大数据的研究和分析论文（精读博士论文大数据信息服务定价研究方案型服务的定价）
大数据的研究和分析论文（精读博士论文大数据信息服务定价研究方案型服务的定价）Today's small series for you to bring the article: 91 research and sharing thesis: intensive reading doctoral thesis-"Research on pricing of big data information services from the perspective of uncertainty analysis" solution-based pricing of big data information services.this is LearningYard.欢迎您的访问！Share interest spread happiness increase knowledge leave beautiful!Dear ones
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今天小编为大家带来文章：91研读分享论文：精读博士论文-《不确定性分析视角下大数据信息服务定价研究》解决方案型大数据信息服务的定价。
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Today's small series for you to bring the article: 91 research and sharing thesis: intensive reading doctoral thesis-"Research on pricing of big data information services from the perspective of uncertainty analysis" solution-based pricing of big data information services.
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今日内容摘要Abstract：
阅读并分析博士论文《不确定性分析视角下大数据信息服务定价研究》中第五章部分的写作。
Read and analyze the writing of the fifth chapter of the doctoral thesis"Research on the pricing of big data information services from the perspective of uncertainty analysis".
正文 body part：
第五章为解决方案型大数据信息服务的定价，核心思想就是对处于不同阶段的大数据信息服务制定不同的定价方法。根据产品各生命周期的特点，文章认为目前大数据信息服务（以下简称为产品）属于导入期和成长期（图1）。进一步地，作者分析了该产品在导入期、成长期以及成熟期面临的不确定性，并提出相应的解决措施。
The fifth chapter is about the pricing of solution-based big data information service. The core idea is to make different pricing methods for the big data information service at different stages. According to the characteristics of each product life cycle the article considers that the current big data information service (hereinafter referred to as product) belongs to the import period and the growth period (Figure 1) . Furthermore the author analyzes the uncertainty faced by the product in the period of introduction growth and maturity and puts forward the corresponding solutions.
图1 Figure 1
其中，导入期（图2-3）面临的不确定性来自于市场、服务属性、服务需求方和提供方。适合的价格策略为免费策略或低价策略（即本文提出的ＰＯＣ定价法和成本导向定价法）。成长期（图4-5）面临的不确定性来自于产品、财务、竞争和渠道四方面。出现的问题表现为需求方有进一步明确大数据信息服务的质量效果（效用）、并根据质量效果决定付费行为的动机，但大数据信息服务的质量效果却难以观察和预估。适合的价格策略为结果导向定价法。成熟期（图6），市场已经趋于饱和，消费者和生产者对服务的质量已经有了全面的了解，为了提高交易的效率，可以根据以往的交易记录来预测未来的交易价格。作者提出的定价方法正是神经网络模拟训练定价法。通过分析每个周期的不确定来源以及探讨解决方案，作者提出了基于产品生命周期理论的大数据信息服务定价体系。
Among them the lead-in period (figure 2-3) faces uncertainties from the market service attributes service demanders and providers. The suitable price strategy is free price strategy or low price strategy (POC pricing and cost-oriented pricing) . The growth period (figure 4-5) faces uncertainty from the product financial competitive and channel four aspects. The problem is that the demander has an incentive to further clarify the quality effect (utility) of the Big Data Information Service and decide to pay according to the quality effect but the quality effect of big data information service is hard to observe and estimate. The appropriate price strategy is result-oriented pricing. At maturity (Figure 6) markets are saturated consumers and producers have a comprehensive understanding of the quality of services and in order to improve transaction efficiency can be based on past trading records to predict future trading prices. The pricing method proposed by the author is the neural network simulation training pricing method. By analyzing the sources of uncertainty in each cycle and exploring the solutions the author puts forward a pricing system of big data information service based on the theory of product life cycle.
图2 Figure 2
图3 Figure 3
图4 Figure 4
图5 Figure 5
图6 Figure 6
在本章的后续小节中，作者对每一种定价方法进行详细论述。在导入期（图7-8），可采用poc定价法-服务提供方对第一个客户收取较低的费用，并在购买方的商业实践中检验服务的有效性，直到有了经验之后，提供方可以据此为本阶段的其他交易进行定价谈判。也可以采用成本导向定价法-将服务定价为服务成本加上预期的利润。作者指出，该定价只能作为交易双方进行价格谈判时的定价基础，适用于提供方具有一定的技术优势且需求方对服务质量不确定的情况。据此，作者提出了适合于该产品定价的多层次型定价法，最终的定价与初期的成本、每层的加成系数、及各层的成本增量相关。
In the following sections of this chapter the author discusses each pricing method in detail. During the lead-in period (figure 7-8) POC pricing can be used-the service provider charges a lower fee for the first customer and tests the effectiveness of the service in the buyer's business practices until experience is gained the supplier may accordingly conduct pricing negotiations for other transactions at this stage. Cost-oriented pricing can also be used-the service is priced as the cost of the service plus the expected profit. The author points out that this pricing can only be used as the basis of price negotiation between two parties in a transaction and is applicable to the situation where the supplier has certain technical advantages and the demander is uncertain about the quality of service. Based on this the author proposes a multi-level pricing method which is suitable for this product. The final pricing is related to the initial cost the added coefficient of each layer and the cost increment of each layer.
图7 Figure 7
图8 Figure 8
在成长期，第一种定价方法为博弈定价法（图9-12）。在双方谈判并最终确定价格期间，经历了讨价还价的过程。作者以轮流出价的鲁宾斯坦博弈模型（无限期完美信息博弈）为例讲解了该过程。分析认为，在该产品的价格谈判过程中，需求方和供给方都倾向于早日得到均衡价格。单独的一个服务提供方和一个服务需求方的讨价还价可以转化为鲁宾斯坦博弈模型，因为双方都想要在谈判中获得更多的利润，此时的利润可以看作鲁宾斯坦模型中的蛋糕。可以求出有贴现的无限期博弈的完美均衡解。该均衡解与双方可以接受的最大价格无关，与贴现因子相关。
During the growth period the first pricing method is the game pricing method (figure 9-12) . During the negotiation between the two sides and the final determination of the price experienced the process of bargaining. The author illustrates this process using the Rubinstein game model of alternating bids (the infinite perfect information game) . The analysis shows that in the process of price negotiation both the demand side and the supply side tend to get the equilibrium price as early as possible. The bargaining between a single service provider and a single service demander can be translated into Rubinstein's game model because both sides want to make more money in the negotiation profits at this point can be seen as the cake in Rubinstein's model. The perfect equilibrium solution of the infinite-period game with discount can be obtained. The equilibrium solution is independent of the maximum price acceptable to both parties and is related to the discount factor.
图9 Figure 9
图10Figure 10
图11 Figure 11
图12 Figure12
成长期的第二种定价方法为（图13-15）结果导向型定价法（也称类版税定价法）。该方法的理论基础来源于版税。在版税的计算中，版税率是关键要素，而版税率的制定和大数据信息服务的定价有相似之处-缺乏统一的标准，版税的收费与大数据信息服务的定价相似在于都与产品的销量效果有关系。基于版税定价法的思想，作者提出了该产品的定价方法。最终的价格包含了一次性的服务费用、版税率加持下的每一个结算周期内的服务新增收益。该方法可以有效避免交易双方由于服务质量的不确定性而无法定价的尴尬处境。现在要解决的问题是如何确定服务的后期收益，即提供方如何追踪本次服务带给需求方的收益。作者据此提出了智能合约交易保障机制（图16-18）。提供方可以在系统中看到以成交额显示的需求方因为本产品获得的后期利润。
The second pricing method for the growth period is the result-oriented pricing method (figure 13 -15) also known as royalty-like pricing method. The theoretical basis of this method comes from royalty. Royalty rates are a key element in the calculation of royalties and the setting of royalty rates is similar to the pricing of big data information services-there is a lack of uniform standards royalty rates are similar to the pricing of big data information services in that they are related to the volume of the product sold. Based on the idea of royalty pricing method the author puts forward the pricing method of this product. The final price includes a one-time service charge and additional revenue from the service for each settlement cycle at a royalty rate. This method can effectively avoid the embarrassing situation that the two parties can not price because of the uncertainty of service quality. The problem now is how to determine the later benefit of the service that is how the provider tracks the benefit of the service to the demander. Based on this the author proposes an intelligent contract transaction protection mechanism (figure 16-18) . The supplier can see in the system by the turnover of the demand side because of the late profit of this product.
图13 Figure 13
图14 Figure14
图15 Figure15
图16 Figure 16
图17 Figure 17
图18 Figure 18
成熟期的大数据信息服务定价（图19-20）可以利用已完成的交易记录来完成。bp神经网络定价法和深度学习定价法的核心思想都是一致的-通过不断地模拟和实验，得到最终交易价格和服务需求方的要求属性、供需双方的合作属性等因素的映射关系。
The mature pricing of big data information services (figure 19-20) can be done using the completed transaction records. The core idea of BP neural network pricing method and deep learning pricing method is consistent-through continuous simulation and experiment finally the mapping relationship between the final transaction price and the Demander's demand attribute the supplier's cooperation attribute and the Demander's cooperation attribute is obtained.
图19 Figure 19
图20 Figure 20
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参考文献：
郭春芳. 不确定性分析视角下大数据信息服务定价研究[D]. 北京交通大学 2019.
英文翻译：Google翻译。
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