The aim of this project is to study the implications of fundamental uncertainty about probabilities, often called ambiguity or model uncertainty, for timing decisions. This includes bidding behaviour in auctions, the optimal time to implement policies such as climate change interventions, or when to bring innovations to the market.
Many economic decisions take place under uncertainty. The literature distinguishes between the case of risk, where the uncertainty can be easily quantified using a probability distribution, or model uncertainty/ambiguity, where this is not easily possible. As an instance of the latter case, a politician evaluating climate change policies may be presented with different probabilistic scenarios when he asks multiple experts.
It is often argued that many decision makers act more cautiously when facing such ambiguity, compared to the case of risk. For instance, they might give a lot of weight to the worst-case scenario. In applications, this may correspond to the model suggested by the most pessimistic experts. However, what is the worst-case scenario can change over time, and consequently, a decision maker may not want to follow through with plans she has formed at earlier stages.
In the first stage of this project, we have investigated when such ambiguity averse behaviour leads to more aggressive bidding in the Dutch auction, an instance of a descending price auction. Future stages will look at timing decisions like when to go public with an innovation or implement a policy change.