Pricing Game Research Articles

Farmer exchange rate category: A Prediction analysis using ANN back propagation

The Farmer Exchange Rate (FER) is one of the indicators in determining the welfare level of farmers. Every month this large FER has a change especially at the end of each season during harvest, the price game especially from producers often occurs so that not a few farmers complain and suffer losses. Therefore, there need to be efforts from the government is looking at past data trends to find the amount of FER in the future so that policies can side with farmers. Artificial Intelligent with a multilayer Back Propagation method is very good for modeling and forecasting of data series in the past with data input in the form of a matrix mxn. The simulation results show that in 2020 in NTB there is an average increase in the exchange rate of farmers of 118.76%, this means that farmers are experiencing a surplus or farmers’ incomes are rising more than their expenditures. However, it appears that from June-August and October-November there was an average decrease of 2%, although overall there was increase of 2.076%. This result was obtained by the construction of a network architecture of two hidden layers where theMSE of 0.12, and MAPE of 0.23.

Research on the evolutionary game of knowledge pricing in an open innovation environment: from the perspective of social dual innovation balance

PurposeThis paper aims to find out the factors that influence the choice of dual innovation strategies in the process of knowledge pricing and transaction between first-mover and late-mover companies in an open innovation environment and also to find the key factors that affect the company's strategic choice in factors such as heterogeneous market environment, demand elasticity, exploration risk intellectual property prices and transaction cost.Design/methodology/approachThis study uses the Cournot equilibrium and Stackelberg two-stage master-slave game model to describe the evolutionary process of knowledge pricing in an open innovation environment of first-mover and late-mover companies.FindingsResearch shows that in an open innovation environment, the formation of a dual innovation strategy in the pricing process of corporate intellectual property transactions is a complex process. Changes in one-time transaction costs and changes in the inverse demand coefficient of the innovation market play the decisive role in the choice of dual innovation strategies. When the demand of the innovation market is moderate, the inverse demand coefficient of the innovation market and the one-time transaction cost has an inverted U-shaped influence relationship. As the innovation market's inverse demand coefficient and the one-time transaction cost increase at the same time, the degree of differentiation of the enterprise's dual innovation strategy choice gradually reduces; when the one-time transaction cost is the largest, the degree of strategy differentiation is minimized.Originality/valueBased on the above relationship, suggestions are made to guide enterprises in the knowledge pricing and transaction process in an open innovation environment, promote enterprises to form a dislocation development and complementary advantages in the knowledge innovation ecological chain and improve the overall innovation efficiency of the industry.

The Effect of Price Perception and Game Features in Online Virtual Game Item Purchase Decisions

This study tries to answer the research objectives, namely to find out how the influence of Price Perception and Game Features on Online Game Virtual Item Purchase Decisions. The results of hypothesis testing using multiple regression analysis with two independent variables and one dependent variable indicate that: The proposed H1 states that Price Perception has no effect on Online Game Virtual Item Purchase Decisions, it can be seen if the tcount value is 0.896 or less than ttable (α : 5%, df : 37 = 2.026192), in addition, the significance value is 0.376 or greater than the model significance value of 0.05. Then H1 is rejected, meaning that this cannot affect the Online Game Item Virtual Purchase Decision. The proposed H2 states that Game Features have an effect on Online Game Virtual Item Purchase Decisions, it can be seen if the tcount value of 1.908 is smaller than ttable (5%, df : 37 = 2.026192), but the significance value is 0.064 or greater than the value the significance of the model is 0.05. Then H2 is rejected, meaning that this cannot affect the Online Game Item Virtual Purchase Decision. The proposed H3 states that Price Perception and Game Features simultaneously affect the Decision to Purchase Virtual Items Online Game, from table 4.12 the Fcount value is 4.751 or greater than the Ftable value (α : 5%, dk numerator : 2, dk denominator : 39 -2-1, Ftable: 3.26), besides the significance value is 0.015 or less than the model significance (0.05), it can be concluded if H3 is accepted, meaning that Price Perception and Game Features simultaneously affect Virtual Purchase Decisions Online Game Items.

Dynamic game analysis on pricing and service strategy in a retailer-led supply chain with risk attitudes and free-ride effect

PurposeThe convenience of online shopping enables the manufacturer to develop direct channels. To counter manufacturer encroachment, the retailer tends to provide presale service to attract more customers. Meanwhile, the service provided by the retailer also has a positive impact on the manufacturer's sale volume, which is usually called the showrooming effect or free-ride. The purpose of this paper is to explore the dynamic game of pricing and service strategy in a dual-channel supply chain with risk attitudes and free-ride.Design/methodology/approachThis paper considers the risk attitude, characterized by mean-variance theory. First, the optimal pricing and service strategy of two static models under two scenarios are derived. Second, dynamic games are then considered to explore the evolution of the decisions. The classical optimization method is used to solve the problem, and numerical experiments are done to analyze the complex characteristic of the system.FindingsThe result shows that the retailer is willing to provide a higher level of service if his risk preference is higher. The offline retail price and online retail price are positively related to the retailer's risk preference. Besides, the free-ride behavior can reduce the offline retail price and the level of service provided by the retailer. Furthermore, the study indicates that the system is more likely to enter chaos if the retailer's risk preference is higher. Additionally, consumers' service sensitivity and cost coefficient affect the stability of the system.Originality/valueThe study provides a different perspective on supply chain management considering risk attitudes and free-ride The findings of the study can offer theoretical and practical guidance for enterprises to choose adjustment measures according to their risk preference.

Pricing Models for the Internet of Things (IoT): Game Perspectives

While large IT companies develop full-stack IoT services with their computing resources, there are many small/medium-sized IoT application/device developers who rely on multiple other service providers for various IoT infrastructure services such as data storage, wireless sensor networks, data analytics, and IoT platforms. While pricing decisions of these IoT services are critical to the sustainable growth of the customer base, there is a paucity of studies on the pricing of a set of services involving multiple service providers. Hence, it is challenging for IoT providers to make informed decisions in regards to the pricing of their IoT devices and services. This paper develops pricing game models involving multiple IoT providers and analyzes both a non-cooperative game and a cooperative game. Nash equilibrium prices for three IoT providers in a non-cooperative game and a cooperative game are derived. The non-cooperative Nash equilibrium price of each provider shows that the Nash equilibrium price consists of the average reservation price of the customers and the provider's cost. While the sum of the providers’ prices affects the IoT customers’ demand, the model shows that in pricing, each provider does not take the other providers’ pricing decisions into account. This paper examines how the cooperative game may generate more profits than the non-cooperative game and applies Shapley solutions for fair and efficient allocation of the payoff of the cooperative game. Contrary to conventional wisdom, consumer welfare and the IoT providers’ profit is not a zero-sum game. The three-provider pricing game model is generalized for any finite number of providers.

Inter-platform competition in a regulated ride-hail market with pooling

This paper studies an aggregate ride-hail market in which two platforms compete with each other, as well as with transit, under different supply and regulatory conditions. The duopoly is built on a general market equilibrium model that explicitly characterizes the physical matching process, including pairing two passengers for a pooling ride. Depending on whether drivers’ work affiliation with a platform is exclusive or not, the duopoly is said to have a single- or multi-homing supply mode. We describe the outcome of the duopoly pricing game as a Nash Equilibrium (NE) and solve it by transforming it into a variational inequality problem (VIP). When a regulatory constraint is imposed, the duopoly equilibrium becomes a generalized NE, which corresponds to a quasi VIP. We show that multi-homing may lead to disastrous outcomes in an unregulated duopoly and demonstrate it through numerical experiments constructed using data from Chicago. Specifically, passenger and driver surplus, as well as platform profits, are all significantly lower in a multi-homing duopoly than in a single-homing counterpart. This disaster arises because (i) the multi-homing duopoly is locked in a self-destructive pricing war analogous to the tragedy of the commons; and (ii) the competition among passengers limits economy of scale in trip production. We show that the negative consequences of this tragedy can be mitigated by (i) discouraging multi-homing behavior; (ii) imposing a minimum wage on both platforms; and (iii) encouraging the platforms to specialize in different services. The results also show the efficiency in matching passengers and drivers is a crucial asset for a platform’s competitiveness, more so in a multi-homing duopoly. In general, the platform with a higher matching efficiency ends up making more money and providing a better level of service.

Performance‐Price‐Ratio Utility: Market Equilibrium Analysis and Empirical Calibration Studies

In this study, as an alternative to the surplus utility, we propose a performance‐price‐ratio (PPR) utility and specialize the customer choice model with a PPR maximization criterion. With the PPR‐based choice model, we investigate a pricing game between retailers in a competitive market. Through transferring the decision variable from price to performance‐price ratio, the first‐order condition of the game becomes a linear equation system, which enables us to develop a closed‐form equilibrium solution. The solution reveals how product performance and price sensitivity affect equilibrium pricing, market share, and revenue. With the developed theoretical results, we carry out empirical studies with a rich data set obtained from the China TV market. At the individual product level and the brand level, we calibrate the PPR‐based choice model and the widely used surplus‐based model with a Bayesian information criteria‐based stepwise multivariate regression method. The regression results suggest that the PPR‐based model fits both the product‐level and brand‐level data better than the surplus‐based model. Through the stepwise selection of independent variables, we find that the leading features affecting a TV’s performance are 3D TV functionality and its screen size. Moreover, we explore how the market equilibrium evolves with the game decision sequence, market structure, and new product entry.

Network Pricing: How to Induce Optimal Flows Under Strategic Link Operators

Regulating Network Pricing Games by Using Price Caps

On pure-strategy Nash equilibria in price–quantity games

This paper examines the existence and characteristics of pure-strategy Nash equilibria in oligopoly models in which firms simultaneously set prices and quantities. Existence of a pure-strategy equilibrium is proved for a class of price–quantity games. If the demand function is continuous, then the equilibrium outcome is similar to that of a price-only model. With discontinuous demand and limited spillover, there are rationing equilibria in which combined production falls short of market demand. Moreover, there might again be an equilibrium reflecting the outcome of a price game. Competition in price and quantity thus yields Bertrand outcomes under a variety of market conditions.

EIHDP: Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems

Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignoring the competition among multiple edge service providers. To address this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We introduce an improved double-layer Stackelberg game model to describe the cloud-edge-client collaboration. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, thereby reducing the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

Research on Big Data Information Service Pricing Based on Uncertainty Analysis

In the era of big data, more and more people use big data technology to provide users with massive data or provide information services based on the results of processing and processing massive data to meet the needs of users for specific information in specific fields. However, with the realization of internal benefits and external effects of big data, the uncertainty of big data information service and the difficulty of quality evaluation make the pricing decision and pricing game of the demander and supplier more complex and difficult to model. Based on the uncertainty of decision matching degree and service quality of big data information service, this paper analyzes the cost-benefit of data asset pricing model through index extraction and mechanism hypothesis definition, and extracts the key factors of data asset service pricing. Through the modeling of return on investment, the important influence factors in the pricing model are analyzed. From the perspective of quality evaluation, it is proposed that quality control and consumer satisfaction need to be balanced by adjusting the benefit function. That is, through the uncertainty analysis of influencing factors and price modeling analysis, the pricing research of two different business characteristics of big data information service is carried out.

Dynamic analysis of pricing model in a book supply chain

Based on two formats of books − paper book and electronic book, book publishers often sell their products to online consumers through e-tailers by adopting reselling mode and agency selling mode simultaneously. This paper develops a dynamic pricing game model on basis of a long-term gradient adjustment mechanism for a book supply chain that consists of a publisher and an e-tailer, and focuses on the impacts of agency fee rate and adjustment speed on the complexity of the dynamic game. We compare the system stability and the average profits for a dynamic decentralized decision and a dynamic centralized decision. The results show that a higher agency fee rate makes the dynamic system more stable, and a higher adjustment speed makes the dynamic system become more unstable with exhibiting a period doubling bifurcation. Moreover, the dynamic decentralized system is more stable than the dynamic centralized system as the adjustment speed increases. Under the dynamic decentralized decision, the profits of the publisher and the supply chain decrease while the e-tailer's profit increases when the game system loses its stability. In addition, it is interesting to find that in a certain parameter region, the profit of the supply chain under the dynamic decentralized decision is higher than that under the dynamic centralized decision.

Limit Pricing and Entry Game of Renewable Energy Firms into the Energy Sector

Governments attempt to provide the energy sector with incentives to replace old technologies with new ones based on renewable energy as the most effective way to combat climate change. Yet, in the energy sector prevail fossil fuel incumbents that inhibit renewable energy entrants. Our paper provides a game-theoretic stylization of competition between those two types of firms. Incumbents set prices and entrants respond with quantity adjustments. In the context of a dynamic limit pricing model, we study the entry dynamics in a market in which the dominant firms (fossil fuel energy suppliers) face the entry of a group of competitive fringe firms (renewable energy suppliers) when the dominant firms have easier access to financial markets, but the fringe firms finance their expansion with internal finance. We also investigate the effect of the public support of renewable energy firms through subsidies. Our model is built on Judd and Peterson (1986, JET), but our solutions are obtained through a non-linear model predictive control algorithm. By this technique, we can predict the outcome of the competition between incumbents and entrants and the impact of financial and fiscal policies considering moving-horizon strategies.

Dynamic Pricing Model of E-Commerce Platforms Based on Deep Reinforcement Learning

With the continuous development of artificial intelligence technology, its application field has gradually expanded. To further apply the deep reinforcement learning technology to the field of dynamic pricing, we build an intelligent dynamic pricing system, introduce the reinforcement learning technology related to dynamic pricing, and introduce existing research on the number of suppliers (single supplier and multiple suppliers), environmental models, and selection algorithms. A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions. The first step is to analyze the pricing strategies of e-commerce platforms in mature markets, analyze the optimal pricing and profits of various enterprises under different strategy combinations, compare different market equilibriums and solve the Nash equilibrium. Then, assuming that all consumers are naive in the market, the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed. By comparing and analyzing the optimal pricing and total profit of each enterprise under different strategy combinations, the subgame refined Nash equilibrium is solved. Finally, assuming that the market includes all experienced consumers, the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.

On pricing and bundling decisions for stackelberg games in parallel channels of substitutable composites

The paper describes competition within a supply network with parallel distribution channels. Each supply chain in the network is composed of a manufacturer and a retailer. Manufacturers sell two complementary products to the retailers, who then deliver to the end consumers. All players can bundle or not bundle their products assuming that the retail market presents the products in a mixed bundling setting. The motivation of this study is to mainly analyze the impact of cost reduction via manufacturers, on how the whole supply network will behave. We have modeled and solved partly and fully sequential game structures well known as Bertrand and Stackelberg games, where the preceding movers are considered to have more market power. Mathematical and numerical analyses reveal interesting propositions and managerial insights for decision makers who are practicing cost cutting strategies. The combination of different ordinal structures have led to exact mathematical comparisons among 24 games. Results indicate both manufacturers and retailers are better off with simultaneous pricing games. This promotes the concept of coordination through layer and channels of the network. Cost reduction with compensation increases payoffs when applied by the manufacturer whose complementary products’ manufacturing costs are more distanced. It is also shown that retailers enjoy a retail advantage on one product at its best when playing retailer leading Stackelberg games.

Research on Crowdsourcing Price Game Model in Crowd Sensing

Research on Crowdsourcing Price Game Model in Crowd Sensing

Hardness of an Asymmetric 2-Player Stackelberg Network Pricing Game

Consider a communication network represented by a directed graph G=(V,E) of n nodes and m edges. Assume that edges in E are partitioned into two sets: a set C of edges with a fixed non-negative real cost, and a set P of edges whose costs are instead priced by a leader. This is done with the final intent of maximizing a revenue that will be returned for their use by a follower, whose goal in turn is to select for his communication purposes a subnetwork of Gminimizing a given objective function of the edge costs. In this paper, we study the natural setting in which the follower computes a single-source shortest paths tree of G, and then returns to the leader a payment equal to the sum of the selected priceable edges. Thus, the problem can be modeled as a one-round two-player Stackelberg Network Pricing Game, but with the novelty that the objective functions of the two players are asymmetric, in that the revenue returned to the leader for any of her selected edges is not equal to the cost of such an edge in the follower’s solution. As is shown, for any ϵ>0 and unless P=NP, the leader’s problem of finding an optimal pricing is not approximable within n1/2−ϵ, while, if G is unweighted and the leader can only decide which of her edges enter in the solution, then the problem is not approximable within n1/3−ϵ. On the positive side, we devise a strongly polynomial-time O(n)-approximation algorithm, which favorably compares against the classic approach based on a single-price algorithm. Finally, motivated by practical applications, we consider the special cases in which edges in C are unweighted and happen to form two popular network topologies, namely stars and chains, and we provide a comprehensive characterization of their computational tractability.

A dynamic pricing game for general insurance market

Insurance contracts pricing, that is determining the risk loading added to the expected loss, plays a fundamental role in insurance business. It covers the loss from adverse claim experience and generates a profit. As market competition is a key component in the pricing exercise, this paper proposes a novel dynamic pricing game model with multiple insurers who are competing with each other to sell insurance contracts by controlling their insurance premium. Different with the existing works assuming deterministic surplus/loss, we consider stochastic surplus and adopt the linear Brownian motion model, i.e., a diffusion approximation to the classical Cramér–Lundberg model, for the aggregate claim amount. The risk exposure of an insurer is assumed to be affected by all insurers in the market. By solving a system of Hamilton–Jacobi–Bellman (HJB) equations, Nash equilibrium premium strategies are explicitly obtained for the insurers who are aiming to maximize their expected terminal exponential utilities. The representation form of the equilibrium strategies relates to the so-called M-matrix, which appears in many economic models. To investigate the robustness of equilibrium pricing strategies under model uncertainty, we further extend the model by allowing insurers to perceive ambiguity towards the aggregate claim loss. Closed-form expression for the robust premium strategies are obtained and comparative statics are carried out for model parameters.

Investigating the Adoption of Hybrid Encrypted Cloud Data Deduplication With Game Theory

Encrypted data deduplication, along with different preferences in data access control, brings the birth of hybrid encrypted cloud data deduplication (H-DEDU for short). However, whether H-DEDU can be successfully deployed in practice has not been seriously investigated. Obviously, the adoption of H-DEDU depends on whether it can bring economic benefits to all stakeholders. But existing economic models of cloud storage fail to support H-DEDU due to complicated interactions among stakeholders. In this article, we establish a formal economic model of H-DEDU by formulating the utilities of all involved stakeholders, i.e., data holders, data owners, and Cloud Storage Providers (CSPs). Then, we construct a multi-stage Stackelberg game, which consists of Holder Participation Game, Owner Online Game, and CSP Pricing Game, to capture the interactions among all system stakeholders. We further analyze the conditions of the existence of a sub-game perfect Nash Equilibrium and propose a gradient-based algorithm to help the stakeholders choose near-optimal strategies. Extensive experiments show the feasibility of the proposed algorithm in achieving the Nash Equilibrium of the Stackelberg game. Additionally, we investigate the effects of parameters related to CSP, data owners and data holders on H-DEDU adoption. Our study advises all stakeholders the best strategies to adopt H-DEDU.

Limited rationality and the strategic environment: Further evidence from a pricing game

A classic issue in behavioral economics is the extent to which agents who make systematic mistakes have large effects on market outcomes. One perspective is that agents who make systematic mistakes have large effects on outcomes in settings characterized by strategic complementarity, but not in settings characterized by strategic substitutability. In this paper, we extend the experimental approach based on this perspective found in Cooper, Schneider, and Waldman (2017) concerning beauty contest experiments to the pricing game initially investigated in Fehr and Tyran (2008). Our main results are as follows: i) given strategic complementarity and multiple identical shocks, convergence to equilibrium play after an initial shock and the initial subsequent shocks is not immediate even though the shocks are identical; ii) the periodic introduction of inexperienced players given strategic complementarity slows down speed of convergence to equilibrium play; and iii) behavior in the pricing game given strategic complementarity shows faster post-shock convergence after later shocks than we found in our earlier paper for the beauty contest. In addition to showing these results, we discuss what the two papers suggest concerning how to model settings characterized by agents who vary in terms of their abilities to process information and form expectations.