A multiple criteria methodology for priority based portfolio selection

This paper presents a new methodology combining multiple criteria sorting or ranking methods with a project portfolio selection procedure. The multicriteria method permits the comparison of projects in terms of their priority based on qualitative and quantitative criteria. Then, a feasible set of projects, i.e. a portfolio, is selected according to the priority defined by the multiple criteria method and satisfying a set of resources and logical constraints. The proposed portfolio selection methodology is called Priority Based Portfolio Selection (PBPS) and can be applied in different contexts. We present an application in the urban planning domain where our approach allows us to select a set of urban projects based on their priority, budgetary constraints, and urban policy requirements. Given the increasing interest of historical cities to reuse their cultural heritage, we applied and tested our methodology in this context. In particular, we show how the methodology can support the prioritization of the interventions on buildings with some historical value in the historic city center of Naples (Italy), taking into account several points of view.     

Quantifying model selection uncertainty via bootstrapping and Akaike weights

Picking one ‘winner’ model for researching a certain phenomenon while discarding the rest implies a confidence that may misrepresent the evidence. Multimodel inference allows researchers to more accurately represent their uncertainty about which model is ‘best’. But multimodel inference, with Akaike weights—weights reflecting the relative probability of each candidate model—and bootstrapping, can also be used to quantify model selection uncertainty, in the form of empirical variation in parameter estimates across models, while minimizing bias from dubious assumptions. This paper describes this approach. Results from a simulation example and an empirical study on the impact of perceived brand environmental responsibility on customer loyalty illustrate and provide support for our proposed approach.