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Big Health System CEOs to Testify Before Congress as Health Care Costs Continue to Skyrocket
WASHINGTON, D.C. – After the U.S. House of Representatives Ways and Means Committee announced a hearing next week with hospital system CEOs, Families USA, the longtime national nonpartisan health care consumer advocacy organization, released the following statement by its executive director, Anthony Wright: “The CEOs of the big hospital chains have some explaining to do […]
The wide-ranging fallout from the Supreme Court’s new terrorism decision, explained
The Court’s Republican majority fractured in a case that could impact everyone from immigrants to consumers.
'The Idea Is Not Dead' - Xbox Boss Explains Why She's Still Keen On A Mobile Store
It seemed to have been killed off, but apparently not!
La Toya Jackson explains why Janet Jackson isn't in the 'Michael' biopic
Director Antoine Fuqua said that although she doesn't appear in "Michael," Janet Jackson still supports her nephew Jaafar Jackson, who stars as the King of Pop in the biopic.
This is the surprising science behind the potato breed in your bag of chips
Michigan professor David Douches explains why the perfect chipping potato is so hard to find.
‘Invincible’: Who Won the Viltrumite War? The Finale Explained
Mark is left with a horrible choice. Lee Pace explains Thragg's ultimate plan.
'Devil Wears Prada 2' director explains why Adrian Grenier cameo didn't happen
'Devil Wears Prada 2' director David Frankel tells EW that he wanted an Adrian Grenier cameo as Nate, and explains why the 'Entourage' star didn't come back for the sequel.
The year’s weirdest game is hard to explain and even harder to put down
Welcome to Titanium Court.
FSU expert explains how FIFA World Cup will be another turning point for sports economics
The upcoming 2026 FIFA World Cup represents another significant evolution in how global sporting events are monetized, shifting away from traditional
FSU expert explains how FIFA World Cup will be another turning point for sports economics
The upcoming 2026 FIFA World Cup represents another significant evolution in how global sporting events are monetized, shifting away from traditional
Stephen and Ayesha Curry are coming for the sports drink market—and their kids were the first focus group
In an interview with Fortune, the Currys explain why they jumped at the chance to work on a Michelle Obama-backed healthy sports drink Plezi Hydration.
The Universe Is Expanding Too Fast and Scientists Can’t Explain Why
The most precise measurement yet shows the Universe is expanding faster than expected, deepening the Hubble tension. The result hints that something may be missing from our current understanding of the cosmos.
My Smartwatch Gives Me Health Anxiety. Experts Explain How to Make It Stop
After dealing with health anxiety caused by tracking my body's metrics on smartwatches, I took matters into my own hands with expert advice.
Top Futurist Sees Major Disruption Ahead For Healthcare
Healthcare is being rapidly reshaped by AI, data, and tech giants. Futurist Thomas Koulopoulos explains why traditional providers must adapt—or be disrupted.
Adobe Enterprise CMO explains how AI helps scale personalization
AI enhances marketing efforts, says Adobe Enterprise's Rachel Thornton, citing Ulta's approach to personalizing customer journeys with AI insights.
Where Are All These Meteors Coming From?
The frequency of fireballs in our planet’s skies seemed to grow in recent months. NASA and other meteor experts can’t agree on what explains it.
Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
How can collaborative care help cancer patients?
Psychiatrist Dr. Kyle Lavin explains the treatment benefits when oncologists team up with mental health care professionals.
Sydney Sweeney cameo cut from “The Devil Wears Prada 2”, source explains why (exclusive)
A source tells EW what would've happened in the "Euphoria" actress' scene.
Sydney Sweeney cameo cut from 'Devil Wears Prada 2,' source explains why
Sydney Sweeney's cameo in 'The Devil Wears Prada 2' was cut from the final film, as a source tells EW what would've happened in the scene and why it was nixed.
A bizarre new state of matter may be hiding inside Uranus and Neptune
Deep inside planets like Uranus and Neptune, scientists may have uncovered a bizarre new state of matter where atoms behave in unexpected ways. Advanced simulations suggest that carbon and hydrogen, under crushing pressures and scorching temperatures, can form a strange hybrid phase—part solid, part fluid—where hydrogen atoms spiral through a rigid carbon framework. This unusual “superionic” structure could reshape how heat and electricity flow inside these distant worlds, potentially helping explain their mysterious magnetic fields.
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Apple Testing Two Small Yet Useful iOS 27 Options
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Bayonetta creator explains what actually happened in Bayonetta 3's ending
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White House spotlights tax perks for small businesses
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Is watermelon good for you? Experts explain calories, sugar content
Watermelon is a summer staple known for its refreshing taste and high water content, but many people wonder how healthy it really is.
How Americans really feel about immigration
The messy politics of immigration reform, explained.
Kash Patel’s Erratic Behavior Could Cost Him His Job
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'AI is transforming businesses across America:' EMJ Capital founder
EMJ Capital founder Eric Jackson explains his 'new Carvana’ theme, highlighting undervalued companies poised for significant growth on ‘Making Money.’
Free Healthy Heart Clinic offers screenings at 9 Henry Ford Health locations
Everything from an EKG to blood sugar levels will get checked, plus doctors will be there to talk to you and explain the results.
‘For All Mankind’ Boss Explains A.J. Jarrett’s ‘Comeback’ Story
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Kingdom Come 2 Lead Celebrates Gayming Award In A Non-Woke Way
Daniel Vávra ties himself up in knots explaining why his gay game's gayness isn't woke
How Elizabeta Gjorgievska Joshevski built a global tech career and is now shaping AI strategy for enterprises
Elizabeta Gjorgievska Joshevski explains how EverCognitive helps companies approach AI transformation with strategy, readiness, and measurable business outcomes.
Participation, Debate and Community: Exploring Ancient Democratic Values in Modern Society | Pomona College in Claremont, California
Associate Professor of Classics Benjamin Keim explains what our methods for assessing potential officeholders have in common with those of the ancient Athenians.
Why Promising Cancer Drugs Failed: Scientists Uncover the Missing Piece
A new study helps explain why a promising class of cancer drugs has often failed to meet expectations in clinical trials.
The making of modern science
Any history of the West's scientific revolution must explain how science generates new discoveries and ways of thinking about the natural world. Social-constructivist approaches have failed to provide a convincing account.
The science behind the flu
As flu season lingers unusually late, UAB experts explain how influenza spreads, why symptoms occur, which prevention strategies truly work, and when vaccination, antivirals and medical care can help reduce severe illness and complications.
Deadline Hollywood
CBS Entertainment President Amy Reisenbach explains why they went with 'NCIS: New York' and did not consider picking up 'Tony & Ziva' for next season. Read more below.
How Dangerous Is Anthropic’s New AI Model? Its Chief Science Officer Explains.
Just how fast is the future coming at us, and how worried should we be? Last week, Anthropic announced Claude Mythos, the latest in what has become the leading family of AI models. In a very short time, AI models from Anthropic and other companies have become shockingly powerful at coding. But Mythos stands out because it is not just able to write code that can hack into critical systems, like those that run financial companies, but also to analyze systems and find and string together vulnerabilities in them, allowing Mythos to evade even sophisticated defenses. Anthropic chose to release Mythos to only about 40 leading corporations, so they could analyze the model’s capabilities and prepare countermeasures, in an effort it calls Project Glasswing. The release of Mythos—and the prospect of similarly powerful AIs to follow—has caused tremors at the top levels of Washington and corporate America. Jared Kaplan, co-founder and chief science officer at Anthropic, is at the center of this breakthrough. We sat down with Kaplan so he could walk us through just how powerful Mythos is, what the implications are for issues like our personal privacy, and what comes next. This conversation has been edited for length and clarity. Sean Fischer: For the layman, how is Claude Mythos different—more powerful, more dangerous—from previous models released by Anthropic? Jared Kaplan: I think most people have experienced the fact that our computers get faster and faster over time and over many decades. There’s this very smooth trend where every 18 months, maybe, computer processing power doubles. I think AI is on a trend in which it’s improving maybe 10 times faster than that. A lot of the earliest work that I did in AI was around identifying this scaling trend in AI. Claude Mythos is the latest model from Anthropic, but it’s not different because it’s qualitatively different. It’s really just kind of the culmination of the trend of AI models getting smarter and smarter at all kinds of general capabilities, from reasoning to software engineering to scientific research to knowledge work. For us, it was the first model to demonstrate very elite-level cybersecurity capabilities. It wasn’t because we trained Claude Mythos to focus on cybersecurity, but it was because, as a byproduct of its general intelligence and its general ability with software, it’s particularly good at identifying vulnerabilities in software and how to exploit them.
The value of free health insurance: Evidence from Mexico’s Seguro Popular
Expanding free health insurance is central to universal health coverage, but its impact depends on how households value it. This column examines Mexico’s Seguro Popular and shows that households value the programme less than its fiscal cost. This helps explain why its effects on labour markets were modest and suggests that overall welfare gains may be smaller than often assumed, despite the programme’s large expansion in coverage.
Scientists Think Dark Matter May Be Filling Our Galaxy With Mysterious Light
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Seniors: Dennis Ouellette, 80
Ontario Orchards owner not slowing down as he grows his farm market By Stefan Yablonski The seeds for Dennis Ouellette’s career path were sown when he was 6 years old — and he isn’t slowing down. “Since age 6, I grew up an agriculturalist. That is 72 years. Started out with a 37-acre farm when I bought it from my parents and built it up to almost 300 acres. Brought it back down to around 200 acres now,” he said. The location of his business was just a barn, a horse barn, he said. “In 1967, it officially became Ontario Orchards Farm Market. My wife [June] and I graduated [high school] in 1964 and three years later we became Ontario Orchards. We have been here more than 60 years,” he explained. “It is the same location. We have expanded over the years as the customers mentioned different things; we have expanded to bring in different products. “What keeps me going is I totally enjoy what I am doing. I enjoy the people that I work with. The customers are wonderful. Mostly, more important than anything, is my family — my wife, my daughters, grandchildren, son-in-law — we are still very much a family business I’m not going to retire just yet.” He also enjoys the changing seasons. “Ontario Orchards is a four-season business. There is always something new; more energy, more interest. Not only for the business but for my staff, the people that work here. Things keep changing, changing. We are always looking forward to something new and exciting,” he said. They work with quite a number of local farmers within the area and throughout the state, throughout the country and from around the world.” “We do have things from different countries around the world. But specifically, we are very excited about the local. We have an over 200-acre farm. We sell just about everything we raise on that farm right here at the farm market. For example, we press cider year-round. We make baked goods every day, year-round. We sell our apples 12 months a year.” “Today, there are so many products in Ontario Orchards that we never would have thought of in 1967,” he continued. “I attribute a lot of that to my daughters. I like the young blood and new energy. That is very crucial to a business, it keeps it vibrant.” What’s his favorite part of the job? “I don’t have a favorite part. I enjoy it all. I have a lot of intertest in other things,” he said. “I have staff that knows so much. I can go to the staff and say, ‘I don’t know what this is, you tell me what this is.’ They are so knowledgeable. It is impossible for one person to know everything in this place. In the summer, staff is about 60; in the winter about 20, 25.” He added that he plans on being around for several more years.
Seniors: John Lemp, 70
Has been working for more than 50 years in the business — M. Lemp Jewelers in Syracuse — that his great-grandfather started in 1890 By Mary Beth Roach Don Lemp owns and represents the fourth-generation of the family business, M. Lemp Jewelers, but he still calls himself “a counter guy.” “I love interacting with couples. It’s a wonderful business. You’re celebrating marriages, engagements, births, anniversaries — all these different significant moments that people invite you into their lives for whatever brief period of time it might be to help them celebrate whatever their important occasion is,” he said. This is one of several reasons why Lemp, at 70, has been working for more than 50 years in the business that his great-grandfather started in 1890. Always located in the downtown Syracuse area, the store has been situated in the building that the family owns on East Fayette and Warren streets since 1971. He jokes that he’s been in the business so long he is now selling engagement rings to the second generation in some families. He also loves the opportunity to learn about the ever-changing industry and gemstones. “You have to know not only diamonds, but all the colored stones, from A to Z, from andalusite to zircons,” he said. Lemp said he had originally thought he wanted to become an attorney, but while working part-time in the store while attending college at Syracuse University, he decided to follow in the family business. His father, Walter D. Lemp Sr., and his mother tried to discourage their son from pursuing retail, reminding him of the hectic hours and lifestyle intrinsic in this business. But the younger Lemp [whose full name is Walter D. Lemp Jr.] prevailed. So, over the years, father and son got to work together in the family business for 35 years. And while many other jewelers retreated from downtown to suburban malls in the 1970s, the Lemps decided to stay downtown. And it’s a decision that Lemp Jr. feels it has worked out well. As he explained, downtown Syracuse is seeing a rebirth. One website, Point2Home.com, has noted that more than 6,000 people live there and the median income is about $62,000. In addition, according to the website that Lemp built years ago, the store can cater to customers throughout the country. His customers, whether from across the country or across Onondaga County, are just one reason that Lemp stays engaged in the business. His employees, the opportunity to continue to learn in an industry, and the challenge to continue to improve business are all other reasons, he noted. He’s also been able to travel to India to buy diamonds and he enjoys buying and selling estates. “I’d rather gallop in the corral than ride into the sunset,” Lemp said.
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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