On decision makers' perceptions of what an ecological computer model is, what it does, and its impact on limiting model acceptance

Abstract

Environmental decision makers are required to understand complex ecological processes and ecological computer models are designed to facilitate this understanding. A set of interviews reveals three main perceptions affecting senior environmental decision makers' trust in ecological computer models as decision facilitation tools: an ecological computer model is perceived as (i) a 'black box', (ii) processing poorly documented, sparse and out-of-date input data, and (iii) whose sensitivity to model parameters enables manipulation to produce desired outcomes justifying pre-conceived decisions. This leads to lack of trust towards both ecological computer models and model-users, including other scientists and decision makers. Model acceptance appears to depend on the amount, currency and geographical origin of input data. This is at odds with modellers' communication style, which typically places more emphasis on highlighting the ecological computer model's features and performance, rather than on describing the input data. Developing 'big data' capabilities could deliver the large, real-time, local data that may enhance acceptance. However, the size and complexity of 'big data' requires automated pre-processing, using modelling and algorithms that are even more inscrutable than current ecological computer models. Future trust in ecological computer models will likely depend on how this dilemma is resolved, which is likely to require improved communication between modellers and decision makers.