1/ Agent-based models for simulating token design.https://twitter.com/laurashin/status/1140986942928097280 …
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2/ I heard
@mZargham (@block_science ),@Fennie_Wang, Natascha Wittenberg, and@DimitriDeJonghe give a talk at#DWeb summit last year that used CSS methods, too.https://decentralizedwebsummit2018.sched.com/event/FfRs/workshop-incentive-design-with-token-engineering …1 reply 1 retweet 3 likesShow this thread -
💥 (wannabe) Ƀreaker of (the Bad) Loops 💫 Retweeted Sean J. Taylor
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@seanjtaylor (who I respect tremendously) had this tweet a while back that was a little disheartening. But, he was right — the did experience a winter. (See also,@EpiEllie's response: https://twitter.com/EpiEllie/status/1113577646238195717 …)https://twitter.com/seanjtaylor/status/1113476519987269644 …💥 (wannabe) Ƀreaker of (the Bad) Loops 💫 added,
Sean J. Taylor @seanjtaylorI think ultimately this methodology could help unlock the promise of agent-based models (ABMs), an older line of research that has been mostly abandoned. If we learn a model of how ABM simulations map to to real empirical results, they may stop being toys.Show this thread2 replies 0 retweets 3 likesShow this thread -
Replying to @generativist @EpiEllie
I was super excited about them before grad school. It felt like such a cool idea that you could study people by programming their behaviors and interactions. My theory is that increasingly available large scale online data was a strong substitute.
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Replying to @seanjtaylor @generativist
The tl;dr on agent-based and other simulation models, at least for
#causalinference, is that for each piece of individual-level data you replace with an assumption (including group-level rates, probs, etc), you need to also add add’l causal assumptions or you’ll get bias.1 reply 1 retweet 4 likes -
The number of causal assumptions can get unmanageably large pretty quickly, but in theory you can write them all out for any given simulation model, so you could use ABMs or similar for causal inference but it’s not an easy task.
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Some day in the near-future — probably after I find my next employment/startup — I really want and need to read more of your work, Ellie :)
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