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Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations, they typically utilise operational hedging strategies in terms of pricing, manufacturing, and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically, we study an integrated problem of procurement, manufacturing, and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain, and they directly affect the system's procurement price, raw material flow rate, and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that, we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We, particularly, investigate how production rate, holding cost, procurement price and demand variabilities, customers' price sensitivity, and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore, our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular, the profit enhancement being achieved with dynamic pricing can reach up to 15%, depending on the problem parameters. [ABSTRACT FROM AUTHOR]

Experience-in-store-and-buy-online (ESBO) is a popular omni-channel strategy. This paper studies the effects of inspection service provision on the interactions of a dynamic-pricing retailer and strategic consumers, i.e., the effects of the ESBO initiative on store operations. Selling a seasonal product over two periods, the omni-channel retailer may allow consumers to inspect this product offline only in the first period (first-period inspection) or in both periods (two-period inspection). First, we find that allowing first-period inspection makes the retailer better off. The retailer will price the product higher in the first period but probably lower in the second period. Even so, more consumers will purchase in the first period: that is, allowing first-period inspection can somewhat deter strategic deferral. Meanwhile, the seemingly negative intertemporal showrooming behavior (i.e., inspect the product offline in the first period but defer online purchase to the second period) benefits the retailer. Compared to first-period inspection, allowing two-period inspection increases the retailer's prices in both periods as well as profit, provided that inspection is definitely available in the second period. On the other hand, when inspection is possible in the second period, it may be profitable to allow inspection only in the first period.. [ABSTRACT FROM AUTHOR]

In this study, we model the dynamics of oil prices based on a dynamic heterogeneous agent model. Two types of agents including fundamentalists and chartists trade in the market, and the transaction costs are taken into consideration. The empirical results show that it is useful to improve forecasting performance if the model allows the agents to learn from their past performance and considers the transaction costs. Examination of the agents' dynamic weights shows that fundamentalists and chartists co-existed in the oil market with almost equal weights, and the fundamentalists dominated the market when the oil prices had large deviations from the fundamental level. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the dynamic-pricing decisions of differentiated products for retailers operating in a competitive environment, for a finite-time horizon, limited initial inventory, and in the presence of the reference effect. Customers learn from the past prices of retailers and form their estimate of sales prices, called the reference price effect, and use it to make a decision on choosing a retailer to make a purchase. The demand is uncertain, and the customer choice behavior is modeled based on a Multinomial Logit model, modified to incorporate the reference effect. The complexity of the problem increases under conditions of competition and demand uncertainty and cannot be analyzed using conventional methods. Therefore, we have used a neural network-based algorithm called Revenue-Based Neural Network (RBNN) to dynamically calculate the competitive price in order to increase the retailer's revenue. We have analyzed the effect of competition and the performance of RBNN algorithm under two scenarios: a monopolistic situation in which a retailer uses the RBNN policy to maximize its revenue, and a duopolistic situation in which one retailer uses the RBNN strategy and the other uses an adaptive policy called Derivative Following (DF). The results of the experiments show that the pricing policy under duopolistic conditions highly affects the income of retailers in the presence of reference price. The RBNN policy outperforms the DF policy due to the learning process on the customers' reference price. By charging higher prices in the RBNN strategy, the seller trades off the current revenue with the long-term revenue resulting from formation of higher levels of the reference price in customers' minds and earns more revenue than its competitor overall. [ABSTRACT FROM AUTHOR]

In product markets, substantial price dispersion exists for transactions of physically identical goods. Moreover, in these markets, incumbent firms sell at higher prices than entrants. This paper presents a theory of price formation under dynamic competition that explains these facts by assuming both that consumers have imperfect access to firms and that their degree of access depends on each firm's sales history. The model has a unique equilibrium that features randomized pricing strategies, with incumbents always posting higher prices than entrants. For a fixed underlying environment, the equilibrium converges to a stationary equilibrium over time. As firms' entry and exit rates approach zero, this stationary equilibrium converges to perfect competition. [ABSTRACT FROM AUTHOR]

Congestion pricing is one of the efficient travel demand management strategies. Many existing researches focus on dealing with the toll optimization problem for a single area. However, the urban network is often composed of several administrative regions. Furthermore, even inside a single administrative region, there may be multiple subnetworks with different traffic dynamics. As a result, the centric pricing scheme may not be applicable. This paper aims to design a coordinated dynamic pricing scheme for such a scenario with multiple adjacent areas which experience an overlapping congested period. Unlike the traditional approach centered on the bi-level mathematical programming, we adopt the regressing Kriging model to estimate the input–output mapping, thus searching for the simulation-based optimal solution in the toll design problem. Two types of coordinated pricing schemes are proposed. The first or unconstrained scheme only focuses on the network performance, while the second or constrained scheme further takes into account the pricing efficiency. The proposed coordinated pricing scheme is further compared with the perimeter control. The results indicate that our scheme is more moderate without imposing traffic burden on the links/corridors heading to protected zones. [ABSTRACT FROM AUTHOR]

In recent years, ride-hailing has played an important role in daily transportation. In the ride-hailing system, how to set the prices for orders is a crucial issue. Considering the variations in vehicle supply and order demand in different regions of the same city, it is necessary to divide regions according to the supply and demand in each region and develop differentiate pricing strategy. However, the traditional fixed region-division strategy is difficult to adapt to the dynamically changed supply and demand over the time. To address this issue, we propose a dynamic region-division based pricing strategy to set prices according to the demand and supply of different regions to maximize the long-term profit of the platform. Specifically, we first design a dynamic region-clustering algorithm based on Deep Q Network and K-Means algorithm to dynamically cluster small zones with similar demand and supply status and nearby locations into the same region. We then propose an adaptive multi-region dynamic pricing algorithm to set unit price for each clustered region to maximize the long-term profit of the ride-hailing platform. We further run extensive experiments based on a real-world dataset to demonstrate the effectiveness of our proposed algorithm. The experimental results show that the platform's profit is increased under different pricing algorithms combined with dynamic region-clustering algorithm. Furthermore, we find that the combination of adaptive multi-region dynamic pricing algorithm with dynamic region-clustering algorithm can bring higher profits, serve more orders and have a higher service rate than benchmark approaches. [ABSTRACT FROM AUTHOR]

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the "curse of dimensionality" and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services. [ABSTRACT FROM AUTHOR]

Background: This paper examines the economic implications of market segmentation on consumer purchasing behavior with a particular emphasis on intertemporal pricing strategies in dynamic markets. Methods: In order to analyze optimal discount rates and the timing for price reductions for consumer segments, including loyal and deal-prone customers, a detailed mathematical model was developed. The model incorporates theories of consumer behavior and pricing elasticity to simulate market responses to price changes throughout a product's lifecycle. Results: This research indicates that market segmentation enhances sales by targeting the distinct preferences of loyal consumers, who are less price-sensitive and who stabilize revenue streams, and deal-prone consumers, who respond to price reductions. Customizing pricing strategies for loyal consumers and deal-prone consumers increases sales volumes and optimizes profitability. Conclusions: This research improves our comprehension of market segmentation and dynamic pricing, providing a practical framework for businesses to create effective pricing strategies that can be promptly implemented. It emphasizes the significance of understanding consumer behavior and price sensitivity in the interest of revenue promotion. This study also emphasizes the social implications of equitable pricing practices, promoting the implementation of transparent and value-based strategies to promote market inclusivity and consumer trust. [ABSTRACT FROM AUTHOR]

Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to develop archetype gray-box models and use them in an economic model-based predictive control algorithm to simulate optimal heating load management in response to a newly-introduced static time-of-use tariff for Québec's residential sector, rate Flex-D. The methodology is evaluated through a case study, wherein in situ measurements from a two-storey unoccupied research house of Hydro-Québec are used to develop an 11R6C network with a heuristic zoning-by-floor approach and compute the sequence of optimal electric heating input for the next control horizon. Properly-tuned economic model-based predictive control under rate Flex-D shows potential for an approximately 30% reduction in daily heating cost compared to the reference operation, with a minimal average deviation of indoor air temperature from the reference setpoint. Also, the analysis of the response's sensitivity to weather forecast uncertainties indicates that the most influential uncontrolled input directing the performance of economic model-based predictive control is the structure price signal, rendering the impact of uncertainty in the weather forecast negligible. [ABSTRACT FROM AUTHOR]

We study how misperceptions of others' tastes influence beliefs, demand, and prices in markets with observational learning. Consumers infer a good's quality from the quantity demanded and price paid by others. When consumers exaggerate the similarity between their and others' tastes, such "taste projection" generates discrepant quality perceptions, which are decreasing in a projector's taste and increasing in the observed price. These biased inferences produce an excessively elastic market demand. We also analyze dynamic monopoly pricing with short-lived taste-projecting consumers. Optimal pricing follows a declining path: a high initial price inflates future buyers' perceptions, and lower subsequent prices induce overadoption. (JEL D42, D83, D91, L15) [ABSTRACT FROM AUTHOR]

The valuation of equity-linked annuity (EIA) has been extensively studied, while few papers consider the impact of long-range dependence (LRD) features on financial and mortality dynamics in EIAs' valuation. To characterise the LRD feature, we adopt the fractional Brownian motion (FBM) in modelling the dynamics of asset prices and mortality intensities, in which clustering jumps are captured by the compound Hawkes process. Besides, the interest rate dynamic is driven by the FBM and correlates to the asset price dynamic. Numerical analyses show that ignorance of the LRD feature and the jump component in the modelling framework could lead to a potential deficiency in the reserve and solvency capital of EIA policies. [ABSTRACT FROM AUTHOR]

Small and medium-scale enterprises (SMEs) need a platform that actively enables collaboration with research institutions and consultants as SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system to enable, execute, and record collaboration activities and to track reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of the results of the collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenized incentives to reward positive contributions to the platform. Cryptocurrencies from which blockchain tokens are derived are volatile, thereby reducing business organizations' interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivizes user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between various groups of users in an implemented blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for users while also providing a dynamic pricing model for the utility value provided by platform tokens. To achieve this objective, we adopted the design science research method, a running case of a blockchain collaboration platform that enables research collaboration, and extensive form games in game theory, first to analyze and simulate token outcomes of users of the collaboration platform. Secondly, the research used a logarithmic model to show the dynamic utility pricing property of the developed token model where the self-sustainability of the token is backed by the availability of an internal resource within the platform. Thirdly, we applied a qualitative approach to analyze potential risks in the designed token model and proposed risk mitigation strategies. Thus, the resulting models and their simulations, such as token distribution models and a dynamic token utility model, as well as the identified token risks and their mitigation strategies, represent the main contributions of this work. [ABSTRACT FROM AUTHOR]

As 5G and early‐stage network technologies mature, a wealth of data and experience is accumulated. The ubiquitous sensing and communication network (USCN) can leverage these existing communication infrastructures to maximize resource utilization and reduce the costs of redeployment through integration with modern 6G and beyond 6G networks. However, implementing future integrated sensing and communication (ISAC) functions in USCN may possess different hardware capabilities and communication protocols, presenting significant challenges. This is due to the lack of a unified framework for managing and optimizing these heterogeneous resources, as well as the absence of reasonable performance metrics. This letter proposes a dynamic pricing coordinated resource allocation (DPCRA) mechanism to realize the emerging ISAC in legacy USCNs. The DPCRA is designed to optimize the spectrum efficiency (SE) of USCN and manages resource interactions between heterogeneous devices through price variables, thereby resolving resource conflicts and deadlocks. The novelties include: (1) employing a price mechanism to foster resource cooperation in non‐cooperative networks; (2) introducing an SE metric incorporating an information similarity factor for optimization; (3) dynamically selecting resources to enhance SE. Simulations demonstrate that the DPCRA effectively resolves device deadlock while maintaining high SE levels, promoting device collaboration. [ABSTRACT FROM AUTHOR]

Under the wave of the digital era, the retail industry is facing unprecedented fierce competition and a rapidly changing market environment. In this context, developing smart and efficient pricing strategies has become a top priority in the industry. Faced with this challenge, traditional pricing methods are inadequate due to their slow response, insufficient adaptability to instant changes in the market, and over-reliance on historical data and human experience. In response to this urgent need, this study aims to design an intelligent pricing model rooted in deep learning to enhance the vitality and competitiveness of the retail industry. The emerging solution adopted in this article combines Temporal Fusion Transformer (TFT), Ensemble of Simplified RNNs (ES-RNN), and dynamic attention mechanisms, aiming to accurately capture and analyze complex time series data through these advanced technologies. TFT processes multivariate and multi-level data, ES-RNN technology integrates multiple simple versions of recurrent neural networks to enhance predictive power, and the dynamic attention mechanism allows the model to dynamically weight the importance of different points in the time series, thereby improving the effectiveness of feature extraction. Test experimental results on four different data sets show that our models all show excellent performance, and the accuracy of predicted product sales far exceeds traditional models. In addition, with its ability to dynamically adjust pricing, the model demonstrates excellent stability and adaptability amid market fluctuations. This research not only promotes the intelligent transformation of retail pricing strategies, but also provides a more strategic tool for enterprises to compete for market share. [ABSTRACT FROM AUTHOR]

Commercial buildings have abundant flexible energy resources for demand response (DR). The electricity price for tenants in the commercial building is generally issued by a subcontracting power supplier (SPS), and the tenants cannot directly interact with the energy retailer. Therefore, the incentive for tenants to participate in DR is insufficient, and their potential is not fully explored. To address these issues, this paper proposes a dynamic pricing method based on the Stackelberg game, helping tenants actively participate in DR. Then, with the optimized energy consumption of the tenants, a virtual energy storage model of the commercial building is constructed by aggregating the adjustable capabilities of flexible energy resources such as air-conditioning (AC) and electric vehicles (EVs) in the public area. Finally, simulation tests are conducted based on a real commercial building to demonstrate the effectiveness of the game-theoretic pricing approach and validate the role of virtual energy storage of the building in DR. [ABSTRACT FROM AUTHOR]

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits. [ABSTRACT FROM AUTHOR]

This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase. [ABSTRACT FROM AUTHOR]

We measure the cost of technical inefficiency for local electricity distribution firms in Sweden using Stochastic Frontier Analysis, and explore how small-scale generation, the number of electric vehicles and the introduction of dynamic pricing schemes affects the transient inefficiency and efficiency scores. Our results show little to no effect of these environmental variables on the cost of technical inefficiency of electricity distribution grids in Sweden. [ABSTRACT FROM AUTHOR]

As the online market rapidly develops, firms will face strategic consumers who differ in buying times. Given that the leadership structure in supply chains affects the chain members' decision‐making and performance, we study this impact in a two‐period dynamic pricing setting with an upstream manufacturer and a downstream retailer, where the patience degree of consumers is the retailer's private information. We examine two leadership scenarios: retailer dominance and manufacturer dominance. The two members play a signaling game under retailer dominance, whereas the manufacturer can only set its wholesale price based on its prior belief under manufacturer dominance. Our analysis indicates that when consumers are more patient, the retailer tends to share its private information with the manufacturer under retailer dominance to avoid the reduction of its retail margin and enhance its profitability. However, the retailer may keep the consumers' patience information private when the manufacturer dominates the supply chain because the manufacturer will set a compromised wholesale price and passively cede profits to the retailer. Finally, we examine consumer surplus and social welfare. We demonstrate that retailer dominance is the Pareto optimal strategy. [ABSTRACT FROM AUTHOR]

For many high‐tech and Internet‐related products, consumers' utility depends partly on the scale of user groups, which is called network externality. This phenomenon plays a crucial role in shaping firms' innovation strategies, especially in markets with price regulation. This paper aims to investigate a monopolist's dynamic product innovation problem in a price‐regulated market exhibiting network externality. We identify the sufficient conditions guaranteeing the existence of optimal steady‐state equilibrium. Our analysis mainly shows the following: (i) the product quality and network size move in opposite directions along the path to the steady‐state equilibrium; (ii) the equilibrium result of product innovation increases with the regulated price but decreases with the intensity of network externality; (iii) the social‐welfare maximizing path of product innovation can be replicated by a dynamic price regulation regime, involving a premium on quality and a premium or a penalty on network size. Numerical experiments are performed to characterize the dynamic patterns of product innovation. We find that firms with high brand power prefer adopting mild product innovation strategy while for firms with low brand power, almost constant high‐quality strategy is better. [ABSTRACT FROM AUTHOR]

With increasing awareness of perishable products, green grocers are forced to be concerned about the deterioration of freshness and adopt a time-based multiple pricing (TMP) policy. However, the reluctance of supply chain members to share their information exacerbates the predicament in the perishable product industry. This paper focuses on the TMP policy with bilateral asymmetric information that the supplier conceals freshness information and the retailer conceals demand information. Differing from existing literature, we adopt the TMP policy to release the retailer from the inferiority of information asymmetry. Based on the Stackelberg game model, three main conclusions are summarized as follows. First, information asymmetry does not always damage profits of the agent in the supply chain. Second, when the retailer agrees to adopt the TMP policy, the TMP policy decreases the price but the retailer can benefit from the low price in perishable product industry. Third, interestingly, the TMP policy can mitigate the impact of information asymmetry that reduces profits of the retailer. However, the mitigation of the TMP policy is limited and it cannot undo the detrimental effects instigated by information asymmetry. Thus, managers in the perishable product industry should adopt the TMP policy as a countermeasure to prevent the reverse caused by information asymmetry. [ABSTRACT FROM AUTHOR]

We develop a marketing-centric framework for delivering affordable, clean energy to consumers by leveraging the marketing 4 Ps to encourage a bi-directional flow of information between firms and consumers. Using a multimethod approach that covers a consumer survey, field experiment, and a decarbonization simulation to test the various aspects of the framework, our findings point to the need for a "system-wide" solution. Specifically, we examine consumer responsiveness to multiple levers within the 4 Ps, showcase the real effects of a combination of an automated solution and dynamic electricity pricing on behavior, and examine the role of dynamic prices and automation in transitioning to 100% clean electricity. We argue that there are ways to achieve affordable, 100% clean energy that many consumers will adopt. We conclude with a set of research questions examining additional aspects of the 4 Ps that can be leveraged to facilitate the wide-spread adoption of clean energy solutions. [ABSTRACT FROM AUTHOR]

Choosing building materials suppliers is crucial as it directly affects the quality, cost, and timeline of a project. Quality suppliers ensure the reliability and consistency of materials, reducing construction risks. The right suppliers offer competitive pricing and flexible delivery schedules, helping control budgets and optimize resource use. Additionally, suppliers' professional support and technical services can enhance construction efficiency and quality. By carefully selecting suppliers, project teams can establish long-term partnerships, improving overall construction standards and market competitiveness. Thus, supplier selection is a key factor in the success of construction projects. The process of selecting and applying building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) method. This study constructs the 2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) approach, which integrates the grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). To validate the method, a numerical example for building material supplier selection is provided, accompanied by comparative studies to highlight the effectiveness of the 2TLNN-CGRA approach. The key contributions of this research include (1) the utilization of the 2TLNN-CGRA approach to address MAGDM under 2TLNSs conditions; (2) the incorporation of both 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) within the 2TLNSs framework in the 2TLNN-CGRA approach; (3) the demonstration of the method through a numerical example for supplier selection; and (4) a series of efficient comparisons with other existing decision-making approaches. [ABSTRACT FROM AUTHOR]

We build a multi-period pricing model between a blockchain-technology-supported platform and a traditional platform, where the blockchain-technology-supported platform provides a higher value for customers. Customers are influenced by network effect, that is, they value a platform more if the platform has more users. As either platform can adopt static pricing or dynamic pricing, four scenarios may occur. By deriving the equilibrium of each scenario, we reveal the 'Matthew effect' caused by network effect, that platform advantage (from adopting blockchain technology) or disadvantage (from not adopting blockchain technology) accumulates as time goes by. Thus, platforms are advised to adopt the blockchain technology antecedent to the competitors. Network effect, which amplifies the benefit of initial users, may intensify price competition and harm both platforms. By comparing the four scenarios, we derive the equilibrium pricing strategies: when network effect is weak, one platform adopts static pricing and the other adopts dynamic pricing; when network effect is medium, the blockchain-technology-supported platform adopts static pricing and the traditional platform adopts dynamic pricing; and when network effect is strong, both platforms adopt dynamic pricing. Dynamic pricing is more desirable for the traditional platform relative to the blockchain-technology-supported platform. [ABSTRACT FROM AUTHOR]

This article discusses research on time-of-use compensation pricing strategies for load aggregators based on user demand response. The study aims to address the issue of mismatch between purchased electricity and market bid volume, which results in punitive costs for companies. The researchers propose a tiered pricing strategy to adjust power distribution and establish a Stackelberg leader-follower game model to promote a mutually beneficial relationship between load aggregators and controllable load users. Through case studies, the paper examines the operational profits of load aggregators and the power adjustment behaviors of controllable load users under tiered and fixed compensation pricing schemes. The results indicate that tiered compensation pricing significantly reduces punitive costs and enhances user participation in demand response. The article also discusses a dynamic compensation pricing model for controllable load power adjustment in power systems, including interactions between load aggregators and controllable load users. A case study is presented to validate the model's effectiveness, and future work will explore the integration of artificial intelligence and machine learning technologies to further optimize demand response strategies. [Extracted from the article]

This study presents a novel Buyback Rail Freight Option (BRFO), leveraging Stackelberg game theory to enhance the strategic management of rail freight transactions. By integrating traditional buyback theory with a multi-phase trigeminal tree pricing model and parameter identification through a nonparametric Ito stochastic method, the research addresses key challenges of information asymmetry and market uncertainty. The proposed methodology emphasizes dynamic pricing strategies and market adaptation, constructing a Nash equilibrium framework within railway freight pricing. The findings suggest significant strategic benefits for railway enterprises, positioning BRFO as a crucial tool for improving competitiveness in the face of alternative transport options. [ABSTRACT FROM AUTHOR]

The integrated energy system (IES) with hydrogen storage has become one of the most important developments in multi-energy coupling field, where the severe conflict of interests between different entities leads to great challenges to the economy and low-carbon operation. A multi-objective robust dynamic pricing and operation strategy optimization method based on the Stackelberg game is proposed for the hydrogen-containing energy storage (HES) IES. Firstly, the HES-IES trading framework is established based on the introduction of an integrated energy operator (IEO) and a load aggregator (LA). Secondly, a multi-objective robust Stackelberg game model is developed with the IEO as the leader and the LA as the follower, considering the minimization of operating costs and carbon emissions of the IEO and the minimization of integrated energy costs of the LA as the objectives. Finally, the compromise planning and the max-min fuzzy are addressed to solve the multi-objective model, which adopts the adaptive differential evolution (ADE) algorithm. In addition, the robust optimization (RO) with adjustable coefficients is employed to tackle uncertainties of source and load. The results show that this method can effectively balance the operating costs and carbon emissions of the system, improve the benefit of the IEO, reduce the costs of the LA, and avoid the uncertainty risk. Compared with traditional algorithms, the ADE algorithm has significant advantages in the number of iterations and solving time. In summary, the multi-objective robust dynamic pricing and operation strategy optimization proposed in this paper could effectively achieve the benefit balance between the IEO and the LA, also further improve the economy and robustness of the system and reduce carbon emissions. [Display omitted] • A multi-objective robust dynamic pricing and operation strategy optimization method based on Stackelberg game is proposed. • The compromise planning, max-min fuzzy method and adaptive differential evolution algorithm are adopted to solve the model. • The robust optimization with adjustable coefficients is employed to deal with the source-load uncertainty. • The economy and robustness of the system are improved, and carbon emissions are reduced. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model's effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization. [ABSTRACT FROM AUTHOR]

The energy scheduling problem (ESP) is a complex NP-hard optimization scheduling problem of finding the best schedule for the energy consumed through a time horizon by smart appliances according to several constraints and dynamic pricing scheme(s). The primary goal of addressing ESP is to optimize electricity bills for users, the highest energy consumption through a time horizon, and the comfort level of users. Several communication and controlling techniques were utilized to interconnect and centralize the smart appliances to be scheduled, where the Internet of Things (IoT) technique is the most helpful. Several studies have proposed different optimization methods to address the ESP optimally. Unfortunately, due to the low performance of the utilized methods and the high number of the problem's constraints, the best schedules were not achieved in many cases. In this paper, a new improved optimization method on the basis of a hybridization approach between two well-established optimization methods, called the marine predators algorithm and particle swarm optimization, is proposed to handle ESP optimally. The proposed method is mainly established and utilized to enhance the schedules with the worse fitness values to improve them to be more acceptable. Furthermore, to avoid the high complexity of the ESP constraints, Renewable Energy Sources (RESs) based on real-world dataset is utilized along the proposed method. Also, the IoT technique is utilized to establish the connection and build the scheduling system. Finally, to optimize the ESP objectives simultaneously, ESP is formulated as a multi-objective problem. In the evaluation stage, the proposed hybrid method with RESs is tested among four different comparison studies and phases, including the adapted methods with and without RESs, hybrid methods with and without RESs, and based state-of-the-arts. The proposed hybrid method with RESs proves its efficiency and high performance in all comparison phases, where it achieved the best results among all compared methods. [ABSTRACT FROM AUTHOR]

Word of mouth (WOM) is crucial in customers' purchasing decisions and affects companies' long-term profits. This study examines the long-term trends in companies' dynamic pricing and profits by using the Hamiltonian function method and dynamic simulation to construct a dynamic equation. It takes into account the intensity of word of mouth faced by companies and analyzes the level of publicity and consumers' predictions of product quality. In this paper, we also discuss the interactive processes between WOM and advertising levels, the two most prominent market factors, and their ultimate impact on companies. The experimental results show that elevated levels of external advertising can potentially prompt companies to establish higher product pricing strategies, particularly in scenarios where the intensity of word of mouth is pronounced. In the initial phases of market development, the saturation level of consumers within the market exerts a negligible influence on companies' long-term profit margins. Conversely, the rate of natural attrition from consumers' upper threshold of product quality expectations distinctly impacts companies' profitability. [ABSTRACT FROM AUTHOR]

A Simple Monotonic Dynamic Pricing Policy We consider a canonical revenue management setting in which a monopolist seeks to maximize expected total revenues by selling a fixed inventory of product to customers who arrive sequentially over time. It is known in the literature that a simple dynamic pricing policy can perform reasonably well in such setting. However, the literature typically assumes that price can move freely over time (i.e., it can either go up or down). In practice, firms often impose certain restrictions on price movement. In this paper, we consider the setting in which price can only move monotonically over time (either only decreasing or only increasing). We devise a simple provably near-optimal dynamic pricing policy. Aside from markdown applications, our policy can also be applied to the setting with strategic customers. We consider a canonical revenue management problem wherein a monopolist seller seeks to maximize expected total revenues from selling a fixed inventory of a product to customers who arrive sequentially over time, and the seller is restricted to implement a pricing policy that is monotonic (either nonincreasing or nondecreasing) over time. Gallego and Van Ryzin [Gallego G, Van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Sci. 40(8):999–1020] show that the simplest monotonic price policy, the fixed price policy, is asymptotically optimal in the high-volume regime in which both the seller's initial inventory and the length of the selling horizon are proportionally scaled. Specifically, the revenue loss of the fixed price policy is O (k 1 / 2) , where k is the system's scaling parameter. Following the publication of Gallego and Van Ryzin [Gallego G, Van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Sci. 40(8):999–1020], several papers have attempted to improve the performance of the fixed price policy. Among them, Jasin [Jasin S (2014) Reoptimization and self-adjusting price control for network revenue management. Oper. Res. 62(5):1168–1178] develops a simple modification of the fixed price policy (that allows prices to move either up or down) with a guaranteed revenue loss of order O (ln k). In this paper, we propose a novel family of monotonic readjustment policy, which restricts the prices to only move in one direction (i.e., either up or down). We show that, if the seller updates the price for only a single time, then the revenue loss of our policy is O (k 1 / 3 (ln k) 2 α) for some α > 1 / 2. If, however, the seller updates the prices with a frequency O (ln k / ln ln k) , then the revenue loss of our policy is O ((ln k) 7 α) for some α > 1 / 2. These results show the power of dynamic pricing even in the presence of monotonic price restriction. We discuss two applications of our policy: markdown pricing and pricing in the presence of strategic customers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2020.0774. [ABSTRACT FROM AUTHOR]

Dynamic resource allocation problems arise under a variety of settings. In "Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis," Balseiro, Besbes, and Pizarro introduce a unifying model for a large class of dynamic optimization problems dubbed dynamic resource-constrained reward collection (DRC2) problems. Surveying the literature, they show that this class encompasses a variety of disparate and classical problems typically studied separately, such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, or order fulfillment. Furthermore, they establish that the DRC2 class is amenable to analysis by characterizing the performance of a central, certainty-equivalent heuristic. Notably, they provide a novel unifying analysis that isolates the drivers of performance, recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and yields new results in specialized settings for which no such characterization was available. Dynamic resource allocation problems arise under a variety of settings and have been studied across disciplines such as operations research and computer science. The present paper introduces a unifying model for a very large class of dynamic optimization problems that we call dynamic resource-constrained reward collection (DRC2) problems. We show that this class encompasses a variety of disparate and classical dynamic optimization problems such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, and order fulfillment, to name a few. Furthermore, we establish that the class of DRC2 problems, although highly general, is amenable to analysis. In particular, we characterize the performance of the fluid certainty-equivalent control heuristic for this class. Notably, this very general result recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and also yields new results in specialized settings for which no such characterization was available. As such, the DRC2 class isolates some common features of a broad class of problems and offers a new object of analysis. Funding: The work of D. Pizarro was supported by the Artificial and Natural Intelligence Toulouse Institute, which is funded by the French "Investing for the Future—PIA3" program [Grant ANR-19-P3IA-0004]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2441. [ABSTRACT FROM AUTHOR]

In "Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and The Value of Dynamic Pricing," Chen, Lei, and Jasin consider a dynamic pricing problem faced by a ride-hailing service provider who manages a fixed number of servers and serves price-sensitive customers within a network. Servers serve arriving customers by relocating from the requested origins to destinations within a certain travel time. The authors first propose a static pricing policy based on the optimal solution to a deterministic relaxation of the original stochastic problem. They show that the proposed static policy matches the best possible asymptotic performance of any static policy. The authors further propose a dynamic pricing policy that adaptively changes the prices in a way that reduces the impact of past demand randomness on the balance of future distributions of servers and customers across the network. They show that the dynamic pricing policy achieves significantly better asymptotical performance. The proposed policies and their performance guarantees are further extended to a case where the firm jointly decides the relocation of vacant servers Motivated by the growth of ride-hailing services in urban areas, we study a (tactical) real-time spatial–intertemporal dynamic pricing problem where a firm uses a pool of homogeneous servers (e.g., a fleet of taxis) to serve price-sensitive customers (i.e., a rider requesting a trip from an origin to a destination) within a finite horizon (e.g., a day). We consider a revenue maximization problem in a model that captures the stochastic and nonstationary nature of demands, and the nonnegligible travel time from one location to another location. We first show that the relative revenue loss of any static pricing policy is at least in the order of n − 1 / 2 in a large system regime where the demand arrival rate and the number of servers scale linearly with n, which highlights the limitation of static pricing control. We also propose a static pricing control with a matching performance (up to a multiplicative logarithmic term). Next, we develop a novel state-dependent dynamic pricing control with a reduced relative revenue loss of order n − 2 / 3 . The key idea is to dynamically adjust the prices in a way that reduces the impact of past "errors" on the balance of future distributions of servers and customers across the network. Our extensive numerical studies using both a synthetic data set and a real data set from the New York City Taxi and Limousine Commission, confirm our theoretical findings and highlight the benefit of dynamic pricing over static pricing, especially when dealing with nonstationary demands. Interestingly, we also observe that the revenue improvement under our proposed policy primarily comes from an increase in the number of customers served instead of from an increase in the average prices compared with the static pricing policy. This suggests that dynamic pricing can be potentially used to simultaneously increase both revenue and the number of customers served (i.e., service level). Finally, as an extension, we discuss how to generalize the proposed policy to a setting where the firm can also actively relocate some of the available servers to different locations in the network in addition to implementing dynamic pricing. Funding: Y. (M.) Lei was partially supported by the Natural Sciences and Engineering Research Council of Canada [Fund 1378108, Grant RGPIN-2021-02973]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2425. [ABSTRACT FROM AUTHOR]

Problem definition: We consider two business models for a two-sided economy under uncertainty: dealership and marketplace with fulfillment services. Although both business models can bridge the gap between demand and supply, it is not clear which model is better for the firm or for the consumers. Methodology/results: We show that, whereas the two models differ substantially in pricing power, inventory risk, fee structure, and fulfillment time, both models share several important features with the revenues earned by the firm from the two models converging when the markets are thick. We also show that, for thick markets, there is a one-to-one mapping between their corresponding optimal policies. Managerial implications: Our results provide guidelines for firms entering two-sided markets: when the market is thick, the two business models are similar; when the market is thin, they should carefully inspect a number of market conditions before making the choice. Funding: The research of G. Li is partially supported by the National Natural Science Foundation of China [Grants 72150002, 72394361] and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. The research of N. Chen is partially supported by the Natural Sciences and Engineering Research Council of Canada Discovery [Grant RGPIN-2020-04038]. The research of G. Gallego is partially supported by Collaborative Research Funding Hong Kong [Grant C6032-21G]. The research of P. Gao is supported by the National Natural Science Foundation of China [Grants 72201234, 72192805], Collaborative Research Funding Hong Kong [Grant C6032-21G], and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0253. [ABSTRACT FROM AUTHOR]

In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property's market performance. Our primary goal was to introduce a solution that can augment property owners' understanding of their property's market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing's financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings' market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals. [ABSTRACT FROM AUTHOR]

We consider the problem of setting the optimal prices and promotions for a large number of products when the firm lacks demand information using adaptive experimentation. We consider the problem of setting the optimal prices and promotions for a multi product category when the firm lacks demand information. At each time, a customer arrives and chooses a product based on a discrete choice model where each product's utility depends on product features, its price and promotion, and the customer's features. Using a Thompson Sampling approach, we develop a regret-minimizing or alternatively, profit-maximizing algorithm for the retailer. We provide the first adaptive algorithm that simultaneously incorporates pricing and promotions into a discrete choice model. To make our algorithm computationally feasible over an infinite space of prices and promotions, we provide a novel method for learning the optimal price and promotion given a set of demand parameters. We also provide theoretical justification for our results and improve upon existing regret guarantees. Using simulations based on real-life grocery store data, we show that our method significantly outperforms existing approaches. In addition, we extend our methodology to a contextual setting, which allows for consumer heterogeneity and personalized pricing and promotion. Compared with existing works, our approach is agnostic to the parametric specification of the utility model and needs no assumptions on the underlying distribution of customer features. History: Olivier Toubia served as the senior editor. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0322. [ABSTRACT FROM AUTHOR]

Realizing the multifunctional value of farmland is essential for regulating the pricing of farmland transfers and stabilizing the rural land market. However, in China, the mismatch between supply and demand leads to improper resource allocation, weakens the explicit value of farmland, and causes unreasonable transfer pricing mechanisms that threaten agricultural production and food security. This study develops an analytical framework to examine the relationship between farmland multifunction and transfer pricing from a supply–demand perspective. An evaluation index system is constructed, considering the physical, value, and material quantities. This study uses the matching index method and bivariate spatial autocorrelation to analyze the supply–demand match of farmland multifunction from 2014 to 2021 and its relationship with transfer prices. Additionally, management methods and strategies for dynamic zoning-based pricing under multifunctional matching trade-offs are proposed. The results show that: (1) There is significant heterogeneity in the supply and demand matching degree of different farmland functions in both space and time. The production and ecological functions of farmland are oversupplied, while the living functions are undersupplied. (2) Different spatial autocorrelation relationships exist between the degree of supply and demand matching of farmland functions and farmland transfer prices. Specifically, the supply and demand matching degrees of the production and living functions show a significant negative spatial correlation with farmland transfer prices. In contrast, the ecological function shows a significant positive spatial correlation with farmland transfer prices, which are continuously strengthening over time. (3) Based on the supply and demand matching situation of different farmland functions and the spatial autocorrelation of farmland transfer prices, nine types of regions are delineated for farmland functions. Among them, the surplus-coordinated development areas have the most cities, accounting for about 40%, with a wide distribution range. This study proposes zoning-based pricing instruments and management strategies. This research provides valuable insights for developing countries seeking to alleviate conflicts in multifunctional land use, enhance the sustainable protection of land resources, and improve land resource assessment frameworks. [ABSTRACT FROM AUTHOR]

In this study, we discuss on the dynamic pricing and investment strategy for fashion products which decay in value, such as fashion clothes. The products are sold by an enterprise under a monopoly market in a finite horizon. Due to the obsolescence of fashion products, their fashion level decreases over time. Obviously, the selling price has a negative effect on demand, while the fashion level has a positive effect. Moreover, we introduce a reference value, which is used to indicate consumers' requirements for fashion level. We assume that investment on products performance or goodwill can reduce consumers' requirements for fashion level, an optimal control model is established. To maximize the enterprise's total profit, we obtain the trajectories of the optimal pricing and investment strategy by utilizing the Pontryagin's maximum principle. The optimal strategy is to gradually lower the selling price and then slightly raise it at the end of the sales period and reduce investment as products become obsolete. Afterwards, we provide a numerical example to illustrate our conclusions. Through the sensitivity analysis of the main parameters, we capture the influence of the parameters on an optimal decision. Moreover, some management implications are given. [ABSTRACT FROM AUTHOR]

Online reviews can effectively mitigate consumers' uncertainty about product valuations, thus affecting firms' pricing decisions. This paper develops a two‐period game model to investigate the impacts of online reviews on the retailer's price commitment strategies in two competing supply chains, each consisting of one manufacturer and one retailer. In the first period, the retailers strategically set prices and decide whether to commit to prices for their products. In the second period, the retailers adjust product prices in response to online reviews, if they choose not to commit to a price. We show that the retailers' price commitment strategies depend on consumers' perceived uncertainty about product valuations, the information value of online reviews and the unit misfit cost. Interestingly, the retailers can benefit from the information value of online reviews by implementing flexible pricing strategies. Furthermore, if the information value of online reviews is sufficiently high, both retailers will choose not to commit to a price in equilibrium. However, if the information value of online reviews is relatively low, the equilibrium in price commitment is asymmetric, that is, the retailer A commits to a price and its competitor chooses not to commit. [ABSTRACT FROM AUTHOR]

Manufacturers introduce updated‐generation products to meet intensified competition under trade‐in programs. Based on this phenomenon, we built a two‐period game‐theoretic model, in which the manufacturer sells the old‐generation product during both periods and considers whether to introduce the updated‐generation product in the second period. We find that under a static pricing strategy, if the costs of both products are low, the updated‐generation product's entry increases the old‐generation product's retail price. The manufacturer cannot benefit from introducing the updated‐generation product if the updated‐generation product's cost is high. Under a dynamic pricing strategy, the updated‐generation product entry benefits both the manufacturer and the consumer. [ABSTRACT FROM AUTHOR]

The paper focuses on a three-echelon supply chain consisting of a green supplier, a green manufacturer and a retailer. Considering the dynamic changes in product greenness, differential games are employed to investigate product prices, green efforts and coordination in the system. By comparing equilibrium results in various scenarios, it is found that supply chain decision-makers invest more in green efforts to maximize their profits under centralized decision, while they select pricing tools under decentralized decision. Importantly, when a local alliance composed of the supplier and manufacturer supplies products to the retailer at a flexible and dynamic wholesale price, the two-part tariff contract can fully coordinate the three-echelon green supply chain. The Rubinstein bargaining model is then used to optimize the profit allocation within the alliance. Additionally, the impact of green effort efficiency on decision-making is explored, revealing that green efforts depend on the efficiency of green efforts, while product pricing has nothing to do with the efficiency of efforts. The paper also points out that decentralization increases the probability of the supplier engaging in more green efforts than the manufacturer. [ABSTRACT FROM AUTHOR]

We present a unified, market-complete model that integrates both Bachelier and Black–Scholes–Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. Unlike the classical Black–Scholes–Merton, we show that option pricing in the unified model differs depending on whether the replicating, self-financing portfolio uses riskless bonds or a single riskless bank account. We derive option price formulas and extend our analysis to the term structure of interest rates by deriving the pricing of zero-coupon bonds, forward contracts, and futures contracts. We identify a necessary condition for the unified model to support a perpetual derivative. Discrete binomial pricing under the unified model is also developed. In every scenario analyzed, we show that the unified model simplifies to the standard Black–Scholes–Merton pricing under specific limits and provides pricing in the Bachelier model limit. We note that the Bachelier limit within the unified model allows for positive riskless rates. The unified model prompts us to speculate on the possibility of a mixed multiplicative and additive deflator model for risk-neutral option pricing. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) receives extensive interest due to enabling many novel applications at lower cost. However in pricing incentive scenes of MCS, utility from mobile users presents complex distributions due to mobility, changes of abilities and resource consumption such as device's energy and memory, especially for submodular MCS with more general distributions. However, existing works only focus on homogeneous and heterogeneous MCS, whether multi-request pricing scene or single-request pricing scene. To the end, in this paper, we investigate online pricing issues for submodular MCS. Moreover, we apply a multiple-stage budget-limited process and robust mean estimators to design budget-limited pricing incentive for submodular MCS. Extensive simulations demonstrate that our mechanisms outweigh existing benchmarks. [ABSTRACT FROM AUTHOR]