AN ANALYTIC APPROACH TO MEASURE INFORMATION AGGREGATION AND EVALUATE THE STABILITY OF INFORMATIONAL CASCADES UNDER INCOMPLETE INFORMATION SETTINGS

Informational cascades describe a situation in which people observe the actions of others and then make the same choice, regardless of their own information. Behavioral conformity prevents information aggregation (Bikhchandani et al., 1992). However, under incomplete information settings, individual’s information is a sample of the whole information pool as we are facing information more than we can handle in daily business routine. As we can rule out the possibility that predecessors get enough information to shatter a cascade if cascade continues, it is reasonable to consider there is information injected into cascade even when decision-maker follows predecessor’s behavior. Taking this belief into consideration, we analyze the threshold point of convergence /deviation, and propose a model to measure Information aggregation and evaluate the stability of informational cascades under incomplete information settings. This model helps to optimize sequential decision-making process by utilizing the statistical aspects of informational cascades