Aryan64

Karma: 5

Aryan64 5 May 2026 21:44 UTC
4 points
0
in reply to: Hide’s comment on: Primary Care Physicians are Incompetent. We Need More of Them.
Having taken a quick look at the source for Claude’s data, it seems like a reasonable read is that the doctors are simply not well-calibrated on the relevance of the patients statements and redirect them away too quickly. Not sure this means they were ‘rude’,
The HCAHPS survey measures ‘courteousness’ by asking patients, and consistently find that in ~85% of cases, doctors are rated ‘always’ respectful and courteous. From my understanding, a further ~10% are rated as ‘usually’ respectful and courteous. This is not a perfect measure, but seems better than using interruption times as a proxy.

Aryan64 5 May 2026 21:09 UTC
1 point
0
in reply to: mabramov’s comment on: Forecasting is Way Overrated, and We Should Stop Funding It
On the usefulness of blockchain, I think the analytical case for our financial system being on blockchain is significantly weaker than the analytical case for better forecasts being useful for policymaking.
With my original comment, I was implicitly drawing a distinction between ‘cannot be useful’ and ‘has not been useful’:
1. If forecasting simply has not been useful, then it leaves us with two possibilities: (1) it cannot be and we should stop funding it; (2) it can be and we should redirect funding to efforts that figure out and solve why it hasn’t been (e.g. distribution).
  
  I feel like the analytical case for at least attempting (2) is pretty strong. I will admit that this may validate skepticism to the tune of, ‘We should reduce funding and seriously consider our theory of change’.
2. If the argument is that no amount of additional funding will justify the ~$100m we’ve already spent, I would argue that it doesn’t need to. Any additional funding only needs to be useful enough to justify itself. Even if the overall forecasting program ends up being overfunded and unjustified, we should treat future funding as independent of any potential bad decisions in the past.
  
  For example, if the program is currently giving us $10m of value (for $100m of funding), and spending an additional $10m would increase that to $30m of value, then we should spend it. Even if the overall program remains a failure, our $10m has given us a 2x return.
Wrt the startup analogy and LLMs, I am not sure it is reasonable to claim that all useful technologies get adopted at this extreme speed, re: seatbelts, childproof medical caps, vaccinations, drunk driving laws, helmets, indoor smoking bans, etc. which all required significant distribution efforts.
To clarify ‘distribution’, I think there’s a difference between ‘everyone has heard of prediction markets’ and ‘we have the right tools to allow policy/decision-makers to adopt prediction markets in their decision-making processes’.

Aryan64 4 May 2026 21:41 UTC
3 points
0
on: Fractional Resignation
I like this! You could avoid the haggling over percentages by having coarse fixed boundaries (e.g. ¹⁰⁄₉₀, ³⁰⁄₇₀, ⁵⁰⁄₅₀ as the three options for fractional resignations). It would make tournaments like professional chess more interesting by letting you cash out a good position without needing to accept a draw where points are split equally.

Aryan64 4 May 2026 21:12 UTC
1 point
0
on: Morale
I wonder if a better formulation is that morale is roughly, “the belief that you are winning for a reason” with a boost from (1) contributing and (2) effort.
I prefer this for a few reasons:
1. ‘Winning’ incorporates both the societal and the individual and folds in cases where you might achieve some goals but not see material change in your conditions. Plus, it incorporates in-group biases and the shared sense of victory when you support a team. For example, when you support a sports team and you win, you feel high morale even though your condition has likely not actually improved as a result.
2. ‘Winning’ also incorporates the importance of mission alignment. Your condition might get better from effort, but you might still not feel a morale boost unless your effort and condition are correlated with what you believe in. You might work 80 hour weeks in a bank and make a bigger bonus, or fly an extra mission and get closer to being discharged, but feel absolutely demoralised because you don’t believe that the effort you are doing gets you to ‘winning’. ‘Winning’ is more than improved conditions. Literature around morale suggests that this kind of mission alignment is pretty important.
3. ‘Contributing’ and ‘effort’ both seem meaningful but distinct additions to morale. The boost from ‘I was the taxi driver for the lead player of the team I support and they won because I got him there on time’ is more contribution oriented. The boost from ‘I have been supporting this team and going to their games for twenty years, and this is the first time they won’ is effort oriented. Both may see huge morale boosts, but don’t involve similar levels of contribution / effort.

Aryan64 4 May 2026 20:24 UTC
1 point
0
on: Forecasting is Way Overrated, and We Should Stop Funding It
I am intrigued by the ‘solution seeking a problem’ framing. Unlike blockchain, there are a number of domains and decisions that would analytically benefit from more accurate predictions about the future (e.g. issue prioritisation, campaigning decisions in politics, domestic policy decisions, geopolitical actions) even if they have practically not adopted forecasting widely. Better forecasts can prevent worse counterfactual worlds, e.g. by preventing planning mistakes leading to rapid inflation, encouraging better resilience planning, etc.
The claim that ‘Forecasting cannot be useful’ seems to have the plausible alternative claim ‘Forecasting can be useful with better distribution’, i.e. we need to find better ways to get policy- and decision-makers access to low-friction, high-quality, defensible forecasts that they can use. This would still mean that existing funding that is focused on improving the resolution and quality of forecasts needs to re-prioritise and focus on the distribution more, but would not go so far as to say we should stop funding it altogether.
The pattern of ‘analytically useful, but practically unadopted’ is not unprecedented. Seatbelts and childproof medical caps, for example, were both analytically beneficial but did not see widespread adoption till concentrated efforts were made on the distribution front. Clear, measurable benefits followed.