Patenting in the Shadow of Independent Discoveries by Rivals

This paper studies the decision of whether to apply for a patent in a dynamic model in which firms innovate stochastically and independently. In the model, a firm can choose between patenting and maintaining secrecy to protect a successful innovation. I consider a legal environment characterized by imperfect patent protection and no prior user rights. Thus, patenting grants probabilistic protection, and secrecy is effectively maintained until rivals innovate. I show that (1) firms that innovate early are more inclined to choose secrecy, whereas firms that innovate late have a stronger tendency to patent; (2) the incentives to patent increase with the innovation arrival rate; and (3) an increase in the number of firms may cause patenting to occur earlier or later, depending on the strength of patent protection. The socially optimal level of patent protection, which balances the trade-off between the provision of patenting incentives and the avoidance of deadweight loss caused by a monopoly, is lower with a higher innovation arrival rate or a larger number of firms.Patenting decisions; Patents; Secrecy; Independent discoveries

AffinityNet: semi-supervised few-shot learning for disease type prediction

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data. The code is freely available at https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure

Interpersonal bundling

This paper studies a model of interpersonal bundling, in which a monopolist offers a good for sale under a regular price and a group purchase discount if the number of consumers in a group—the bundle size—belongs to some menu of intervals. We find that this is often a profitable selling strategy in response to demand uncertainty, and it can achieve the highest profit among all possible selling mechanisms. We explain how the profitability of interpersonal bundling with a minimum or maximum group size may depend on the nature of uncertainty and on parameters of the market environment, and we discuss strategic issues related to the optimal design and implementation of these bundling schemes. Our analysis sheds light on popular marketing practices such as group purchase discounts, and it offers insights on potential new marketing innovation

Experience Goods and Consumer Search

We introduce a search model where products differ in variety and unobserved quality (`experience goods'), and firms can establish quality reputation. We show that the inability of consumers to observe quality before purchase significantly changes how search frictions affect market performance. In equilibrium, higher search costs hinder consumers' search for better-matched variety and increase price, but can boost firms' investment in product quality. Under plausible conditions, both consumer and total welfare initially increase in search cost, whereas both would monotonically decrease if quality were observable. We apply the analysis to online markets, where low search costs coexist with low-quality products