Retail Pricing Behavior for Perishable Produce Products in the US with Implications for Farmer Welfare

The typical model of retail pricing for produce products assumes retailers set price equal to the farm price plus a certain markup. However, observations from scanner data indicate a large degree of price dispersion in the grocery retailing market. In addition to markup pricing behavior, we document three alternative leading pricing patterns: fixed (constant) pricing, periodic sale, and high-low pricing. Retail price variations under these alternative pricing regimes in general have little correlation with the farm price. How do retailers’ alternative pricing behaviors affect farmers’ welfare? Using markup pricing as the baseline case, we parameterize the model to reflect a prototypical fresh produce market and carry out a series of simulations under different pricing regimes. Our study shows that if harvest cost is sufficiently low, retail prices adjusting only partially, or not at all, to supply shocks tends to diminish farm income and exacerbate farm price volatility relative to the baseline case. However, we also find that if harvest cost is sufficiently large and the harvest-cost constraint places a lower bound on the farm price, increased farm price volatility induced by retailers’ alternative pricing strategies may result in higher farm income, compared to markup pricing. Our study is the first to evaluate the welfare implications for producers of the diversified pricing strategies that retailers utilize in practice and the resulting attenuation of the relationship between prices at retail and at the farm gate.Agribusiness, Demand and Price Analysis,

"Asymmetric Market Shares, Advertising, and Pricing: Equilibrium with an Information Gatekeeper"

We analyze the impact of market share on advertising and pricing decisions by firms that sell to loyal, non-shopping customers and can advertise to shoppers through an information intermediary or "gatekeeper." In equilibrium the firm with the smaller loyal market advertises more aggressively but prices less competitively than the firm with the larger loyal market, and there is no equilibrium in which both firms advertise with probability 1. The results differ significantly from earlier literature which assumes all prices are revealed to shoppers and finds that the firm with the smaller loyal market adopts a more competitive pricing strategy. The predictions of the model are consistent with advertising and pricing behavior observed on price comparison websites such as Shopper.com.online markets, E-commerce, market share, information gatekeeper, equilibrium price dispersion, advertising

Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation

A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template-based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two automatic metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.Comment: ACL 201

Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning

In this paper, an impedance control strategy is proposed for a rigid robot collaborating with human by considering impedance learning and human motion intention estimation. The least square method is used in human impedance identification, and the robot can adjust its impedance parameters according to human impedance model for guaranteeing compliant collaboration. Neural networks (NNs) are employed in human motion intention estimation, so that the robot follows the human actively and human partner costs less control effort. On the other hand, the full-state constraints are considered for operational safety in human-robot interactive processes. Neural control is presented in the control strategy to deal with the dynamic uncertainties and improve the system robustness. Simulation results are carried out to show the effectiveness of the proposed control design

A teleoperation framework for mobile robots based on shared control

Mobile robots can complete a task in cooperation with a human partner. In this paper, a hybrid shared control method for a mobile robot with omnidirectional wheels is proposed. A human partner utilizes a six degrees of freedom haptic device and electromyography (EMG) signals sensor to control the mobile robot. A hybrid shared control approach based on EMG and artificial potential field is exploited to avoid obstacles according to the repulsive force and attractive force and to enhance the human perception of the remote environment based on force feedback of the mobile platform. This shared control method enables the human partner to tele-control the mobile robot’s motion and achieve obstacles avoidance synchronously. Compared with conventional shared control methods, this proposed one provides a force feedback based on muscle activation and drives the human partners to update their control intention with predictability. Experimental results demonstrate the enhanced performance of the mobile robots in comparison with the methods in the literature