Same frustration here. It’s somewhat painful for me to type but using dictation on the iphone is so terrible I prefer the physical pain.
As for names, I an also baffled. Most people in my family have either a Brazilian Portuguese or German name, but my work life is in English, so guess what, no getting anyone’s name right!
One of the (not so many) things about Windows that I loved was the zen simplicity of the Notepad. I saw it through Windows 3.1 all the way to the bloated oblivion it was driven to, and I did not like to see that sad, final chapter. (Broader theme, do I miss the simpler computer times!)
I always preferred money stuff. I feel like the hosts have better chemistry and matt levine is one of the most read/respected financial journalists in the last 20 years. I always look forward to it coming out on a Friday.
I agree with your points about the tone. Money Stuff is definitely more "fun".
I find the content differs between the two, not just the presentation. Odd Lots goes into the broad scale (national, global) backstory a lot more; Money Stuff dives deep into specific businesses, people, or the technical details of a trade. Maybe your circumstances and habits mean you get more from one than the other?
I wish Bloomberg would find presenters for UK or European centric versions of both shows.
Hi, HN! Co-author here (I don't know if own papers are also Show HN, happy to adjust if so). We explored LLM strategic choices in a simple but intriguing game theoretical setting, the ultimatum game.
In this game, a Proposer proposes to distribute a share of the amount at stake with the Responder. If the Responder accepts, both get their proposed amounts; if the proposal is rejected then no one gets anything. This game shines a light on how these models could behave when their payoffs depend on the opponent's choices too.
We document three main findings.
First, LLM behavior is heterogeneous but predictable when conditioning on stake size and player types.
Second, some models approximate the rational benchmark and others mimic human social preferences, but we also observe a distinct "altruistic" mode emerging - in this case, LLMs propose hyper-fair distributions (greater than 50%).
Third, when we calculate the expected payoff, LLMs actually leave a lot of money on the table. They forgo a large share of total payoff, and an even larger share when the Responder is human.
As for names, I an also baffled. Most people in my family have either a Brazilian Portuguese or German name, but my work life is in English, so guess what, no getting anyone’s name right!
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