Work and pleasure, arts and science

One can dream up uncountable categories in any profession, of course, but among academics, and perhaps especially among economists, two types stand out for me: the athlete and the artist/entrepreneur.

Athletes want to be the best in whatever competition they perceive to be in. Rankings are all that matters: all admiration goes to those in the Top 3. Athletes have a strong sense of who is ‘in’ and who is ‘out’: you want to associate with people who are ‘in’ because they publish in all the cool journals, go to the cool conferences and some of that coolness may someday rub off on you. Like real athletes, these academics choose their game, learn the rules, and try to be really good at it. Does this field require me to eschew interdisciplinary research, and prove difficult mathematical propositions? Then by heck I’m going to be the best at it. An athlete is another athlete’s competitor, first and foremost: if other athletes score he cringes his teeth in jealousy and swears to beat them in the next game.

Artists/entrepreneurs want to make a good product. A product is good if they themselves think it is good (the artist) or if it is good enough for a sufficient number of people (the entrepreneur). Artists/entrepreneurs don’t choose games or follow rules: they invent their own game, their own rules. When other scientists produce a great product, like a highly original paper, an artist/entrepreneur is eager to read it, and learn from it. Where athletes are driven by a constant comparison of themselves with others, artists/entrepreneurs are intrinsically motivated: they want to make something they themselves can be proud of.

Fiddle Tunes and IIFET

I have a lot more affinity with artists/entrepreneurs than with athletes – no surprises there. The current system in academia is largely geared towards athletes, with its emphasis on journal citation scores, H-index, and university rankings. This worries me. Athletes may be rule-followers, they are also more likely to cheat – just witness the doping scandals in bicycle racing and other sports. Rule-following also kills creativity – an essential ingredient of science.

IMG_4434 smallI had plenty of opportunity to reflect on the importance of creativity and inventing your own rules in science in the past three weeks. The first week of July I was at the Festival of American Fiddle Tunes in Port Townsend, Washington. It was an overwhelming experience to immerse myself in the music and hospitality of all the folks at this beautiful spot on a peninsula at the Puget Sound. One of the highlights was an improvisation workshop by bluegrass fiddler Tatiana Hargreaves. I don’t want to give away too many details about what we did (perhaps to preserve the secret but actually just because the truth is too embarrassing), but an important lesson that scientists might want to draw from it is that to get out of your comfort zone you should not take yourself too seriously!

DSC00089_smallAnd then there was the conference of the International Institute of Fisheries Economics and Trade (IIFET) in Seattle, in the third week of July. It was my second IIFET meeting but I’m sure it won’t be my last. One of the things I like about IIFET is its broadness, including not only economists but also policy scientists, sociologists, and people from NGOs and the fishing industry. Where the environmental economics conferences can feel like a gathering of athletes, IIFET is the place to go for artists/entrepreneurs. I was also excited to hear that 2020 will see the second edition of MSEAS, a conference on marine social-ecological systems, in Japan! The first edition, in Brest in 2016, yielded what must be the first comic in a peer-reviewed journal – another example of how art and science can make a happy marriage. More of that please!

In case you were wondering what I’m doing in the Australian winter

I’m here for two very interrelated goals.

I’m having another assessment meeting in September 2014; this time it’s about a possible promotion to associate professor. So Goal #1 is to take a good look again at my research and education vision, and discuss it with whoever I can discuss it with. I got quite some inspiration from the keynotes and discussions at IIFET2014. Not that I went there with a blank slate, but it was good to see my ideas confirmed, in a way, and complemented by other people’s ideas.

I have decided long ago that I will focus on the economics of coastal and marine ecosystems. My background is mainly in bioeconomic modelling, so it is logical to focus my research on the kind of questions that require such modelling. But then the question arises: aren’t many other people doing that already? People have been doing theoretical fisheries economics since the 1950s (or longer, if you consider Jens Warming’s work). And there are gigabytes of applied bioeconomic fisheries models like FishRent and Mefisto, and wholesale ecosystem models like Atlantis, where fishers are included as some sort of predators.

But that’s it, actually: either the models are very abstract and qualitative, so that they can be analysed on paper, or they are very detailed and quantitative, so that they can be used for policy assessment or scenario analysis. The problem with the first is that they lack realism; the problem with the second is that they lack transparency. Either you can explain what drives your results, but then your results are close to useless for policy makers, or you can advise policy makers but you cannot explain where your advice comes from.

What has not yet happened much (I know there are people doing it, but not many), is to take the theoretical models, and make them more realistic to the point where you can maintain some intuition as to what drives your results, even though you cannot prove fancy theorems anymore. Macroeconomists and financial economists have reached that stage long ago: where their models get too complicated to be solved by some math magic, they use computation. This way you can add more realism, while maintaining a fair amount of insight into the mechanisms at work. My intention is to apply such computational methods to problems with coastal and marine ecosystems. This includes a lot of fisheries, but also other ecosystem uses, goods, and services.

Which brings me to Goal #2. The Crawford School of Public Policy of Australian National University has among its staff a number of people who have applied computational economic tools to fisheries problems, like Tom Kompas, Hoang Long Chu, and Quentin Grafton. I’m here to learn at least some of the methods they use. Originally I wanted to stay about two months, but for several reasons I only have about two weeks. But in the short time frame I have I’m trying to get the most out of it.

And lo and behold, I have a first result to show you. My first hurdle was writing a perturbation model in a program I can work with. Their models run in a combination of Matlab and Maple, but I don’t have a license for either of them, and I’m not well-versed in Maple. Hoang Long Chu was so kind to give me a paper by Kenneth Judd and Sy-Ming Guu on writing perturbation models in Mathematica – another program I don’t use, but luckily the paper explains the method well and it presents the entire Mathematica code for a simple optimal growth model. So I decided to write the same method in Python – my language of choice for its elegance, simplicity, and speed (ok, compared with R, which is neither elegant, nor simple, nor speedy). It took me a few days but here it is: the Python code and a pdf with some notes on the paper and the model.