Unobserved preferences and dynamic platform pricing under positive network externality

We study the dynamic optimization of platform pricing in industries with positive direct network externalities. The utility of the network for the consumer is modeled as a function of three components. Platform price and participation rate affect the consumer’s decision to join the platform. The platform operator is assumed to know the consumer’s sensitivities with respect to these components. In addition, the consumer’s utility is a function of other attributes, such as network privacy policies and environmental effects of the service. We assume that the distribution of these unobserved preferences in the potential customer base is known to the platform operator. We show analytically how the unobserved preferences affect the dynamic platform price design. Both static and rational expectations with respect to the platform participation are presented. We simulate an electricity market demand side management service application and show that the platform operator sets low prices in the launch phase. The platform operator can set higher launching prices if it can affect customers’ preferences, expectations or adjustment friction.

The impacts of pre-apprenticeship training for young people

Apprenticeships are the key means by which the UK government aims to build skills and tackle the problem of youth unemployment. However, not all young people are able to secure an apprenticeship. Traineeships, a voluntary six-month programme of work placements and work preparation training, were introduced in England in 2013 to help equip young people with the skills and experience required to secure an apprenticeship or employment. The analysis in this paper uses linked administrative data on the population of trainees and a comparison sample of non-trainees to evaluate the impact of the programme on employment and apprenticeships. It uses a local instrumental variable approach, which allows selection into a traineeship to be influenced by unobserved preferences and for impacts to vary according to these preferences. The heterogeneous impacts can be aggregated to form an estimate of the average impact of treatment for all participants. The results show no overall impact on employment for younger trainees (16-18 year-olds) but an across-the-board positive impact on the probability of becoming an apprentice. For older trainees (19-23 year-olds), no significant impact on either employment or apprenticeships is evident among participants as a whole but the results suggest that, for those more resistant to participating, traineeships may actually reduce the probability of becoming an apprentice. These results confirm the effectiveness of traineeships as a means of facilitating apprenticeships among younger people. As such, they support the policy target of achieving 3 million apprenticeships by 2020.

Travel Data Sequence from Multi-Source Recommendation System

Due to different sort of preferences and restrictions of a trip such as time source limitation and every tourist’s destination points the travel based recommendation has become a challenging task. Most importantly the data generated by the geo-tagged social channel from the geo based tag tweets, snapshots of credentials. Due to examining this, extended data allows us to invent the profiles, daily mobility patterns, and results of the user’s. To resolve the issues and challenges of capacity providing their personalized and sequential travel to make package recommendation to a topical package model and to take using social media info in which mechanically mine person travel interest with another quality like time, cost, and period of wayfaring. Here, we had a proposal that a travel data sequence after a multi source recommendation system. We implemented a location recommendation system that derives personal preferences while accounting for restraints irremissibly by road capacity in order to change the demand of travel. We first infer unobserved preferences using a machine learning technique from data mining records. It extends our method to provide personalized suggestions based on user geo co-ordinates points. By utilizing the tree based hierarchal graphs (TBHG), location histories of the multiple users’ have been modeled. In order to collect the selected places interest level and travel knowledge of user’s, the HITS model had developed based on TBHG. Finally, hybrid filtering approach based on HITS is utilized to get the global positioning system (GPS) based personalized recommendation system. And for image based search similar images with the tag information are retrieved for the query image users.