Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokemon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have bought us various degrees of digital transformation. The term `metaverse' has been coined to further facilitate the digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalysed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. This survey paper presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems, and illustrates the possibility of the digital `big bang'. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously - Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors -- Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for the development of the metaverse.

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Uncertainty. Incomplete markets (M. Magill, W. Shafer). The theory of value in security markets (D. Duffie). Sunspot equilibria in sequential markets models (P.A. Chiappori, R. Guesnerie). Utility theory with uncertainty (E. Karni, D. Schmeidler). Infinite Economies. Equilibrium theory in infinite dimensional spaces (A. Mas-Colell, W.R. Zame). Overlapping generations (J. Geanakoplos, H. Polemarchakis). Non-Convexity and Imperfect Competition. Equilibrium analysis with non-convex technologies (D. Brown). Monopolistic competition (J.-P. Benassy). Computation and Mathematical Methods. Computation and multiplicity of equilibria (T. Kehoe). Nonstandard analysis with applications to economics (B. Anderson). Nonlinear dynamical systems: Instability and chaos in economics (W.A. Brock, W.D. Dechert).

Handbook of Mathematical Economics

Introduction In the following paper I will introduce two main theories about the market procedure. The first one will be the „Competition as a discovery procedure” by Friedrich August von Hayek, economist and social scientist of the Austrian School, noted for his defense of liberal democracy and free-market capitalism against a rising tide of socialist and collectivist thought in the mid-20th century. The second will be “The problem of social cost” by Ronald Coase which suggests that well defined property rights could overcome the problems of externalities on the market. At the end I will summarize the main contradictory and common points in their works.

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Legitimization of the free market from two aspects: Hayek: Competition as a discovery procedure vs. Coase: The Problem of Social Cost

‘The Age of Surveillance Capitalism — The Fight for a Human Future at the New Frontier of Power’

We consider a demand management problem of an energy community, in which several users obtain energy from an external organization such as an energy company, and pay for the energy according to pre-specified prices that consist of a time-dependent price per unit of energy, as well as a separate price for peak demand. Since users' utilities are their private information, which they may not be willing to share, a mediator, known as the planner, is introduced to help optimize the overall satisfaction of the community (total utility minus total payments) by mechanism design. A mechanism consists of a message space, a tax/subsidy and an allocation function for each user. Each user reports a message chosen from her own message space, and then receives some amount of energy determined by the allocation function and pays the tax specified by the tax function. A desirable mechanism induces a game, the Nash equilibria (NE) of which result in an allocation that coincides with the optimal allocation for the community. 
As a starting point, we design a mechanism for the energy community with desirable properties such as full implementation, strong budget balance and individual rationality for both users and the planner. We then modify this baseline mechanism for communities where message exchanges are allowed only within neighborhoods, and consequently, the tax/subsidy and allocation functions of each user are only determined by the messages from her neighbors. All the desirable properties of the baseline mechanism are preserved in the distributed mechanism. Finally, we present a learning algorithm for the baseline mechanism, based on projected gradient descent, that is guaranteed to converge to the NE of the induced game.

Mechanism Design for Demand Management in Energy Communities

In the modern era of the mobile apps (the era of surveillance capitalism - as termed by Shoshana Zuboff) huge quantities of surveillance data about consumers and their activities offer a wave of opportunities for economic and societal value creation. ln-app advertising - a multi-billion dollar industry, is an essential part of the current digital ecosystem driven by free mobile applications, where the ecosystem entities usually comprise consumer apps, their clients (consumers), ad-networks, and advertisers. Sensitive consumer information is often being sold downstream in this ecosystem without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are `compromising' beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.

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Preference-Based Privacy Markets

The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are `compromising' beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.

Preference-Based Privacy Trading.

Notice of Violation of IEEE Publication Principles: Data Trading with Competitive Social Platforms: Outcomes are Mostly Privacy Welfare Damaging

This letter argues that data of strategic individuals with heterogeneous privacy valuations in a distributed online social network (e.g., Facebook) will be under-priced, if traded in a monopoly buyer setting, and will lead to diminishing utilitarian welfare. This result, for a certain family of online community data trading problems, is in stark contrast to a popular information economics intuition that increased amounts of end-user data signals in a data market improves its efficiency. Our proposed theory paves the way for a future (counter-intuitive) analysis of data trading oligopoly markets for online social networks (OSNs).

Data Trading with a Monopoly Social Network Outcomes Are Mostly Privacy Welfare Damaging

In the current era of social media culture, almost everyone's social life is inseparable from media. Especially since the outbreak of COVID-19, the way most people communicate has shifted to online, and the release of information on social media has seen an explosion. However, the information spread on social media platforms is not all positive, and there is many fake news mixed in and continuously spread. Different types of publishers have different effects on their audiences, and their audiences react differently. This paper uses thematic analysis and selects the content of a fake news tweet published by a journalist, and studies the content of its popular comments to summarize the audience's response. The results turned out that the responses of its audience fall into three broad themes: unequivocal belief, decisive denial,

https://www.atlantis-press.com/article/125967333.pdf

Yixuan Wang

Papers

Preference-Based Privacy Markets

Preference-Based Privacy Trading.

Notice of Violation of IEEE Publication Principles: Data Trading with Competitive Social Platforms: Outcomes are Mostly Privacy Welfare Damaging

Data Trading with a Monopoly Social Network Outcomes Are Mostly Privacy Welfare Damaging

The Study of Audience’s Response to the Fake News About Covid-19 That Posted by KOL on Twitter