Introduction: Why do we need simple models?

Assessment of 1000⁺ issues hampered by incomplete and incompatible data. Over the last century, production and consumption has intensified and diversified. As a result, 1000⁺ chemical substances potentially affect 1000⁺ biological species and 10⁺ social categories at 1000⁺ geographic sites ^{[Figure 1]}. Setting the right priorities in environmental problems and selecting the best alternatives among sustainable solutions thus requires proper assessment ^{[Hendriks 2013, Hendriks 2021]}. Yet, information is often incomplete because financial, practical and ethical constraints hamper generation of new data. For instance, exposing caged wildlife to test impact of chemicals is unaffordable, unfeasible and illegal. In addition, data collected from different studies are often incompatible because of sectoral and disciplinary boundaries. For example, toxicological endpoints in standard tests do not correspond to ecological indicators of habitat fragmentation or climate change.

Figure 1. Assessment of multiple substances, sites, species and categories is achieved by relating data and models such as SIMPLEBOX and OMEGA.

Assessment of multiple issues facilitated by simple models. Statistical regressions allow interpolation to cases not investigated empirically. Yet, abundant measurements to achieve significant relationships. Mathematical equations explicitly include knowledge of the system potentially reducing data hunger. Yet, incorporating many detailed mechanisms in models still requires new lab and field studies for extrapolation to other cases. Hence, if multiple substances, sites, species and categories need to be assessed, one inevitably has to rely on models made up of a few mathematical equations and default parameters based on overarching principles.