The importance of painting a complete picture (sometimes)

How often have you heard, read, or thought about a situation akin to ‘A’ causes ‘B’, where ‘B’ is considered undesirable to some agenda? The obvious resolution to such a scenario is to stop, reduce, or take some preventative measure against ‘A’ if possible. However, without proper consideration of how this action against ‘A’ will affect things ‘C’, ‘D’, ‘E’, etc., and how these other things may impact the broader agenda, it is not necessarily clear if preventing ‘A’ is a good thing or not.

Consider a situation based in the real world. I am interested in the impact of trade on biodiversity. Some commodities require a lot of land for production and can have a negative impact on biodiversity in the regions of production. An example of this is soy in the Brazilian Cerrado, which is home to over 5% of the world’s species (Green et al., 2019). It is well documented that soy plantations, including those in Brazil, contribute heavily to deforestation. Additionally, it is known that change in land use, such as turning forests into soy plantations, is a major contributor to species extinctions (Curran et al., 2011). A similar example of land use change contributing to biodiversity loss, also directly linked to deforestation, is the production of Brazilian beef (zu Ermgassen et al., 2020).

Given our initial considerations regarding ‘A’ causing negative thing ‘B’ and thus deciding to prevent ‘A’, if deforestation in Brazil is contributing to species extinctions, then it seems that deforestation should be regulated more severely. Implementing such a policy may be the correct action, but it is first necessary to ascertain how this change would impact the big picture. If deforestation was regulated more severely, making less land available with which to produce commodities, then there would be less supply. Consumer demand might then not be met, so consumers would have to either change their behaviour or source their soy and beef from ‘elsewhere’. To meet demand, this ‘elsewhere’ may have to reappropriate land for commodity production, with new local consequences for biodiversity.

Thinking more broadly, it is also unclear whether a change in supplier would cause a temporary, or even long-term, supply shortage or some other distortion of the market. In that case, the global economy could be affected in other ways that are not well understood due to the complexity of international trade. The aim of my work is to use mathematical methods to better understand this complexity, how better decisions can be made, and how these decisions can impact real-world biodiversity. Humans often find that such complex networks of interactions are difficult to understand. These systems can operate in different places and at different scales so, especially when any decisions rely on information which might be incomplete or unreliable, the data can easily be misinterpreted.

Maybe it would all be fine, and maybe there would be adequate soy for everyone, served with some lab-grown beef for a moral burger, and fewer species extinctions. Hopefully these are outcomes that policy makers are aiming for. However, the point here is not to solve a complex problem but to highlight the importance of thinking about problems properly. Making a policy change before understanding its implications in a complete and precise way risks an outcome against the policy maker’s best interests. In conclusion, if ‘A’ and ‘B’ are known to be isolated, so affect only each other, then any change in them will not impact the rest of the world and can safely be changed if needed. Otherwise, it is important to at least check, with things ‘C’, ‘D’, ‘E’, etc., if an apparently good decision is actually good.