Evolution and Outlook of Resource Allocation Mechanism in the Age of Internet
He Da’an Ren Xiao＊
Abstract: In today’s society, the Internet, big data and artificial intelligence are being integrated and the mechanisms of resource allocations are also evolving,mainly because of the changes in background factors, means, and processes that influence resource allocation. To analyze and study this evolution, we need to combine resource allocation, industrial organization, and industrial regulation theories to determine the driving and restricting factors new technologies bring to business-to-business and business-to-consumer interactions and how the combination of internet, big data and AI will impact productivity and pricing. We must also analyze the differences between today’s supply-demand and pricing processes and those of the industrial age to discover whether there are special resource allocation mechanisms in this era and how to summarize them theoretically. This paper reviews the evolved resource allocation mechanisms at the present stage of the integration of the Internet, big data and AI based on resource allocation theories from classical Marxian, neoclassical, and modern western economics.
Keywords: Internet; resource allocation; big data; artificial intelligence; Internet-based resource allocation mechanism
Introduction
Resource allocation is a fundamental issue in the study of economic theories. Since classical economics based on cost and price theories was founded, economists① Smith, 1988；Ricardo, 2009 have studied resource allocation focusing on how product value and price fluctuations are influenced by the supply-demand relationship. Marxian economists have developed theories of resource allocation mainly in two ways. First, they founded the theory of socially necessary labor which has two aspects; analyzing the rational allocation of manpower, material and financial resources under the labor theory of value, ② Marx, 1975 and resource allocation, driven simply by average rates of profit and production, a market mechanism commonly applied in the capitalist world, would lead to a huge waste of social labor. Although Marx built a preliminary skeleton of resource allocation theory in his Economic and Philosophic Manuscripts of 1844③ Central Compilation and Translation Bureau, 1998 and other early works, he did not negate the effect of the market mechanisms on resource allocation. However, he highlighted the significant role of the government’s macro control in this regard and advocated a plan-based resource allocation mechanism. Setting aside the practice of Marxian economic theories in the past, these theories, in terms of their influence on the world of economics, have been a major ideological source for Western Economics with respect to resource allocation under government’s macro control.
需求情况：基层农需欠佳，秋季用肥需求正在缓慢启动。下游复合肥企业正在进行秋季备肥，开工率较前周持稳；经销商提货速度略增，但钾肥整体需求仍无明显起色，实际成交欠佳。
Alfred Marshall’s Marginal Analysis presented a neoclassical perspective on resource allocation theories in economics. As an intermediary, neoclassical economics is the parent of modern mainstream economic theories. However, all neoclassical thoughts on resource allocation were derived from the age of Industrialization and were developed by taking technological factors into account as inherent variables. In fact, the changes in resource allocation is partially driven by technological development which impact not only manufacturer’s strategies regarding what to make, how to make it and how much to make, but also the consumer’s decisions on what to buy, how to buy and how much to buy. Thus, technological development, on some level and to a certain extent, are a function of market mechanisms and the government’s macroeconomic control policies and therefore determine the output and price of goods and services. It is noticeable from the economic literature concerning resource allocation that economists tend to study this topic from the general and local equilibrium of economic operations. Centering on market mechanisms, their research was carried out on the grounds of rational choice theory and theoretically reflected people’s choice behaviors in the age of industrialization with the assumption that technology remains static.
General equilibrium is a classical economic theory used to explain resource allocation. The general equilibrium of total supply and demand and quantitative composition of goods and services was first discussed by Gosson④ Gosson, 1927 and Jevons⑤ Jevons, 1957 and modeled by Walras.⑥ Walras, 1954 Pareto,⑦ Pareto, 1971 on the other hand, demonstrated the well-known Pareto Optimality based on the inference that general equilibrium is achievable when budget constraint conditions for the market demand function are met. Economists have debated Walras’ analysis and the Pareto Optimality in their research by discussing either the equilibrium of productivity under the constant returnsto-scale model from the technological point of view, ① Koopmans, 1951; Dantzig, 1951 or the equilibrium of productivity under the constant returns-to-scale model,② Hayek, 1945 or the constraints hindering the achievement of competitive equilibrium while focusing on the Pareto Optimality.③ Arrow, 1951 Most of the aforesaid research were carried out on the assumption and inference of “economic man” or “rational economic man,” where the preference, cognition, utility and other constraints are certain, featuring realistic basis and logics typical in the age of industrialization. In the era of the Internet, however, the resource allocation equilibrium should be further investigated as it is now possible to collect and process massive amounts of data with the help of technology.
1. A theoretical explanation to changes in investor’s choice behaviors due to the latest technology
Investment choice behaviors, a factor that impacts or determines resource allocation in the human society, are changing in the age of the Internet. This can be briefly explained with fundamental theories: the neoclassical description of “preference consistency”④ John & Oskar, 1947; Arrow & Debreu, 1954 which is completely helpless for explaining investor’s preferences today. Ideas from modern mainstream economics related to information collection, integration,categorization and processing, as well as how perception is developed, are incapable of explaining the development of investor’s perception in this era; ⑤ Although modern mainstream economics does not indicate that people need to collect, integrate, categorize and process information to make rational choices,it does imply some related thoughts, according to studies of preference, perception and utility. and the conclusions made by modern heterodox economics that the expected utility varies in the process of making investment choices⑥ Kahneman & Tversky,1973; Kahneman & Tversky, 1974; Kahneman & Tversky, 1979 also fails to effectively expound variations of investor’s expected utility these days. The reason, is that how investors contemplate and make their choices is changing under the combined influence of the Internet, big data, machine learning/artificial intelligence (AI) and other cutting-edge technologies.
第三方物流是百安居双引擎供应链中的副引擎，只负责区域物流中心到各门店、各门店到顾客和小部分供应商到区域物流中心的配送，功能较简单，但第三方物流的配送占比大约有80%左右，所以其运作能力的高低对整条供应链的效率有着很大的影响，而且，如果顾客在百安居购买产品需要送货上门，那么最终面对顾客的也是百安居的第三方物流服务商，也就是说，第三方物流服务商从一定程度上就等同于百安居在顾客心中的形象。因此，对于第三方物流的管理和考核相当重要。
Using AI actually represents an extended scope of analysis of the Internet and the IoT. Theoretically, the investor’s choice preference is a function of utility. If investors were able to acquire accurate and/or precisely targeted information from the hybrid of the Internet, big data and AI, their choice preferences would neither be ‘consistent,’ as argued in neoclassical economics, or unpredictable, as indicated in modern mainstream or heterodox economics. Instead, the concept of preference similarity stands out, i.e. the investor’s choice preference tends to be similar with that of smart brains that have acquired expertise from big data, the Internet, and AI and know how to combine them to maximize utility. Unlike the age of industrialization,the development of smart brains’ perception will be gradually released from restrictions due to incomplete information as they gain insights by digging, collecting, integrating, categorizing and processing big data because the perceptions of smart brains allow utility to approach its maximum, while average brains generally follow smart perceptions when they are making investment choices. This is defined as investor perception simulation.
能否在不同地形条件下实现平稳运动，是一个机器人是否具有实际使用价值的评判标准。斜面是自然界中常见的地形，一些崎岖的地形也可以等效为斜面。因此，研究四足机器人在斜面上的运动控制具有重要意义。经典控制和现代控制在设计过程中一般是基于被控对象的精确数学模型。对于四足机器人来说，因其具有非线性、高耦合、模型复杂等特点，采用传统的控制理论和方法往往存在局限性，难以取得满意的控制效果[1]。模糊控制是建立在模糊集合论基础上的一种基于语言规则与模糊推理的控制理论，在控制非线性、高耦合及参数和结构不确定的复杂过程中具有较好的控制效果。
Apart from how exactly smart brains use big data to acquire accurate information and push utility toward the maximum, let’s look solely at the influence of their preferences and perceptions on the investment choices of average brains, who make the majority of investment choices and whose simulation of investment preferences and perceptions therefore reflect changes in the investment choices in the age of the Internet. In addition, smart brains do not adjust their utility expectations as they develop their preferences and perceptions by fusing the Internet, big data and AI, and can acquire accurate information before they make investment decisions. On the contrary, average brains are unable to extract accurate information from big data. They simply follow suite. The average brains’ utility expectations reflect their anticipation for the utility of investment choices made by the smart brains. Obviously, the classification of smart and average brains is a prerequisite for analyzing preference simulation, perception simulation and utility expectation.
在收集了丰富的资料后，对这些资料进行分类汇总，首先按照案例和动画、FLASH资料分成两大类。其次又把案例按照工程材料、铸造、锻造、焊接、机械加工这些工艺特点分类。然后交由课程组评阅、讨论，再进行案例的修改，加注标准格式。最后交给课程组负责人审阅完善，再分门别类整合进案例库框架中。
女人跟杨剑的爱恋也从那一刻开驶启程，他们约会的场所就是那家叫金典的咖啡厅，当然是两个人喝茶聊天的时候。两个人做爱的时候就去城西或城北的几家宾馆里开房间，也在郊外杨剑的轿车里或者僻静的山坡上。
Given the fact that average brains are a majority in the community of investors, we could describe the changes in investment choices today as preference simulation → perception simulation → utility expectation to align with fundamental theoretical analysis of preference, perception and utility in the rational choice theory. Certainly, the said description of change is derived from the thought that the integration of the Internet,big data and AI allows for grouping investors, and it stresses the assumption that the investment choice of average brains are guided by smart brains. If this description could reveal the truth of investment choices in the age of the Internet, we might explore this analytical approach further and carry out practical research about the evolution of resource allocation mechanisms.
2. Evolution of resource allocation mechanism in the age of Internet
When a company’s capabilities to mine, collect, integrate and categorize big data are not considered,its strength of data intelligence depends on how well it can process big data, which directly determines the extent and scale of the Internet-based resource allocation mechanisms. In the future, companies will expand the scope of parameters, replace a single well-engineered model with many simple models, and intelligently handle the supply and demand relationships and pricing through various machine learning methods. This“data-driven method” will be the main approach for companies to achieve data intelligence in the future.① Wu, 2016 Investigation of companies using historical and current data for the purpose of data intelligence indicates that the “data-driven method” has gained some progress in determining outputs and prices. Whether this method will be applicable to processing future data or will we need to develop the “predictive reinforcement learning,”a machine learning method dedicated to future data, we will need to wait for considerable advances in the research of artificial intelligence.
Machine learning means to analyze the data with algorithms. At present, all types of machine learning deals with historical or current data, be it supervised learning with sample identification, unsupervised learning without sample identification, reinforcement learning functioning in a trial-and-error mode in a dynamic environment, or deep learning that combines low-level and high-level data. If future data is not included in machine learning for AI-based matching of big data, then it is difficult to really establish Internetbased resource allocation mechanisms. Current discussions on how computers can acquire the ability to learn without specific programming highlight how to enable big data matching with artificial intelligence,① Taddy, 2017 but this will only be using historical and current data supported by real events. As to the methods for machine learning of future data, we should first figure out how to do data mining and then find a proper way to learn. A possible option is that we can leverage the completeness and relevance of historical and current data for the mining of future data, i.e. we can, based on the reinforcement and deep learning of these two types of data, utilize their multidimensionality and relevance to predict future data. This machine learning of future data may be referred to as “predictive reinforcement learning.”② In fact, it’s not so important as to what kind of concept is developed to describe machine learning of future data. What really matters is how to mine future data and bring such machine learning into reality. For example, Alibaba Group's “new retail” investment strategy, which combines online and offline sectors,is an attempt to obtain future data by mining and processing the multi-dimensionally correlated historical and current data, in order to explore the algorithm for future data through the "predictive reinforcement learning" defined in this paper to eventually develop a thorough understanding of the supply and demand relationships.
Evolution process 1: As the Internet, big data and AI are fused organically, behavioral interactions among investors will be a key part in the evolution of resource allocation mechanisms.
The investment choices of smart brains lead to the preference/perception simulation and utility expectation of average brains, which reflects the behavioral interactions between investors. Economics has long been an individualistic methodology taking ‘individual behavior’ as the basic analysis element and inter-individual behavioral interaction as the reference of analysis. The reason is that the age of industrialization lacked the technological base that triggers group behavioral interaction like it does in the age of the Internet. Yes, if we review the development of resource allocation mechanisms from a majority choice point of view, scattered individuals do interact with each other under the effect of price, supply and demand and other market signals, and somehow generate a collective force that affects resource allocation but, unfortunately, is not strong enough to consolidate itself in individual behaviors, and therefore cannot result in group behavioral interactions. In the era of the Internet, the preference and perception simulation and utility expectation of average brains impacted by the investment choices of smart brains obviously lead to a collective force ② Economists tried to describe simulation behavior in individual choice under the framework of individual methodology. Some of the well-known concepts include Butterfly Effect, herd behavior, information overlaps, and framework dependency. For instance, in his analysis of financial market, Robert Shiller (2001)discussed simulation behavior induced by market mechanisms by applying catalytic factors, feedback loops, chain reactions and amplification mechanisms.But, the analysis, which targeted individual behavioral interactions, does not apply to group behaviors of average brains by simulating the smart brains in the age of the Internet. that is strong enough to drive group behavioral interactions. It is because of this collective force that behavioral interactions among investors has become a key part in the evolution of resource allocation mechanisms.
While smart brains may collect, integrate, categorize and process market data to obtain complete and precisely targeted information, the investment choices of average brains made by simulating the smart brains can be seen as a choice behavior that indirectly benefits from big data analysis. Academically, can we take the “behavioral interactions” between smart and average brains as a basic analysis element and introduce collectivistic methodology into the study of economic theories? Clearly, this academic thought acknowledges the fact that the investments by average brains represent a large portion of the total investment volume of society, and the “behavioral interactions” between the two groups reflect the evolution of resource allocation mechanisms in the mode of choice. On the Internet, the behavioral interactions among investors is reflected in click-through rates, notability, real-time reviews, shares of experiences and other aspects of goods and services. Once becoming a key step in the evolution of resource allocation mechanisms, these interactions will have an extensive and deep influence on the evolution of the factors manufacturers consider when they are making investment or operational choices.
Evolution process 2: the application of big data and AI in microeconomic activities is reducing the dominant effect of price and supply and demand relationships on market resource allocation.
During the industrialization age, manufacturers made decisions on what to invest and what/when/how much to produce mainly according to supply and demand relationships and price fluctuations if the government did not intervene in economic activities or did not implement industrial regulations. This resource allocation theory, which is regarded as a classic in neoclassical economics and modern mainstream economics,has never got rid of the predicament of market failure, hence it is difficult to achieve Pareto optimality. This is essentially because manufacturers only obtained partial information from limited data in their operations,and the digging, collection, integration, categorization and processing of necessary information, as well as the development of their perceptions based on such information, were bridled by their bounded rationality and limited technical means. The major reason for the bounded rationality was that the manufacturer was unable to figure out the possible results of their investment/operational decisions since they couldn’t gather complete information; as to technical means, it was mainly because of the lack of technical capabilities necessary for the manufacturer to make market signal-based investment/operational decisions that met the effective demands.We can see that optimal resource allocation mechanisms lead to Walras’ general equilibrium and guarantee proper allocations and quantitative compositions based on effective demand, providing that manufacturers can acquire complete and accurate information, break the constraint of bounded rationality, and are using new technology to deal with the totality of supply and demand and its quantitative composition in the market clearing sense as an algorithm.
In today’s world where the Internet, big data and machine learning/AI integrate organically, the exponential growth of big data will eventually contain all market information including price fluctuations and supply-demand relationships. According to mainstream economic theory, the supply-demand relationship determines price volatility, which determines manufacturers’ investments and operations, and that is how the resource allocation mechanisms works. The truth is, however, that the supply and demand relationships between goods and services are determined, considering certain factors such as employment and income distribution, mainly by the choice preferences and perceptions that reflect the consumption trend, the manufacturer’s perception of investment choices, and the utility expectations of both consumers and manufacturers. Smart manufacturers do not tie their investments directly to price fluctuations and supply and demand relationships; instead, they study and categorize the big data reflecting people’s preferences,perceptions, and utility expectations on the cloud platform, do match-making by using cloud computing and AI, and acquire clear and true information of price fluctuations and supply and demand relationships before making investment/operational decisions. Although price and supply and demand relationships still play a role in the Internet-based resource allocation mechanisms, their dominant effect has been largely weakened.
改革开放进入“深水区”，面对越来越难啃的“硬骨头”，以习近平同志为核心的党中央，带领全国人民涉险滩、攻难关，坚定不移将改革开放进行到底。正如今年10月，习近平在广东考察调研时向世界宣示：中国改革开放永不停步！下一个40年的中国，定当有让世界刮目相看的新成就！
Evolution process 3: a vital way for establishing the Internet-based resource allocation mechanisms is that smart brains make investment/operational choices through machine learning of big data and AI-based matching of big data.
During the age of industrialization, people did not have access to adequate information. They could only make investment choices by collecting, integrating, categorizing and processing information from what had happened. In the era of the Internet when big data includes not only what has happened and what is happening,it can also reveals information of what will happen with the help of machine learning, ① Current researches about how to use “algorithms” for data analysis by machine learning discuss how computers can acquire the ability to learn without specific programming, and what methods can be developed for AI-based data matching. The academia has classified machine learning, by the learning characteristics,into supervised/unsupervised, reinforcement and deep learning. However, researchers have only studied machine learning of past and on-going events so far,not touching the prediction of the future. This is discussed in the sections below. i.e. big data, a combination of digital and non digital data (pictures, texts, drawings, videos, sounds, fingerprints, images…)contains the information necessary for making wiser, better informed investment choices. If we have managed to obtain massive data from the Internet, IoT, sensors, social media and GPS devices and, leveraging the comprehensiveness, multidimensionality and relevance of big data, acquired accurate and/or precisely targeted information by a range of machine learning technologies, then we can build the technological foundation for rational and accurate resource allocation. When companies try to obtain accurate information about supply and demand of goods and services and pricing through the analysis of big data, they operate under an Internetbased resource allocation mechanism, which has been upgraded from its predecessor, developed in the age of industrialization, and has evolved to include the choice modes of individuals, businesses, government agencies, technological development, markets and policies.
The theoretical basis is that the exponentially growing big data contains all the information that is needed to make decisions. Before mining, control and utilization of big data is possible, all the information that affects the company’s investments and operations is merely something in existence objectively. In the age of the Internet, big data or AI, companies are able to mine and collect big data with the Internet, IoT, mobile devices,sensors, social media, GPS, etc. However, it is one thing to acquire big data containing all the information but another thing to extract all the information the data may contain to further derive precisely targeted information. To achieve market-clearing results, the Internet-based resource allocation mechanisms must enable an overall balance between total supply and demand through company-level data intelligence. This requires companies to gain the capabilities to attain data intelligence when determining output and price.
Evolution process 4: As the supply and demand relationships of goods and services are transformed into an algorithm, industrial organizations will change from a vertically integrated architecture to a networked collaborative architecture, which will enable the Internet-based resource allocation mechanisms.
A prominent feature of industrial organizations in the era of industrialization was the joint effects of pricing mechanisms, product attributes, supply and demand relationships, and geographic locations, which resulted in an industrial cluster that demonstrated the interactions between businesses. Such a industrial cluster corresponded to industrial chain that formed a market structure where businesses were linked by upstream and downstream relations with quantitative ratios for different industries. The integration of resources was vertically associated in this market structure. In the age of the Internet, new competition and monopolization methods and approaches have changed the industrial organization as businesses are not relying fully on market signals any longer, but are mining, collecting, integrating, classifying and processing big data for intensive cloud computing, and using AI to analyze big data in order to determine outputs and prices. Futurists have interpreted this new technology as an algorithm① Noah, 2017; Kelly, 2014 when they talk about the economic, political, cultural, and ideological impact of AI-based analyses of big data. When it comes to the application of this new technology,economists explain it as data intelligence, and imply that data intelligence will change resource allocation mechanisms and industrial organizations.
The process of new technology reshaping resource allocation mechanisms is reflected not only in production but also in distribution. Aiming at market scenarios featuring the fusion of big data, the Internet,IoT and machine learning/AI, some scholars have attached more importance to the research of network synergy, an Internet-based collaboration that, on the basis of data intelligence, moves investments, production,operations and other inter-business activities to operating platforms such as the Internet or IoT through big data processing. Network synergy is a way of leading to competition and monopoly in the Internet era, and it co-functions with data intelligence in the process of driving the establishment of the Internet-based resource allocation mechanisms and reshaping industrial organizations. If data intelligence is a necessary condition for establishing Internet-based resource allocation mechanisms, then network synergy is a sufficient condition for that purpose. It is of great significance to understand the connection between the two, which will help us develop a clearer vision for the future of Internet-based resource allocation mechanisms.
3. Presence and prospects of the internet-based resource allocation mechanisms
3.1 Data intelligence is a fundamental factor that makes the Internet-based resource allocation mechanisms possible. Determining the output and price based on intelligent data is a specific reflection of the function of the Internet-based resource allocation mechanisms.
A large part of the current theoretical analyses of companies determining output and price based on big data focus on abstract theories in which it is taken as an algorithm, rather than specifically analyzing how the Internet-based resource allocation mechanisms operate from company-level data intelligence. When determining output and price based on data intelligence, companies must be capable of processing big data, in addition to mining, collection, integration and categorization of big data. The former requires the capability of intensive cloud computing; the latter requires machine learning and decision-making by AI-based analysis of big data. Only when companies reach a necessary level of data intelligence can they accurately forecast market demands and set a price leading to market clearing. Given the deep integration of the Internet, big data, IoT and machine learning/AI, it is reasonable to envision, as a prediction of the future resource allocation pattern,that the Internet-based resource allocation mechanisms will be fully functional when most or all companies can apply data intelligence to determine output and price.
Considering the determination of supply and demand for goods and services, “predictive reinforcement learning” is a crucial process for establishing Internet-based resource allocation mechanisms. To determine the exact supply and demand, manufacturers must use intensive cloud computing methods for mining, collection,integration, categorization and processing of historical, current and future data, and acquire machine learning capabilities for handling them. Machine learning is vital to the transformation of resource allocation mechanisms as it forecasts real economic operations. Since the future supply and demand relationships of goods and services are uncertain in the economic world, it is impossible for economists to solve uncertain challenges through models developed with definite programming conditions. This will lead to failures of the market and the government. Machine learning will function as an important intermediary in the fusion of the Internet, big data and AI, largely because it can transform manufacturer’s investments and operations into an algorithm, and the utility function of the Internet-based resource allocation mechanisms will be greatly compromised without such an algorithm or if it is unscientifically engineered.
In the age of the Internet when time and space restrictions are released, channels synchronized, and realtime reviews are possible, motivation, preference, perception, utility and other factors affecting the decisionmaking process of investors all present in a scenario where “everything is connected.” Choice behaviors of investors, be they individuals, businesses or government agencies, will be gradually influenced by the fusion of big data, the Internet and AI. As to the change, we should pay attention to these two aspects: (1) how big data, the Internet and AI technologies are fused, and (2) how such fusion interacts with the investor’s choice preference, perception development, and adjustment of expected utility. For the first aspect, the facts are:the data acquired annually by society has increased beyond the limits of Moore’s Law because of the deep penetration of the mobile Internet, the Internet of Things (IoT), sensors, social media and GPS devices; and the tremendous volume, multidimensionality and comprehensiveness of big data have enabled the generation,by machine leaning (AI), of accurate or even precisely targeted information, which in turn exerts influence on investment choices of individuals, businesses and government agencies. In this case, we could take big data as the soul of such fusion, The Internet and IoT as the vector, and AI as the approach in order to clarify the relationships among the Internet, big data and machine leaning/AI.
Economic studies on resource allocation have evolved from focusing solely on market mechanisms to taking government control into account. Analytical approaches include: (1) the assumption of theoretical analysis has changed from complete information to incomplete information, and the reference system and analysis method of resource allocation theory are different; (2) determination of price and output, as well as formation of monopoly, are explained differently; and (3) the studies are always based on the grounds that the market or government goes out of control. Nonetheless, considering mechanism, majority, and behaviors, the resource allocation depends on the investment choices, which are limited by the sufficiency of information obtained.
3.2 Data intelligence and network synergy of companies are correlated and jointly maintain the existence and development of the Internet-based resource allocation mechanisms.
The relationship between data intelligence and network synergy is both sides of a copper plate. Intelligent data reflects how big data is matched by supply-related AI capabilities, and it is an operational process to determine the output and price through machine learning. Network synergy represents a multi-party interaction (between businesses, or businesses and consumers) on the Internet or IoT; it reflects the application of demand-related big data and verifies whether the output and price related big data processing is accurate in the operation of data intelligence. Network synergy is the market spillover of data intelligence mainly because the effect of network synergy is generated when companies analyze big data with algorithms and then make investment/operational decisions by utilizing data intelligence. This spillover, featuring the effect of network synergy, allows verification of the data intelligence quality through network synergy. The effect of network synergy is embedded in the Internet-based resource allocation mechanisms.
Theoretically, the effect of network synergy is a utility function of big data, the Internet, and AI, which integrate and demonstrate the effect of the Internet-based resource allocation mechanisms. In the era of the Internet, companies have begun to pay attention to the network synergy between businesses and between businesses and consumers when they are intelligently processing historical output and price data while trying to explore the current and future data that reflect supply and demand relationships through such network synergy. For example, companies will pay attention to click-through rates, notability and real-time reviews of goods and services on the Internet or IoT, and even promotions by influencers. Some Chinese researchers have analyzed these phenomena from the perspective of resource aggregation, pointing out a trend that companies aggregate market resources through the Internet platform, production resources through the IoT/industrial chain platform, and fragmented resources through the Internet-based platforms of the sharing economy.① Jiang, 2017 This means that they have virtually identified the existence of the Internet-based resource allocation mechanisms and observed that network synergy is a specific manifestation of such mechanisms.
Research of economic theory needs a future-oriented perspective for development of the Internetbased resource allocation mechanisms. As to the future of big data and AI, futurists tend to believe that the composition and movement of all matters, organic and inorganic, will become an algorithm. Regardless of the feasibility of this vision, we can look at the basis of the analysis by futurists, which at least includes the following conditions: (1) We have made considerable progress in developing the technologies for mining and collecting big data and are now at a level high enough for us to probe for accurate information; (2) Big data processing technologies have also reached to a very high level and the mining and processing of historical,current and future data can be done through machine learning; and (3) the Internet and IoT platforms are sufficiently extensive to cover all areas of human activities, and the interpersonal behavioral interactions are fully released from the limits of space and time and synchronized in a way that the relationships between all behavioral interactions are clearly revealed by analyzing big data concerning perceptions, experiences and evaluations of real behaviors.
It can be easily noted that when analyzed as economic activities, the first and second conditions are the technologies required for data intelligence of the Internet-based resource allocation mechanisms, while the third condition is for the platforms required for network synergy of the Internet-based resource allocation mechanisms. Given that balanced macroeconomic operation relies on the rational allocation of resources,macroeconomic benefits in the era of the Internet are therefore mainly dependent on interpersonal behavioral interactions, i.e. the effect of network synergy, providing that data intelligence technology is fully developed.In this sense, the effect of network synergy can be regarded as a utility function of the Internet-based resource allocation mechanisms. Unlike the relatively specific utility functions of the manufacturer’s investments and operations, this is an abstract utility function for the macroeconomic benefits caused by the Internet-based resource allocation mechanisms and is difficult to express with a specific formula. Nevertheless, as a macrolevel utility function, the effect of network synergy is always connected with the two explanatory variables, i.e.data intelligence and network synergy. Is it reasonable to indirectly transform our vision for the Internet-based resource allocation mechanisms to a vision for the network synergy?① It’s necessary to note that network synergy includes two aspects: first, the micro-level behavioral interactions between a single business and its customers generated by the business matching big data on the Internet platform using artificial intelligence for investments and operations (data intelligence); second, the macro-level behavioral interactions between all businesses and their customers. When we consider the effect of network synergy as a utility function of the Internet-based resource allocation mechanism, such an effect refers particularly to the results from the macro-level behavioral interactions. If yes, will this vision involve more macroeconomic issues?
3.3 When the best effect of network synergy is achievable based on the economic operations of high-level data intelligence, we may draw a theoretical inference about implementation of a planned economy.
The prospect for the Internet-based resource allocation mechanisms is actually for the data intelligence,network synergy and corresponding effects of network synergy. Data intelligence and network synergy can be fully realized and analyzed by taking the Internet-based resource allocation mechanisms as an algorithm.When society-wide data intelligence is fully realized, it means that: first, we will be able to mine and collect the encompassing big data with the Internet, IoT, mobile devices, sensors, social media, GPS, etc.; second,with the help of artificial intelligence, we can obtain near-complete information necessary for decisionmaking from the encompassing big data; third, people can identify real information and even acquire accurate information through data intelligence and know the outcomes of investments or operational decisions. On the other hand, when society-wide network synergy is fully realized, it signifies the maximum macroeconomic benefit (optimal effect of network synergy). For this fascinating vision, supporters believe that it is possible to implement a planned economy in the future, while opponents argue that the planned economy is not an option even if this vision is correct. ② For this issue, Ma Jack and Qian Yingyi (2017) had a typical debate on Sina.com. Mr. Ma believed that a planned economy is feasible in the future based on the vision of data intelligence that everything can be rendered as an algorithm, while Mr. Qian, by referring to the economic practice before the reform and opening-up policy, negated the viability of a planned economy. Zhang Xukun (2017) asserted that a planned economy is not feasible by taking the lessons learned from the failure of the European Utopia Commune as an example.Xu Chenggang (2017) based his arguments on the general mechanism of economic operation and believed that big data and AI cannot enable a planned economy. Anyway most economists stand against a planned economy, leaving Mr. Ma alone in an unsupported position. The truth is, however, the debate between the two sides had not completely probed into the key points of data intelligence, and this issue remains to be studied. We can interpret this debate about the implementation of a planned economy as an argument about the existence and role of the Internet-based resource allocation mechanisms. Whether plan-based socio-economic operations are acceptable depends on whether we can obtain accurate information about total supply and demand, and the quantitative composition of the data.
Here are a few questions to discuss: (1) If we can collect the big data containing complete information; (2)If we can acquire complete and accurate information from big data; and (3) If we can achieve network synergy.One of the key indicators of top-level data intelligence is the ability to mine big data for behavioral interactions and to coordinate behavioral interactions through artificial intelligence. Another important indicator is the ability to mine and process big data for future events. If this is the case, the “plan” is market-oriented. For that reason, we can assume that if we can obtain accurate and precisely targeted information through the mining and processing of historical, current and future data, then it is possible for us to implement a market-oriented planned economy under the effects of the Internet-based resource allocation mechanisms.
本厂风电试验站，被试验双馈风力发电机需要配套转子变频器才能运行，而转子变频器就属于典型的电力电子类非线性负载。为此设计上选择了使用机组电源。
（一）材料 2011年1月～12月，对本区的20个大型生猪集约化养殖场，按每个猪场随机抽选种母猪以及15～45日龄的仔猪各10头份，每头份采集全血3～5 ml，并记录好各养殖场的免疫程序。将编号好的猪血分别移至1.5 ml灭菌EP管内，3 000 r/min离心10 min，共计获得了350份合格样品血清。根据养殖场的免疫记录，此次采样所抽选猪群均未接种猪圆环病毒2型、猪繁殖与呼吸综合征病毒和猪传染性胸膜肺炎的疫苗，有部分猪场在采样近一个月内曾接种过猪瘟弱毒苗，另外所有猪场均接种过猪口蹄疫及猪伪狂犬病毒活疫苗。
3.4 In the age of Internet when AI and automation impact employment and income distributions,we can forecast and control the impacts as a vision for the effects of the Internet-based resource allocation mechanisms.
The Internet-based resource allocation mechanisms will have an impact on employment, mainly due to AI and AI-enabled automation. According to analytical models which classify low-skilled, repetitive, highlyskilled and non-repetitive production activities,① Autor & Dorn, 2013. our vision of the impact of the Internet-based resource allocation mechanisms on jobs should take into account the impact of AI on low-skilled and highly-skilled jobs caused by differences in repetitive and non-repetitive production. Benzell et al. ② Benzell, Kotlikoef, Lagarda & Sachs, 2015. believed that while artificial intelligence replaces low-skilled jobs to a large extent, it also replaces some high-skilled jobs as well.Acemoglu③ Acemoglu, & Restrepo, 2016 argued that the cost of labor and capital, under equilibrium conditions, will result in a reduction of low-skilled jobs due to artificial intelligence while creating some highly skilled jobs. By scrutinizing these latest viewpoints, it is easy to note that they, while demonstrating the role of factors and the correlation of their cost and artificial intelligence, certainly imply that the Internet-based resource allocation mechanisms will affect employment.
At present, most mainstream views on the impact of AI on employment hold that the impacts on lowskilled and highly-skilled jobs are complementary. Diving deeper into this point of view, it also involves the issue of income distribution for jobs at different levels or even the same level. In fact, AI has different influences on GDP, employment, income distribution and social welfare in different countries. When investigating AI and analyzing the impact of the Internet-based resource allocation mechanisms on income distribution, in a purely theoretical sense, we should base our analysis on the algorithm. However, as an algorithm is scenario-related, it is difficult to accurately implement an algorithm for analyzing employment and income distributions, where multiple variables and complicated scenarios are involved. Therefore, without strong capabilities of machine learning and AI-based matching of big data, it is challenging to accurately predict the impact of AI and AI-enabled automation on employment and income distributions when the Internet-based resource allocation mechanisms take effect.
Our society is about to embrace the era of the Internet-based resource allocation mechanisms with big data as its soul. Data intelligence, network synergy and the corresponding effects of network synergy will adjust resource allocation which is an issue focusing on the totality of supply and demand. For the objectively existing Internet-based resource allocation mechanisms, economists need to carry out in-depth analyses and forecasts within the context of developing of new technologies.
（6）防治疾病。当进行高密度养殖时，无论是中华绒螯蟹还是小龙虾疾病的预防工作都是不能放松的。扣蟹和小龙虾下塘之前要进行体表消毒，防止把病原体带进池内，定期用生石灰消毒池水，1.0m水深用量为15.0kg/亩，使用时注意生石灰化水泼洒时要避开水草，不能直接泼洒在水草上，以免将水草烧死而影响水质。经常向养殖池塘加注新水，保持池水清洁卫生。
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*He Da’an, doctoral advisor/professor, School of Economics, Zhejiang Gongshang University; professor, Modern Business Research Center of Zhejiang Gongshang University, a key research institute under the administration of MOE.
Ren Xiao, PhD, Zhejiang Gongshang University.
*Foundation item: this paper is sponsored by Zhejiang Key Research Institute of Humanities and Social Sciences at Universities (Applied Economics, Zhejiang Gongshang University).
(Translator: Tan Xiaomei; Editor: Xu Huilan)
This paper has been translated and reprinted with the permission of Economist, No. 10, 2018.
标签：School of Economics论文; Zhejiang Gongshang University论文; Modern Business Research Center of Zhejiang Gongshang University论文; a key research institute under the administration of MOE论文;