The impact of time limitation: insights from a queueing experiment

We experimentally explore the effects of time limitation on decision making. Under different time allowance conditions, subjects are presented with a queueing situation and asked to join one of the two given queues. The results can be grouped under two main categories. The first one concerns the factors driving decisions in a queueing system. Only some subjects behave consistently with rationality principles and use the relevant information efficiently. The rest of the subjects seem to adopt a simpler strategy that does not incorporate some information into their decision. The second category is related to the effects of time limitation on decision performance. A substantial proportion of the population is not affected by time limitations and shows consistent behavior throughout the treatments. On the other hand, some subjects’ performance is impaired by time limitations. More importantly, this impairment is not due to the stringency of the limitation but rather to being exposed to a time constraint.

Context-aware Approach for Determining the Threshold Price in Name-Your-Own-Price Channels

Key feature of a context-aware application is the ability to adapt based on the change of context. Two approaches that are widely used in this regard are the context-action pair mapping where developers match an action to execute for a particular context change and the adaptive learning where a context-aware application refines its action over time based on the preceding action’s outcome. Both these approaches have limitation which makes them unsuitable in situations where a context-aware application has to deal with unknown context changes. In this paper we propose a framework where adaptation is carried out via concurrent multi-action evaluation of a dynamically created action space. This dynamic creation of the action space eliminates the need for relying on the developers to create context-action pairs and the concurrent multi-action evaluation reduces the adaptation time as opposed to the iterative approach used by adaptive learning techniques. Using our reference implementation of the framework we show how it could be used to dynamically determine the threshold price in an e-commerce system which uses the name-your-own-price (NYOP) strategy.