Fueled by the widespread adoption of algorithms and artificial intelligence, the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging the two design features identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response probability. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response probability, conversation length, and the probability of order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms—and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform.This paper has been This paper was accepted by Felipe Caro for the Special Issue on the Human-Algorithm Connection.Funding: This study is supported by the National Natural Science Foundation of China [Grants 72172169 and 91646125], Program for Innovation Research at the Central University of Finance and Economic, and Shanghai Pujiang Program.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03833 .

With the rapid development of information technology, the gig labor marketplace is fast growing, with digital platform-based instant messaging (IM) playing an important role in raising freelancers’ orders, serving the intention for the crowdsourcing platforms to increase capacity to balance supply and demand. Using a large-scale field experiment on a crowdsourcing freight platform, this study investigates the impact of IM on freelancers’ response rate of orders. Our findings suggest the effects of IM depend on its content and information richness level. Task-relevant information in IM increases the freelancers’ response rate, especially for the priority commitment information, compared with order price information. In addition, although adding task-irrelevant information in IM decreases the freelancers’ response rate, it does not mean the less task-irrelevant information results in a weaker negative IM effect. Rather than that, including task-irrelevant information with a medium information richness level in IM harms the freelancers’ response to the most significant extent. Moreover, our findings reveal crowdsourcing platforms’ actions of IM to increase freelancers’ response rate are consistent with the actions to improve the order acceptance rate, thus demonstrating the critical role of increasing freelancers’ response rate in raising their interest in the final acceptance of the order serving. Our findings guide crowdsourcing platforms to design effective digital platform-based IMs to communicate with freelancers to arouse their response and interest in serving the orders. The capacity of crowdsourcing platforms thus can be dynamically adjusted and expanded to benefit their profitability.

EDITORIAL article Front. Phys., 10 April 2023Sec. Social Physics Volume 11 - 2023 | https://doi.org/10.3389/fphy.2023.1199879

The development of logistics accelerates the transportation process, which in turn increases the exchanging efficiency between cities. To understand such exchanging interactions among cities, we build a city logistics network based on a unique data set, which contains millions of online freight orders spanning a period of one year. It is found that this network exhibits a disassortative configuration and its link weight (respectively, node strength) follows a Weibull (an exponential) distribution. We also find that the hierarchical structure of the city logistics network is highly consistent with the strategic layout of constructing modern comprehensive transportation system plan in China's "Thirteenth Five-Year Plan". Furthermore, it is observed that the cities with high Gross Domestic Product (GDP) have high centrality in the city logistics network and the logistics flows between two cities are proportional to the product of their GDPs. Our results not only uncover the topological and hierarchical structure of the city logistics network in China, but also provide an understanding of the structural shaping force on the growth of city logistics network.

Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability pij from one city to another. The transportation probabilities are calculated from the radiation model based on the geographic distance and its cost-based version based on the driving distance as the proxy of cost. For each model, we consider both the population and the gross domestic product (GDP), and find quantitatively very similar results. We find that the transportation probabilities have nice power-law tails with the tail exponents close to 0.5 for all the models. The two transportation probabilities in each model fall around the diagonal pij=pji but are often not the same. In addition, the corresponding transportation probabilities calculated from the raw radiation model and the cost-based radiation model also fluctuate around the diagonal pijgeo=pijcost. We calculate four sets of highway truck transportation diversity according to the four sets of transportation probabilities that are found to be close to each other for each city pair. It is found that the population, the gross domestic product, the in-flux, and the out-flux scale as power laws with respect to the transportation diversity in the raw and cost-based radiation models. It implies that a more developed city usually has higher diversity in highway truck transportation, which reflects the fact that a more developed city usually has a more diverse economic structure.

Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin & Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extract important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the composite sector is an information source of the whole stock market, while the non-bank financial sector is the information sink. We also find that the non-bank finance, bank, computer, media, real estate, medical biology and non-ferrous metals sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the non-bank finance and bank sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector acts as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks.

Highway freight transportation (HFT) plays an important role in the economic activities. Predicting HFT networks is not only scientifically significant in the understanding of the mechanism governing the formation and dynamics of these networks, but also of practical significance in highway planning and design for policymakers and truck allocation and route planning for logistic companies. In this work we apply parameter-free radiation models to predict the HFT network in mainland China and assess their predictive performance using metrics based on links and fluxes, which can be done in reference to the real directed and weighted HFT network between 338 Chinese cities constructed from about 15.06 million truck transportation records in five months. It is found that the radiation models exhibit relatively high accuracy in predicting links but low accuracy in predicting fluxes on links. Similar to gravity models, radiation models also suffer difficulty in predicting long-distance links and the fluxes on them. Nevertheless, the radiation models perform well in reproducing several scaling laws of the HFT network. The adoption of population or gross domestic product in the model has a minor impact on the results, and replacing the geographic distance by the path length taken by most truck drivers does not improve the results.

The gravity law has been documented in many socioeconomic networks, which states that the flow between two nodes positively correlates with the strengths of the nodes and negatively correlates with the distance between the two nodes. However, such research on highway freight transportation networks (HFTNs) is rare. We construct the directed and undirected highway freight transportation networks between 338 Chinese cities using about 15.06 million truck transportation records in five months and test the traditional and modified gravity laws using GDP, population, and per capita GDP as the node strength. It is found that the gravity law holds over about two orders of magnitude for the whole sample, as well as the daily samples, except for the days around the Spring Festival during which the daily sample sizes are significantly small. Accordingly, the daily exponents of the gravity law are stable except during the Spring Festival period. The results also show that the gravity law has higher explanatory power for the undirected HFTNs than for the directed HFTNs. However, the traditional and modified gravity laws have comparable explanatory power.

In this paper, a stochastic optimization framework is proposed to address the microgrid energy dispatching problem with random renewable generation and vehicle activity pattern, which is closer to the practical applications. The patterns of energy generation, consumption and storage availability are all random and unknown at the beginning, and the microgrid controller design (MCD) is formulated as a Markov decision process (MDP). Hence, an online learning-based control algorithm is proposed for the microgrid, which could adapt the control policy with increasing knowledge of the system dynamics and converges to the optimal algorithm. We adopt the linear approximation idea to decompose the original value functions as the summation of each per-battery value function. As a consequence, the computational complexity is significantly reduced from exponential growth to linear growth with respect to the size of battery states. Monte Carlo simulation of different scenarios demonstrates the effectiveness and efficiency of our algorithm.

In this paper we study spatial quantile regression estimation of multivariate threshold time series models. Bahadur’s representations for our estimators are established, which naturally lead to asymptotic normality of the estimators. Simulations and a real example are used to evaluate the performance of the proposed estimators.

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student’s t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and are normally followed by a trend reversal that corrects the previous over reaction. In this paper, we construct an indicator to measure the magnitude of the super-exponential growth of stock prices, by measuring the degree of the price network, generated from the price time series. Twelve major international stock indices have been investigated. Error diagram tests show that this new indicator has strong predictive power for financial extremes, both peaks and troughs. By varying the parameters used to construct the error diagram, we show the predictive power is very robust. The new indicator has a better performance than the LPPL pattern recognition indicator.

Identifying unambiguously the presence of a bubble in an asset price remains an unsolved problem in standard econometric and financial economic approaches. A large part of the problem is that the fundamental value of an asset is, in general, not directly observable and it is poorly constrained to calculate. Further, it is not possible to distinguish between an exponentially growing fundamental price and an exponentially growing bubble price. We present a series of new models based on the Johansen-Ledoit-Sornette (JLS) model, which is a flexible tool to detect bubbles and predict changes of regime in financial markets. Our new models identify the fundamental value of an asset price and crash nonlinearity from a bubble calibration. In addition to forecasting the time of the end of a bubble, the new models can also estimate the fundamental value and the crash nonlinearity. Besides, the crash nonlinearity obtained in the new models presents a new approach to possibly identify the dynamics of a crash after a bubble. We test the models using data from three historical bubbles ending in crashes from different markets. They are: the Hong Kong Hang Seng index 1997 crash, the S&P 500 index 1987 crash and the Shanghai Composite index 2009 crash. All results suggest that the new models perform very well in describing bubbles, forecasting their ending times and estimating fundamental value and the crash nonlinearity. The performance of the new models is tested under both the Gaussian and non-Gaussian residual assumption. Under the Gaussian residual assumption, nested hypotheses with the Wilks statistics are used and the p-values suggest that models with more parameters are necessary. Under non-Gaussian residual assumption, we use a bootstrap method to get type I and II errors of the hypotheses. All tests confirm that the generalized JLS models provide useful improvements over the standard JLS model.

The Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with finite-time singular crash hazard rates has been developed to describe the dynamics of financial bubbles and crashes. It has been applied successfully to a large variety of financial bubbles in many different markets. Having been developed for more than one decade, the JLS model has been studied, analyzed, used and criticized by several researchers. Much of this discussion is helpful for advancing the research. However, several serious misconceptions seem to be present within this collective conversation both on theoretical and empirical aspects. Several of these problems appear to stem from the fast evolution of the literature on the JLS model and related works. In the hope of removing possible misunderstanding and of catalyzing useful future developments, we summarize these common questions and criticisms concerning the JLS model and offer a synthesis of the existing state-of-the-art and best-practice advices.

We present an extension of the Johansen-Ledoit-Sornette (JLS) model to include an additional pricing factor called the "Zipf factor", which describes the diversification risk of the stock market portfolio. Keeping all the dynamical characteristics of a bubble described in the JLS model, the new model provides additional information about the concentration of stock gains over time. This allows us to understand better the risk diversification and to explain the investors' behavior during the bubble generation. We apply this new model to two famous Chinese stock bubbles, from August 2006 to October 2007 (bubble 1) and from October 2008 to August 2009 (bubble 2). The Zipf factor is found highly significant for bubble 1, corresponding to the fact that valuation gains were more concentrated on the large firms of the Shanghai index. It is likely that the widespread acknowledgement of the 80-20 rule in the Chinese media and discussion forums led many investors to discount the risk of a lack of diversification, therefore enhancing the role of the Zipf factor. For bubble 2, the Zipf factor is found marginally relevant, suggesting a larger weight of market gains on small firms. We interpret this result as the consequence of the response of the Chinese economy to the very large stimulus provided by the Chinese government in the aftermath of the 2008 financial crisis.

Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreements (repos) market size is a very important element in calculating the overall leverage in a financial market. Therefore, studying the behavior of repos market size can help to understand a process that can contribute to the birth of a financial crisis. We hypothesize that herding behavior among large investors led to massive over-leveraging through the use of repos, resulting in a bubble (built up over the previous years) and subsequent crash in this market in early 2008. We use the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles and behavioral finance to study the dynamics of the repo market that led to the crash. The JLS model qualifies a bubble by the presence of characteristic patterns in the price dynamics, called log-periodic power law (LPPL) behavior. We show that there was significant LPPL behavior in the market before that crash and that the predicted range of times predicted by the model for the end of the bubble is consistent with the observations.

Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framework to understand and diagnose financial bubbles from rational expectations and was recently extended to negative bubbles and rebounds. Using the JLS model, we develop an alarm index based on an advanced pattern recognition method with the aim of detecting bubbles and performing forecasts of market crashes and rebounds. Testing our methodology on 10 major global equity markets, we show quantitatively that our developed alarm performs much better than chance in forecasting market crashes and rebounds. We use the derived signal to develop elementary trading strategies that produce statistically better performances than a simple buy and hold strategy.

We introduce the concept of "negative bubbles" as the mirror image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with a hazard rate describing the collective buying pressure of noise traders. The price fall occurring during a transient negative bubble can be interpreted as an effective random downpayment that rational agents accept to pay in the hope of profiting from the expected occurrence of a possible rally. We validate the model by showing that it has significant predictive power in identifying the times of major market rebounds. This result is obtained by using a general pattern recognition method which combines the information obtained at multiple times from a dynamical calibration of the JLS model. Error diagrams, Bayesian inference and trading strategies suggest that one can extract genuine information and obtain real skill from the calibration of negative bubbles with the JLS model. We conclude that negative bubbles are in general predictably associated with large rebounds or rallies, which are the mirror images of the crashes terminating standard bubbles.