We develop a Bayesian model for decision-making under time pressure with endogenous information acquisition. In our model, the decision-maker decides when to observe (costly) information by sampling an underlying continuous-time stochastic process (time series) that conveys information about the potential occurrence/non-occurrence of an adverse event which will terminate the decision-making process. In her attempt to predict the occurrence of the adverse event, the decision-maker follows a policy that determines when to acquire information from the time series (continuation), and when to stop acquiring information and make a final prediction (stopping). We show that the optimal policy has a rendezvous structure, i.e. a structure in which whenever a new information sample is gathered from the time series, the optimal date for acquiring the next sample becomes computable. The optimal interval between two information samples balances a trade-off between the decision maker's surprise, i.e. the drift in her posterior belief after observing new information, and suspense, i.e. the probability that the adverse event occurs in the time interval between two information samples. Moreover, we characterize the continuation and stopping regions in the decision-maker's state-space, and show that they depend not only on the decision-maker's beliefs, but also on the context, i.e. the current realization of the time series.

We develop a Bayesian model for decision-making under time p ressure with endogenous information acquisition. In our model, the decisi on-maker decides when to observe (costly) information by sampling an underlying c ontinuous-time stochastic process (time series) that conveys informa tion about the potential occurrence/non-occurrence of an adverse event which will t erminate the decision-making process. In her attempt to predict the occurrence of t he adverse event, the decision-maker follows a policy that determines when to acquire information from the time series (continuation), and when to stop acquiring information and make a final prediction (stopping). We show that the optimal polic y has a " rendezvous" structure, i.e. a structure in which whenever a new informat ion sample is gathered from the time series, the optimal "date" for acquiring the ne xt sample becomes computable. The optimal interval between two information s amples balances a trade-off between the decision maker's "surprise", i.e. th e drift in her posterior belief after observing new information, and "suspense", i. e. the probability that the adverse event occurs in the time interval between two inf ormation samples. Moreover, we characterize the continuation and stopping re gions in the decision-maker's state-space, and show that they depend not only on th e decision-maker's beliefs, but also on the "context", i.e. the current realiza tion of the time series.

Technical quality: This paper proposes an interesting model and derives three main theorems. The paper demonstrates when the decision maker acquires new information (continuation) and when makes a final prediction (stop) in a scenario where the deadline depends on the sensory information. It also shows that the subject's belief state is a supermartingle, and that the optimal policy has a "rendezvous" structure. The continuation and stopping region in this model depend both on the subject's belief state and "context" of the sensory observations. The paper is interesting and technically sound.

We develop a Bayesian model for decision-making under time pressure with endogenous information acquisition. In our model, the decision-maker decides when to observe (costly) information by sampling an underlying continuous-time stochastic process (time series) that conveys information about the potential occurrence/non-occurrence of an adverse event which will terminate the decision-making process. In her attempt to predict the occurrence of the adverse event, the decision-maker follows a policy that determines when to acquire information from the time series (continuation), and when to stop acquiring information and make a final prediction (stopping). We show that the optimal policy has a "rendezvous" structure, i.e. a structure in which whenever a new information sample is gathered from the time series, the optimal "date" for acquiring the next sample becomes computable. The optimal interval between two information samples balances a trade-off between the decision maker's "surprise", i.e. the drift in her posterior belief after observing new information, and "suspense", i.e. the probability that the adverse event occurs in the time interval between two information samples.