Recent developments in Behavioural Public Policy: IBPPC 2022

Editorial

Tensorized multi-view subspace representation learning

Self-representation based subspace learning has shown its effectiveness in many applications. In this paper, we promote the traditional subspace representation learning by simultaneously taking advantages of multiple views and prior constraint. Accordingly, we establish a novel algorithm termed as Tensorized Multi-view Subspace Representation Learning. To exploit different views, the subspace representation matrices of different views are regarded as a low-rank tensor, which effectively models the high-order correlations of multi-view data. To incorporate prior information, a constraint matrix is devised to guide the subspace representation learning within a unified framework. The subspace representation tensor equipped with a low-rank constraint models elegantly the complementary information among different views, reduces redundancy of subspace representations, and then improves the accuracy of subsequent tasks. We formulate the model with a tensor nuclear norm minimization problem constrained with ℓ2,1-norm and linear equalities. The minimization problem is efficiently solved by using an Augmented Lagrangian Alternating Direction Minimization method. Extensive experimental results on diverse multi-view datasets demonstrate the effectiveness of our algorithm

Sophisticated and naïve procrastination: an experimental study

The model of time-inconsistent procrastination by O'Donoughe and Rabin shows that individuals who are not aware of their present-bias (naïve) procrastinate more than individuals who are aware of it (sophisticated) or are not present-biased (time-consistent). This paper tests this prediction. We classify participants into types using a novel measure, and require them to perform a real-effort task on one out of three dates. We find that sophisticated participants perform the task significantly later than naïve participants. Our data suggest that this result may be explained by habit formation