Modeling signal-based decisions in online search environments: A non-recursive forward-looking approach
Consider a rational decision maker (DM) who must acquire a finite amount of information sequentially online from a set of products. The DM receives signals on the distribution of the product characteristics. Each time an observation is acquired, DMs redefine the probability of improving upon the products observed. The resulting information acquisition process depends on the values of the characteristics observed, the number and potential realizations of the remaining observations, and the type of signal received. We construct two functions determining the information acquisition behavior of DMs and illustrate numerically the importance of the characteristic on which signals are issued.
signals information acquisition product improvement learning utility theory sequential search