Default contribution rates for 401(k) pension plans powerfully influence workers' choices. Potential causes include opt-out costs, procrastination, inattention, and psychological anchoring. We examine the welfare implications of defaults under each of these theories. We show how the optimal default, the magnitude of the welfare effects, and the degree of normative ambiguity depend on the behavioral model, the scope of the choice domain deemed welfare-relevant, the use of penalties for passive choice, and other 401(k) plan features. Depending on which theory and welfare perspective one adopts, virtually any default contribution rate may be optimal. Still, our analysis provides reasonably robust justifications for setting the default either at the highest contribution rate matched by the employer or -contrary to common wisdom -at zero. We also identify the types of empirical evidence needed to determine which case is applicable.A data appendix is available at: http://www.nber.org/data-appendix/w17587 6 In principle, these two effects are separable (e.g., upon electing a contribution rate of 3%, the default for the next period could change to 4%), but in practice they always go hand-in-hand (in the same example, the new default would be 3%).7 It is also natural to assume that τ (0) = 0 and τ (x) < 1.

We document some early effects of how mobile devices might change Internet and retail commerce. We present three main findings based on an analysis of eBay's mobile shopping application and core Internet platform. First, early adopters of mobile e-commerce applications appear to be people who already were relatively heavy Internet commerce users. Second, adoption of the mobile shopping application is associated with both an immediate and sustained increase in total platform purchasing, with little evidence of substitution from the core platform. Third, differences in user behavior across the mobile applications and the regular Internet site are not yet so dramatic.

Abstract. The purpose of data browsers is to help users identify and query data effectively without being overwhelmed by large complex graphs of data. A proposed solution to identify and query data in graph-based datasets is Pivoting (or set-oriented browsing), a many-to-many graph browsing technique that allows users to navigate the graph by starting from a set of instances followed by navigation through common links. Relying solely on navigation, however, makes it difficult for users to find paths or even see if the element of interest is in the graph when the points of interest may be many vertices apart. Further challenges include finding paths which require combinations of forward and backward links in order to make the necessary connections which further adds to the complexity of pivoting. In order to mitigate the effects of these problems and enhance the strengths of pivoting we present a multi-pivot approach which we embodied in tool called Visor. Visor allows users to explore from multiple points in the graph, helping users connect key points of interest in the graph on the conceptual level, visually occluding the remainder parts of the graph, thus helping create a road-map for navigation. We carried out an user study to demonstrate the viability of our approach.

Default contribution rates for 401(k) pension plans powerfully influence choices. Potential causes include opt-out costs, procrastination, inattention, and psychological anchoring. Using realistically parameterized models, we show how the optimal default, the magnitude of the welfare effects, and the degree of normative ambiguity depend on the behavioral model, the scope of the choice domain deemed welfare-relevant, the use of penalties for passive choice, and other 401(k) plan features. While results are theory-specific, our analysis provides reasonably robust justifications for setting the default either at the highest contribution rate matched by the employer or—contrary to common wisdom—at zero. (JEL D14, D91, J26, J32)

In this paper we give an account of the current state of practice in ontology engineering based on the findings of a six months empirical survey we performed between October 2008 and March 2009 that analysed 148 ontology engineering projects from industry and academia. The survey focused on process-related issues and looked into the impact of research achievements on real-world ontology engineering projects, the complexity of particular ontology development tasks, the level of tool support, and the usage scenarios for ontologies. The main contributions of this survey compared to other works in the ontology engineering community are twofold: Firstly, the size of the data set the results are grounded on is by far larger than every other similar endeavour published in the last years. Secondly, the findings of the survey confirm the fact that ontology engineering is an established engineering discipline in respect of the maturity and level of acceptance of its main components, methodologies, methods and software tools, whereas further research should target the customization of existing technology to the specifics of vertical domains, as well as economic aspects of ontology engineering.