Polymarket is America, America is Polymarket

As the US celebrates its 250th birthday, prediction markets have already taken over—now they are coming for you

‘Unbelievable how accurate’: How paid influencers hype Polymarket’s odds

The company’s chief marketing officer, Matthew Modabber, used a personal PayPal account to send money to content creators, according to records reviewed by POLITICO.

How Everything Became Gambling

It’s not just DraftKings and Polymarket — the logic of gambling undergirds everything coming out of Silicon Valley.

Comment on “Banning the purchase of sex increases cases of rape: evidence from Sweden”

Journal of Population Economics -

Polymarket on Twitter / X

JUST IN: AI dating startup Ditto reveals 160,000+ students have signed up for its app that automatically picks their match & sends them a fully planned date itinerary.— Polymarket (@Polymarket) August 22, 2026

Issue 102 – The public will pay

Justin Sun buys his way out of an SEC fraud case, Iranian transactions on Binance draw DOJ scrutiny as the exchange sues the newspaper that reported on them, and crypto super PACs dump millions into Tuesday’s primaries in Illinois.

Exclusive: ICE agents investigated for sexual assault, bribery, drug smuggling

Records obtained by Newsweek reveal 148 allegations of misconduct and potential crimes.

Prospect Theory, Mental Accounting, and Differences in Aggregated and Segregated Evaluation of Lottery Portfolios

If individuals have to evaluate a sequence of lotteries, their judgment is influenced by the presentation mode. Experimental studies have found significantly higher acceptance rates for a sequence of lotteries if the overall distribution was displayed instead of the set of lotteries itself. Mental accounting and loss aversion provide an easy and intuitive explanation for this phenomenon. In this paper we offer an explanation that incorporates further evaluation concepts of Prospect Theory. Our formal analysis of the difference in aggregated and segregated portfolio evaluation demonstrates that the higher attractiveness of the aggregated presentation mode is not a general phenomenon (as suggested in the literature) but depends on specific parameters of the lotteries. The theoretical findings are supported by an experimental study. In contrast to the existing evidence and in line with our theoretical results, we find for specific types of lotteries an even lower acceptance rate if the overall distribution is displayed.

Yeah, so... Substack, I'm out.

The Polymarket partnership is the last straw.

Pluralistic: The worst possible antitrust outcome (03 Sep 2025) – Pluralistic: Daily links from Cory Doctorow

Last year, Google lost an antitrust case to Biden's DoJ. The DoJ lawyers beat Google like a drum, proving beyond a shadow of a doubt that Google had deliberately sought to create and maintain a monopoly over search, and that they'd used that monopoly to make search materially worse, while locking competitors out of the market.

Unwillingness to pay for privacy: A field experiment

We measure willingness to pay for privacy in a field experiment. Participants bought at most one DVD from one of two competing online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often.

Private Markets Charge More

Also dual-class SpaceX stock, fake Iranian crypto fees and Global Tetrahedron M&A.

Algorithmic Bias in Lending: Evidence from a Fintech Audit

Algorithmic lending has transformed the consumer credit landscape, with machine learning models commonly facilitating underwriting decisions. To comply with fair lending laws, these algorithms exclude legally protected characteristics, such as race and gender. Yet algorithmic underwriting can still inadvertently favor certain groups, prompting concerns about whether lending algorithms exhibit discriminatory behavior. Using proprietary loan-level data from a major U.S. fintech platform, we audit lending decisions across approximately 80,000 personal loans. We find that loans made to men and Black borrowers yielded lower profits than loans to other groups, suggesting that men and Black borrowers benefited from relatively favorable pricing. We trace these disparities to miscalibration in the platform's underwriting model, which overestimates risk for women and underestimates risk for Black borrowers. We then show that one could correct this miscalibration -- and the corresponding disparities -- by including race and gender in underwriting models, illustrating a tension between competing notions of fairness.