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St. Raphael’s Church announces season of dining and entertainment
St. Raphael’s Episcopal Church has announced a full lineup of special events for the 2026 season, marking a joyful return to the church’s parish hall. With a mix of beloved traditions and exciting live entertainment, the season is designed to keep the community well fed and thoroughly entertained. “We are thrilled to be back in […]
Microsoft Finally Ups FAT32 Size Limit
You probably don’t spend a lot of time using the FAT32 file system anymore, since it’s thoroughly been superseded many times over. Even so, Microsoft has seen fit to deliver an upgrade …
Valverde: “I never imagined being Real Madrid’s captain”
Real Madrid midfielder Fede Valverde talked to Uruguayan TV AUF and shared some thoughts about the upcoming FIFA World Cup and also about the club. “Wearing the armband is something incredible; I never imagined being captain for Real Madrid, and I thoroughly enjoy every opportunity to wear the armband. It carries some pressure too. When […]
'We played the right type of game'
Birmingham City boss Chris Davies tells BBC WM he was thoroughly happy with his side's performance in Wednesday's 2-1 win over Preston.
Connecticut reports 23 cases of cyclosporiasis as health officials urge produce washing
Connecticut health officials are urging people to thoroughly wash fresh produce after the state reported 23 cases of cyclosporiasis as of July 1. The parasitic illness spreads through eating contaminated food, according to doctors. The Connecticut Department of Public Health said it has not been able to determine whether the cases are concentrated in one area and is working to…
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
Virginia Tech’s Season Begins Saturday Night Against Maryland
Virginia Tech enters Saturday night's game against the Maryland Terrapins, sitting at 2-0 on the season for the first time in five years. For the Hokies, it's been a solid start to the James Franklin era. Tech has thoroughly beaten two overmatched opponents at home in Blacksburg, and will now head on the road for …
🚨 A rout is on! Italy are tearing Turkey apart
Italy, thoroughly revamped by coach Roberto Mancini, is working to be seen again as “Bella Italia.” In the Nations League clash against Türkiye, the Azzurri are at least showing that the change i...
Google and Apple are warning some visa employees not to leave the US due to 'significant' return delays of up to a year
The US State Department said it was "prioritizing thoroughly vetting" each visa case, which could cause delays.
Introducing: The New Glashütte Original 70s Collection, a Reworked Take on the Integrated Sports Watch
The Seventies model thoroughly revamped, becoming the Glashütte Original 70s with a smaller case, textured dials and a new-gen movement.
Ryan calls complaints against former BPS superintendent 'disturbing'
Ryan said the school board needs to build trust with the community through transparency and thoroughly investigate each claim against the administration.
Cuba: US Embassy issues health alert due to GI pathogens like norovirus, Shigella
The US Embassy in Havana issued a health alert last week after seeing a significant increase in diarrheal illness across Cuba, associated with the continuing degradation of the water and energy infrastructure. This degradation affects water supply, and food storage and temperature control. Recent cases have included E. coli, norovirus, Shigella, and other gastrointestinal pathogens. This comes after a health alert dated August 28 which noted that Cuba’s water delivery infrastructure is increasingly unstable. The Embassy is aware of reports of residential neighborhoods that have gone for long periods without receiving water from municipal or public sources. Dengue cases surge in the Tampa Bay area Prolonged and worsening water issues and power outages caused by the regime’s mismanagement of public infrastructure impact the entire island. Water often only flows to residences when city power is on for a sufficient amount of time. Media reports indicate wells are running dry across the country due to drought, aggravating the ability to supplement water delivery. The lack of water, coupled with power shortages that have disrupted cold chain networks, can present significant health and safety challenges. We remind U.S. citizens to be aware of the risks when traveling to Cuba and take precautions. Embassy officials offer the following precautions to prevent illnesses: Avoiding Foodborne Illness: Be cautious of meat, poultry, seafood, eggs, dairy, and other perishable foods that may have been inadequately stored. Avoid foods that appear to have been sitting at room temperature for extended periods. Avoid raw or improperly washed vegetables. Be cautious of food cooked and then cooled, stored, or reheated, as improper temperature control can allow pathogens or toxins to develop. Wash hands thoroughly with soap and water before eating and after using the restroom. Hand sanitizer may be less effective against certain gastrointestinal pathogens, including norovirus. Outbreak News Today is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Water Safety: Use only water known to be safe for drinking, brushing teeth, making ice, and cooking. Use bottled or appropriately treated water; check that bottle seals are intact before opening. Avoid ice unless the water source is known to be safe. Do not use untreated tap water to prepare beverages or food. Be aware that contamination can occur when food, utensils, and surfaces are washed with contaminated water. If You Become Ill: Stay hydrated, especially with frequent diarrhea or vomiting. Most illnesses are self-limited and improve within several days. Seek medical attention for persistent or severe diarrhea, bloody stools, high fever, significant abdominal pain, repeated vomiting, signs of dehydration, or symptoms not improving. Common medications (e.g., acetaminophen, ibuprofen, antibiotics) are frequently unavailable in Cuba; consider bringing your own supply. If you like the reporting on Outbreak News Today, please consider a paid subscription for $5/month or $30 a year. Help support the cause of this niche journalism. It is appreciated.
Shaky political "science": breakdown of the peer review process
[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 December 2025, we examined over 40 studies focused on ranked choice voting (RCV). 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 four articles in our series: the first one summarizing the overall results of our report; the second one analyzed 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; a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research; and a fourth article that analyzed seven studies sponsored by New America’s political reform program that deployed flawed methodology based on “simulated” elections instead of real-world elections, which when combined with some basic misunderstandings of RCV, produced odd and misleading results]. In this fifth article in our “Shaky Political Science” series, we examine two flawed studies on the impact of RCV on voter turnout authored by San Francisco State University professor Jason McDaniel. McDaniel’s research in these two papers stands out for its puzzling methodology, inadequate and cherry-picked data, and faulty analyses that not only misunderstands ranked choice voting but reflects a basic misunderstanding of politics in general. The first of the McDaniel studies actually passed through a peer review process and then was subsequently published by the Journal of Urban Affairs, despite its obvious errors. It is perplexing that a jury of peer political scientists did not catch the errors and send it back to McDaniel for a rewrite or outright rejection. This calls into question the credibility of the peer review process itself. The peer review process is supposed to represent the intellectual pinnacle of the profession – a way for academics to certify the quality of each other’s work, and to act as a check against sloppy research. In the case of the McDaniel study, the peer review process failed. Yet his flawed study has been cited dozens of times by other political scientists in their own flawed works — an unvirtuous circle of academic research. Here is the first Jason McDaniel study that we analyzed: “Writing the Rules to Rank the Candidates: Examining the Impact of Instant-Runoff Voting on Racial Group Turnout in San Francisco Mayoral Elections,” by Jason McDaniel, 2016, Journal of Urban Affairs. This study covered San Francisco’s use of ranked choice voting (RCV) in mayoral elections, purporting to show that RCV reduces voter turnout overall, increases voter errors, and has a negative impact on “marginal populations,” i.e. racial minorities, mainly as the result of the alleged complexity of RCV asking voters to rank candidates and having to be familiar with multiple candidates and related dynamics. McDaniel reached these conclusions by examining five San Francisco mayoral elections taking place in odd years (i.e. where there are no federal or state races on the ballot) from 1995 to 2011, comparing three earlier non-RCV races (in 1995, 1999 and 2003) with the later two RCV races (in 2007 and 2011). Only two RCV races were studied, and as McDaniel acknowledged, only the contest in 2011 was “competitive” (that contest in 2007 was the first mayoral election using RCV, and incumbent mayor Gavin Newsom won easily in the first round of counting, earning 74% of first choices and far ahead of the second place candidate who had just 6.3% of first choices). Yet even the allegedly competitive contest in 2011was won by an incumbent with a landslide margin of nearly 20 points. No other elections were studied by McDaniel, even though San Francisco elects 17 other offices besides mayor with RCV, including the 11 seats on the Board of Supervisors (the name for the city council) in even years, and six other citywide offices in odd years. Previous academic research has found that competitive elections can stimulate greater voter involvement and higher turnout. For unexplained reasons, this study ignored at least 34 other RCV races between the years 2004 to 2011, many of which were very competitive and had high voter turnout. Instead, McDaniel’s analysis based its conclusions on data from only a single, marginally-competitive mayor’s race using RCV. These 2007 and 2011 mayoral elections were the only two RCV elections that McDaniel used as the sole basis for his small data set to determine the impact of RCV on voter turnout. Despite having no meaningful data to inform his study, McDaniel nonetheless concluded that RCV lowered turnout when compared to the previous three mayoral non-RCV elections using two-round runoffs. Then, using the same inadequate database of San Francisco elections, McDaniel also found that “Instant-runoff voting is associated with a significant decline in Black voter turnout and White voter turnout…On average, Black voter turnout decreases by 18 points and White voter turnout decreases by 16 points …” But how could he credibly arrive at that conclusion when his study was based on voter turnout in two RCV contests that were not even remotely competitive? Beyond that, McDaniel tacitly admits that the key determining factor driving Black voter turnout was not RCV vs non-RCV elections, but the presence of a viable Black candidate. McDaniel wrote, “Black voter turnout was about 17 percentage points higher for the two elections that featured an African American candidate compared to the three elections that did not” — but one of those three elections did not use RCV. The same dynamic was present for White voter turnout, in which higher turnout was not correlated with RCV vs non-RCV elections, but the presence of a viable White candidate. Beyond that, McDaniel’s voter turnout results, whether correlated with the race of the candidates or RCV vs non-RCV elections, showed no clear patterns. In his Figure 1, he shows low Asian voter turnout in the non-RCV elections in 1999 and 2003 when there was no viable Asian candidate running, but in the 2007 mayoral election using RCV, there is still no viable Asian candidate, yet Asian turnout increases dramatically. For Latinos, voter turnout is virtually the same in 1999 (no viable Latino candidate, non-RCV elections), 2003 (leading Latino candidate, non-RCV elections), 2007 (no viable Latino candidate, RCV elections) and 2011 (leading Latino candidate, RCV elections). Meanwhile White voter turnout increased dramatically from a low in the 2007 mayoral election, in which the leading candidate was a white incumbent, to the 2011 election in which there were no leading white candidates that finished any higher than sixth place with 5.6% of the vote. Clearly other factors were determining voter turnout than either the race of the candidates or RCV vs non-RCV elections. Despite these inconsistent findings, McDaniel boldly marches forth to render conclusions about the impact of RCV on different groups of racial voters, even though his conclusions are not remotely supported by his data. This looks like the worst kind of “data cherry picking” to cover up a puzzling conclusion based on a poor data set involving only two RCV elections, neither of which were remotely competitive. McDaniel’s cherry picking didn’t stop there. In calculating voter turnout for the three non-RCV mayoral elections in 1995, 1999 and 2003, he failed to mention that in each of these years there was a two-round runoff election, with a first round election in November in which no candidate garnered a winning majority of the vote, followed by the decisive runoff in December. So there were two non-RCV elections in each election year. But McDaniel only decided to include one of the two elections in his data set, apparently the December runoff in each year (which had higher turnout in two out of the three mayor’s races, and lower turnout in the third). So he ignored the November 1995 election, the November 1999 election when turnout was only 45 percent, and the November 2003 election when turnout was only 45.7 percent. This is flawed methodology based on blatant cherry picking, since McDaniel simply omitted three elections worth of data that did not fit within his frame. His study should never have made it through a peer review process, and it is an embarrassment to political science that not only was it approved for publication, but it has been uncritically cited by dozens of other political scientists. Share A “study” without wider context or credible data Lacking a data set that included even a single competitive RCV mayoral race, McDaniel declared that RCV lowered voter turnout in 2011, when turnout was 42.5 percent. But he also ignored -- or did not realize -- that voter turnout in mayoral elections during 2011 had sharply declined in all big cities, including far more steeply in nearby Los Angeles and other cities. In fact, San Francisco’s election in 2011 had the second highest turnout of any mayoral election in the nation’s 22 largest cities from 2008-2011. Was 2011 just a particularly low turnout year all across the country, perhaps because the country was still reeling from the aftermath of the housing market collapse in 2008-2010? McDaniel ignored this factor and did not search for alternative explanations beyond his flawed thesis. Furthermore, while McDaniel suggested that RCV leads to more “overvotes” – which is an invalidated ballot in which a voter, in either a non-RCV or RCV race, makes a mistake and selects two candidates for a single choice – he failed to address that in the June 2012 US Senate primary contest, which used the typical “one-choice” plurality method and did not use RCV, there was more than five times as many overvotes in San Francisco and Oakland than in the RCV mayoral elections in those cities. Additionally, turnout had been significantly higher and more representative in RCV elections for the San Francisco Board of Supervisors races -- that McDaniel didn’t even bother to study -- as compared to previous “delayed two-round runoffs” for those offices, often higher by as much as 40% in the RCV races. Perhaps the most surprising irony was that an earlier study co-authored by McDaniel (Overvoting and the Equality of Voice under Instant-Runoff Voting in San Francisco by Francis Neely and Jason McDaniel, 2015, California Journal of Politics and Policy) found that overvotes are often more common in lower-income precincts, but that the pattern of overvoting is similar in both RCV and non-RCV contests. Wrote Neely and McDaniel: “[Overvote] errors appear to be a function of complexity in general and not IRV per se…. the pattern of overvoting is similar in both IRV and non-IRV contests…Even among votes cast in a top-of-the-ticket race like U.S. Senate, overvoting occurs in an uneven fashion” (IRV, or ‘instant runoff voting,’ is another name for ranked choice voting in single-winner contests). So McDaniel not only contradicted his own conclusion in his earlier study with Neely, but the earlier study had been an important corrective for one of the most frequently made mistakes made by anti-RCV political scientists – a continual failure to provide crucial context by pairing a comparative analysis of real world RCV elections with an analysis of real world plurality or other electoral methods. Unfortunately McDaniel’s earlier study does not get cited much by other researchers, and McDaniel did not even cite it in his own flawed study analyzed above. Incredibly, McDaniel cherrypicked McDaniel, even as his flawed study has been cited dozens of times. Despite being a textbook example of poor political “science,” featuring both inadequate as well as cherrypicked data, the shelf life of McDaniel’s study has been extended endlessly by other political scientists and media outlets who have cited it uncritically. Equally troubling, the Journal of Urban Affairs put this paper through its peer review process and published it despite its numerous flaws. McDaniel gets it wrong, Part II Not content with producing one shoddy, confused paper, McDaniel produced another one a few years later, here is the link: Electoral Rules and Voter Turnout in Mayoral Elections: An Analysis of Ranked-Choice Voting by Jason McDaniel. San Francisco State Univ. (McDaniel, 2019). In this paper, McDaniel extended his previous flawed research focused on San Francisco, about voter turnout and voter impact, to multiple RCV cities through the 2018 elections. In his data set he included San Francisco, Minneapolis, St Paul, Oakland, Berkeley, San Leandro and Santa Fe. He then compared their turnout in mayoral elections to non-RCV cities, and to mayoral elections in those cities held before first RCV usage, and concluded “a significant decrease in voter turnout of approximately 3–5 percentage points in RCV cities after the implementation of RCV.” But he replicated many of the same mistakes as his first study presented above. While in this study McDaniel attempted to distinguish between various factors, such as odd year vs. even year elections, competitive races vs. noncompetitive, contests with open seats and other factors, the analysis is superficial and inadequate. He attempts to incorporate so many often-conflicting variables that his conclusions are once again unsupported by the actual data, and ultimately unconvincing (this second study never underwent a peer review process). For example, McDaniel declared that for RCV cities “in elections that are more competitive than average (margin of victory less than 20%), there is no significant RCV effect on voter turnout.” Yet McDaniel is a professor in a city, San Francisco, in which the 2018 special election using RCV to fill a mayoral vacancy was exceedingly close, decided by a margin of only one percentage point, and resulted in the second highest turnout in San Francisco history for a mayoral election. Illustrating the complexity involved in assessing the critical factors in turnout, that special election was held on the same election day as federal and state offices, which tends to boost turnout. Yet more voters participated in that RCV mayoral election than voted on the same day in the non-RCV primary for governor in which former San Francisco mayor Gavin Newsom was running. Even more puzzling, in this study McDaniel inaccurately cited his own previous study from 2015 with Neely. McDaniel wrote, “Neely and McDaniel (2015) examine individual ballot image files within a natural experiment, and find higher rates of ballot errors in RCV elections compared to non-RCV elections” (emphasis added). However, we quoted from that 2015 study several paragraphs above, and in actual fact Neely and McDaniel actually concluded the opposite -- that the pattern of overvoting – which is the most common form of ballot error – is similar in both RCV and non-RCV contests. McDaniel also ignored the fact that in Bay Area RCV elections held in even years – at the same time as federal and state elections, when turnout is highest -- the “undervote” (that is, the dropoff in ballots cast for governor or mayor) has declined, which contradicts his thesis that the act of ranking ballots in RCV elections is too complex and presents a barrier for certain demographics of voters. To his credit, McDaniel actually acknowledged another contradiction in his own paper, which found a negative RCV turnout effect in some odd-year elections but not others, admitting that “may present a challenge to the theory that increased complexity [from RCV] will have a marginal negative effect on voter turnout.” But he tried to wallpaper over that contradiction by blaming it on an unlikely source -- bad election administration. McDaniels wrote, “This would suggest that the negative impact of RCV on voter participation may be alleviated by high quality election administration,” without presenting any data or even anecdotes to support such an unfounded theory. While competent election administration of any election, whether RCV or non-RCV, is crucially important, there is no credible research that supports the far-fetched notion that the quality of election administration has much of an impact on voter turnout in US elections, whether using RCV or otherwise. Upgrade to a $5 subscription The unvirtuous circle of academic research It is puzzling how McDaniel’s first study could have survived the peer review process for the Journal of Urban Affairs. The mistakes in McDaniel’s first study above should have been obvious to anyone with a basic understanding of ranked choice voting as well as elections in general, and elections in San Francisco specifically. It undermines faith in the credibility of the peer review process to think that a jury of peer political scientists did not catch the egregious errors and reject McDaniel’s sloppy research and paper. And it remains a black mark on political science that this thoroughly discredited study has been cited dozens of times by other researchers. Indeed, in our research paper it became apparent that many political scientists cite each other’s flawed research to pad their list of citations and to legitimize their own flawed research, creating an unvirtuous circle of political science. More broadly, McDaniel’s voter turnout studies, like other studies evaluating RCV’s impact on turnout, lacked a recognition that no credible opinion or research can claim that RCV increases voter turnout merely because so many voters are excited to vote for their favorite candidates instead of “lesser evil” candidates without worrying about spoilers. Instead, with respect to turnout, the advantage of RCV has always been that, in most places where it has been used, it has eliminated a pre-November primary or post-November runoff, which usually saw about half the turnout of the November general election. This is especially true during even years when there are state and federal elections on the ballot which substantially drive turnout more than local contests. Any study that does not adequately account for this non-November primary/runoff vs November general election dynamic, or odd year vs even year election factors, is simply not credible. Both of McDaniel’s studies largely failed on those grounds. Steven Hill and Paul Haughey Share Upgrade to a $5 subscription Thanks for reading DemocracySOS…your digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
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Ruby Ridge Book Explores Roots of American Conspiracy Politics at Vroman’s Tonight
Author Chris Jennings will discuss his critically acclaimed book “End of Days: Ruby Ridge, the Apocalypse, and the Unmaking of America” at Vroman’s Bookstore on Monday, March 30, in a 7 p.m. conversation with journalist and author Jim Newton, a Pasadena resident. The book, published in February by Little, Brown and Company, examines the 1992 Ruby Ridge standoff — in which federal agents surveilled a cabin in Boundary County, Idaho, as part of an operation to arrest white separatist Randy Weaver — and traces a line from that 11-day siege to the conspiratorial politics that now mark American public life. By the time Weaver surrendered, his wife, his son, and a U.S. Marshal were dead. Jennings, a former editorial staffer at The New Yorker and the author of “Paradise Now: The Story of American Utopianism,” argues that the strain of apocalyptic Christianity that gripped the Weaver family can be traced to biblical prophecy readings dating to the 1870s and through the rise of 20th-century fundamentalism, according to the publisher’s description. The events at Ruby Ridge, Jennings contends, acted as an accelerant for this worldview. The New York Times Book Review called the book a thoroughly researched chronicle written with precision, according to a review published in February. Kirkus Reviews described it as a vivid reconstruction of a pivotal moment in the history of right-wing extremism. Newton, who will lead the conversation, spent 25 years at the Los Angeles Times as a reporter, editor, bureau chief, and columnist, including a stint as editor of the editorial pages from 2007 through 2010. He is the author of four books, including biographies of Chief Justice Earl Warren and Gov. Jerry Brown. Newton teaches at UCLA, where he founded Blueprint magazine, and is a columnist for CalMatters. He received the American Political Science Association’s
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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HARRISBURG – Attorney General Dave Sunday and the Office of Attorney General’s Antitrust Section announced a court-approved settlement that will protect patients, physicians, and health plans potentially impacted by Allegheny Health Network’s (AHN) recent acquisition of Heritage Valley Health Systems (HVHS). The Office of Attorney General thoroughly reviewed the transaction and outlined a number of […]
“Victor Wembanyama thoroughly embodies the spirit of …
“Victor Wembanyama thoroughly embodies the spirit of …
Let's Stop Saying "Metroidvania"
Happy birthday, Metroid. Now let's all celebrate by forgetting the term "metroidvania" as thoroughly as we've all forgotten Metroid: Other M.
11 Stats to explain Cavs 112-84 win over Nets
The Cavs thoroughly dominated both sides of the floor in their run-away victory.
47 revelatory hours with Assassin’s Creed Black Flag Resynced
To review this month’s supposed remake of 2013’s Assassin’s Creed IV: Black Flag, I must first correct the record. Assassin’s Creed Black Flag Resynced isn’t a game about pirates (and assassins) in the 18th century Caribbean. Sure, sure, that’s what the trailers would have you believe. They’ve go their clips of Edward Kenway swinging his swords and steering his pirate ship into cannon-blasting combat against British frigates. Is Resynced truly a pirate game? Of the 47 hours I spent playing Resynced over the past few weeks, I will admit I spent maybe 44 (?) of them collecting plunder with Blackbeard, drinking rum with Anne Bonny and debating the pros and cons of the lawless pirate-run settlement Nassau with Benjamin Hornigold. I may in fact have spent dozens of hours sailing to remote islands, reading treasure maps, opening messages left in bottles, sinking the odd Man o’ War with a well-timed broadside, collecting sea shanties for my crew, befriending a pet monkey, and hunting whales. I am nevertheless neither so Gullible nor so Narrow-Minded as to call the magnificent Resynced a “pirate remake.” Oh no. Forget any reviews of Assassin’s Creed Black Flag Resynced you may have read. Forget what you yourself may have played, if you’ve spent any time with the game since its launch. What Assassin’s Creed Black Flag Resynced is really about I’m here to give you the scoop (spoilers for Resynced’s Animus menu lore, which many players are probably not going to see): Assassin’s Creed Black Flag Resynced, the new game whose development was led by Ubisoft Singapore, is set in the year 2096. It’s about a societal split, about revolution, about an old Assassin’s Creed adventure repurposed in the series’ distant future as propaganda or samizdat. It’s about the overheated future of 2096, essentially conquered by the series’ well-established nefarious Templar-run company Abstergo. (In this future, Abstergo is, among other things, remaking a 2013 pirate movie called Devils of the Caribbean, but this time in VR. The VR version will be backed by a marketing plan that involves AI influencers, a 10,000-avatar “metaverse premiere event” and that manufacturing of memes aimed at “50 million VR nodes.” See what I mean about Abstergo being evil?) Resynced is about a late 21st-century population mollified by the widespread use of Abstergo’s Animus devices which let people experience the lives of historical figures culled from “genetic memories.” It’s about an insurgent faction of Assassins. And it’s about a plan to wake the population into revolutionary fervor by showing them the memories of 18th century pirate Edward Kenway, who—as depicted in the original Black Flag and again in Resynced—gradually comes to an understanding about the Assassins of his day and their centuries old fight for freedom. Resynced is also about an AI developed within the late 21st-century Animus, called Ego. It has found a way to alter genetic memories, to create and show Animus users false ones. The game is about Ego’s efforts to tell the people experiencing Edward Kenway’s memories that Kenway was wrong, that fighting against order is folly, that we should crave a world where “compliance is rewarded with power.” Who are we in this game? We’re an unnamed Animus user in 2096 who experiences Kenway’s journey virtually. At four moments during Kenway’s exploits in the Caribbean our virtual jaunt can be interrupted for 10-15 minutes, as we explore Animus “rifts” where Ego explicitly tries to tell us how wrong Kenway was. None of this is what 2013’s Assassin’s Creed IV: Black Flag was about, of course. That was a different game, even if you also spent dozens of hours pirating as Kenway in that game, across missions and a map that’s been faithfully reintroduced and rebuilt for Resynced. The 2013 Black Flag was about you playing as an unnamed researcher newly hired in 2013 by Abstergo Entertainment, the video game development wing of the Templars’ evil-corp. In that game, you were assigned to use the Animus to dive into the genetic memories of Desmond Miles, a man who died in Assassin’s Creed III and who was descended from a long line of Assassins (and at least one Templar) whose adventures were featured across the prior AC games. In 2013’s Black Flag, your mission was to plumb the Edward Kenway experience for material that Abstergo could use in productions such as the motion picture Devils of the Caribbean, though the Templars had some ulterior motives for the assignment. Resynced is therefore not a remake of 2013’s Black Flag. It’s a sequel. It is a new game about a new exploration of Edward Kenway’s pirate/assassin journey, set 83 years after the framing sequences of the original game. Resynced provides a new narrative framework that, thanks to the concept of an AI that wants to alter historical memories, arrives with creative justification to tweak a now-classic 13-year old game. That makes Resynced, already a hot seller, a tantalizing hint of where Ubisoft could go with a line of Assassin’s Creed remakes. They could remake the first game, the Ezio games, even the recent ones set in ancient Egypt and Greece. And for each, they could not just modernize the graphics and controls but expand and alter their sequences of events to use them as reference points for a conflict set in the late 21st century, as rival factions try to weaponize historical memories. Game File is a reader-supported publication. To receive new posts and support my work, even when that work consists of ravings about Assassin’s Creed’s modern day plot, please consider becoming a free or paid subscriber. Subscribe A story most players will probably miss If you didn’t know any of this, I’m not surprised. If you played the game and still didn’t catch it, I’m not a bit shocked. If Ubisoft has no plans to do more of this, I won’t fall down. Almost all of what I’ve described is optional and missable in Black Flag Resynced. I nearly missed it myself, as I planned for a breezy playthrough of an old Assassin’s Creed favorite. Instead, I found myself in a 40+ hour hunt for the “real” story, buried deeper in Resynced than any gold-filled chest. The story of 2096 is Resynced’s meta-story, told in two remote pockets of the game: 1) four optional playable “rifts” that players may find as they proceed through Resynced’s pirate fantasy , 2) in dozens of text files tucked three menus deep in the game’s barely-interactive Animus hub. Assassin’s Creed games have almost always had a meta-story about people in the 21st century (usually matching the game’s release year) and their connections to the main history portions you play. Those meta-stories have always been squeezed into the margins, though seldom as severely and as, uh, graphiaclly simple, as in recent games. Back in 2013, the modern-day exploits of you as an Abstergo Entertainment employee were prominent. They were portrayed via first-person portions that occasionally interrupted the third-person Kenway experience and could be explored for hours, complete with their own missions, side quests and puzzles. Resynced’s 2096 story—of Assassin “Watchers” vs. Abstergo/Tempar goons, of rogue AIs, of panicking scientists, of a woman wondering why her Animus-addicted husband is muttering about Mary Read (“What does he mean by Assassins?”), of a fight for the population’s hearts and minds via varying interpretations of Edward Kenway’s life choices—is mostly told through unlockable text files. The files are interesting. Offered via diary entries, intercepted chats and news reports, they present varied perspectives of a world in crisis. One set works as a narrative puzzle as it reveals the Rashomonic interpretations of a massacre, or was that a successful Assassin hit? Another favorite reveals the rising bitterness of a person who, through Edward Kenway’s journey, now shakes at the ignored evil around him. One surmises that Ubisoft is averse to putting the Assassin’s Creed’s meta-story in the way of more marketable pirate playgrounds and ninja/samurai epics. Instead, we get a better-than-nothing text-in-menus approach introduced in 2025’s Assassin’s Creed Shadows. In Shadows, players unlocked the meta-story text files largely via time-gated missions that called on the user to kill X number of enemy type Y, clear three enemy castles, re-win a three-phase fighting tournament, and other grinds. Time restrictions on accessing these missions and unlocking the files meant it could take players months to access the full story in-game. But even in the most hardcore Assassin’s Creed hangouts, the fanbase instead seemed to throw up their hands rather than grind to keep up. In Resynced, Ubisoft has shown some mercy, diminishing the amount of new text files that are tied to timed grinds. Now, most of the text-based 2096 lore are associated with “data file” collectibles that players can find in the world. That includes a batch of written entries that explain a historical link between the feudal Japan era in Shadows and the piracy adventures in Black Flag. Players who explore a lot will find dozens of data files, though the game does not appear to offer any in-game tracker for finding each one. You just need to keep looking for them. As of this writing, I have most of them, but not all. I can’t find any full guide for them online. And even Ubisoft’s optional $5 microtransaction that reveals lots of items in-game does not appear to reveal the locations of the text files (at least the official description omits them from its collectibles list). That’s not a huge bother, because tracking the data files down compelled me to re-explore the waves and islands of Black Flag. That was a thoroughly pleasant task to do for the first time in a dozen years. I expect many players, though, will be content to wait for the files to be reproduced in an online AC wiki somewhere, the easier to read on a web page. It may not seem enticing to play a video game in order to gradually unlock pages of text to read. Why not, you may ask, just read a book, where they put the pages in order? For one, they’ve not tucked this latest installment of Assassin’s Creed’s meta saga in a book. I also enjoy the unordered delivery, the dawning comprehension of a story about 2096’s desperate people and questionable tech, which I glimpsed first at its middle, then the end, then the front, then other moments in between1. I am also enamored with the idea of the Assassin’s Creed games I’ve previously played, including Black Flag, being repurposed for an information war. The ideas in Resynced’s meta-story, which builds off characters and ideas in Shadows’ chunks of text, are strong. The potential of where they could go with it is exciting. As for the pirate stuff? Looks great, controls pretty well, is fun to play and oh so speedy in terms of how much of a good time you can have with each sit-down. It never gets old to chase a new shanty page across a rooftop, bombard a fort with upgraded mortars or use a diving bell to explore sunken wrecks (hats off to the team at Ubisoft Barcelona that worked on the underwater portions in Resynced; that team is now facing layoffs, such is the misery of modern gaming and oft-troubled Ubisoft). New pirate missions in Resynced involving new ally characters are brief, dramatic and add more to Edward’s story, even if the gameplay perks they provide barely enhance the gameplay. A new denouement for high-class pirate Stede Bonnet is affecting; a new hunt for Blackbeard’s treasure is a welcome extra moment. A new epilogue to hunt down a pirate killer, on the other hand, over-complicates Kenway’s character. The ending credits remain my favorite of any game I’ve ever played. Black Flag provides great source material. The revisit is well-made and a great sign of what could be. It is offered in a different wrapper that hints at an opportunity for more interesting remakes to come, should Ubisoft choose to pursue such treasures—and as we brace to resist or comply with an Abstergo future. 1 The messages that Edward Kenway finds in bottles tell another out-of-order story, one about a man reckoning with the strange memories and voices he’s heard in his head since he was a boy. That tale was also in the 2013 game and develops into one of Black Flag’s most compelling character arcs. In Resynced, it has lost its climax, because the bottle story previously paid off in the halls of the first-person Abstergo Entertainment, which is exclusive to the older game. An altered resolution, written by the 2013 game’s lead writer is fine, doesn’t invalidate the 2013 version, but makes for an inferior final note.
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
How the O.J. Simpson Trial Inspired My Career in Forensic Science
Inspired by a famous trial, a NIST researcher makes sure the science law enforcement relies on is accurate and thoroughly tested.