_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _24_ The model explains around 95% of variation in the data with only 11 indicators. At this point there are diminishing returns associated with additional complexity. Next, being able to project the covariates accurately or have reasonable scenario values for them is critical to the application and limits the data that can be used. Most importantly, the model cannot rely on future unknowns for which we have only historical data.<sup>26</sup> Others have also put forward broader arguments in favor of parsimony. Baylis et al. (2021) argue that the use of domain knowledge of economic mechanisms is critical to interpretability of prediction results in the agricultural domain. Zhou et al. (2022) focus more concretely on narrowing down on inputs in their call for transparent machine learning models for food security tailored to specific policy needs, by proposing to carve out the path for future efforts and adoption with a simple prototype model. McBride et al. (2022) similarly observe that related but distinct objectives for food insecurity modeling will require choosing to use distinct data sources and urge for careful consideration of the purpose and use cases when making such decisions.

Expanding the set of input features well beyond what can intuitively be grasped immediately diminishes usability from these perspectives. Andr ́ee (2021a) explain that food crises result from complex interactions between conflict, poverty, extreme weather, climate, and food price shocks; see the analyses by Misselhorn (2005); Headey (2011); Singh (2012); D’Souza and Jolliffe (2013) for more focus on individual elements, that compound in the presence of long-standing structural factors, as pointed out also by Maxwell and Fitzpatrick (2012). The vast complexity of food insecurity provides ample directions for more data and rich opportunity to develop a complex model. The intrinsic assumption behind the relatively simple model developed here is that while the exact causal mechanisms that describe how food crisis shocks play out may be vastly complex, much of the impacts are endogenous to the economic state of a country. The corollary is that a good estimate of vulnerability may thus be produced from a few broad-based indicators that capture this internalized fragility.<sup>27</sup> The idea is supported by the good accuracy obtained in the empirical application; the 2019 data was forecast

> 26If food insecurity could accurately be predicted from say an indicator of the quality of governance, but a breakdown in that indicator itself is as equally hard to forecast into the future as an outbreak of food insecurity itself, then little progress has been made in terms of developing a future outlook. Instead, the only thing achieved is to state one unknown in terms of another unknown. Keeping such an important indicator fixed in the outlook in turn bakes in the assumption that food insecurity will remain stable. Essentially, this restricts the exercise from using the wide variety of indicators whose unforeseen drastic deterioration go hand in hand with a drastic deterioration in food insecurity, including protests, political violence, violent conflict, weather impacts, and other indicators that have previously been used to model acute food insecurity situations in previously cited work and related targets such as conflict outbreaks (Celiku and Kraay, 2017).

> 27This is essentially a more elaborate way of summarizing Amartya Sen’s famous statement that no famine ever occurred in a democracy; see a discussion by Rubin (2012), or de Waal’s statement that all modern famines are man-made (De Waal, 2018), both which are ways to say that critical food insecurity outcomes are produced fully endogenously from rising vulnerabilities in a country. It follows that if systems in a country are so broken or corrupt to produce such devastating outcomes, then surely the impacts should be visible across a wide spectrum of socio-economic outcomes and a simple reading of basic economic indicators should provide ample predictive signal.