lynettebye

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Hmm, I'm not certain where you're getting that. I interpreted this as the amount of deliberate practice contributed to success in some fields much more than it did in other fields. (Which could be explained by some fields not having developed techniques and training methods that enable good DP, or could be explained by everyone maxing out practice, or by practice not mattering in those fields.) DP still makes a difference among top performers in music and chess, indicating that not all top performers are maxing out deliberate practice in those areas.  

I considered that early on during my exploration, but didn't go deep into it after seeing Scott's comment on his post saying:

These comparisons held positions (specialist vs. generalist) constant. Aside from whether someone is a specialist or not, I don't think there's any tendency for older doctors to get harder cases.

Now, after seeing that the other fields also match the same pattern of decline, I'd be somewhat surprised by evidence that taking on harder cases explained the majority of skill plateaus in middle age for doctors. 

Note: I was treating the 2009 study as a psudo-replication. It's not a replication, but it's a later study on the same topic that found the same conclusion, which had allayed some of my concerns about old psychology research. However, I since looked deeper into Dan Ariely's work, and the number of accusations of fraud or academic misconduct makes me less confident in the study. https://en.m.wikipedia.org/wiki/Dan_Ariely#Accusations_of_data_fraud_and_academic_misconduct

I agree with the line of reasoning, but I'd probably err on the side of adding a deadline even for designing  your office -  if you want to make sure the task gets done at some point, setting the deadline a month away seems better than not having one at all. 

I agree that adopting high variance strategies makes sense if you think you're going to fail, but I'm not sure the candle task has high variance strategies to adopt? It's a pretty simple task.   

I feel like being the code master for Codenames is a good exercise for understanding this concept. 

I wasn't thinking of shards as reward prediction errors, but I can see how the language was confusing. What I meant is that when multiple shards are activated, they affect behavior according to how strongly and reliably they were reinforced in the past. Practically, this looks like competing predictions of reward (because past experience is strongly correlated with predictions of future experience), although technically it's not a prediction - the shard is just based on the past experience and will influence behavior similarly even if you rationally know the context has changed. E.g. the cake shard will probably still reinforce eating cake even if you know that you just had mouth-changing surgery that means you don't like cake anymore.

(However, I would expect that shards evolve over time. So in the this example, after enough repetitions reliably failing to reinforce cake eating, the cake shard would eventually stop making you crave cake when you see cake.) 

So in my example, cleaner language might be: For example, I more reliably ate cake in the past if someone was currently offering me the slice of cake, compared to someone promising that they will bring a slightly better cake to the office party tomorrow. So when the "someone is currently offering me something" shard and the "someone is promising me something" shard are both activated, the first shard affects my decisions more, because it was rewarded more reliably in the past. 

(One test of this theory might be whether people are more likely to take the bigger, later payout if they grew up in extremely reliable environments where they could always count on the adults to follow through on promises. In that case, their "someone is promising me something" shard should have been reinforced similarly to the "someone is currently offering me something" shard. This is basically one explanation given for the classic Marshmallow Experiment - kids waited if they trusted adults to follow through with the promised two marshmallows; kids ate the marshmallow immediately if they didn't trust adults.) 

Cool, I'm happy if you're relaxing with a leisure activity you enjoy! The people I spoke with were explicitly not doing this for fun. 

Time inconsistency example: You’ve described shards as context-based predictions of getting reward. One way to model the example would be to imagine there is one shard predicting the chance of being rewarded in the situation where someone is offering you something right now, and another shard predicting the chance you will be rewarded if someone is promising they will give you something tomorrow. 

For example, I place a substantially better probability on getting to eat cake if someone is currently offering me the slice of cake, compared to someone promising that they will bring a slightly better cake to the office party tomorrow. (In the second case, they might get sick, or forget, or I might not make it to the party.)

I have lots of points of contact with the world, but it feels really effortful to be always mindful and noting down observations (downright overwhelming if I don't narrowing my focus to a single cluster of datapoints I'm trying to understand)

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