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Consumer-to-consumer (C2C) platforms have become a major engine of growth in Internet commerce. This is especially true in countries such as China, which are experiencing a big rush toward e-commerce. The emergence of such platforms gives researchers the unique opportunity to investigate the evolution of such platforms by focusing on the growth of both buyers and sellers. In this research, we build a utility-based model to quantify both cross and direct network effects on Alibaba Group’s Taobao.com, the world’s largest online C2C platform (based in China). Specifically, we investigate the relative contributions of different factors that affect the growth of buyers and sellers on the platform. Our results suggest that the direct network effects do not play a big role in the platform’s growth (we detect a small positive direct network effect on buyer growth and no direct network effect on seller growth). More importantly, we find a significant, large and positive cross-network effect on both sides of the platform. In other words, the installed base of either side of the platform has propelled the growth of the other side (and thus the overall growth). Interestingly, this cross-network effect is asymmetric with the installed base of sellers having a much larger effect on the growth of buyers than vice versa. The growth in the number of buyers is driven primarily by the seller’s installed base and product variety with increasing importance of product variety. The growth in the number of sellers is driven by buyer’s installed base, buyer quality, and product price with increasing importance of buyer quality. We also investigate the nature of these cross-network effects over time. We find that the cross-network effect of sellers on buyers increases and then decreases to reach a stable level. By contrast, the cross-network effect of buyers on sellers is relatively stable. We discuss the policy implications of these findings for C2C platforms in general and Taobao in particular. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0976 .

Two-sided platforms connect two or more distinct user groups. Agents on such a platform not only value the participation of users from a different group but are also affected by the same-side network effects that arise from the participation of agents in their own group. We study how negative same-side network effects among sellers affect the participation levels and profit of a monopoly platform. We use a novel specification of the CES utility function to model our consumer preferences, where taste for variety and substitutability are not interrelated. We find that when the platform implements subscription pricing on both sides, an increase in the intensity of competition (higher negative same-side network effects) amongst sellers leads to more participation from both buyers and sellers and there is an increase in the profit of the platform. On the other hand, when the platform can only charge a fee from the seller, participation on both sides first rises and then falls. The platform’s profit also follows the same trend. We also briefly discuss how prices of competing platforms change when there is an increase in the intensity of competition amongst sellers.

The dynamics of network effects present challenges for platforms’ management strategies across development stages, which have been overlooked in existing literature. Using data from a Chinese prominent freight exchange platform, this paper explores the evolution of direct network effects and offers an explanation for the inconsistent findings in existing literature. We find that direct network effects are positive initially but gradually lose significance and eventually turn negative as the market thickens. We consistently observe asymmetry in direct network effects, initially favoring carriers but shifting to shippers over time. Additionally, shippers experience earlier changes in direct network effects compared to carriers. We attribute the changes over time to the diverse perceptions of platform value resulting from an increased number of peers, as different forces dominate under different market thickness conditions. Our study contributes to the debate on direct network effects, providing insights into their variability based on market thickness.

We study deep neural networks and their use in semiparametric inference. We establish novel nonasymptotic high probability bounds for deep feedforward neural nets. These deliver rates of convergence that are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second-step inference after first-step estimation with deep learning, a result also new to the literature. Our nonasymptotic high probability bounds, and the subsequent semiparametric inference, treat the current standard architecture: fully connected feedforward neural networks (multilayer perceptrons), with the now-common rectified linear unit activation function, unbounded weights, and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed-width, very deep networks. We establish the nonasymptotic bounds for these deep nets for a general class of nonparametric regression-type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focusing on causal parameters for concreteness, and demonstrate the effectiveness of deep learning with an empirical application to direct mail marketing.

Harnessing placebo and nocebo effects has significant implications for research and medical practice. Placebo analgesia and nocebo hyperalgesia, the most well-studied placebo and nocebo effects, are thought to initiate from the dorsal lateral prefrontal cortex (DLPFC) and then trigger the brain’s descending pain modulatory system and other pain regulation pathways. Combining repeated transcranial direct current stimulation (tDCS), an expectancy manipulation model, and functional MRI, we investigated the modulatory effects of anodal and cathodal tDCS at the right DLPFC on placebo analgesia and nocebo hyperalgesia using a randomized, double-blind and sham-controlled design. We found that compared with sham tDCS, active tDCS could 1) boost placebo and blunt nocebo effects and 2) modulate brain activity and connectivity associated with placebo analgesia and nocebo hyperalgesia. These results provide a basis for mechanistic manipulation of placebo and nocebo effects and may lead to improved clinical outcomes in medical practice.

Post-stroke fatigue (PSF) is highly prevalent and lacks of effective management. Recent evidence suggest the use of transcranial direct current stimulation (tDCS) to reduce PSF. However, the effect was not lasting and the working mechanisms was unclear. The purpose of this study is to determine the behavioral and neurophysiological effects of five daily sessions of tDCS on PSF. This will be a double-blind randomized controlled trial targeting an enrollment of 32 participants with subacute-chronic stroke and significant fatigue (average Fatigue Severity Scale (FSS) > 4). Participants will be equally randomized to either anodal tDCS or sham tDCS groups. The anodal tDCS group will receive 20 minutes of 2-mA anodal tDCS applied to the ipsilesional primary motor cortex (M1) for five consecutive days. The sham tDCS group will receive the same protocol except there will be no active current delivered. Outcome assessments will take place at baseline (prior to randomization), immediately after the intervention, and at one-month follow-up. The primary behavioral outcome will be the FSS and the primary neurophysiological outcome will be an input-output curve of motor cortex excitability derived using transcranial magnetic stimulation. Secondary behavioral outcomes will include Fatigue Scale for Motor and Cognitive Function, Visual Analog Scale-Fatigue, Borg Rating of Perceived Exertion, and Paas Mental Effort Rating Scale. Secondary neurophysiological outcome will be the functional connectivity of the fronto-striato-thalamic network acquired using resting state functional Magnetic Resonance Imaging (MRI). Repeated measure ANOVA or ANCOVA will be conducted for all outcomes to compare the change between groups. Little is known about effective treatments for PSF and the underlying mechanisms of PSF. tDCS is a promising tool to provide targeted intervention to reduce PSF symptoms. However, its lasting effect and working mechanism on PSF is elusive. The results of this clinical trial will offer critical information for PSF management and investigation. This trial was registered in February 1 2024 with ClinicalTrials.gov under the registration number NCT06088914.

This paper investigates a long-standing question about the effect of surface roughness on turbulent flow: What is the equivalent roughness sand-grain height for a given roughness topography? Deep neural network (DNN) and Gaussian process regression (GPR) machine learning approaches are used to develop a high-fidelity prediction approach of the Nikuradse equivalent sand-grain height $k_s$ for turbulent flows over a wide variety of different rough surfaces. To this end, 45 surface geometries were generated and the flow over them simulated at ${Re}_\\tau =1000$ using direct numerical simulations. These surface geometries differed significantly in moments of surface height fluctuations, effective slope, average inclination, porosity and degree of randomness. Thirty of these surfaces were considered fully rough, and they were supplemented with experimental data for fully rough flows over 15 more surfaces available from previous studies. The DNN and GPR methods predicted $k_s$ with an average error of less than 10 % and a maximum error of less than 30 %, which appears to be significantly more accurate than existing prediction formulae. They also identified the surface porosity and the effective slope of roughness in the spanwise direction as important factors in drag prediction.

People are exposed to persuasive communication across many different contexts: Governments, companies, and political parties use persuasive appeals to encourage people to eat healthier, purchase a particular product, or vote for a specific candidate. Laboratory studies show that such persuasive appeals are more effective in influencing behavior when they are tailored to individuals’ unique psychological characteristics. However, the investigation of large-scale psychological persuasion in the real world has been hindered by the questionnaire-based nature of psychological assessment. Recent research, however, shows that people’s psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook Likes or Tweets. Capitalizing on this form of psychological assessment from digital footprints, we test the effects of psychological persuasion on people’s actual behavior in an ecologically valid setting. In three field experiments that reached over 3.5 million individuals with psychologically tailored advertising, we find that matching the content of persuasive appeals to individuals’ psychological characteristics significantly altered their behavior as measured by clicks and purchases. Persuasive appeals that were matched to people’s extraversion or openness-to-experience level resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or unpersonalized counterparts. Our findings suggest that the application of psychological targeting makes it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. We discuss both the potential benefits of this method for helping individuals make better decisions and the potential pitfalls related to manipulation and privacy.

•Attention plays an important role in pain modulation.•Changing the excitability at the rDLPFC through tDCS can modulate the placebo effect.•Repeated tDCS at the rDLPFC can modulate the functional connectivity of the FPN and DAN.•DAN connectivity changes after tDCS are correlated with the placebo analgesia. Literature suggests that attention is a critical cognitive process for pain perception and modulation and may play an important role in placebo and nocebo effects. Here, we investigated how repeated transcranial direct current stimulation (tDCS) applied at the dorsolateral prefrontal cortex (DLPFC) for three consecutive days can modulate the brain functional connectivity (FC) of two networks involved in cognitive control: the frontoparietal network (FPN) and dorsal attention network (DAN), and its association with placebo and nocebo effects. 81 healthy subjects were randomized to three groups: anodal, cathodal, and sham tDCS. Resting state fMRI scans were acquired pre- and post- tDCS on the first and third day of tDCS. An Independent Component Analysis (ICA) was performed to identify the FPN and DAN. ANCOVA was applied for group analysis. Compared to sham tDCS, 1) both cathodal and anodal tDCS increased the FC between the DAN and right parietal operculum; cathodal tDCS also increased the FC between the DAN and right postcentral gyrus; 2) anodal tDCS led to an increased FC between the FPN and right parietal operculum, while cathodal tDCS was associated with increased FC between the FPN and left superior parietal lobule/precuneus; 3) the FC increase between the DAN and right parietal operculum was significantly correlated to the placebo analgesia effect in the cathodal group. Our findings suggest that both repeated cathodal and anodal tDCS could modulate the FC of two important cognitive brain networks (DAN and FPN), which may modulate placebo / nocebo effects.

Abstract Background Although transcranial direct current stimulation (tDCS) has shown to potentially mitigate drug craving and attentional bias to drug-related stimuli, individual differences in such modulatory effects of tDCS are less understood. In this study, we aimed to investigate a source of the inter-subject variability in the tDCS effects that can be useful for tDCS-based treatments of individuals with methamphetamine (MA) use disorder (IMUD). Methods Forty-two IMUD (all male) were randomly assigned to receive a single-session of either sham or real bilateral tDCS (anodal right/cathodal left) over the dorsolateral prefrontal cortex. The tDCS effect on MA craving and biased attention to drug stimuli were investigated by quantifying EEG-derived P3 (a measure of initial attentional bias) and late positive potential (LPP; a measure of sustained motivated attention) elicited by these stimuli. To assess the association of changes in P3 and LPP with brain connectivity network (BCN) topology, the correlation between topology metrics, specifically those related to the efficiency of information processing, and the tDCS effect was investigated. Results The P3 amplitude significantly decreased following the tDCS session, whereas the amplitudes increased in the sham group. The changes in P3 amplitudes were significantly correlated with communication efficiency measured by BCN topology metrics (r = −0.47, P = .03; r = −0.49, P = .02). There was no significant change in LPP amplitude due to the tDCS application. Conclusions These findings validate that tDCS mitigates initial attentional bias, but not the sustained motivated attention, to MA stimuli. Importantly, however, results also show that the individual differences in the effects of tDCS may be underpinned by communication efficiency of the BCN topology, and therefore, these BCN topology metrics may have the potential to robustly predict the effectiveness of tDCS-based interventions on MA craving and attentional bias to MA stimuli among IMUD.