Marginal quality‐based long‐term incentive mechanisms for crowd sensing

Crowd sensing is a new paradigm that leverages pervasive sensor‐equipped mobile devices to provide sensing services like forensic analysis, documenting public spaces, and collaboratively constructing statistical models. Extensive user participation is indispensable for achieving good service quality. Nowadays, most of existing mechanisms focus on guaranteeing good service quality based on instantaneous extensive user participation for crowd sensing applications. Little attention has been dedicated to maximizing long‐term service quality for crowd sensing applications due to their heterogeneous interests, preferences, selfish behaviors, and so on. To fill these gaps, a closed‐form expression of the marginal sensing data quality based on the monopoly aggregation in economics is derived in this paper. Furthermore, we design marginal quality‐based incentive mechanisms for long‐term crowd sensing applications not only to enhance extensive user participation by maximizing the expected total profits of mobile users but also to stimulate mobile users to produce high‐quality contents by applying the marginal quality. Finally, simulation results show that our mechanisms outperform the existing solutions. Copyright © 2015 John Wiley & Sons, Ltd.     

A RBF based finite difference method for option pricing under regime-switching jump-diffusion model

Abstract

In this article, we presented a method for option pricing problem under regime-switching jump-diffusion models. We have proposed a numerical method for solving a partial integro-differential equation (PIDE) for pricing European option and for solving linear complementarity problem (LCP), to evaluate the price of American options. We use implicit explicit method for time semi discretization, followed by radial basis function based finite difference (RBF-FD) method for spatial discretization to solve PIDE. The proposed method is further extended to solve the LCP by coupling it with operator splitting method. Numerical simulation is done for European and American option to demonstrate efficiency and accuracy of the proposed method.     

A simulation‐based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation‐based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high‐dimensional parameters, it uses the efficient smoothed functional gradient estimator.     

Dynamic pricing with stochastic reference effects based on a finite memory window

Inspired by the latest empirical studies, we propose a new updating model for reference prices by assuming that consumers’ memories are limited and their recall of previous prices obeys a first-order Markov stochastic process. We investigate a dynamic pricing model with stochastic reference effects and finite memory. Consistent with the exponential smoothing model, we indicate that reference effects lead to monotonic convergence of the optimal price path to an expected steady-state price. The steady-state range tends to widen as consumers become loss-averse. The results of our numerical experiments differ from findings of certain models under the assumption of stochastic recall memory of consumers. The optimal price path fluctuates consistently around the steady state instead of remaining constant. The effect of the first price on the memory window and long-term profits decreases as the length of memory window increases.     

Water pricing in Tunisia: Can an original rate structure achieve multiple objectives?

In the context of anthropic pressures, the Tunisian Water Utility tries to achieve three objectives: financial (achieving full-cost recovery), social (ensuring access for all), and environmental (incentivizing users to save a scarce resource). After a theoretical review of water pricing, the paper presents how successive reforms in Tunisia have gradually set up an original pricing structure that has become the main instrument of the sector's policy. Using different data and methods, we investigate how each of the three objectives is reached through pricing. We conclude that this pricing policy has led to a relatively stable but partial equilibrium.     

Effects of demand response programs on distribution system operation

This paper discusses an analytical approach illustrating the effects of demand response (DR) programs on network operation efficiency. The contribution of DR is estimated using price elasticity of demand under two mechanisms of dynamic pricing: Critical Peak Pricing (CPP) tariff and Hourly Pricing (HP). Numerical examples are provided to explore the effects of DR on distribution networks’ operation.     

Cryptocurrency value formation: An empirical study leading to a cost of production model for valuing bitcoin

This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoin’s growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.Using cross-sectional empirical data examining 66 of the most widely used cryptocurrencies, a regression model was estimated that points to three main drivers of cryptocurrency value: the level of competition in the network of producers, the rate of unit production, and the difficulty of algorithm used to “mine” for the cryptocurrency. These amount to relative differences in the cost of production of one digital currency over another at the margin, pointing to differences in relative cost of production – electricity goes in, cryptocurrency comes out. Using that as a starting point, a no-arbitrage situation is established for Bitcoin-like cryptocurrencies followed by the formalization of a cost of production model to determine the fair value of a bitcoin.     

Multi-coefficients goal programming

In today’s increasingly competitive business environment, maintaining profit margins or quantities of goods sold is an important issue for businesses. Accordingly, more and more industries use group pricing discrimination strategy to attract potential customers in order to increase competitive advantage. Hence, to find ways maximize profit and to minimize total cost, group pricing discrimination strategy has become an important issue for decision makers. Unfortunately, these types of problems cannot be solved by any current goal programming models. The objective of this study is to deduce a new method, which we call the multi-coefficients goal programming, for group pricing discrimination problems. In addition, an example is given to illustrate the correctness and usefulness of the proposed model.     

Assessing the rebound effect using a natural experiment setting: Evidence from the private transportation sector in Israel

Subsidizing energy-efficient technologies is considered by energy and environmental organizations to be one of the most effective policies for decreasing energy consumption. In the transportation sector such policies are becoming ever more popular, and have been implemented in a considerable number of countries in recent years. Because these policies promote energy-efficient cars with lower usage costs, they may rebound and increase the distances traveled by households that have switched to energy-efficient cars. From an econometric perspective, a subsidization policy can be used as a valid instrument to identify the households’ choice of energy efficiency levels of the cars they own. This identification, in turn, can be utilized to account for endogeneity in the estimation of a rebound effect. The present study uses a natural experiment setting of such a policy implemented in Israel in 2009. The empirical results indicate a fairly large average rebound effect of 40%. The results also indicate that while the policy indeed encouraged the purchase of energy-efficient cars, households that bought a new or used car during the surveyed period did not generate a rebound effect of a different magnitude compared with other households that did not. We discuss the implications of our findings.     

Joint optimisation of tracking capability and price in a supply chain with endogenous pricing

Tracking systems have been widely used to resolve the issues of product recall and food safety. Thus far, few researches have been done on designing the tracking capability from the perspective of supply chain. In this paper, using the traceable unit size at the manufacturer level to measure the tracking capability, we propose a non-convex non-linear programming to jointly optimise the tracking capability and price considering the tracking cost and recall cost in a supply chain with endogenous pricing. Results show that, in both centralised and decentralised supply chains, there is a unique tracking capability and retailing/wholesale price with closed-form solutions to optimise the supply chain profit. When the cost ratio (unit tracking cost/unit recall cost) is sufficiently large and small, the optimal tracking strategy is barcode tracking and unit tracking, respectively, and otherwise, the optimal tracking strategy is batch tracking with an economic traceable unit size which depends on the cost ratio, quality inspection threshold, supply defection rate and the supplier’s tracking capability. Furthermore, in the context of large and small cost ratio, we find that improving tracking capability will enlarge and mitigate the effect of double marginalisation, respectively. In particular, we find that the strict tracking regulation policy is more robust than the subsidy policy to improve the supply chain tracking capability.