Did you know that there is a website called Whale.fm
where you can do your part to help marine biologists decipher the eerily beautiful language of whales?
There are all sorts of similar research projects out there tapping into the wisdom of the crowd
to deal with difficult problems and large datasets. It's been called crowd science
or citizen science
. Thanks to the Internet, humans are inventing new ways of thinking that aggregate our grey matter.
Someday I'll have a genealogy or some other dataset large enough so that the time investment in developing a citizen science platform would be worthwhile. For now, I'm thinking about embarking on what I will call open source noodling
. And I want you to tell me whether or not this is a really bad idea.
Open source noodling puts together two terms, "open source" and "noodling". Open source, of course, refers to a mode of production that involves free access to and distribution of an end product's design. Many argue that open source bolsters innovation and levels the playing field. Scholars in many disciplines, including my own
, are debating a shift to open access peer review from the corporate, for-profit peer-reviewed journal model. Open access is very similar to the open source philosophy.
Noodling is a term I first heard in conversation with Richard McElreath, an anthropologist at UC Davis who studies cultural transmission and lots of other stuff. Basically, it's the use of abstract mathematical models to answer concrete questions like, "Why are humans so reliant on social learning?" or..well..anything really, if you build the model properly.
If you put open source and noodling together you get the development of a mathematical model to answer a question, which is built collaboratively using a system that allows open distribution and free access to information.
What I want to do is take a mathematical model I've been working on, and which is basically finished, write a draft manuscript, and post it in a medium that allows for open discussion and possibly even open editing. This sounds like a terrible idea but I'm playing with it, and want you to come and play with it, too.
The benefits of open source are manifold. I could tap into a huge knowledge base to solve a problem that I think is really interesting. It bypasses the slow process of peer review while also potentially improving a manuscript in preparation for the peer review format. Yet open source also poses new problems about who gets credit for the research, how to filter information, and how to aggregate the wisdom of the crowd into a singular product.
How would you set something like this up? I encourage open (heh) discussion about it. Or contact me personally, whatever. It's an idea I'd like to develop into a systematic method of producing and distribution knowledge.
I'd also appreciate it if you told me if I was reinventing the wheel, and if there are already venues out there where open source noodling can be done in a way that maximizes the benefits but minimizes the costs.
Anyway, here's an example of something I'd like to submit to open source noodling. I have other things, too, but here's one for starters
In between genealogical interviews with key informants and preparation for the rest of my field surveys, I'm working on some side projects
to pass the down time.
The one I'm focusing on now is part of a project I'm calling "the tragedy of hawkish altruism". The tragedy of hawkish altruism is this:
If individuals who are aggressive to members of their own group make that group better prepared for competition with other groups, it may drive the evolution of aggressive behavior that leads to more conflict within groups. In this scenario, hawkish bastards are also tragic heroes because they risk their necks training for and maneuvering on the evolutionary battlefield against other groups.
Think about warriors risking severe injury to realistically train for battle. Think about office intrigue and backstabbing that prepares people for the rigors of negotiation with other firms.
The tragedy of hawkish altruism ties into a broader debate in evolutionary biology about the strength of natural selection among groups versus individuals. Natural selection needs variation in order to happen. When individuals move freely between groups, it causes all groups to be more similar to one another and for most variation to exist among individuals. For this and other reasons, evolutionary biologists have traditionally considered individual selection to be stronger than group selection.
Yet a few evolutionary biologists argue that group selection may still be an important force in some cases. For example, the tendency of humans to conform to the customs most common in their current group may allow variation among groups to increase relative to variation among individuals, causing group selection to overpower individual selection.
Researchers have used this reasoning to support a hypothesis for why humans are an especially cooperative species, even in large groups of unrelated individuals, and even when cooperation requires altruism, which would be selected against among individuals but favored among groups (that characterization of humanity, by the way, is itself a hypothesis that I don't think has been adequately tested; at least not the part where cooperation is assumed to be altruistic).
Anyway, this so-called cultural group selection explanation for the cooperativeness of humans is contentious for many but compelling for some. Unfortunately, some people I've spoken to during academic conferences and after-school happy hours have clearly misinterpreted the argument as meaning that group selection leads inexorably to socially desirable outcomes.
Um. No. In fact, some research
suggests (though not without substantial controversy) that group selection could potentially explain warfare if warfare entails individually costly contributions toward common defense or offensive raids that benefit a group. Hard to argue that warfare between groups is socially desirable.
Back to the tragedy of hawkish altruism. I've developed a mathematical model showing that group selection cannot only explain conflict BETWEEN groups. It might also explain conflict WITHIN them.
Where the crowd science and open source noodling comes in is that I want to post an early version of my manuscript and hold an open discussion about it before I submit it for publication. Hell, maybe the discussion might result in a collaboration. Scientists share their unpublished manuscripts all the time with their colleagues. And there are many many many scientists out there who could be my virtual colleagues. Why limit it to the people I know personally?
My ultimate goal with this tragedy of hawkish altruism stuff is to develop several quantitative models of increasing complexity while amassing a large, cross-cultural dataset to test the assumptions and qualitative predictions of those models. In fact, one of my post-PhD plans is to apply for a Harry Guggenheim Research Grant
(they fund research on dominance and violence) to do this research.
Anyway, I'm looking for suggestions on how and if to do this kind of open source noodling in a way that maximizes the coolness and soundness of the science while minimizing the possibility for unforeseen bad stuff to happen. So how about it?