In Which Jake Questions the Free Market

One of the things that makes my work with Zome different from a lot of other approaches to modulating energy demand is that we aren't trying to solve the problem by using price-signalling or any other sort of market-based approach.

Market-based approaches are the dominant line of thinking for how to control demand, and when you are first approaching the problem, the reasoning behind it makes sense:

• A lot of things in our society manage supply/demand relations pretty well by markets, where changes in supply lead to changes in price that lead to change in demand, until equilibrium is reached.  This is total Econ 101 material.
• At the macro-scale, power in the US grid is pretty much entirely managed by markets, and that works pretty well (if you ignore the whole Enron thing).
• Pilot studies where you give people information about power prices (e.g., through a red light that glows when the price is high), and they moderate their demand, or you give the same information to appliance controllers (e.g., thermostats with a budget and a cooling goal) and they shift their use accordingly.

I don't trust it though.  You see, I just think that price is a pretty lousy and impoverished signal, and there's no reason to restrict ourselves to that when we're running an algorithm on computers.  If you commit to using a market-based approach, you're throwing out pretty much the whole world of distributed algorithms and other engineered self-organization approaches.  And most engineered systems don't use markets for good reason---they're a lot of complex hassle to get right, give you lots of ways to shoot yourself in the foot with unexpected emergent effects, and really just beg for exploitation if you get any real money involved.

If you assume you have to solve the problem with a market, you probably can (I'm pretty sure market-based approaches are Turing complete, if you allow complex enough structures and derivatives), but you might have to twist the problem into a pretzel to do so.

So I tend instead to think the right way to approach a complex distributed control problem like energy demand shaping is to start by solving the distributed control problem, and then figure out how to match it well with external incentives.  Maybe the answer will be a market, but usually it won't, just because markets are only one tiny corner of a really big design space.

And I might be on the way to saying something much more definitive about it.  Just recently, when looking at how the Zome approach compares to price-signalling approaches, I noticed that it's really easy for a distributed demand response market to end up completely failing and always being very far from equilibrium.  And this result just might be more general...

Making the cover of ACS Synthetic Biology

Just a brief note today... the current issue of ACS Synthetic Biology, which includes our papers on our TASBE tool-chain for designing organisms and our MatchMaker algorithm for selecting genetic regulatory elements, has the following lovely cover image and blurb:

This cover depicts bio-design automation’s transformative effect on synthetic biology. DNA design increasingly will be a computational effort culminating in software “toolchains” which cover the specification, design, and assembly of novel biological systems.

Dear reader, I am proud, because that image is an illustration inspired by our TASBE tool-chain paper. That is all.

Publish and Perish

Dear reader, if I can beg your indulgence for a little while, I'd like to do some philosophical maundering for a bit.  Before my paternity leave, I'd been through a hard stretch of "publish and perish" recently (we had a bit of a perfect storm of anticipated deadlines, unanticipated deadlines, and requests for revision) and so the nature of publication and science has been much upon my mind.

One of the standard maxims of science that every scientist knows is "publish or perish"---if you don't get your ideas out there, you can't have an impact.  Of course, there's all the other meanings of that statement as well, having to do with career and funding and all that, but I personally tend to look at it through the filter of the impact of my ideas.

Here, I find the philosophy of Bruno Latour compelling (though I disagree strongly with him in certain other areas): in their anthropology of science, he and Woolgar present a "cycle of credit" that summarizes the scientific world as an economic enterprise.  Scientists then act as investors, building research capital by transforming one type of scientific resource into another.

The cycle (filtered through my interpretation and mutation-inserting memory) is roughly:

Position and publications give a reputation that can be used to secure funding.  Funding allows the production of scientific data.  Data in turn supports the production of publications, and so on around the cycle, with established researchers likely to have significant research capital moving through all stages at the same time.

Where, you may ask, are ideas in there?  Why at every link!  That's the broader job of the scientist.  The standard image of science being merely about production of data and testing of hypotheses is only part of the picture, though a cornerstone on which the whole enterprise rests.  Drop any of the other ingredients out as well, though, and the whole enterprise founders.

I know that some might find this "economic" view of science as crass and unsavorily political.  I don't think so, though, because I think about science as being not just about knowledge, but able knowledge that matters in some way.  Oh, it doesn't necessarily have to matter any time soon, and it doesn't necessarily need to be practical---some things are worth knowing simply because it shapes our understanding of the universe that we live in (an aside: one of my undergraduate degrees is in theoretical mathematics.  I always assumed that things like abstract algebra, topology, and measure theory would be purely useless in the "real world," but enjoyed them simply for the sheer power they gave over the abstract world.  To my great surprise, I now rely on pretty much all of the theoretical mathematics I even learned.).  So when I look at the cycle of scientific credit, I see each of these steps as marking the motion of ideas outward.  Data is how you know your ideas are meaningful, publications are how you spread them to others where they will have an impact, and position and funding are amplifiers that the world gives you as it starts believing your ideas are worthwhile.

What I do notice is missing from Latour's cycle, however, is a clear location for professional service, like organizing or public outreach or teaching.  For myself, at least, that's been a very important part of keeping forward momentum, as well as something I think is really important if you take the "impact of knowledge" view of science that I do.  Service doesn't really fit the diagram neatly: in my experience, it tends to stem from publications, funding, and itself and it feeds into all of these by indirect means.  But I think it doesn't really fit because it's largely a different type of reputation---but, well, any model only takes you so far.

So: publish or perish.  True, I suppose, but I prefer it when it's for the right reason.  Not for fear of perishing, but because I've got things that I have learned that I need to communicate and an audience I want to communicate them to.  And that, I suppose, is why I'm not snobbish when it comes to Impact Factor of my publication venues.  There are places I publish because there are people I want to talk to, others I publish because I want to broadcast an idea, and others simply because I want to archive a key (but possibly obscure) result for later reference.  Only for broadcasting an idea does impact factor really matter---the others are all about getting a strong enough peer validation and placing the ideas where the right group of people will be able to easily find them.  Publish or perish?  We'll see what what happens, but I'm content as long as I'm neither hiding nor overselling the things that I accomplish.

On the Evaluation of Space-Time Functions

Back in May, I discussed my ongoing obsession with first class space-time functions---programs that can be dynamically created, moved around, and operate on one another, even though they're being run distributed over many devices scattered across space and time.

We've now made another step forward, with the publication of a new paper, On the Evaluation of Space-Time Functions.  This paper is an extension of last year's Spatial Computing Workshop paper, generalizing the results to be not just about Proto, but in fact any distributed computing system (of which Proto is simply our prototypical example).

I'm rather proud of this paper, and think it's a fairly readable discussion of these concepts---though still not for the faint of heart.