Optimal natures

Recently, the Natural Capital Project released its new tool for watershed-based ecosystem services decision-making, the Resource Investment Optimization System, or RIOS (spanish for rivers). It builds on InVEST, NCP's tool for mapping and valuing all sorts of services. Where InVEST could tell you for instance where to invest in a watershed to achieve the best water quality gains (efficiency), RIOS is geared to help you decide between different sets of investment (optimization).

RIOS joins a fast-growing cadre of other ecosystem services decision-making software tools. A short list includes:

Social Values for Ecosystem Services (SOLVES) - the USGS's tool of choice
Integrated Water Resources planning suite  - led by the Army Corps of Engineers
Simple and Effective Resource for Valuing Ecosystem Services (SERVES) - from Earth Economics
i-Tree - USFS built this one
ARtificial Intelligence for Ecosystem Services (ARIES)

These models literally instantiate ecosystem services as a framework by providing the means for framing services - ES is a framework for understanding tradeoffs in managing nature and here are the algorithms for modeling them. One of the key points the tools have in common is that they are spatially-explicit; what might distinguish them is whether they aim to inform either investment or policy decisions. Or, since ecosystem service policy tends toward treating nature as always already an investment (or lack thereof), the distinction is probably: what kind of investment (public or private)?

These tools parallel a number of data analytics firms working with so-called Big Data on the environment. Many, like Cloudera and Ayasdi work with oil and gas companies to visualize optimize the use of their drilling equipment, in the name of preventing future environmental catastrophes. Others, like Remsoft's suite of tools aim to improve forestry practices by incorporating extensive data on tree health, location, etc. - Google and Microsoft are working on similar software for "seeing the trees and the forest."

In short, the stated goal of these models is to "optimize" environmental management, which, for many of them, also means optimizing business practice. Is there a difference between optimal and efficient? For some, maybe not. But Remsoft's tools, they claim, allow you to "understand and manage the supply-demand balance, identify current and future supply chain bottlenecks, manage production and delivery capacity, forecast costs and revenues, and generate plans that stay within budget." Clearly something more than the sense of efficiency as input/output is going on here. Indeed, optimization, in the language of mathematics and computer programming, means to choose the best from among several alternatives given a particular criteria. Yes, the criterion for Remsoft might be $, but that may or may not be the case for USFS's community forestry tool, i-Tree.

Where does all this talk of optimization come from? That's hard to say, and 600 pg. tomes have been written about it. But there is a curious perpendicular conversation happening in the weird realm of biology, computer programming, and artificial intelligence themselves meet: where NCP, Remsoft, and others want to optimize nature, these researchers think nature optimizes. They "use and abuse" evolutionary concepts (note: optimization is not necessarily about selection pressure) as metaphor for informing tech design, their goals ranging from the everyday to the lethal. Researchers have found that ants respond to disaster and disruption - to their environment - in ways that may inform optimal transmission of information over internet protocols. The US military has enrolled apiologists to use bee swarms as an analogue for drone maneuvering. The goal, of course, being to optimize surveillance and kill rates. What brings together the "optimize nature" modelers and the "nature optimizes" researchers and designers is the idea that the environment serves as a model for our treatment of it.

This is not to get us lost in the thickets of environmental philosophy or social theory. The question is: on the ground, what is lost and gained by thinking in terms of optimizing ecosystem services? Who stands to win and lose? These models are meant to inform land use decisions, and in doing so, they help to bring about the optimized world they only purport to represent. If you model it, they will come. In this performance, the way the models are programmed matters. And what differences are there between the flavor of optimization led by the conservationists using NCP and the timber managers using Remsoft's Spatial Optimizer? One has to inform policy, the other business - can optimization serve as an adequate guiding concept for both?