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The mechanism design theory can be applied not only in the economy but also in many fields, such as politics and military affairs, which has important practical and strategic significance for countries in the period of system innovation and transformation. As Nobel Laureate Paul said, the complexity of the real economy makes it difficult for "Unorganized Markets" to ensure supply-demand balance and the efficient allocation of resources. When traditional economic theory cannot explain and calculate the complex scenes of reality, we require a high-performance computing solution based on traditional theory to evaluate the mechanisms, meanwhile, get better social welfare. The mechanism design theory is undoubtedly the best option. Different from other existing works, which are based on the theoretical exploration of optimal solutions or single perspective analysis of scenarios, this paper focuses on the more real and complex markets. It explores to discover the common difficulties and feasible solutions for the applications. Firstly, we review the history of traditional mechanism design and algorithm mechanism design. Subsequently, we present the main challenges in designing the actual data-driven market mechanisms, including the inherent challenges in the mechanism design theory, the challenges brought by new markets and the common challenges faced by both. In addition, we also comb and discuss theoretical support and computer-aided methods in detail. This paper guides cross-disciplinary researchers who wish to explore the resource allocation problem in real markets for the first time and offers a different perspective for researchers struggling to solve complex social problems. Finally, we discuss and propose new ideas and look to the future. [ABSTRACT FROM AUTHOR]

This paper studies the problem of dividing m indivisible items among n agents fairly and efficiently. Specifically, this research concentrates on pairwise maximin share (PMMS), which is defined to be the maximum value that an agent can guarantee for herself if she were to repartition the items with another agent and receive the bundle with the minimum value. PMMS is an important concept in the fair division. However, whether PMMS for indivisible items exists is still open. This work concentrates on PMMS by proving the existence of PMMS on linear graphs with binary valuation functions. Besides, this paper designs an algorithm to approximate PMMS in the case where different agents have identical valuations among the same items, achieving a ratio strictly greater than 0.8, which outperforms the state-of-the-art ratio of 0.781 from Kurokawa [22]. The time complexity of our FFD-based algorithm is O(mn + mlog m). [ABSTRACT FROM AUTHOR]

This work contributes an (approximately) incentive-compatible and computationally efficient bargaining mechanism for pricing fog computing resources. In network settings (e.g., fog computing), it is plausible to think that self-interested and incompletely informed players (represented by software agents) will attempt to maximize their own benefits at the expense of others. Hence, it is crucial that fog bargaining mechanisms give incentives to agents for behaving in a manner consistent with the desired outcome where every agent's benefit is maximized. Equilibrium analyses validate that the fog bargaining mechanism in this work is approximately Bayesian incentive compatible because every agent can approximately maximize its expected utility by adhering to the strategy recommended by the bargaining mechanism given that all other agents also adhere to their equilibrium strategies. That is, if every agent in the market adheres to the strategy recommended by the bargaining mechanism, then the strategy profile of the agents forms an approximate Bayesian Nash equilibrium. Given that a fog resource market has a large number of buyers and a large number of sellers, computational efficiency is also imperative since every agent needs to process a huge number of trading alternatives. Computational complexity analyses validate that 1) the procedure for carrying out the bargaining strategy has a linear time complexity, and with every passing round, the search space dwindles but the solutions become progressively better, 2) the number of rounds for each agent to complete bargaining is logarithmic in the number of its opponents, and 3) each agent has a linear message complexity. [ABSTRACT FROM AUTHOR]