Why shouldn't I collect more data? Reconciling disagreements between intuition and value of information analyses

Abstract

Abstract Value of information (VoI) analysis is a method for quantifying how additional information may improve management decisions, with applications ranging from conservation to fisheries. However, VoI studies frequently suggest that collecting more data will not substantially improve management outcomes. This often contradicts the intuition of ecologists and managers who usually believe new information is critical for management. This inconsistency is exacerbated by the perception that VoI is a black‐box method. A lack of understanding as to why VoI is usually lower than ecologists expect is hampering on‐ground uptake. There is an urgent need to identify the factors that drive VoI methodology to produce low values. Here, we use a rigorous approach to provide insights into why VoI values are often low. We first derive analytic solutions and upper bounds for a VoI problem with two uncertain states, two actions, and four management outcomes. We show how VoI changes with respect to the benefit (i.e. utility) of implementing actions in each state, and the probability the system is in each state. We apply our formulation to a published frog population management case study and extend the results numerically to 10 million randomly generated larger‐sized problems. Zero VoI occurred half of the time in our two‐action two‐state simulations, corresponding to when one action is best, or equal best, across all states. Even when VoI values were positive, they were typically low. However, on average, VoI tended to increase with the number of states and actions. Our analytic expression for VoI, in the case where VoI is positive, demonstrates that VoI is characterized by the state probabilities and, the utility gaps, that is the difference in utility of deploying each action in each state. Our derived bounds reveal that, in all two‐action two‐state systems, VoI cannot be larger than half the largest utility gap. Our simple, yet powerful, analysis provides precious insight into the important factors that drive VoI analysis. Our work provides an essential stepping stone towards increasing the interpretability of VoI analysis in more complex settings, ultimately empowering managers to use VoI to help inform their decisions.

Keywords

• decision making
• ecological management
• expected value of perfect information
• uncertainty
• value of information