Science and policy: a tenuous mutualism
Mutualisms occupy a romantic place in the popular interpretation of ecology -- one species helping another and benefiting itself along the way. The challenge is that these relationships, when viewed this way, are nearly impossible to reconcile with Darwinian evolution. When I teach my ecology students about mutualisms I tell them that test relationships are far easier to understand when they are viewed the other way around -- two species each acting in their own best interest, that result in a net benefit, though invariably some costs, for each. Perhaps it's because that lecture is coming up soon this semester, but that view of mutualism was on my mind over the last week during my most recent foray into linking science and policy -- a trip to Miyazaki, Japan to discuss a collaboration between an international modeling consortium, Miyazaki University, and the prefectural government to use simulation models to help plan for and respond to livestock disease outbreaks.
Miyazaki is famous in Japan for its livestock and has, for years, won the national prize for the best beef -- it puts your local gastro pub's Kobe beef sliders to shame. In 2010, Miyazaki was devastated by an outbreak of foot-and-mouth disease that shut their livestock industry and led to the preemptive slaughter of hundreds of thousands of pigs and cattle. Understandably, the local government is keen to learn as much as possible from that experience about how to more effectively respond in the future if such an outbreak were to recur.
Enter an international group of disease modelers from government agencies and universities in the UK, US, Europe, Australia, and New Zealand. For us, this outbreak reflects a major increase in the available data in which to understand the dynamics of this diseases -- the bulk of the modeling work has been focussed on the Well characterized 2001 outbreak in the UK -- and an opportunity to test whether the insights gained from extensive analysis of the UK outbreak have generality outside of that one outbreak.
And here we were, in a golf resort in Miyazaki -- divided by language, culture, and objectives -- trying to work out whether it was worth their trusting us as partners and our committing time and resources to developing analyses for Japan. Ostensibly, we all wanted the same thing in the end -- to understand this disease, and help ensure that the impact of outbreaks can be minimized in the future. But our approaches, and intermediate goals were fundamentally different. They need to satisfy national and local stake holders, assure producers that they are taking adequate steps to protect them from future outbreaks, and protect consumer confidence in their vital industry. We need to publish papers, secure grant funding, train graduate students and post-docs, and learn lessons from this outbreak that we can bring back to our home countries.
In this setting we began the common game of back and forth -- the policy side presenting specific questions that they want answered, and the modeling responding with tools and simulations that we can can produce. To an outsider, this conversation probably sounds something like:
Policy makers: we really need to know how to bake a cake
Modelers: we are really good at designing houses
Policy makers: but that cake has to be chocolate and my child had a wheat allergy
Modelers: usually the houses we design have a kitchen - or at least several rooms, one of which could be reasonably interpreted as a kitchen - or maybe a bathroom- it has a sink either way.
There is no good way around these conversations, and at best they will lead to subsequent conversations, perhaps in which figure out how we can design a room in the house in which the child's birthday party can be held. The challenge is that these conversations too often end at the first stage, in which we realize that neither can provide the other what we want right now.
Here in lies the analogy to mutualisms. We hear a lot of bluster from universities and funding agencies about translational science, and linking science and policy. But doing so is never as simple as doing great science that will necessarily find its way into practice. The application of science to policy must emerge as a continuing interaction in which each side continually makes the case for their agenda. In the end, successful ventures may look as though they were designed as such -- like plants rewarding pollinators for facilitating their reproduction. But they will never reach that point if each side is not continually receiving some individual benefit - decision support for policy, papers and practical relevance for the academics - at the expense of the other - data, time, personnel.
Often, we ignore the necessary antagonism of the relationship between science and policy -- happily discussing the long-term benefits while glossing over the inherent conflicts -- data sharing, funding, publication approval and credit -- that often slow the process. In stead, we should address these conflicts head on; acknowledging that we both want something from the other, and only proceeding if there is a reasonable chance that the net result will leave us both in the black.
Cynical as that may sound, I'm quite positive about the potential collaboration in Japan (not least because the beef is really that tasty)! Though our first conversation had lots of missteps, they seemed really engaged about the potential benefits of working with our group -- at least enough to carry us to the next conversation, in which we might be able to bring out collective goals a bit more in line.
