Why Branded Firm may Benefit from Counterfeit Competition

A durable good monopolist sells its branded product over two periods. In period 2, when there is entry of a counterfeiter, the branded firm may charge a high price to signal its quality. Counterfeit competition thus enables the branded firm to commit to high future price in period 2, alleviating the classic time-inconsistency problem under durable good monopoly. This can increase the branded firm's profit by encouraging consumer purchase without delay, despite the revenue loss to the counterfeiter. Total welfare can also increase, because early purchase eliminates delay cost and consumers enjoy the good for both periods

Price-directed Consumer Search

We extend Stahl's (1989) model to a setting with differentiated products to study the effects of price-directed consumer search. Consumers engage in costly search to find out whether products meet their needs. Consumer search is directed by prices when they are observable before search, in contrast to the case in which prices are discovered only after search, where search is naturally random. The equilibrium under price-directed search differs substantially from that under random search, despite certain similarities. We show that as search costs decrease, sales become more likely and firms earn higher expected profits under price-directed search, whereas the opposite holds under random search. Moreover, compared with random search, under price-directed search firms' expected profits are always lower, but consumer surplus and total welfare are higher provided that the search cost is sufficiently small

Advance Selling Programs: When to Introduce and What to Inform Consumers

We study a two-stage model in which the information processed by consumers at the first stage (advance selling stage) is endogenously determined. In the model, the firm decides whether to introduce an advance selling program, chooses what attribute information to disclose and determines an advance selling price and a retail price. Forward-looking consumers strategically choose, based on the disclosed information, to buy in advance or to make a purchase decision at the second stage (retail stage) when all information is revealed. We characterize the firm's optimal choice on the advance selling program and the strategy of information disclosure. In particular, we show that the firm always prefers to introduce the advance selling program except when underlying consumer preferences are extremely homogenous. In addition, we find that fully revealing horizontal product information at the advance selling stage is never optimal to the firm, but revealing either partial or no product information can be optimal depending on the underlying consumer preferences. Our finding that partial information disclosure is sometimes optimal to the firm is in contrast to the result in the literature of horizontal information provision that a firm maximizes profit by revealing either no or full information to consumers

Price-directed Consumer Search

We extend Stahl's (1989) model to a setting with differentiated products to study the effects of price-directed consumer search. Consumers engage in costly search to find out whether products meet their needs. Consumer search is directed by prices when they are observable before search, in contrast to the case in which prices are discovered only after search, where search is naturally random. The equilibrium under price-directed search differs substantially from that under random search, despite certain similarities. We show that as search costs decrease, sales become more likely and firms earn higher expected profits under price-directed search, whereas the opposite holds under random search. Moreover, compared with random search, under price-directed search firms' expected profits are always lower, but consumer surplus and total welfare are higher provided that the search cost is sufficiently small

Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch under Cyber-Attacks

False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation

Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.Comment: Accepted as CIKM'23 Applied Research Trac

On-Device Model Fine-Tuning with Label Correction in Recommender Systems

To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices. To further deal with cross-device data heterogeneity, the offloaded models normally need to be fine-tuned with each individual user's local samples before being put into real-time inference. In this work, we focus on the fundamental click-through rate (CTR) prediction task in recommender systems and study how to effectively and efficiently perform on-device fine-tuning. We first identify the bottleneck issue that each individual user's local CTR (i.e., the ratio of positive samples in the local dataset for fine-tuning) tends to deviate from the global CTR (i.e., the ratio of positive samples in all the users' mixed datasets on the cloud for training out the initial model). We further demonstrate that such a CTR drift problem makes on-device fine-tuning even harmful to item ranking. We thus propose a novel label correction method, which requires each user only to change the labels of the local samples ahead of on-device fine-tuning and can well align the locally prior CTR with the global CTR. The offline evaluation results over three datasets and five CTR prediction models as well as the online A/B testing results in Mobile Taobao demonstrate the necessity of label correction in on-device fine-tuning and also reveal the improvement over cloud-based learning without fine-tuning

Asynchronous Opinion Dynamics with Online and Offline Interactions in Bounded Confidence Model

Open Access journalNowadays, in the world, about half of the population can receive information and exchange their opinions with others in online environments (e.g. the Internet), while the other half obtain information and exchange their opinions in offline environments (e.g. face to face) (see eMarketer Report, 2016). The speed at which information is received and opinions are exchanged in online environments is much faster than in offline environments. To model this phenomenon, in this paper we consider online and offline as two subsystems in opinion dynamics, and there is asynchronization when the agents in these two subsystems update their opinions. We show that asynchronization strongly impacts the steady-state time of the opinion dynamics, the opinion clusters and the interactions between the online subsystem and offline subsystem. Furthermore, these effects are often enhanced the larger the size of the online subsystem