QuestaGame Now Rewards Wildlife Expertise with “Pays-to-Know” Program
Over the last couple months I’ve written several posts about AI (often voicing my concerns). But QuestaGame is not about AI. It’s about CI, or Collective Intelligence.
More specifically, it’s about CI that’s powered by information economics, a subject founded by such esteemed economists as Friedrich Hayek, Michael Spence, Joseph E. Stiglitz. This is an important qualifier. Collective Intelligence is often confused with crowd-sourcing — a kind of “intelligent” crowd-sourcing, if you will. Crowd-sourcing with “up votes” and “down votes.”
But CI powered by info-economics is a different creature entirely. It’s something my partner and I have been exploring, with mixed success, not just through our involvement in QuestaGame, but for the last 20 years (from the unaffiliated fringes of scholarship with its steady tumbleweed of dismissal, its hot winds of despair). In fact, it may be something that could help resolve the sort of “knowledge bubble” problem a lot of Internet users are suddenly awakening to — but that’s another post.
It’s a good time to discuss this, as well, because just this week QuestaGame has released its “Pays-to-Know” program and published the real-time earnings of its expert community (see http://questagame.com/paystoknow). Yes, real money for the ability to identify wildlife in online photos. It’s early days, and not much money at this point, but it provides a working model for how we believe the future economy might raise the value of environmental knowledge and create meaningful jobs.
What’s more, the model is scalable and starting to grow.
So how does all this work?
Let’s take a recent tweet from Jade Craven: “If I’m just after an ID [for a sighting], is it easier to email a museum or use @QuestaGame?”
We can remove the “@QuestaGame” bit. Museum, Google forum, Facebook group, could be anything. In this case, what Jade is asking is: “What’s the easiest way for me to get an identification of this creature I’ve just photographed?” It’s a question about the user experience. Is it easier to email a photo attachment? Or use a smartphone app? Or something else?
But from an information economics perspective, the question could also be about the knowledge trading process — I give a photo, you give an ID — and particularly about the value of an “identification.” It may be easier, for example, to email a photo attachment to a museum; but if it takes six weeks for the identification to come back, the ease of submission may not be worth it. Ditto if an identification comes back from an online source but it’s wrong; or if it only identifies the specimen to an order level when the user wants a genus or species-level ID.
In other words, from an info-economics perspective, the identification has a value that’s determined by numerous variables. If you’re selling an equal number of green pencils and red pencils, and people buy twice as many green pencils as red pencils, it’s safe to conclude — assuming the pencils are similar in every other way — that one variable affecting the demand for pencils is colour.
When it comes to trading knowledge, one such variable is speed. A species ID is likely to be more valuable to me if I receive it in 10 minutes than if I receive it in 10 days or 10 years. (Remember, we’re living in the biggest extinction event in 66 million years; every three and half minutes a species on this planet is going extinct; and more often than not, this species has not yet been described by science. So time matters not just for us, individually, but for the planet as well).
I realise this seems obvious. But things get interesting when we match the “speed” variable against its arch nemesis, “Quality.” That is, what if the beautiful paint on a pencil, for all the desire it ignites in the hearts of buyers, also makes the pencil more slippery and difficult to write with? Colour and utility impact each other; and impact demand.
Quality can take time. It involves someone — or more often, several people — with expertise and proven qualifications. It can involve objectivity, agreed-upon standards. But it’s also subjective. It may depend on the buyer’s idea of quality. Or it may depend on accuracy, detail, precedent, and so much more.
The point of all this, however, is that these variables can be exposed. They can also be adjusted by the consumer — e.g. I’m only interested in knowledge that’s been fact-checked. A well-designed CI system meanwhile — through, for example, expertise management and instant, blind, anonymous, on-the-fly, randomly-generated peer review — can not only account for the variables, but process and reward them in a way that creates a sustainable market.
In an earlier post, I wrote about why I feel it’s important for citizen scientists to be paid (see “We all Benefit when Citizen Scientists Get Paid”). The same, in fact, can be said about citizen journalists. Unfortunately (thank you, Ayn Rand), Silicon Valley has invested so heavily in systems designed to exploit people — be it to market to them, to build big-data products and train dehumanising AI devices like Amazon Echo or Google Home — that CI research and development was overlooked.
The old dichotomy prevailed. The debate was always about whether real scientists (with their peer reviewed papers), or real journalists (with their double-sourced, fact-checked stories), would be heard over the din of the untrained masses. Would quality research institutes survive? Would newspapers live or die? Even now the discussion is between real news and fake news, as if there’s a clear line between them, and that line is going to be decided in the board rooms of big data companies like Google or Facebook.
With CI systems powered by info-economics, it’s not an either-or situation. It’s not professional vs amateur; paid vs volunteer. The products are not the people, the influencers, the celebrities. Rather, the products are the many different kinds of information that exist in the world, each with its own value and price point.
It may sound complicated, but for an information network it’s much simpler than the sort of deep learning algorithms that decide which clickbait to display in your news feed (“and number 10 will blow your mind!”). The people who benefit most, meanwhile, can be the ones with the desired knowledge; not just the ones with the biggest following or stock portfolio.
We believe QuestaGame is an early example of such a network. Interestingly, if you look at http://questagame.com/paystoknow, you’ll see that the average earning per correct ID is different for different users. That’s because the users don’t all earn the same amount for each sighting. Some users are able to identify more challenging sightings than others, and therefore earn more per ID (the demand for accuracy amongst QG players and researchers is pretty consistent).
It’s by no means perfect. It’s still early days. The system is going to evolve and improve. But when it comes to species identification, it’s beating any AI system by a long shot and will continue to do so. And if you believe in the fundamental cause — in raising the social value of the environment by rewarding environmental expertise — this could be an effective and scalable way to achieve it. We look forward to your feedback, involvement, suggestions for improvement, or any support you can offer.