The Limits of Reputation in Platform Markets
Video from talk at the Algorithmic Game Theory and Practice Conference, at UC Berkeley.
I'm pretty new to Steem, and definitely don't know how everything works yet, technically, or culturally.
But something I definitely pick up on is all the discussion and energy devoted to how to encourage (incentivize) certain types of behavior, and how to protect stuff from getting abused ("gamed").
As somebody who works with social media for my day job, these questions are pretty familiar, even if the specifics are new. These are the challenges faced by all social platforms. Not just to dodge bad actors like spammers, but to encourage certain types of participation (subjectively "good" behavior).
Some of these questions are defined and discussed in the overlapping fields of Mechanism Design and Algorithmic Game Theory.
I'm no expert in either of these fields. Not even an amateur. But following these topics has still informed the frameworks I use to think about social platform stuff. Plus, it's straight up provides fascinating "inside baseball" direct from employees at social companies when they participate in the field in the form of research papers and talks at academic conferences.
One event that provided all sorts of social platform inside baseball was the Algorithmic Game Theory and Practice conference in November 2015. (Here's a whole YouTube playlist full of the talks.) I'll be sharing a few videos from the event here on Steem. But I'm starting this with this one because it has some really interesting ideas about Reputation, which is a big concept here on Steem.
Description from event page:
Reputation mechanisms used by platform markets suffer from two problems. First, buyers may draw conclusions about the quality of the platform from single transactions, causing a reputational externality. Second, reputation measures may be coarse or biased, preventing buyers from making proper inferences. We document these problems using eBay data and claim that platforms can benefit from identifying and promoting higher quality sellers. Using an unobservable measure of seller quality we demonstrate the benefits of our approach through a large-scale controlled experiment. Highlighting the importance of reputational externalities, we chart an agenda that aims to create more realistic models of platform markets.