We (jacobjacob and Ben Pace) have finally settled on the allocation of the $800 bounty for this question. All the motivations are summarised in this comment, together with links to the relevant prize-winning answer/comment.
We will also post individual notices with motivations next to each comment for ease of discussing them.
We’ll PM all prize winners to sort out logistical details of payment.
Main post
David Manheim (answer and additional points made in discussion) $150
This answer offers relevant and robust evidence about the role of info-cascades in forecasting environments, together with a discussion of its interpretation.
Jan Kulveit (answer and additional comment below) $200
This answer seems to offer a learnt summary of the relevance of network science (which offers a complementary perspective on the phenomenon to the microeconomic literature linked by other commenters), which not implausibly took Jan at least an order of magnitude less time to compile than it would have taken us. (For example, the seemingly simple fact of using a different Google scholar keyword than “information cascade” might have taken several hours to realise for a non-expert.) It also attempts to apply these to the case of forecasting (despite Jan’s limited knowledge of the domain), which is a task that would likely have been even harder to do without deep experience of the field.
These answers compile a useful summary of the literature (we learnt a lot from going through on of the papers linked), and it attaches handy links to everything, which is a task which is on the one hand very helpful to other people, and on the other tedious and without many marginal benefits for the writer, and so likely to be under-incentivised.
It offers a novel mechanism which is relevant to the context of intellectual progress, and ties it in with literature cited in the OP
Rather than just linking the paper, it distills a technical paper, which is a valuable service that is usually underfunded (academic institutions comparatively incentivise novel and surprising insights)
This answer offers a practical example of a cascade-like phenomenon, which is both generally applicable and has real economic consequences. Also, the fact that it comes with a game to understand and practice responding is rare and potentially quite valuable (I (jacobjacob) am of the opinion that deliberate practice is currently a neglected virtue in the rationality/EA spheres).
It references existing (and novel) work in economics and mechanism design, which might have been time-consuming to discover otherwise
It distills a technical paper, which is a valuable service that is usually underfunded (academic institutions comparatively incentivise novel and surprising insights)
The insights provided are quite action-guiding, and caused me (jacobjacob) to have ideas for how one can experiment with new kinds of forecasting tournaments that use a threshold-mechanism to change participant incentives
UPDATE.
We (jacobjacob and Ben Pace) have finally settled on the allocation of the $800 bounty for this question. All the motivations are summarised in this comment, together with links to the relevant prize-winning answer/comment.
We will also post individual notices with motivations next to each comment for ease of discussing them.
We’ll PM all prize winners to sort out logistical details of payment.
Main post
David Manheim (answer and additional points made in discussion) $150
This answer offers relevant and robust evidence about the role of info-cascades in forecasting environments, together with a discussion of its interpretation.
Jan Kulveit (answer and additional comment below) $200
This answer seems to offer a learnt summary of the relevance of network science (which offers a complementary perspective on the phenomenon to the microeconomic literature linked by other commenters), which not implausibly took Jan at least an order of magnitude less time to compile than it would have taken us. (For example, the seemingly simple fact of using a different Google scholar keyword than “information cascade” might have taken several hours to realise for a non-expert.) It also attempts to apply these to the case of forecasting (despite Jan’s limited knowledge of the domain), which is a task that would likely have been even harder to do without deep experience of the field.
Pablo (1 and 2) $100
These answers compile a useful summary of the literature (we learnt a lot from going through on of the papers linked), and it attaches handy links to everything, which is a task which is on the one hand very helpful to other people, and on the other tedious and without many marginal benefits for the writer, and so likely to be under-incentivised.
Michael McLaren $50
This answer:
It offers a novel mechanism which is relevant to the context of intellectual progress, and ties it in with literature cited in the OP
Rather than just linking the paper, it distills a technical paper, which is a valuable service that is usually underfunded (academic institutions comparatively incentivise novel and surprising insights)
Ways of responding
David Manheim $50
This answer offers a practical example of a cascade-like phenomenon, which is both generally applicable and has real economic consequences. Also, the fact that it comes with a game to understand and practice responding is rare and potentially quite valuable (I (jacobjacob) am of the opinion that deliberate practice is currently a neglected virtue in the rationality/EA spheres).
rossry $250
This answer does several important things.
It references existing (and novel) work in economics and mechanism design, which might have been time-consuming to discover otherwise
It distills a technical paper, which is a valuable service that is usually underfunded (academic institutions comparatively incentivise novel and surprising insights)
The insights provided are quite action-guiding, and caused me (jacobjacob) to have ideas for how one can experiment with new kinds of forecasting tournaments that use a threshold-mechanism to change participant incentives