As digital transformation accelerates, data has become a core driver of technological innovation and economic growth. However, a key challenge in data utilization is the semantic misalignment between data supply and the demands of business scenarios. This misalignment significantly hinders efficient data flow and collaborative utilization. To address this issue, this article proposes a federated service solution integrating blockchain and decentralized autonomous organizations and operations (DAOs), large language models (LLMs) and scenarios engineering, federated learning and edge computing, as well as encryption technologies and privacy-computing. A five-layer federated service framework is introduced, consisting of the foundation layer, the data-scenario layer, the semantic coordination layer, the incentive-security layer, and the application layer, which is designed to ensure efficient and context-aware data supply–demand matching while preserving privacy and scalability. Moreover, the core mechanisms for semantic coordination are proposed, and a detailed solution process for resolving semantic misalignment with these mechanisms, as well as an illustrative example, is also presented. The proposed federated service framework offers an effective solution to semantic misalignment in supply–demand matching, fostering seamless data collaboration across diverse business scenarios. This work provides an intelligent service paradigm that leverages distributed data co-governance to address semantic challenges in the digital economy.

去中心化自治组织(DAO)的兴起, 为传统的社会协作关系带来全新的可能性, 而DAO组织的核心驱动力和权利基础就在于其创新的运作机制. 然而, DAO作为一个同时具有社会复杂性和工程复杂性的复杂系统, 其机制需要能够动态适应不断变化的外部环境, 这同时也是其面临的巨大挑战. 传统的自上而下的设计方法并不能有效解决这些问题. 循环因果理论将复杂系统的运作视作是一个持续演化的动态过程, 从而为解决以上问题提供了新的视角. 因此, 基于平行智能理论和循环因果理论, 提出一种用于DAO机制设计和验证的工程方法. 在这种方法中, 采用了计算机辅助复杂自适应系统的动态设计工具(cad2CAS)简化DAO机制的设计, 并使用复杂自适应系统的计算机辅助动态设计系统(casCAD2)验证和引导这些机制, 从而建立一个因果循环. 通过提出这种方法, 希望能提高DAO治理系统的效率、安全性和适应性, 为更加鲁棒和弹性的去中心化组织发展奠定基础.

The governance technology of decentralized autonomous organizations (DAOs) provides an effective solution for solving existing organizational management issues. Governance mechanisms of DAOs are usually encoded in smart contracts in the form of rule sets and executed automatically. However, the mechanism’s logical flaws and code errors expose DAOs to unpredictable risks. Complex adaptive dynamics computer-aided design (CadCAD) can test the effectiveness of the mechanisms through simulation. Nonetheless, as DAOs are typical complex systems with social and engineering complexity, managing, controlling, and supervising their operation through traditional methods are difficult. The parallel intelligence theory based on artificial societies, computational experiments, and parallel execution (ACP) method provides an effective research framework and practical method for solving DAOs’ governance issues. Therefore, in this article, we propose a parallel mechanism verification method and execution system, namely, complex adaptive systems for computer-aided dynamic design (casCAD2) as an extension of cadCAD. Leveraging parallel intelligence and cyber–physical–social systems (CPSS), casCAD2 is capable of probing into the laws that govern system evolution within a simulated environment. It serves as a robust tool for verifying the efficacy of DAOs’ mechanisms and predicting their potential risks. We also build a parallel market-based anchoring mechanism (MAM) system to demonstrate how it can be used for DAOs’ mechanism verification. This study can provide a new research method and application system for DAOs’ effective governance.

In the context of the Societies 5.0, a series of discussions on the emerging Fukushima treated radioactive water (FTRW) event was carried out, which has an impact on sustainable development in multiple fields including the economy, culture, and society. In order to comprehensively understand emerging topics and their evolution, explore the impact of people's cognition on their participation, and focus on people's attitudes and public participation in the FTRW event, we propose an evolution analysis framework (EAF) to analyze the massive multilingual comments and news collected from social media platforms in several countries. We design a multilingual topic extraction model (XLM-topic) to detect the patterns of topics and analyze their evolution. Potential relations between the FTRW event's elements are explored by relational reasoning based on a knowledge graph, which is established by entities and relations extracted from comments and news. Moreover, we predict the public attitudes and participation toward the FTRW event by utilizing our custom-designed public opinion cellular automata (POCA). The proposed POCA simulates the information dissemination, cognitive changes, and topic evolution among social groups in virtual spaces. It collaborates with XLM-topic to analyze trends in both physical and virtual spaces. Analysis results indicate that participants in different regions and countries have different attitudes and reactions toward the FTRW event, and the public's cognition on this event will interact with itself. Our study is conducive to promoting the integration and interaction of virtual space and physical space in the context of Societies 5.0, providing decision-making support for building a more harmonious and stable social environment.

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.

This article proposes a novel parallel management mode based on decentralized autonomous organizations (DAOs) for enterprises by utilizing the artificial systems, computational experiments, parallel execution (ACP) approach, parallel intelligence theory, and blockchain technologies, to realize the distributed management of an enterprise. The artificial enterprise DAO (EnDAO) corresponding to the actual enterprise is constructed, and they constitute a parallel system via virtual–real interaction and parallel execution. Through the non-fungible token (NFT)-based incentive mechanism, metaverse-based virtual learning and training, as well as DAO-based distributed management and decision-making, the management and control of the actual enterprise as well as its employees can be carried out. By virtue of the virtual–real interactions of three types of employees, as well as the virtual–real feedback of three closed loops in the parallel systems, DAO-based parallel management for enterprises can realize descriptive intelligence, predictive intelligence, and prescriptive intelligence. On this basis, this article takes the recruitment-oriented key performance indicator (KPI) management of a startup technology enterprise as the case to introduce the operation processes and illustrate the superiorities of the proposed DAO-based enterprise parallel management mode.

The insurance industry plays a crucial role of the national financial system, and the operating efficiency of insurance companies has always been a significant subject of academic research. This study proposes a multiperiod DEA model to dynamically evaluate the operating efficiency of insurance companies, which not only overcomes the defect of the traditional DEA method ignoring the internal structure of decision-making units, but also extends the limitation of the leader–follower model in evaluating single-period efficiency. By analyzing the efficiency of seven listed insurance companies in China from 2009 to 2018, the following conclusions are drawn. The multiperiod DEA model demonstrates advantages over the leader–follower model. The profitability of insurance companies during the first five years is higher compared with the last five years. There is a significant correlation between premium financing efficiency and overall efficiency. Throughout both periods, the loss ratio, loss reserve ratio, and consumer price index (CPI) are always positively correlated with the efficiency of the insurer, while the gearing ratio is negatively related to the efficiency of the insurer. The correlation among gross domestic product (GDP), total insurance value, tradable financial assets, and corporate efficiency varies over time.

Threshold Elliptic Curve Digital Signature Algorithm (ECDSA) has attracted a lot of attention due to the wide applications of ECDSA in crypto asset. Although several variants of threshold signature protocols can provide functions, such as key generation and signing, they suffer from two shortfalls. First, these schemes only discuss a single signature computation task in a synchronous algorithm context, which is difficult to adapt to real crypto-asset applications, such as custody. Second, these schemes are computing intensive and not scalable, hence can hardly support large-scale processing operations in real life even after traditional optimization, such as multithreading, is applied. In this article, we propose an innovative computation method called asynchronous threshold ECDSA with batch processing, based on the interactive threshold signature protocols. The method provides a reliable solution for critical operational scenarios, such as threshold signing and distributed key generation (DKG) in crypto-asset custody, and can be a future reference in secure data distribution mechanisms. The performance and scalability of our methods are validated through a benchmark testing.

As the next-generation Internet characterized by readability, writability, and ownability, Web 3.0 necessitates the fusion of blockchain and artificial intelligence (AI) technologies to realize its vision of decentralization, user autonomy, and intelligent openness. To this end, this article proposes the integration of “AI for blockchain” and “blockchain for AI” to form a bidirectional enhancement loop, for establishing genuinely intelligent blockchains and ushering in a new paradigm referred to as blockchain intelligence. On this basis, the technical architecture of intelligent blockchains is proposed, which infuses intelligence into every layer of traditional blockchain architectures while enables the parallel execution between virtual and artificial intelligent blockchain systems. This architecture facilitates blockchain systems to cultivate an ecosystem of intelligence, extending from foundation intelligence to application intelligence. Moreover, the core attributes of blockchain intelligence are examined, from the perspectives of smart contracts, data, identity, and governance. Furthermore, the main challenges and research issues faced by blockchain intelligence are outlined. This article is committed to the advancement of blockchain intelligence, laying the groundwork for Web 3.0 and the impending era of smart societies.

With the advent of Web 3.0, the evolution of cities and societies is increasingly oriented toward virtual spaces. This shift signifies an inevitable trend where the integration of virtual and real elements becomes vital to their development. Such a transition will bring huge changes to the organizational structure and methods, development modes, as well as operating mechanisms of cities and societies. In the virtual-real integrated cities and societies, traditional economic and management principles and models are no longer applicable. Consequently, it is crucial to explore new economic and management models tailored to Web 3.0. This article integrates virtual cities/societies with actual cities/societies, and proposes the innovative paradigm of MetaCities/MetaSocieties. Based on parallel intelligence theory and metaverse technologies, the research framework of MetaCities/MetaSocieties is established, and its main participants and operating mode are discussed. In addition, in view of the new economic and management issues faced in MetaCities/MetaSocieties, the innovative paradigms of MetaEconomics and MetaManagement are proposed, and the operational logic and models of MetaEconomics, as well as the MetaManagement big models and management-oriented operating systems, are proposed. This work aims to offer valuable insights for the evolution of cities and societies in the upcoming intelligent era, and inspire the development of new MetaEconomics and MetaManagement models in MetaCities/MetaSocieties.

This paper presents Management-Oriented Operating Systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social Systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making.

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems.

Decentralized Autonomous Organizations (DAOs) have been gaining popularity in recent years due to their promise of realizing the decentralized Web 3.0. However, most DAOs rely heavily on token-centric value systems as well as allocate decision-making authority and yield-sharing rights according to the held tokens, which often lead to monopolization of power and rights. To address this issue, this paper contributes to propose a truly democratic organization model, named True Autonomous Organizations and Operations (TAOs), that does not count upon tokens and is guided by principles of contribution-based and on-demand allocation. We first discuss the design of TAOs, including their infrastructures, power structures, and value systems, and then provide a technical roadmap for implementing TAOs in the DeSci context. This research can provide a valuable guidance for the construction and application of TAOs.

Current evidence indicates that the semantic representation of question and answer sentences is better generated by deep neural network-based sentence models than traditional methods in community answer selection tasks. In particular, as a widely recognized language model, the self-attention model computes the similarity between the specific word and the whole sets of words in the same sentence and generates new semantic representation through the similarity-weighted summation of semantic representations of the whole words. However, the self-attention operation entirely considers all the signals with a weighted sum operation, which disperses the distribution of attention, which may result in overlooking the relation of neighboring signals. This issue becomes serious when applying the self-attention model to online community question answering platforms because of the varied length of the user-generated questions and answers. To address this problem, we introduce an attention mechanism enhanced local self-attention (LSA), which restricts the range of original self-attention by a local window mechanism, thereby scaling linearly when increasing the sequence length. Furthermore, we propose stacking multiple LSA layers to model the relationship of multiscale$n$-gram features. It captures the word-to-word relationship in the first layer and then captures the chunk-to-chunk (such as lexical$n$-gram phrases) relationship in its deeper layers. We also test the effectiveness of the proposed model by applying the learned representation through the LSA model to a Siamese and a classification network in community question answer selection tasks. Experiments on the public datasets show that the proposed LSA achieves a good performance.

Welcome to the second issue of the IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (TCSS) of 2023. According to the latest update of CiteScoreTracker from Elsevier Scopus released on February 5, 2023, the CitesSore of TCSS has reached a historical high of 9.6. Many thanks to all for your great effort and support.

The new generation of digital intelligence technology enables knowledge creation, dissemination, and application to undergoing parallel changes. Scientific systems face an increasingly uncertain, diverse, and complex environment, making adopting multidisciplinary, interdisciplinary, and transdisciplinary approaches to research issues inevitable. Existing scientific systems follow linear value streams, leading to problems, such as inefficiency, unfairness, and knowledge monopoly. Decentralized science (DeSci) is a new scientific development paradigm based on Web3, Metaverses, and decentralized autonomous organizations and operations (DAOs) technologies, that can solve organizational and management problems in scientific systems through organizing, coordinating, and executing techniques. However, new economic theories and methods are still needed to effectively solve the problem of linear value flow in scientific systems. Metaeconomics based on the parallel intelligence theory, also known as decentralized economics (DeEco), provides a new approach and idea for redesigning the economic system of scientific markets. Thus, this article proposes a research framework and core mechanisms of DeSci MetaMarkets based on parallel economic theory to provide effective and practical methodologies for scientific system governance.

In the future, management in smart societies will revolve around knowledge workers and the works they produce. This article is committed to explore new management framework, model, paradigm, and solution for organizing, managing, and measuring knowledge works. First, the parallel management framework is presented that would allow for the virtual-real interactions of humans in social space, robots in physical space, and digital humans in cyberspace to realize descriptive, predictive, and prescriptive intelligence for management. Then, the management foundation models are proposed by fusing scenarios engineering with artificial intelligence foundation models and cyber–physical-social systems. Moreover, the new management paradigm driven by decentralized autonomous organizations and operations is formulated for the advancement of smart organizations and intelligent operations. On these basis, the management operating systems that highlight features of simple intelligence, provable security, flexible scalability, and ecological harmony are finally put forward as new management solution.

Empowered by blockchain and Web3 technologies, decentralized autonomous organizations (DAOs) are able to redefine resources, production relations, and organizational structures in a revolutionary manner. This article aims to reanalyze DAOs from the perspectives of organization and operation, and provide a more precise definition of DAOs as Decentralized Autonomous Organizations and Operations. Based on this, the fundamental principles and requirements of DAOs are explained, while the infrastructure based on cyber–physical–social system (CPSS) and parallel intelligence, as well as the supporting technologies, such as digital twins, metaverse, and Web3, are discussed. Besides, a five-layer intelligent architecture is presented, and the closed-loop equation and new function-oriented intelligent algorithms are also proposed. Moreover, the governance mechanisms from the individual, organizational and social perspectives are discussed, and the incentive mechanisms for the human, robot, and digital human are analyzed. This article can be regarded as a stepping stone for further research and developments of DAOs.

Welcome to the third issue of IEEE Transactions on Computational Social Systems (TCSS) of 2022. According to the latest update of CiteScoreTracker from Elsevier Scopus released on April 6, 2022, the CitesSore of IEEE TCSS has reached a historical high of 8.4. Many thanks to all for your great effort and support.

Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) of 2022. We would like to take this opportunity to express our sincere thanks to our associate editors, reviewers, authors, and readers for your great support and effort devoted to IEEE TCSS. Happy New Year to you all, and cheers to health, happiness, and high-producing in 2022!

Any successful change in an organization requires an appropriate change management method and a process for involved staff and department to accept the change and become engaged in order to achieve its success. It is even more important and difficult to adopt a novel change management method to bring multiple organizations across the business value chain into the change implementation. This research does not focused on change management within a single organization but rather emphasizes a change management method, including an appropriate change framework, well-defined critical success factors (CSFs), and related tools for implementing change in multiple organizations. This article introduces one kind of change management method to support a process change through global value chain (GVC) in multiple organizations, and the method is used in a case study to achieve a successful change. In order to succeed in optimizing GVC performance, this research applies the proposed change management method to the case GVC, to support technical change by obtaining the staff’s full commitment and engagement. The achieved results from the case study prove that successful change comes not only through technical solutions implemented in the problem process throughout the GVC but also through strong support and engagement from all organizations and involved staff. The proposed change management method not only helped the case GVC to implement change successfully but also can help the relevant multiple organizations to improve the GVC performance and add value by optimizing their problem process.

This special section of IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS is a selection of nine articles presented in the Special Issue on “Advanced Machine Learning on Cognitive Computing for Human Behavior Analysis.” This special issue aims to provide a forum for researchers from the perspective of cognitive computing to present recent progress on state-of-the-art methods and applications to human behavior analysis. Our purpose is to review the new progress and achievements on deep learning, transfer learning, and their applications on cognitive computing for human behavior analysis in recent years.

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) this year. First, I am grateful to report that, as of February 7, 2021, theCitescoreof TCSS has leapfrogged back to 5.8, a new high, which indicates the high quality and relevance of IEEE TCSS in the field of social computing and computational social systems research. Many thanks to all of you for your great effort and support.

Welcome to the last issue of IEEE Transactions on Computational Social Systems (IEEE TCSS) in 2021. For IEEE TCSS, 2021 is an exciting year. TCSS has been added to the ISI Web of Science Sources Citation Index Expanded (SCIE) database in 2021, and all articles published since 2018 have been indexed by SCIE. This is an important milestone in the development of TCSS. We would like to take this opportunity to thank and congratulate everyone for their great efforts and supports. We are looking forward to working together to further improve the publication quality and speed up the review process of TCSS in the upcoming 2022.

Welcome to the fifth issue of Ieee Transactions on Computational Social Systems (TCSS) of 2021. As usual, we would like to share some great news first. Since April 2021, IEEE TCSS has been added to the Science Citation Index Expanded (SCIE) database in Clarivate Web of Science. We are excited to report that all TCSS articles published since 2018 have been backtracked and indexed by SCIE.

Welcome to the fourth issue of IEEE Transactions on Computational Social Systems (TCSS) this year. I am excited to share some great news.

Federated ecology can provide an effective solution forthe serious isolated data island issues caused by data privacy protection and information security requirements in the era of artificial intelligence (AI). As the data foundation of federated ecology, federated data include the data of all the nodes in the federation, as wellas their storage, computation, and communication resources. For privacy-preserving, federated data are divided into private data and non-private data, and through the federated control of these data, data federalization can be realized. In data-driven AI technologies, federated data play an important role, and it can help realize effective data retrieval, pre-processing, processing, mining, and visualization for AI-based applications. It can also provide effective solutionsfor the dilemmas faced by AI technologies, such as training AI models without sufficient data, increasing the generality of AI models for different application scenarios and establishing a unified processing workflow for data security and privacy control in AI-based applications.

Since early 2020, the COVID-19 global pandemic has significantly impacted almost every aspect of the human society throughout the world. Until now, middle of 2021, although with all the efforts on pandemic intervention and vaccination, COVID-19 is still hovering around the world, resulting in more than 177 million confirmed cases and 3.8 million deaths.

In proof-of-work (PoW) consensus protocol-based blockchain mining, the pools can increase their rewards by utilizing block withholding attack. As such, how much computational power should be used to attack other pools becomes an important decision issue faced by the pools. This article mainly studies the block withholding attack issue faced by mining pools. Considering the case that there are two pools, where only one pool can attack the other pool, we propose an optimal block withholding strategies for pools. We also illustrate that attacking is not always the optimal strategies for the pools and present the conditions for attacking. With computational experiment approach, we designed several experiments to validate our proposed strategies, and our results can provide important managerial insights for pools in blockchain mining.

Traditional organization management typically follows a top-down pyramid structure, which is widely believed to have many problems in releasing innovation potentials. In the new era of intelligent technologies, knowledge automation is required to meet the urgent demand for rapid acquisition and application of knowledge. With the rapidly deepened integration of the real world and the virtual society, cyber-physical-social system (CPSS)-oriented parallel management proves to be an effective and efficient way in solving these problems. In this article, we utilize blockchain technology and smart contracts in knowledge automation and investigate blockchain-based knowledge automation, which can be used for CPSS-oriented parallel management. We also propose a management framework based on the smart contract and discuss a case study.

Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) of 2020, and Happy New Year to You! We would like to take this opportunity to express our sincere thanks to our editors, reviewers, authors, and readers for your great support and effort devoted to the TCSS, along with our best wish and hope that everyone has a happy, healthy, and fruitful 2020.

Welcome to the fifth issue of the IEEE Transactions on Computational Social Systems (TCSS) this year. Seventeen regular articles and a brief discussion on social manufacturing (SM) are presented here. In addition, a special issue on “Human-Centric Cyber Social Computing” is included.We would like to take the opportunity to thank the Guest Editors for their time and effort devoted to the special issue.

Welcome to the third issue of the IEEE Transactions on Computational Social Systems (TCSS) in 2019. Thanks to the efforts of the editors, reviewers, authors, and readers of TCSS, the influence of TCSS is rapidly increasing. According to the latest statistics released by Elsevier, the CiteScore of TCSS in 2018 reaches 4.00, and ranks eighth out of the 255 journals (top 3%) in the field of social sciences. This is a solid improvement compared with the corresponding data in 2017 (CiteScore: 2.36, Rank: 17/226, and top 8%). Thanks and congratulations to our authors, reviewers, and members of our editorial board. The current issue includes 20 regular papers and a brief discussion on social energy.

With the development of the blockchain technology, Bitcoin mining has become more and more popular. This paper aims to provide a three-level framework of the economic issues in Bitcoin mining research, from the levels of mining pools, individual miners and blockchain network. We also offer an overview of relevant research efforts in literature. Considering the uncertainty, diversity and complexity of the Bitcoin ecosystems, we propose a novel research framework based on the ACP theory, which can be used to explore the economic issues in the Bitcoin ecosystems. This paper aims to provide a preliminary investigation to the economic issues faced by participants in the Bitcoin ecosystems, and stimulate the attentions and interests of researchers in this field.

With the rapid development of cryptocurrency and its underlying blockchain technologies, platforms such as Ethereum and Hyperledger began to support various types of smart contracts. Smart contracts are computer protocols intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract. Smart contracts have broad range of applications, such as financial services, prediction markets and Internet of Things (IoT), etc. However, there are still many challenges such as security issues and privacy disclosure that await future research. In this paper, we present a comprehensive overview on blockchain powered smart contracts. First, we give a systematic introduction for smart contracts, including the basic framework, operating mechanisms, platforms and programming languages. Second, application scenarios and existing challenges are discussed. Finally, we describe the recent advances of smart contract and present its future development trends, e.g., parallel blockchain. This paper is aimed at providing helpful guidance and reference for future research efforts.

With the increasing difficulty of solo mining in blockchain mining, pool mining has become more and more popular, and most of the miners would like to join a mining pool and contribute their computational power to the pool. When the pool finds a valid block and get the reward from the blockchain network, it will distribute the reward to its miners according to its reward mechanism. In practice, the Pay-Per-Last-N-Shares (PPLNS) mechanism is one of the most commonly used mechanisms by pools, and the pool adopting PPLNS mechanism will distribute the reward to the miners whose reported shares are in the last N shares, according to their proportion of the number of shares in the last N shares. In the PPLNS mechanism, different reporting strategies may bring different rewards for miners. Thus, how to report their found shares to the pool has become an important issue faced by the miners. In this paper, we study the share reporting problem faced by the miners in PPLNS pools, and establish a share reporting optimization model for the miners. We also study the effect of the parameter N in the PPLNS mechanism on the optimal reporting strategies of the miners. With the computational experiments approach, we design experiments to evaluate our proposed share reporting strategies. This work is the first attempt to study the share reporting issue faced by miners in PPLNS pools, and it can provide useful managerial insights for miners when making their share reporting decisions in such pools.

Since the increasing popularization of the emerging blockchain technology, blockchain mining has attracted more and more attention. Due to the difficulty of solo mining, typically miners choose to join a mining pool. As there are many mining pools and different mining pools may adopt different reward mechanisms, how to choose the appropriate mining pool has become one of the most important issues faced by miners, since miners can get different rewards in different pools. In practice, there are three commonly used reward mechanisms for the mining pools to distribute the reward among their miners, namely, the proportional mechanism, the pay-per-share mechanism, and the pay-per-last-N-share mechanism. In this paper, we study the pool selection problem faced by the miners, and model it as a risk decision problem since different reward mechanisms have different risks. We establish a pool selection model based on the maximum-likelihood criterion and also study the effect of N on the miners' optimal pool selection decisions. By utilizing the computational experiments approach, we validate our proposed pool selection strategies. Our results can provide important managerial insights for miners when making their pool selection decisions.

Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) for 2018, and Happy New Year to everyone. According to the Chinese lunar calendar, this is the year of the Dog, which in Chinese culture represents trust, loyalty, dedication, and energy. As such, I would like to take this opportunity to express my best wishes of a happy, healthy, and high-producing 2018 to each and every one of our readers, reviewers, and editors.

Welcome to the last issue of the IEEE Transactions on Computational Social Systems (IEEE TCSS) in 2018. Starting from the first issue next year, our Transactions will be a bimonthly publication, entering a new stage for the IEEE TCSS.

With the rapid development of big data analytics in online marketing, real-time bidding (RTB) has emerged as a promising business model in recent years, and now becomes one of the major online advertising channels. Based on analysis of Web Cookies, RTB platforms are able to precisely identify the features and preferences of target audiences visiting publishers' websites, and forward the generated ad impressions to competing advertisers who submit bids for their best-matched audience in real-time ad auctions. In RTB markets, reserve price serves as an important tuner to exclude advertisers with low estimated values, and hence can guarantee a desirable result for the publisher from ad impression auctions. In this paper, we strive to study publishers' strategy on the reserve price, and probe the impact of reserve price on their revenues. We first analyze the ad impression auction under a direct auction mechanism. We then introduce the reserve price and study its impact on publishers' revenues under an indirect auction mechanism, and our research findings indicate that a rational positive reserve price will always improve publishers' revenues even if it is not optimal. Also, the optimal reserve price is figured out based on the advertisers' bid distributions for publishers' revenue maximization. Finally, experiments using empirical log data from real-world RTB markets are designed to validate our model and analysis, and the results provide strong support to our theoretical analysis. The experimental results also indicate that although the number of bids does not impose any influence on the optimal reserve price, it has significant impacts on publishers' revenues.

Real time bidding (RTB) has become an emerging online advertising with the development of Internet big data in recent years. In the whole RTB ecosystem, the Demand Side Platform (DSP) plays a central role, and it realizes the programmatic and accurate buying of the advertisements for the advertisers via a two-stage auction. In RTB business logics, DSP plays as an intermediary between the advertisers and the center platform. Due to the principle-agent relationship between the advertisers and the DSP, the DSP aims not only to maximize the revenue for the advertisers, but also gain its revenue in this process. So far there are two revenue modes for DSP, namely the two-stage resale model and the commission model, respectively. In this paper, we mainly consider the revenue model for DSP in RTB advertising market. We aim to study the properties of the two revenue models, and compare the revenues for the DSP and the advertisers under these two models. We also provide an example to illustrate our proposed models and their properties. The results show that under small ratio of the commission, the advertisers are more likely to choose the commission model, but the DSP is more likely to choose the two-stage resale model and set a larger weight, while under large ratio of the commission, the advertisers are more likely to choose the two-stage resale model, but the DSP is more likely to choose the commission model. Our research work highlights the importance of the revenue model on the revenues of the advertisers and the DSP, and is intended to provide a useful reference for DSPs in RTB advertising markets.

With the ever-growing popularization of Real Time Bidding (RTB) advertising, the Ad Exchange (AdX) platform has long enjoyed a dominant position in the RTB ecosystem due to its unique role in bridging publishers and advertisers in the supply and demand sides, respectively. A novel technology called header bidding emerged in the recent one or two years, however, is widely believed to have the potential of challenging this dominant position. Compared with RTB markets, header bidding establishes a priority sub-market allowing bidding partners of the publisher submit their bids before the ad impression delivered to the open AdX platform, resulting in a decreased winning probability and revenue for the AdX. As such, there is a critical need for the AdX to tackle this challenge so as to better coexist with header bidding platforms. This need motivates our research. We utilize stochastic programming approach and establish a stochastic optimization model with risk constraints to optimize the pricing strategy for the AdX, considering that the highest bids from the bidding partners can be characterized by random variables. We study the equivalent forms of our proposed model in case when the randomness is characterized by uniform or normal random variables. With the computational experiment approach, we validate our proposed model, and the experimental results indicate that both the risk tolerance of the AdX and the distribution of randomness of the highest bid from the bidding partners can greatly affect the optimal strategy and the corresponding optimal revenue of the AdX. Our work highlights the importance of the risk level of the AdX and the distribution of the randomness generated by the partners to the decision making process of the AdXs in header bidding markets.

With the rapid development of big data analytics in online marketing, real-time bidding (RTB) has emerged as a promising business model in recent years and now becomes one of the major online advertising channels. Based on analysis of Web Cookies, RTB platforms are able to precisely identify the features and preferences of target audiences visiting publishers' websites, and forward the information to competing advertisers submitting bids for their best-matched audience in real-time ad auctions. As the supplier of ad impressions, publishers typically have multiple channels to sell their ad impressions (i.e., ad inventory), making their strategies for allocating ad inventory one of the most critical research problems. In this paper, we strive to study publishers' optimal strategy of allocating ad inventory across online channel of RTB-based auctions and offline channel prevailingly realized in the form of guaranteed contracts. Considering the ad reserve price as the control variable, we establish the optimization model. We also explicitly take the default penalty in offline channels into consideration, so as to balance the short-term online revenue and long-term offline revenue. In our work, we analyze altogether three kinds of strategies for publishers to allocate their ad inventory in pursuit of the optimal strategy, and validate our model and analysis via computational experiments. We find that there is no dominant strategy that can outperform others in all cases, and interestingly, publishers using the hybrid-channel strategy do not always gain more revenues than those using the single-channel strategy.

Real Time Bidding (RTB) is an emerging business model and a popular research topic of online advertising markets. Using cookie-based big-data analysis, RTB advertising platforms have the ability to precisely identify the features and preferences of online users, segment them into various kinds of niche markets, and thus achieve the precision marketing via delivering advertisements to the best-matched users. The segmentation granularity used by such platforms, typically referred to as the Demand Side Platforms (DSPs), plays a central role in the effectiveness and efficiency of the RTB ecosystem. In practice, fine-grained user segmentations may lead to increased value-per-clicks and bid prices from advertisers, but at the same time reduced competition and possibly decreased bid prices in each niche market. This motivates our research on the optimal segmentation granularity to solve this dilemma faced by DSPs. Using a RTB market model with two-stage resales, we analyzed DSPs' segmentation strategies taking the revenues of both advertisers and DSPs into consideration. We also validated our proposed model and analysis using the computational experiment approach, and the experimental results indicate that with the increasing of segmentation granularity, the weighted sum of the DSP and advertisers' revenues tends to first rise and then decline in all weight-value cases, and the optimal granularity is greatly influenced by the value of weights. Our work highlights the need for DSPs of moderately using, instead of overusing, the online big data for maximized revenues.

How to rationally allocate the limited advertising budget is a critical issue in sponsored search auctions. There are plenty of uncertainties in the mapping from the budget into the advertising performance. This paper presented some preliminary efforts to deal with uncertainties in search marketing environments, following principles of a hierarchical budget optimization framework (BOF). We proposed a stochastic, risk-constrained budget strategy, by considering a random factor of clicks per unit cost to capture a kind of uncertainty at the campaign level. Uncertainties of random factors at the campaign level lead to risk at the market/system level. We also proved its theoretical soundness through analyzing some desirable properties. Some computational experiments were made to evaluate our proposed budget strategy with real-word data collected from reports and logs of search advertising campaigns. Experimental results illustrated that our strategy outperforms two baseline strategies. We also noticed that 1) the risk tolerance has great influences on the determination of optimal budget solutions; 2) the higher risk tolerance leads to more expected revenues.

Budget optimization is one of the primary decision-making issues faced by advertisers in search auctions. A quality budget optimization strategy can significantly improve the effectiveness of search advertising campaigns, thus helping advertisers to succeed in the fierce competition of online marketing. This paper investigates budget optimization problems in search advertisements and proposes a novel hierarchical budget optimization framework (BOF), with consideration of the entire life cycle of advertising campaigns. Then, we formulated our BOF framework, made some mathematical analysis on some desirable properties, and presented an effective solution algorithm. Moreover, we established a simple but illustrative instantiation of our BOF framework which can help advertisers to allocate and adjust the budget of search advertising campaigns. Our BOF framework provides an open testbed environment for various strategies of budget allocation and adjustment across search advertising markets. With field reports and logs from real-world search advertising campaigns, we designed some experiments to evaluate the effectiveness of our BOF framework and instantiated strategies. Experimental results are quite promising, where our BOF framework and instantiated strategies perform better than two baseline budget strategies commonly used in practical advertising campaigns.