emissions and increasingly frequent and intense climate disasters. Around the world, people are paying a terrible price.”

The direction of travel has left earth on track for what the UN Environment Programme (Unep) called a “catastrophic” 2.8C of global warming by the end of the century. That is far off the Paris Agreement goal, pledged by the UK and nearly 200 other countries, of limiting temperature rises to 1.5C or “well below” 2C.

In a wide-ranging report, Unep found inadequate action last year and a 1.3 per cent increase in emissions to 57 billion tonnes of carbon dioxide meant the 1.5C goal “will soon be dead”.

“Every year of insufficient action puts us in a worse position. We only have six years to cut global emissions by 42 per cent to hit 1.5C. But there has been non-measurable global progress in ambition and action, and the growth in emissions last year even surpassed the average annual growth rate of the decade preceding covid-19,” said Anne Olhoff, the report’s chief scientific editor.

The assessment found that China is no longer only the world’s biggest carbon polluter today, but also one of the largest of all time. The country’s historical emissions are now on a par with the European Union, at 12 per cent of carbon dioxide released since 1850, though still dwarfed by the US on 20 per cent.

Environmental groups often cite the historical emissions of nations as a reason for them to pay climate finance to smaller emitters.

The Unep team said that one glimmer of hope, of predictions last year that 2024 could be a landmark moment when global emissions peak, was now looking less likely. “While renewables continue to break records, surging energy demand means that it is less likely that emissions fall in 2024 than before,” said Neil Grant, an author of the report.

The findings come less than three weeks before the UK and nearly 200 other countries meet for the Cop29 climate summit in Azerbaijan. Top of the agenda will be how much money rich nations such as the UK will pledge to give poorer, more vulnerable countries to tackle and adapt to climate change.

Unep said the bulk of future emissions cuts need to come from the G20, which was responsible for nearly four fifths of carbon pollution in 2023. By comparison, the 47 least developed countries, such as Afghanistan, Chad and Haiti, account for only 3 per cent.

 

Ed Miliband, the energy secretary, is heading to Cop29, and is expected to announce soon how strong the UK’s carbon target should be for 2035, before a UN deadline of February. Unep found that the G20 as a whole should cut its emissions 78 per cent by 2035.

“We need global mobilisation on a scale and pace never seen before — starting right now, before the next round of climate pledges — or the 1.5C goal will soon be dead and well below 2C will take its place in the intensive care unit,” said Inger Andersen, executive director at Unep.

Britain’s existing UN goal for 2030 is for a 68 per cent reduction. On Friday the government’s advisers, the Climate Change Committee, will issue their advice on how steep the cut should be.

[Vesper 28,807]

Minimum wage to rise by more than 6% in budget

https://www.thetimes.com/uk/politics/article/minimum-wage-to-rise-6-percent-uk-budget-dnj87k0wz

Ministers promised to “raise the floor” on wages and ultimately want parity for 18 to 20-year-olds

MONKEY BUSINESS IMAGES/GETTY IMAGES

 

More than a million low-paid workers will get a pay rise of more than 6 per cent next year as the minimum wage is increased at the budget.

Rachel Reeves, the chancellor, is due to announce an increase that is well above inflation and even higher than predicted last month.

Younger workers will get an even bigger increase as ministers say that 18 to 20-year-olds should eventually be paid the same as older workers.

While ministers are expected to herald good news for “working people”, businesses have warned of the impact of a rise that is expected to be announced alongside an increase in the national insurance contributions they must pay on wages.

About 1.6 million people receive the “national living wage” of £11.44 an hour, the minimum wage for over-21s. It will rise to more than £12.12 after ministers promised to “raise the floor” on wages.

Ministers have told the Low Pay Commission that the national living wage must not drop below two thirds of median earnings. This was a target set by the Conservatives and achieved this year after almost a decade of above-inflation increases, but ministers have signalled they want to go further to “boost low earnings”.

Last month the commission said that it expected to recommend an increase of 5.8 per cent, taking the living wage from age 21 to £12.10, but said it strong earnings growth could lead to a higher recommendation.

A government source said that the final figure was now more than 6 per cent, suggesting a new rate closer to £12.20.

Workers aged 18 to 20 can legally be paid a lower rate of £8.60 an hour but ministers want a “single adult rate” and Reeves is expected to announce a bigger increase for younger staff to get closer to the over-21 rate.

Angela Rayner, the deputy prime minister, has also told the commission to recommend an increase that “takes into account the cost of living”, but at a time when inflation has fallen close to 2 per cent, analysts do not expect that to significantly impact its recommendations this year.

Nye Cominetti, the principal economist at the Resolution Foundation, said: “Millions of low earners are set for good news in the budget when the chancellor announces the latest rise in the minimum wage”.

He said that government plans to were “actually less ambitious than the previous government’s record” after years where the minimum wage has increased by up to 10 per cent, saying “business should be used to the minimum wage rising at least in line with median pay, which is what we expect to happen.”

But Cominetti added: “A bigger surprise is the expected increase in employer national insurance contributions. As a result of the two together, some businesses will legitimately say that their wage costs have gone up quite a bit as a result of this budget.”

Tina McKenzie of the Federation of Small Businesses said: “It is businesses that pay people’s wages, plus all the tax government charges on top, which must be factored in when deciding on the Living Wage rate.”

She said an increase “must be accompanied with powerful government measures to help small businesses create and sustain jobs”, calling for more generous tax breaks for small employers facing “difficult choices” to cope with rising staff costs.

But Paul Nowak, general secretary of the TUC, said that experts on the commission “clearly believe that employers can absorb the rise”, and added: “At a time when the cost of living is still very high the lowest paid would really benefit from a decent increase in the minimum wage. We know that low-paid workers spend more of their cash in their local economies. So any increase in their spending power will benefit local firms too.”

He added: “Every time the minimum wage goes up there are some voices who predict this will drive up unemployment. Every time they are wrong.”

Edited October 29 by Vesper

[Vesper 28,807]

 

The Trends I’m Watching During Election Week (Part 1)

Examining the factors that will tell the story of this election.

https://www.liberalpatriot.com/p/the-trends-im-watching-during-election

One week from today, we could very well know who the next president of the United States will be, though the final result may not be known for a few days. In the weeks that follow, pundits, operatives, and analysts alike will be pouring over the data to discern what the “story” of the election was. More partisan types will likely be looking for ways to spin the outcome in their favor, especially those on the winning side.

To help better focus these conversations on relevant considerations, I want to share some questions I’ll be seeking to answer as the results become clearer and historical trends I’ll be comparing this year’s results against. Fair warning: there are many! So below I’ll offer the first bunch, and next Monday, before Election Day, we’ll post the second half of them. After we have results and other election data, I will come back to these questions and see if we can answer them.

The gender gap among young voters

Democrats have long held an advantage among voters aged 18–29. They have won these voters in presidential elections by at least nine points all but once since 19921 and by at least 20 points since 2008. What’s more: in contrast to other age groups, both young women and young men have supported Democratic candidates. In 2020, 18–29-year-old men were the only male age cohort to support Joe Biden, doing so by an 11-point margin, 52–41 (young women, meanwhile, supported Biden by a much larger 35 points, 67–32).

There are some signs that this dynamic could be changing. According New York Times/Siena College polling, Donald Trump now leads Harris among young men, and it’s not particularly close: 58–37. But Harris’s lead among young women is even wider: 67–28 (this is also wider than Biden’s advantage was four years ago). If this polling is accurate, it’s unclear how a growing divide like this might impact the final results, but suffice it to say that it’s a wild card.

However, it’s not clear that Republicans do have an advantage among young men. The latest Harvard IOP poll of young voters found that in fact Harris continues leading with this cohort by 10 points. Moreover, the younger men who support Trump also appeared to be less certain that they would vote, while Harris held a 17-point lead among likely voters.

Key questions:

• Which survey do the early exit polls suggest was more accurate: polling from the Times’ or Harvard?

• Does the data show both young men and women moving further from their 2020 baselines or only one of those groups?

• Does a potential growing gender gap extend to other age brackets as well?

Racial depolarization

A trend we at TLP have been watching for some time has been the rightward movement of nonwhite voters. For example, between 2016 and 2020, Hispanic, a Democratic-leaning group, swung toward Trump by 12 points. This cycle, Hispanics as well as black Americans appear to be moving even further rightward. The latest polling crosstab averages from both the Cook Political Report and Democratic pollster Adam Carlson show that Harris’s advantage with black voters is roughly 19–20 points below Biden’s, and with Hispanics she is 8–11 points behind. These swings could make a big difference in battleground states where the final margin is only a point or two.

Meanwhile, though, there have been signs that Harris is making up ground among white Americans, who have traditionally voted more Republican than Democratic and constitute an outsized share of the electorate (72 percent in 2020). This includes white non-college voters, who are overrepresented in swing states like Michigan, Wisconsin, and Pennsylvania as well as their college-educated peers. If Harris outperforms Biden with white voters, it will mark the second straight election in which the Democratic nominee has done better than their predecessor.

One thing to watch is whether the nonwhite voters moving in Trump’s direction are are among those who are slightly less likely to vote. As the Times’ Cohn has observed previously, Trump’s support with these voters may be coming from those who are less politically engaged. Extensive reporting has also indicated that Trump’s ground game and turnout operation are wanting, which means he may have trouble turning his poll support into actual votes. So it’s unclear whether the extent to which his gains with these groups will ultimately materialize—or the impact it will have if they do.

Key questions:

• Do the polls showing Trump’s substantial gains with nonwhite voters square with the final results?

• If Trump does make further gains with nonwhite voters relative to 2020, do they make the difference in any state?

• If Harris loses ground with black or Hispanic voters but improves with white voters, is that enough to offset the former losses?

The growing class divide

Another topic we have covered extensively is how the rise of educational polarization has come to reshape the two parties’ coalitions, with Democrats increasingly becoming the party of the college-educated class while the Republicans earn more votes from working-class voters. After Obama won non-college voters by four points in 2012, they swung to Trump in 2016, backing him by six. They did so again by roughly the same margin in 2020. College-educated voters have moved in the opposite direction, voting Democratic at the presidential level by margins of four (2012), 15 (2016), and 18 (2020) points. This cycle, both college- and non-college-educated voters have moved rightward relative to four years ago by roughly 2–3 points, a sign that this gap isn’t likely to shrink much, if at all.

There appear to be similar movements happening when looking at household income. Between 2012 and 2020, voters whose median earnings below $50,000 annually swung rightward by 12 points, going from backing Obama by 22 to supporting Biden by just 10. At the same time, the wealthiest Americans (those earning at least $100,000) have moved toward Democrats, going from a Romney +10 group to narrowly breaking for Democrats by two points in each of the previous two elections. The latest polling averages show Harris doing about the same as Biden among high income earners but underperforming among low-income households by 10–12 points.2

Finally, one other constituency whose performance may offer more context to the picture of class in America is union households. For decades, union voters have been strongly Democratic, but since 1992, that support (and the group’s vote share) have steadily declined. After Bill Clinton won them by around 30 points, they backed Obama by roughly 19. Hillary Clinton then carried them by only nine before Biden, who regularly touted his support for organized labor, bounced back, taking them by 15 points. The picture this cycle is a little murky, with little pre-election polling on this group. But the party’s struggles with this core constituency are undeniable.

If these trends hold on Election Day, it may be bad news for the Democrats. Working-class and union voters are overrepresented in several pivotal states, while, college-educated and higher-income voters both represent small (though reliable) shares of the electorate.

Key questions:

• Will recent class trends continue to change the makeup of the two parties’ coalitions, or will one or both claw back ground they have lost?

• Does Harris hold the line with union voters, or was the Teamsters’ internal polling a canary in the coal mine for further rightward drift?

• Is there a racial component to the class divide? (E.g., Do white working-class voters swing left but non-white ones swing right, or not?)

The impact of geography

In addition to educational attainment, the other growing fault line in American politics has been the politics of place—namely, whether you live in an urban, rural, or suburban area. These communities tend to be deeply Democratic, deeply Republican, and a mix of both, respectively. However, what’s become more interesting in the last few elections is the directions in which each one is trending.

Though urban areas across America are reliably Democratic, almost without exception, the party has seen some erosion of support in these places. As I wrote in my inaugural TLP piece last year, major cities in key swing states have experienced a decline in voter turnout as well as Democratic support. To be sure, they continue to vote blue by huge margins, but even these small losses matter. For example, in 2016, had Hillary Clinton simply matched Obama’s vote totals in Detroit and Milwaukee, she would have carried Michigan and Wisconsin. The latest polling averages indicate that Democrats may be losing further ground in urban America, with Harris underperforming Biden by nearly 12 points.

Meanwhile, much of rural America has become extremely hostile territory for Democrats over the previous decade. In 2008, John McCain only won rural counties by about eight points. By 2020, Trump carried them by 23. Democrats’ image in this part of the country is a longstanding problem. However, recent polling averages indicate that the party might be bouncing back.3 Carlson’s tracker has shown Harris over-performing Biden’s 2020 margins every month, usually by around seven or eight points. If her urban losses do materialize, gains in rural America could help her stay competitive.

Finally, suburban areas are likely where this election will be won or lost, just as they have in most elections. Since at least 2008, suburban voters have composed a whopping 55 percent of the electorate, and as they have gone, so too has the election. The latest polling averages show Harris narrowly winning them this time around by about three points. Though this might sound like good news for her, it represents a six-point decline from Biden’s 2020 performance.

Key questions:

• Are the polling crosstab averages showing Trump gaining in urban areas accurate? And if he eats into Harris’s margins in places like Detroit or Milwaukee, does she make up ground elsewhere?

• Does Harris do better than Biden in rural communities? If so, is there anything we can glean from the ones where she achieves this? (E.g., have they grown in population since the COVID pandemic?)

• If Harris carries the suburbs but by a lower margin than Biden did, is that enough to win?

 

1

The lone exception was the 2000 election, when Democrats only won them by two.

2

Averages for middle income earners are not readily available, though this is historically a Republican-leaning group.

3

Interestingly, this improvement began while Biden was still in the race, which may serve to counter claims that picking Tim Walz as her running mate helped Harris bounce back.

 

Edited October 29 by Vesper

[cosmicway 1,323]

I wonder if all those opinion polls are based on the swing theory or not.
The swing theory is essentially a variance reduction technique.
What is it ?
It is we ask the participants not only which way they are going to vote but also which way they voted last time.
Then we work with ratios instead of sums.
So if the accuracy without swing measurement is ±2%, now it becomes ±1.5% or ±1%.
Are they doing that ?
You could n't apply swing theory in the brexit referendum - difficult. But in general elections we can.

 

[Vesper 28,807]

The forces of chance

Social scientists cling to simple models of reality – with disastrous results. Instead they must embrace chaos theory

https://aeon.co/essays/without-chaos-theory-social-science-will-never-understand-the-world

Bockscar en route to Nagasaki, 9 August 1945. US Air Force photo

The social world doesn’t work how we pretend it does. Too often, we are led to believe it is a structured, ordered system defined by clear rules and patterns. The economy, apparently, runs on supply-and-demand curves. Politics is a science. Even human beliefs can be charted, plotted, graphed. And using the right regression we can tame even the most baffling elements of the human condition. Within this dominant, hubristic paradigm of social science, our world is treated as one that can be understood, controlled and bent to our whims. It can’t.

Our history has been an endless but futile struggle to impose order, certainty and rationality onto a Universe defined by disorder, chance and chaos. And, in the 21st century, this tendency seems to be only increasing as calamities in the social world become more unpredictable. From 9/11 to the financial crisis, the Arab Spring to the rise of populism, and from a global pandemic to devastating wars, our modern world feels more prone to disastrous ‘shocks’ than ever before. Though we’ve got mountains of data and sophisticated models, we haven’t gotten much better at figuring out what looms around the corner. Social science has utterly failed to anticipate these bolts from the blue. In fact, most rigorous attempts to understand the social world simply ignore its chaotic quality – writing it off as ‘noise’ – so we can cram our complex reality into neater, tidier models. But when you peer closer at the underlying nature of causality, it becomes impossible to ignore the role of flukes and chance events. Shouldn’t our social models take chaos more seriously?

The problem is that social scientists don’t seem to know how to incorporate the nonlinearity of chaos. For how can disciplines such as psychology, sociology, economics and political science anticipate the world-changing effects of something as small as one consequential day of sightseeing or as ephemeral as passing clouds?

 

On 30 October 1926, Henry and Mabel Stimson stepped off a steam train in Kyoto, Japan and set in motion an unbroken chain of events that, two decades later, led to the deaths of 140,000 people in a city more than 300 km away.

The American couple began their short holiday in Japan’s former imperial capital by walking from the railway yard to their room at the nearby Miyako Hotel. It was autumn. The maples had turned crimson, and the ginkgo trees had burst into a golden shade of yellow. Henry chronicled a ‘beautiful day devoted to sightseeing’ in his diary.

Nineteen years later, he had become the Unites States Secretary of War, the chief civilian overseeing military operations in the Second World War, and would soon join a clandestine committee of soldiers and scientists tasked with deciding how to use the first atomic bomb. One Japanese city ticked several boxes: the former imperial capital. The Target Committee agreed that Kyoto must be destroyed. They drew up a tactical bombing map and decided to aim for the city’s railway yard, just around the corner from the Miyako Hotel where the Stimsons had stayed in 1926.

Stimson pleaded with the president Harry Truman not to bomb Kyoto. He sent cables in protest. The generals began referring to Kyoto as Stimson’s ‘pet city’. Eventually, Truman acquiesced, removing Kyoto from the list of targets. On 6 August 1945, Hiroshima was bombed instead.

If such random events could lead to so many deaths, how are we to predict the fates of human society?

The next atomic bomb was intended for Kokura, a city at the tip of Japan’s southern island of Kyushu. On the morning of 9 August, three days after Hiroshima was destroyed, six US B-29 bombers were launched, including the strike plane Bockscar. Around 10:45am, Bockscar prepared to release its payload. But, according to the flight log, the target ‘was obscured by heavy ground haze and smoke’. The crew decided not to risk accidentally dropping the atomic bomb in the wrong place.

Bockscar then headed for the secondary target, Nagasaki. But it, too, was obscured. Running low on fuel, the plane prepared to return to base, but a momentary break in the clouds gave the bombardier a clear view of the city. Unbeknown to anyone below, Nagasaki was bombed due to passing clouds over Kokura. To this day, the Japanese refer to ‘Kokura’s luck’ when one unknowingly escapes disaster.

Roughly 200,000 people died in the attacks on Hiroshima and Nagasaki – and not Kyoto and Kokura – largely due to one couple’s vacation two decades earlier and some passing clouds. But if such random events could lead to so many deaths and change the direction of a globally destructive war, how are we to understand or predict the fates of human society? Where, in the models of social change, are we supposed to chart the variables for travel itineraries and clouds?

In the 1970s, the British mathematician George Box quipped that ‘all models are wrong, but some are useful’. But today, many of the models we use to describe our social world are neither right nor useful. There is a better way. And it doesn’t entail a futile search for regular patterns in the maddening complexity of life. Instead, it involves learning to navigate the chaos of our social worlds.

Before the scientific revolution, humans had few ways of understanding why things happened to them. ‘Why did that storm sink our fleet?’ was a question that could be answered only with reference to gods or, later, to God. Then, in the 17th century, Isaac Newton introduced a framework where such events could be explained through natural laws. With the discovery of gravity, science turned the previously mysterious workings of the physical Universe – the changing of the tides, celestial movements, falling objects – into problems that could be investigated. Newtonian physics helped push human ideas about causality from the unknowable into the merely unknown. A world ruled by gods is fundamentally unknowable to mere mortals, but, with Newton’s equations, it became possible to imagine that our ignorance was temporary. Uncertainty could be slain with intellectual ingenuity. In 1814, for example, the French scholar Pierre-Simon Laplace published an essay that imagined the possible implications of Newton’s ideas on the limits of knowledge. Laplace used the concept of an all-knowing demon, a hypothetical entity who always knew the positions and velocities of every particle in Newton’s deterministic universe. Using this power, Laplace’s demon could process the full enormity of reality and see the future as clearly as the past.

These ideas changed how we conceived of the fundamental nature of our world. If we are the playthings of gods, then the world is fundamentally and unavoidably unruly, swayed by unseen machinations, the whims of trickster deities and their seemingly random shocks unleashed like bolts of lightning from above. But if equations are our true lords, then the world is defined by an elegant, albeit elusive, order. Unlocking the secrets of those equations would be the key to taming what only seemed unruly due to our human ignorance. And in that world of equations, reality would inevitably converge toward a series of general laws. As scientific progress advanced in the 19th and 20th centuries, Laplace’s demon became increasingly plausible. Better equations, perhaps, could lead to godlike foresight.

‘Small differences in the initial conditions produce very great ones in the final phenomena’

The search for patterns, rules and laws wasn’t limited only to the realm of physics. In biology, Darwinian principles provided a novel guide to the rise and fall of species: evolution by natural selection acted like an ordered guardrail for all life. And as the successes of the natural sciences spread, scholars who studied the dynamics of culture began to believe that the rules of biology and physics could also be used to describe the patterns of human behaviour. If there was a theoretical law for something as mysterious as gravity, perhaps there were similar rules that could be applied to the mysteries of human behaviour, too? One scholar who put such an idea in motion was the French social theorist Henri de Saint-Simon. Believing that scientific laws underpinned social behaviour, Saint-Simon proposed a more systematic, scientific approach to social organisation and governance. Social reform, he believed, would flow inexorably from scientific research. The French philosopher Auguste Comte, a contemporary of Saint-Simon and founder of the discipline of sociology, even referred to the study of human societies as ‘social physics’. It was only a matter of time, it seemed, for the French Revolution to be understood as plainly as the revolutions of the planets.

But there were wrinkles in this world of measurement and prediction, which the French mathematician Henri Poincaré anticipated in 1908: ‘it may happen that small differences in the initial conditions produce very great ones in the final phenomena. A small error in the former will produce an enormous error in the latter.’

The first of those wrinkles was discovered by the US mathematician and meteorologist Edward Norton Lorenz. Born in 1917, Lorenz was fascinated by the weather as a young boy, but he left that interest behind in the mid-1930s when he began studying mathematics at Harvard University. During these studies, the Second World War broke out and Lorenz spotted a flyer recruiting for a weather forecasting unit. He jumped at the chance to return to his childhood fascination. As the war neared its end in 1945, Lorenz began forecasting cloud cover for bombing runs over Japan. Through this work, he started to understand the severe limitations of weather prediction – forecasting was not an exact science. And so, after the war, he returned to his mathematical studies, working on predictive weather models in the hope of giving humanity a means of more accurately glimpsing the future.

One day in 1961, while modelling the weather using a small set of variables on a simple, premodern computer, Lorenz decided to save time by restarting a simulation that had been stopped halfway through. The same simulation had been run previously, and Lorenz was running it again as part of his research. He printed the variables out, then programmed the numbers back into the machine and waited for the simulation to unfold as it had before.

The control panel on an LGP-30 computer, similar to that used by Edward Norton Lorenz. Courtesy Wikipedia

At first, everything looked identical, but over time the weather patterns began to diverge dramatically. He assumed there must have been an error with the computer. After much chin-scratching and scowling over the data, Lorenz made a discovery that forever upended our understanding of systemic change. He realised that the computer printouts he had used to run the simulation were truncating the values after three decimal points: a value of 0.506127 would be printed as 0.506. His astonishing revelation was that the tiniest measurement differences – seemingly infinitesimal, meaningless rounding errors – could radically change how a weather system evolved over time. Tempests could emerge from the sixth decimal point. If Laplace’s demon were to exist, his measurements couldn’t just be nearly perfect; they would need to be flawless. Any error, even a trillionth of a percentage point off on any part of the system, would eventually make any predictions about the future futile. Lorenz had discovered chaos theory.

 

The Lorenz attractor is the iconic representation of chaos theory. Courtesy Wikipedia

The core principle of the theory is this: chaotic systems are highly sensitive to initial conditions. That means these systems are fully deterministic but also utterly unpredictable. As Poincaré had anticipated in 1908, small changes in conditions can produce enormous errors. By demonstrating this sensitivity, Lorenz proved Poincaré right.

Chaos theory, to this day, explains why our weather forecasts remain useless beyond a week or two. To predict meteorological changes accurately, we, like Laplace’s demon, would have to be perfect in our understanding of weather systems, and – no matter how advanced our supercomputers may seem – we never will be. Confidence in a predictable future, therefore, is the province of charlatans and fools; or, as the US theologian Pema Chödrön put it: ‘If you’re invested in security and certainty, you are on the wrong planet.’

Most of the genomic tweaks driving evolution are fundamentally arbitrary, even accidental

The second wrinkle in our conception of an ordered, certain world came from the discoveries of quantum mechanics that began in the early 20th century. Seemingly irreducible randomness was discovered in bewildering quantum equations, shifting the dominant scientific conception of our world from determinism to indeterminism (though some interpretations of quantum physics arguably remain compatible with a deterministic universe, such as the ‘many-worlds’ interpretation, Bohmian mechanics, also known as the ‘pilot-wave’ model, and the less prominent theory of superdeterminism). Scientific breakthroughs in quantum physics showed that the unruly nature of the Universe could not be fully explained by either gods or Newtonian physics. The world may be defined, at least in part, by equations that yield inexplicable randomness. And it is not just a partly random world, either. It is startlingly arbitrary.

Consider, for example, the seemingly ordered progression of Darwinian evolution. Alfred Russel Wallace, who discovered evolution around the same time as Charles Darwin, believed that the principles of life had a structured purpose – they were teleological. Darwin was more sceptical. But neither thinker could anticipate just how arbitrary much of evolutionary change would turn out to be.

In the 1960s, the Japanese evolutionary biologist Motoo Kimura discovered that most of the genomic tweaks driving evolution at the molecular level are neither helpful nor harmful. They are fundamentally arbitrary, even accidental. Kimura called this the ‘neutral theory of molecular evolution’. Other scientists noticed it, too, whether they were studying viruses, fruit flies, blind mole rats, or mice. Evidence began to accumulate that many evolutionary changes in species weren’t driven by structured or ordered selection pressures. They were driven by the forces of chance.

The US biologist Richard Lenski’s elegant long-term evolution experiment, which has been running since 1988, demonstrated that important adaptations that help a species (such as E coli) thrive can emerge after a chain of broadly meaningless mutations. If any one of those haphazard and seemingly ‘useless’ tweaks hadn’t occurred, the later beneficial adaptation wouldn’t have been possible. Sometimes, there’s no clear reason, no clear pattern. Sometimes, things just happen.

E coli populations from Richard Lenski’s long-term evolution experiment, 25 June 2008. Courtesy Wikipedia

Kimura’s own life was an illustration of the arbitrary forces that govern our world. In 1944, he enrolled at Kyoto University, hoping to continue his intellectual pursuits while avoiding conscription into the Japanese military. If Henry Stimson had chosen a different destination for his sightseeing vacation in 1926, Kimura and his fellow students would likely have been incinerated in a blinding flash of atomic light.

How can we make sense of social change when consequential shifts often arise from chaos? This is the untameable bane of social science, a field that tries to detect patterns and assert control over the most unruly, chaotic system that exists in the known Universe: 8 billion interacting human brains embedded in a constantly changing world. While we search for order and patterns, we spend less time focused on an obvious but consequential truth. Flukes matter.

Though some scholars in the 19th century, such as the English philosopher John Stuart Mill and his intellectual descendants, believed there were laws governing human behaviour, social science was swiftly disabused of the notion that a straightforward social physics was possible. Instead, most social scientists have aimed toward what the US sociologist Robert K Merton called ‘middle-range theory’, in which researchers hope to identify regularities and patterns in certain smaller realms that can perhaps later be stitched together to derive the broader theoretical underpinnings of human society. Though some social scientists are sceptical that such broader theoretical underpinnings exist, the most common approach to social science is to use empirical data from the past to tease out ordered patterns that point to stable relationships between causes and effects. Which variables best correlate with the onset of civil wars? Which economic indicators offer the most accurate early warning signs of recessions? What causes democracy?

Social science became dominated by one computational tool above all others: linear regressions

In the mid-20th century, researchers no longer sought the social equivalent of a physical law (like gravity), but they still looked for ways of deriving clear-cut patterns within the social world. What limited this ability was technology. Just as Lorenz was constrained by the available technology when forecasting weather in the Pacific theatre of the Second World War, so too were social scientists constrained by a lack of computing power. This changed in the 1980s and ’90s, when cheap and sophisticated computers became new tools for understanding social worlds. Suddenly, social scientists – sociologists, economists, psychologists or political scientists – could take a large number of variables and plug them into statistical software packages such as SPSS and Stata, or programming languages such as R. Complex equations would then process these data points, finding the ‘line of best fit’ using a ‘linear regression’, to help explain how groups of humans change over time. A quantitative revolution was born.

By the 2000s, area studies specialists who had previously done their research by trekking across the globe and embedding themselves in specific cultures were largely supplanted by office-bound data junkies who could manipulate numbers and offer evidence of hidden relationships that were obscured prior to the rise of sophisticated numerical analysis. In the process, social science became dominated by one computational tool above all others: linear regressions. To help explain social change, this tool uses past data to try to understand the relationships between variables. A regression produces a simplified equation that tries to fit the cluster of real-world datapoints, while ‘controlling’ for potential confounders, in the hopes of identifying which variables drive change. Using this tool, researchers can feed a model with a seemingly endless string of data as they attempt to answer difficult questions. Does oil hinder democracy? How much does poverty affect political violence? What are the social determinants of crime? With the right data and a linear regression, researchers can plausibly identify patterns with defensible, data-driven equations. This is how much of our knowledge about social systems is currently produced. There is just one glaring problem: our social world isn’t linear. It’s chaotic.

Linear regressions rely on several assumptions about human society that are obviously incorrect. In a linear equation, the size of a cause is proportionate to the size of its effect. That’s not how social change works. Consider, for example, that the assassination of one man, Archduke Franz Ferdinand, triggered the First World War, causing roughly 40 million casualties. Or think of the single vegetable vendor who lit himself on fire in central Tunisia in late 2010, sparking events that led to the Syrian civil war, resulting in hundreds of thousands of deaths and the fall of several authoritarian regimes. More recently, a bullet narrowly missed killing Donald Trump in Pennsylvania: if the tiniest gust of wind or a single bodily twitch had altered its trajectory, the 21st century would have been set on a different path. This exemplifies chaos theory in the social world, where tiny changes in initial conditions can transform countless human fates.

Another glaring problem is that most linear regressions assume that a cause-and-effect relationship is stable across time. But our social world is constantly in flux. While baking soda and vinegar will always produce a fizz, no matter where or when you mix them together, a vegetable vendor lighting himself on fire will rarely produce regional upheaval. Likewise, many archdukes have died – only one has ever triggered a world war.

Timing matters, too. Even if the exact same mutation in the exact same coronavirus had broken out in the exact same place, the economic effects and social implications of the ensuing pandemic would have been drastically different if it had struck in 1990 instead of 2020. How would millions of people have worked from home without the internet? Pandemics, like many complex social phenomena, are not uniformly governed by stable, ordered patterns. This is a principle of social reality known to economists as ‘nonstationarity’: causal dynamics can change as they are being measured. Social models often deal with this problem by ignoring it.

Most linear regressions are also ineffective at modelling two fundamental facets of our world: sequencing, the critical order in which events take place; and space, the specific physical geography in which those events occur. The overarching explanations offered by linear regression ignore the order in which things happen, and though that approach can sometimes work, at other times the order of events is crucial. Try adding flour after you bake a cake and see what happens. Similarly, linear regressions cannot easily incorporate complex features of our physical geography or capture the ways that humans navigate through space. Social models tend to conceptualise changes at the macro level, through economic output figures or democracy scores, rather than seeing diverse, adaptive individuals who are constantly interacting on specific terrain. Life looks very different for people living in Antarctica compared with people living in downtown Mumbai or the Andes or outback Australia.

We produce too many models that are often wrong and rarely useful. But there is a better way

By smoothing over near-infinite complexity, linear regressions make our nonlinear world appear to follow the comforting progression of a single ordered line. This is a conjuring trick. And to complete it successfully, scientists need to purge whatever doesn’t fit. They need to detect the ‘signal’ and delete the ‘noise’. But in chaotic systems, the noise matters. Do we really care that 99.8 per cent of the Titanic’s voyage went off without a hitch, or that Abraham Lincoln enjoyed most of the play before he was shot?

The deeply flawed assumptions of social modelling do not persist because economists and political scientists are idiots, but rather because the dominant tool for answering social questions has not been meaningfully updated for decades. It is true that some significant improvements have been made since the 1990s. We now have more careful data analysis, better accounting for systematic bias, and more sophisticated methods for inferring causality, as well as new approaches, such as experiments that use randomised control trials. However, these approaches can’t solve many of the lingering problems of tackling complexity and chaos. For example, how would you ethically run an experiment to determine which factors definitively provoke civil wars? And how do you know that an experiment in one place and time would produce a similar result a year later in a different part of the world?

These drawbacks have meant that, despite tremendous innovations in technology, linear regressions remain the outdated king of social research. As the US economist J Doyne Farmer puts it in his book Making Sense of Chaos (2024): ‘The core assumptions of mainstream economics don’t match reality, and the methods based on them don’t scale well from small problems to big problems.’ For Farmer, these methods are primarily limited by technology. They have been, he writes, ‘unable to take full advantage of the huge advances in data and technology.’

The drawbacks also mean that social research often has poor predictive power. And, as a result, social science doesn’t even really try to make predictions. In 2022, Mark Verhagen, a research fellow at the University of Oxford, examined a decade of articles in the top academic journals in a variety of disciplines. Only 12 articles out of 2,414 tried to make predictions in the American Economic Review. For the top political science journal, American Political Science Review, the figure was 4 out of 743. And in the American Journal of Sociology, not a single article made a concrete prediction. This has yielded the bizarre dynamic that many social science models can never be definitively falsified, so some deeply flawed theories linger on indefinitely as zombie ideas that refuse to die.

A core purpose of social science research is to prevent avoidable problems and improve human prosperity. Surely that requires more researchers to make predictions about the world at some point – even if chaos theory shows that those claims are likely to be inaccurate.

We produce too many models that are often wrong and rarely useful. But there is a better way. And it will come from synthesising lessons from fields that social scientists have mostly ignored.

Chaos theory emerged in the 1960s and, in the following decades, mathematical physicists such as David Ruelle and Philip Anderson recognised the significance of Lorenz’s insights for our understanding of real-world dynamical systems. As these ideas spread, misfit thinkers from an array of disciplines began to coalesce around a new way of thinking that was at odds with the mainstream conventions in their own fields. They called it ‘complexity’ or ‘complex systems’ research. For these early thinkers, Mecca was the Santa Fe Institute in New Mexico, not far from the sagebrush-dotted hills where the atomic bomb was born. But unlike Mecca, the Santa Fe Institute did not become the hub of a global movement.

Public interest in chaos and complexity surged in the 1980s and ’90s with the publication of James Gleick’s popular science book Chaos (1987), and a prominent reference from Jeff Goldblum’s character in the film Jurassic Park (1993). ‘The shorthand is the butterfly effect,’ he says, when asked to explain chaos theory. ‘A butterfly can flap its wings in Peking and in Central Park you get rain instead of sunshine.’ But aside from a few fringe thinkers who broke free of disciplinary silos, social science responded to the complexity craze mostly with a shrug. This was a profound error, which has contributed to our flawed understanding of some of the most basic questions about society. Taking chaos and complexity seriously requires a fresh approach.

One alternative to linear regressions is agent-based modelling, a kind of virtual experiment in which computers simulate the behaviour of individual people within a society. This tool allows researchers to see how individual actions, with their own motivations, come together to create larger social patterns. Agent-based modelling has been effective at solving problems that involve relatively straightforward decision-making, such as flows of car traffic or the spread of disease during a pandemic. As these models improve, with advances in computational power, they will inevitably continue to yield actionable insights for more complex social domains. Crucially, agent-based models can capture nonlinear dynamics and emergent phenomena, and reveal unexpected bottlenecks or tipping points that would otherwise go unnoticed. They might allow us to better imagine possible worlds, not just measure patterns from the past. They offer a powerful but underused tool in future-oriented social research involving complex systems.

The study of resilience in nonlinear systems would drastically improve our ability to avert avoidable catastrophes

Additionally, social scientists could incorporate chaotic dynamics by acknowledging the limits of seeking regularities and patterns. Instead, they might try to anticipate and identify systems on the brink, near a consequential tipping point – systems that could be set off by a disgruntled vegetable vendor or triggered by a murdered archduke. The study of ‘self-organised criticality’ in physics and complexity science could help social scientists make sense of this kind of fragility. Proposed by the physicists Per Bak, Chao Tang and Kurt Wiesenfeld, the concept offers a useful analogy for social systems that may disastrously collapse. When a system organises itself toward a critical state, a single fluke could cause the system to change abruptly. By analogy, modern trade networks race toward an optimised but fragile state: a single gust of wind can twist one boat sideways and cause billions of dollars in economic damage, as happened in 2021 when a ship blocked the Suez Canal.

The theory of self-organised criticality was based on the sandpile model, which could be used to evaluate how and why cascades or avalanches occur within systems. If you add grains of sand, one at a time, to a sandpile, eventually, a single grain of sand can cause an avalanche. But that collapse becomes more likely as the sandpile soars to its limit. A social sandpile model could provide a useful intellectual framework for analysing the resilience of complex social systems. Someone lighting themselves on fire, God forbid, in Norway is unlikely to spark a civil war or regime collapse. That is because the Norwegian sandpile is lower, less stretched to its limit, and therefore less prone to unexpected cascades and tipping points than the towering sandpile that led to the Arab Spring.

There are other lessons for social research to be learned from nonlinear evaluations of ecological breakdown. In biology, for instance, the theory of ‘critical slowing down’ predicts that systems near a tipping point – like a struggling coral reef that is being overrun with algae – will take longer to recover from small disturbances. This response seems to act as an early warning system for ecosystems on the brink of collapse.

Social scientists should be drawing on these innovations from complex systems and related fields of research rather than ignoring them. Better efforts to study resilience and fragility in nonlinear systems would drastically improve our ability to avert avoidable catastrophes. And yet, so much social research still chases the outdated dream of distilling the chaotic complexity of our world into a straightforward equation, a simple, ordered representation of a fundamentally disordered world.

When we try to explain our social world, we foolishly ignore the flukes. We imagine that the levers of social change and the gears of history are constrained, not chaotic. We cling to a stripped-down, storybook version of reality, hoping to discover stable patterns. When given the choice between complex uncertainty and comforting – but wrong – certainty, we too often choose comfort.

In truth, we live in an unruly world often governed by chaos. And in that world, the trajectory of our lives, our societies and our histories can forever be diverted by something as small as stepping off a steam train for a beautiful day of sightseeing, or as ephemeral as passing clouds.

Parts of this essay were adapted from Fluke: Chance, Chaos, and Why Everything We Do Matters (2024) by Brian Klaas.

 

[cosmicway 1,323]

In prediction theory there is a magic number.
The Shannon number - representing the total information count.
So your mulitparametric system is manipulated so as to maximize the Shannon number of the pre-posterior observations.
That's how it works.
Other models are unstable.
With many parameters computations become tediously slow - even with the fastest computers.

Edited October 29 by cosmicway

[Vesper 28,807]

8 minutes ago, cosmicway said:

I wonder if all those opinion polls are based on the swing theory or not.
The swing theory is essentially a variance reduction technique.
What is it ?
It is we ask the participants not only which way they are going to vote but also which way they voted last time.
Then we work with ratios instead of sums.
So if the accuracy without swing measurement is ±2%, now it becomes ±1.5% or ±1%.
Are they doing that ?
You could n't apply swing theory in the brexit referendum - difficult. But in general elections we can.

 

By introducing a secondary independent variable (and this goes for the common random numbers (CRN) variance reduction technique as well) you can potentially set up a negative correlation, thus increasing variation (dispersion) and the decreasing predictive accuracy of the data set.

[cosmicway 1,323]

51 minutes ago, Vesper said:

By introducing a secondary independent variable (and this goes for the common random numbers (CRN) variance reduction technique as well) you can potentially set up a negative correlation, thus increasing variation (dispersion) and the decreasing predictive accuracy of the data set.

It is not independent.
Independent is toothpaste Colgate versus republicanism.
Swing theory reduces the variance.
Someone I know made huge money with brexit. What he observed was the Newcastle result, the first to be announced.
Newcastle voted remain but by a lot less than was expected in a preponderantly Labour constituenct.
I could n't follow because betfair is not allowed in Greece.

Also the late lamented Greek minister Akis Tsohatzopoulos (n.b. accused of economic crimes) was an expert in mathematical statistics
and by using swing theory he always predicted the exact result.

Edited October 29 by cosmicway

[Vesper 28,807]

Here is a partial list of RW, Republican push pollsters that flood the zone with bullshit polls, thus skewing agregator averages towards Trump and other Republican candidates (US House, US Senate, state Governor races, etc)

they VASTLY outnumber Democratic dodgy pollster (this election probably 25 to 50 to one)

the top 15  or so, especially the top 10 listed, are just relentless with deluge of RW skewed polls

    
Trafalgar Group

ActiVote

Redfield & Wilton Strategies   

Fabrizio, Lee & Associates

AtlasIntel

InsiderAdvantage

OnMessage

Patriot Polling

Remington Research Group

Cherry Communications

HarrisX

HighGround

RMG Research

Cygnal

McLaughlin & Associates    

Victory Insights

Targoz Market Research

The Tyson Group 

P2 Insights

SoCal Strategies

TIPP Insights

Spry Strategies

Peak Insights

Iron Light

Echelon Insights

Moore Information Group

Ascend Action

Clout Research

Normington, Petts & Associates

FM3 Research

Repass

Global Strategy Group

North Star Opinion Research

Fabrizio Ward

 

 

 

 

[Vesper 28,807]

47 minutes ago, cosmicway said:

It is not independent.
Independent is toothpaste Colgate versus republicanism.
Swing theory reduces the variance.
Someone I know made huge money with brexit. What he observed was the Newcastle result, the first to be announced.
Newcastle voted remain but by a lot less than was expected in a preponderantly Labour constituenct.
I could n't follow because betfair is not allowed in Greece.

Also the late lamented Greek minister Akis Tsohatzopoulos (n.b. accused of economic crimes) was an expert in mathematical statistics
and by using swing theory he always predicted the exact result.

please post a link to swing theory

[Vesper 28,807]

48 minutes ago, cosmicway said:

It is not independent.

Yes it is.

Any variable that can be attributed a value without automatically attributing a value to any other variable is called an independent variable.

Who a person voted for in the past does not automatically pass through to who they will now vote for in a future election.

Yes, there is often correlation (even strong correlation), but it is not anywhere near 100 per cent, and injecting that variable introduces statistical drift masquerading as certainty of outcome.

Ask Trump about 2016 Trump voters who switched to Biden in 2020.

[Vesper 28,807]

Election 2024: $500 million in crypto-fueled queer-baiting

https://prospect.org/politics/2024-10-29-brown-moreno-ohio-senate-race/

Ohio Republican Senate candidate Bernie Moreno speaks to constituents during a bus tour stop in Columbus, Ohio on Oct. 28, 2024.

 

NORTHEASTERN OHIO—Bernie Moreno is a Bogota-born junior oligarch with a long list of ex-employees, business partners and competitors who claim he amassed his mega-millions by cheating and stealing. He also falsely claimed to have an MBA and a “lower middle class” background, and his own party dubbed him “Ohio’s George Santos.” During the primary, the Democrats were sufficiently unafraid of his candidacy that the Senate Majority PAC plowed $3 million into ads knocking two of his competitors.

Now, somehow, this gazillionaire car dealer looks like he might beat Sherrod Brown, the disheveled populist and three-term senator who has represented Ohioans in one elected office or another since 1975, when he was sworn in as a 22-year-old legislator representing Lorain, a mill town just west of Cleveland. D.J. Byrnes, whose substack The Rooster covers Ohio state politics, got a bad feeling when he was chatting with a friend about the three-term senator and his six-year-old son excitedly piped up: “Sherrod Brown: he’s too liberal for Ohio!”

Byrnes blames the cash tsunami that has put the matchup on track to be the nation’s first half-billion-dollar Senate election. (To put that figure in perspective, it’s more than George W. Bush and John Kerry raised in 2004—combined.) Two weekends ago, his local Fox affiliate in Columbus missed the kickoff of the Cincinnati Bengals game against the Carolina Panthers to broadcast a commercial promising Moreno would “end inflation.” The station’s owner, Sinclair Broadcasting, recently revised its projected political ad revenue for the year by about sixty million dollars, largely due to the insatiable thirst for air time in Ohio.

Brown has raised more than $80 million, quadruple his haul during his last race in 2018 and nearly quadruple what Moreno has managed to raise himself. But dark money groups, financed most aggressively by three cryptocurrency giants, likely enthused both by Brown’s reluctance to embrace crypto deregulation and Moreno’s own status as a blockchain entrepreneur, have fallen over themselves to plow funds into anti-Brown attack ads. Even the senator’s supporters are worried the propaganda onslaught will prove too toxic to bear. “We finally have a really formidable candidate in Bernie Moreno,” says Dale Fellows, a GOP official in Lake County.

More from Maureen Tkacik

To an extent this is true. Where the last ultra-rich asshole to attempt to unseat Brown flew around the state in the borrowed private plane of a strip club owner buddy and looked a bit too much like the used car salesman his successor actually is, Moreno is articulate and polished, with an attractive family who look like sort of people who would spend time with Kamala Harris and Doug Emhoff. But in an ocean of phonies, sycophants and brazen hypocrites, Moreno’s MAGA credentials are some of the most dubious around. He owes his fortune to foreign cars and DEI; his brother, who until 2020 was the president of the Inter-American Development Bank, literally negotiated free trade pacts on behalf of Colombia, the country of his birth; his deleted tweets from as recently as three years ago condemned Trump as a “fake Republican” exploiting “ignorance in our society” to “stoke hatred and fear” and who “deserves lots and lots of blame” for January 6.

And perhaps most surreally, the primary focus of Moreno’s campaign commercials, depicting a nascent transgender invasion of teenage girls’ bathrooms, sports programs and schools, is almost completely at odds with his long, well-documented history of gay rights advocacy. As early as 1995, after an irate reader wrote the Providence Journal in 1995 calling for a boycott of the Saturn auto brand over its ad campaigns in Out Magazine, Moreno responded with a letter wondering “how one could become so hateful of a specific group that it would affect their purchase of a product made available to that group… how many people are still left out there who would not consider purchasing a Saturn if they knew we advertised in Ebony and Jet.” Moreno also sponsored the 2014 Gay Games, co-authored a latter condemning an anti-trans hate crime at the community college where he served as a trustee, praised the ABC sitcom Modern Family for normalizing gay parenting, and turned up in a 2016 hack of the sex solicitation site Adult Friend Finder for having registered an account to solicit “1-on-1 gay sex”—though he claims that last part was the work of a prankster intern. While an undergraduate at Brown University, Moreno’s son Adam was the star of a gender-fluid pole-dancing troupe called the Poler Bears. “The show culminated grandly in a sensational performance by Adam Moreno ’18 in tall platform black combat boots to Madonna’s ‘Like a Prayer’ as a smoke machine blasted vapor across the stage,” reads a Brown Daily Herald review of a Poler Bears Christmas performance. “The crowd went wild.”

“You turn on the TV and It’s basically like, Sherrod Brown’s gonna dig up your dead grandfather and turn him trans,” laughed Josh Sponsler, a Toledo manufacturing worker who says he is far more worried about Brown’s race than that of his beloved congresswoman (and Brown’s longtime ideological comrade) Rep. Marcy Kaptur (D-OH), who is seeking a 22nd term in the House. “But Sherrod is way too decent of a guy to use any of that stuff against him.”

PERHAPS INEVITABLY, THE BROWN CAMPAIGN has also steered clear of dragging Moreno’s soon-to-be-ex son-in-law, Max Miller, into its attack ads, even though the young real estate scion’s 2022 marriage to Moreno’s daughter Emily at Trump’s Bedminster Golf Club appears to have sealed the MAGA endorsement for his Senate campaign. Miller was one of the former president’s favorite aides, despite or perhaps because of his lifelong penchant for violent outbursts and misogynistic insults. While working in the White House, Miller dated a fellow staffer who later accused him of physical abuse; an in-depth Politico profile published a month before the wedding detailed multiple episodes of him violently assaulting women; and over the summer Emily accused him of abusing drugs and threatening her safety and moved with their infant daughter out of the house they had shared into another house Miller accuses the Moreno family of having purchased secretly for her.

Somehow, the only campaign ad that makes any mention of the sordid tale is an online commercial produced by the shoestring operation of Dennis Kucinich, the former presidential candidate who is running a long-shot campaign to unseat Miller from the suburban Cleveland district he won with Trump’s help in 2022. The spot makes no mention of Moreno, who “basically sold his daughter to get Trump’s endorsement,” in Byrnes’ joking characterization.

“You turn on the TV and It’s basically like, Sherrod Brown’s gonna dig up your dead grandfather and turn him trans,” laughed Josh Sponsler, a Toledo manufacturing worker.

The tawdriest attack ad Democrats have produced on Moreno is a somewhat confusing spot, “Shady Projects”, that accuses the car dealer’s family of funneling “our tax dollars” into “so-called development projects” in Latin America, then investing the proceeds of those projects into “the perfect cover”—Moreno’s auto empire, which comprised 55 dealerships at its peak—until Moreno began selling them off to finance his crypto car titles business. The commercial, which was produced by a Democratic Super PAC, has not been in heavy rotation, and it’s hard to say how effective it would be if it were. So much about the Moreno family’s riches, and the motivation behind their MAGAfication, is still unclear. “The negative campaigning against Bernie Moreno hasn’t been as vicious as I would expect,” a Republican official in northeastern Ohio told the Prospect. “He is widely disliked in the auto sales world, and that’s not coming through.”

Instead, Brown has mostly focused his messaging around some obnoxious comments Moreno made about abortion rights (“Sadly, by the way, there’s a lot of suburban women, a lot of suburban women that are like…‘If I can’t have an abortion in this country whenever I want, I will vote for anybody else.’ OK. It’s a little crazy…especially for women who are like past 50, I’m thinking to myself, ‘I don’t think that’s an issue for you..’”) along with his vow to restore “the dignity of work,” a motto he first embraced while briefly testing the waters for a presidential campaign in 2019.

One day's worth of 2024 election mailers is seen on the floor of a voter from Warren, Ohio.

 

The phrase, which Moreno has panned on the stump as a “fake slogan”, elicits an eyeroll from Byrnes: “‘Work is cool’ doesn’t really resonate to me, especially from a guy who has literally never had a job,” he quips. But the Republican official, who supports Brown privately but is hesitant to do so publicly for fear of antagonizing their own supporters, says that critique is unfair. “That man works,” they said of Brown during a recent conversation at a cafe in Warren, Ohio, a charming town fifteen minutes north of Youngstown. “His constituent services are truly the gold standard. For Ohio to lose him would be… a real blow.”

Warren is the seat of Trumbull County, the single “swingiest” county in Ohio and home to perhaps the largest population of Obama-Trump voters in America. Obama won Trumbull by 23 points in 2012 and Trump won by 11 points in 2020; between 2012 and 2016 the Democrats lost 25 points. But back in 1992, 25 percent of Trumbull County voted for Ross Perot, making it the only place in Ohio where Perot outperformed Bush.

Deindustrialization is a core memory in northeast Ohio, says Josh Nativio, the manager of a beloved comic book shop on Warren’s main drag who, like many people the Prospect has encountered in its travels, terrifyingly intimate with the ins and outs of state politics.

“This area has been in recession since 1977,” Nativio says, referencing the devastating shutdown of Youngstown Sheet & Tube that is widely understood as the hard launch of deindustrialization. “People here have gotten accustomed to the cycles of absorbing and adapting to macroeconomic shocks. At this point you have people in their thirties mourning the loss of steel mills that shut down before they were born. They’re still not happy about it.” 

Ohio lost nearly four in ten of the 1.1 million manufacturing jobs that fled the Midwest between 1990 and 2019, and no one I spoke to in northeast Ohio believed that was reversible. But merely acknowledging that pain and loss provided a kind of catharsis, said a Democratic poll worker in Youngstown who identified herself as Jackie. “Trump was talking a lot of sense when he first came on the scene,” she said while handing out sample ballots to residents stopping by an early voting station.

In 2017, the former president visited Warren and warned attendees not to sell their houses, because he was going to save the local Lordstown General Motors plant, which had been hemorrhaging jobs since the 1990s. “I don’t think anyone actually took him seriously,” says Nativio. The plant manufactured compact sedans; Trump relaxed fuel efficiency requirements and GM shut it down entirely in 2019. “But only Democrats were actually mad about that,” recalls Nativio, whose own politics are leftist. What Trump had discovered, he believes, was that wave after wave of economic dislocations had produced a collective trauma that could be wielded as a kind of eternal culture war issue with very little consequence.

Almost no one in northeast Ohio talks about Trump without bringing up late Youngstown congressman Jim Traficant, an amazingly coiffed populist Democrat who successfully defended himself against federal racketeering charges and savaged the “carnage of NAFTA” before getting expelled from the House and ultimately imprisoned for taking bribes and filing false tax returns in 2002. But Traficant earned the adoration of his constituents through a genuinely heroic confrontation with the deep state: As sheriff of a county with the nation’s highest unemployment rate during the collapse of the American steel industry in 1982, he refused to carry out foreclosure orders on behalf of banks and bashed Ronald Reagan for gutting his community. The media lambasted him with a contemptuous zeal that is in hindsight dismayingly familiar. “It was the morally correct thing to do,” says the anonymous Republican legislator, who was in elementary school at the time but remembers vividly how “my grandparents absolutely adored Jim Traficant.”

“I’m 36, and I’m pretty resigned at this point to the fact that I’ll probably never own a house,” says Sponsler. “But the people at work who are just a little older than me, they remember the people who trained them: they had houses, and pensions, legitimately nice lives. I recall Trump in 2016 saying a lot of things that those people wanted to hear; I mean, however you want to interpret the coded language ‘make it how it used to be’; to those people it is pretty straightforward. And a lot of them thought, we’ll give this a shot because Obama didn’t do anything for me,” he said. “And then the Democrats never wanted them back. They’d rather have ten suburban wine moms than ten guys who wear steel-toed boots.”

What Trump had discovered, he believes, was that wave after wave of economic dislocations had produced a collective trauma that could be wielded as a kind of eternal culture war issue.

AFTER THE 2016 ELECTION, DEMOCRATS INVESTED invested considerable effort into winning back Ohio’s Obama-Trump voters. The party ran candidates in all 99 districts represented in the state house in 2018; Byrnes, who made a living at the time blogging about Ohio State football, got his start in state politics after being drafted to run in one, an experience memorably captured in the 2021 documentary Touch the Boulder. After blocking a Republican-sponsored amendment that would have canceled Trump’s steel tarriffs, Brown won his election that year by 7 points, against Republican congressman Jim Renacci, who had accumulated a net worth of nearly $100 million running nursing homes and Harley-Davidson dealerships. The author Brian Alexander, whose heart-wrenching book Glass House: The 1% Economy and the Shattering of the All-American Town about the private equity bust-out of a glass factory in central Ohio had hit shelves just weeks after Trump’s inauguration, recalls numerous Democratic operatives inviting him in those days to speak to politicians and pundits about the problems of financialization and the malaise of the working class Obama-Trump voter.

At the time, he recalls, both Brown and Kaptur invited him to brief their staffs on potential policy solutions to private equity disinvestment, but Kaptur, whose politics are essentially identical to Brown’s, voiced more frustration. “She put her hands on my shoulders and asked me, ‘How do we get the national Democratic Party to care about these problems?’ And I said, ‘Madam, you’ve been in Congress nearly 40 years, if you don’t know how to do it, I don’t know if it can be done,” Alexander said.

Kaptur, who appeared in Michael Moore’s financial crisis documentary Capitalism: A Love Story and was one of a handful of Democratic politicians who endorsed Bernie Sanders in 2015 while Brown endorsed Clinton, had spent her entire career struggling to convince party leadership to prioritize the problems of the working class. She made enough of an impression that Ross Perot had approached her to be his running mate for his 1996 candidacy—she declined—but cycle after cycle, she’d been marginalized and shut out of leadership spots, which were invariably reserved for members in deeper-pocketed districts on the coasts. Still, she persevered, winning elections despite repeated attempts by state Republicans to gerrymander away her district, which currently stretches thinly across the top of northwest Ohio to the Indiana border. The 9th District voted for Trump by four points in 2020; prior to then it was a Democratic vote sink that stretched all the way east to Cleveland, putting Kaptur into the same seat as Kucinich. Kaptur prevailed in that Dem-on-Dem race.

Sponsler, who lives in Toledo, predicts Kaptur will keep winning elections by comfortable margins—she beat her last opponent, an amateur rapper who lied about serving in Afghanistan, by more than 13 points—until she decides to retire, solely on the basis of the loyalty she had engendered in 41 years in office. He recalled how helpful her staffers were when he worked as a letter carrier and their labor union needed help navigating budget cuts. “Her staff was like, scarily well-informed about the plight of the postal carriers, and that’s just such a typical story about her. You fill a small room with people from Toledo and someone will have a story along those lines.” At the same time, he wonders if Kaptur’s lack of association with Democratic Party leadership liberates her un-woke fans to support her more openly: he’s seen a lot of Trump/Kaptur lawns in the suburbs, he said, and sent the Prospect a photo of one.

Trump-Brown voters are harder to find. “There was a guy here last week from Bloomberg, and he went two whole days looking for a Trump-Brown voter and I don’t think he found anyone,” said Dave Bell, a retired teacher campaigning for a Democratic county commissioner outside the Youngstown early voting center. But the anonymous Republican official in Warren who plans on voting for Brown promised the Prospect that others like them exist, as did Andrew Six, an Akron supermarket butcher who has spent the past several weekends canvassing members of his labor union. More than 80 percent of them told him they were voting for Brown, while only “one or two” said they were voting for Kamala. (“A lot of them actually left the president spot blank,” he said; a few more were voting for Trump.)

Six himself cast his presidential vote for Claudia De La Cruz, an activist running on the Party for Socialism and Liberation ticket, in protest of the genocide in Gaza. But he campaigned for Brown despite the senator’s AIPAC endorsement, because the rumpled senator “is a real person, not some awful ghoul like Kamala Harris or Bernie Moreno.”

Edited October 29 by Vesper

[Vesper 28,807]

MAGA House of Horror

[Vesper 28,807]

https://prospect.org/power/2024-10-29-elon-musk-spacex-putin-treason/

Elon Musk cheers at a Donald Trump rally at Madison Square Garden in New York on Oct. 27, 2024. Another speaker at the rally called Puerto Rico a "floating island of garbage."

 

Just as Elon Musk has gone all in for Donald Trump, donating a total of $132 million, commandeering Trump’s ground game operation, posting even more over-the-top tweets than the campaign’s own messages, and hoping to take over several realms of policy, we learn courtesy of The Wall Street Journal that Musk has had several backchannel conversations with Vladimir Putin. This is the same Musk whose SpaceX company is used by NASA for the majority of its rocket launches, and whose Starlink system of low-orbit satellites is the basis for some military communications.

All of this has occurred at a time the U.S. has been redoubling its efforts to keep sensitive technologies with military uses out of the hands of China. But when it comes to the most sensitive space and communications satellite technologies, the fox is not only already in the chicken coop. The fox owns the chicken coop.

According to the Journal, in February Musk called on his echo chamber to lobby the Senate to vote down an aid package for Ukraine. “There is no way in hell that Putin is going to lose,” Musk said during a February audio event on X. 

Early in the war, the U.S. thanked Musk for helping Ukraine by providing Starlink for Ukraine’s battlefield communications. But later, it was revealed that Musk had denied a request by Ukraine to enable Starlink in Crimea to allow an attack on Russian ships. In effect, Musk has his own foreign policy, and it increasingly tilted toward Russia.

In its most recent investigative story, the Journal revealed that Musk has had several conversations with Putin. At one point, Putin asked Musk to avoid activating his Starlink satellite internet service over Taiwan as a favor to Chinese leader Xi Jinping. SpaceX, which operates Starlink, won a $1.8 billion classified contract in 2021 and is the primary rocket launcher for the Pentagon and NASA. 

The heavy dependence of U.S. military and space systems on Musk did not begin with Trump. It goes back to the Obama and Bush presidencies, and you can characterize it as one part the hollowing out and quasi-privatization of NASA, and one part the growing concentration of the aerospace industry.

After this wave, with the number of in-house engineers cut by half, NASA was more reliant on contractors. It’s not as if defense and space officials were blasé about Musk’s monopoly position. They tried, and substantially failed to find other suppliers.

According to Tim Fernholz, a former Prospect writing fellow who is author of the authoritative book on Musk’s rise to dominance in the space, satellite, and military communications industries, Rocket Billionaires, “The Defense Department has spent billions trying to get United Launch Alliance, the Boeing/Lockheed Martin joint venture that was the incumbent monopolist prior to SpaceX, and Jeff Bezos’ company Blue Origin to field competing launch vehicles, but both of those programs are years behind schedule.”

Despite Musk’s personal weirdness and his corrupt propensity for mixing business and political interests that rivals even Trump’s, his companies do make good cars and good rockets. “The main reason that SpaceX dominates the launch vehicle market is that its technology and execution is better and cheaper than that of its competition,” Fernholz told me. “Musk’s critics often try to hand-wave around this, but the fact is that SpaceX is a well-run company. It’s baffling to many people in the industry that Blue Origin has been around for longer than SpaceX but still hasn’t launched anything to orbit. United Launch Alliance has suffered from problems that we see in many traditional military engineering projects like the F-35 program, including delivery delays and rising prices.”

It is illegal for a private citizen to have their own foreign policy, least of all with an enemy great power. Some might dare call it treason.

Musk was also a risk-taker. The design for SpaceX’s Starlink network was seen by others in the satellite communications industry as too risky. “A similar network called Teledesic was attempted in the nineties and went out of business,” says Fernnolz. “By deploying a network that most of the industry didn’t think would work, SpaceX was able to get a huge head start over its competitors.” 

Walter Isaacson, Musk’s biographer, concurs. He told The New York Times that no other company “has been able to make reusable rockets, or get astronauts into orbit, or get some of these heavy satellites into high-Earth orbit.”

Should Trump be elected, we can imagine an even tighter technical and political alliance with Musk, unless Musk becomes even more flagrant in trying to usurp Trump’s power. It’s one thing to have a private foreign policy when Biden is president, but quite another when Trump is president. In a dictatorship, there is only room for one dictator at a time.

But if Harris is elected, the defense and space programs will need to redouble their efforts to find alternatives to Musk. This of course cannot be done overnight. For now, both agencies have been negotiating deals to try to gain more control of technology, even when partly reliant on Musk. The Pentagon’s Space Development Agency, says Fernholz, is purchasing its own network of Starlink satellites called Starshield that it will control explicitly. The Defense Department is also working with Amazon, which is building a Starlink competitor expected to launch in the next twelve months.

It may be necessary for government to de-privatize some of NASA, and for defense agencies to take back some of their own technical competence from contractors.

As for Musk, he is vulnerable on a few grounds. He has top-secret clearance, and he has backchannel conversations with Putin. He could lose his clearance and access to data necessary to his NASA and Pentagon contracts. It is illegal for a private citizen to have their own foreign policy, least of all with an enemy great power. Some might dare call it treason.

Musk could also have antitrust problems. There have been reports that SpaceX has used its dominant position in anti-competitive ways—underpricing its rocket launches to make it more difficult for launch vehicle competitors, and negotiating concessions of radio spectrum when launching competing satellites.

If Harris is elected, Musk will be a monumental headache. And if Trump wins, they will be two scorpions in a bottle.

[cosmicway 1,323]

33 minutes ago, Vesper said:

Yes it is.

Any variable that can be attributed a value without automatically attributing a value to any other variable is called an independent variable.

Who a person voted for in the past does not automatically pass through to who they will now vote for in a future election.

Yes, there is often correlation (even strong correlation), but it is not anywhere near 100 per cent, and injecting that variable introduces statistical drift masquerading as certainty of outcome.

Ask Trump about 2016 Trump voters who switched to Biden in 2020.

Search psephology.
What swing theory essentially does is it guards the pollster against "wasp nests" that is if for example your responders are from a Trump country, unbeknownst to you.
The bloke with Newcastle is one such example.

[Vesper 28,807]

21 minutes ago, cosmicway said:

Search psephology.
What swing theory essentially does is it guards the pollster against "wasp nests" that is if for example your responders are from a Trump country, unbeknownst to you.
The bloke with Newcastle is one such example.

'swing' is the actual amount of votes that move between parties from one election to another

please explain how you would use that to make a future prediction (and introduce it into polling on a valid statistical basis)

I could see one way (albeit fraught with predictive dangers) would to be to ask poll respondents how they voted in 2020, 2016, and 2012 (some will not have voted in all 3 or even 2 or 1 of those, and then see the 'swing' they exhibit.

I can see some utility in that, and I would be interested in see such polls.

Do you know of state polls (mainly care about the 'swing states', ironically, lol) that employ this methodology?

Edited October 29 by Vesper

[Vesper 28,807]

24 minutes ago, cosmicway said:

psephology

BTW

as I am a fair play (or at least strive to be) poster

I give you credit for knowing what psephology is

👍🏽

Edited October 29 by Vesper