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CJ
@cjh
"Using BioDAO tokens in onchain prediction markets means that new mediums of financial & social gravity can be used to forecast scientific outcomes by extracting sentiment from leading minds in the industry." https://paragraph.xyz/@limitlessexchange/vita-desci-prediction-markets
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cyrus
@cyrus
wdyt @gmo ?
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ayan 🧬
@gmo
Technical and Ethical considerations that come up, here are a few: 1. Scientific research is lengthy, complex, multivariate & topically quite niche to the point where a subset of scientists can actually meaningfully engage in prediction 2. Prediction markets require meaningful and a significant number of participation from knowledgeable people, DeSci does not have that currently (there are very few academic scientists, scientists are generally resistant to web3, & no significant onboarding strategies) 3. Scientific research is a long tedious process without reward for years and years (if at all), shared IP incentives therefore are very long term & do not align with the instant or delayed gratification culture of web3 3. Ethically, the scientific questions with the most financial incentives are not necessarily the questions most worth pursuing or funding. This perpetuate academias incentives problem by displacing it to another group of stakeholders with possibly less knowledge or authority on the subjects
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CJ
@cjh
I believe there are a lot of really great academic scientists in or funded by the DeSci space I admit when you compare that to TradSci, they may be few & far between but that wasn’t really the point of the article. also, I believe that it’s hard to predict things in general, not only scientific outcomes, which is why prediction markets extract wisdom from crowds. even just a few professional traders can iron out inconsistencies in forecasts bc there is economic incentives to do so. there are also other interesting use cases. for example, let’s say that there is an IP Token that you’ve invested in- you may actually bet against it in a prediction market to hedge your position. and there are fun use cases. btw, in terms of what to fund, I think DeSci is great in funding early stage & experimental research so it can hit key milestones then grow to a broader funding base. the more times DeSci is right with those bets, the more TradSci will respect the movement
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ayan 🧬
@gmo
There are definitely not a lot of academic scientists in DeSci, but there are some being funded. I do see the incentives and potential collective gains from introducing prediction markets from a financial perspective. However, I think the claims in this article for furthering science discovery and results/tapping into collective wisdom through this approach does require an actual quality pool of knowledgeable people. From scenarios like making predictions to how best to use the generated IP for maximal impact. So as much as this is not the point of the article. I think some major claims here can’t be made without acknowledging that point. I would say the most that can be said at this stage is what you said in your last point. This model can fund early stage research that perhaps is not yet able to tap into other avenues of funding, which is cool.
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