Dealers' position in the trading network is shown to have a significant impact on asset prices. 1 However, it remains challenging to account for the structure of trading networks during the estimation of dealer and asset features' impact on asset prices. Structural approaches usually rely on specific network structures to reduce complexity in estimation (e.g. Pint er and Usl u, 2022; Eisfeldt et al., 2023; Cohen et al., 2024), which limits the accuracy and generalizability of the estimation method. Reduced-form approach uses centrality measures to capture dealers' position in the network(e.g. Di Maggio et al., 2017; Hollifield et al., 2017; Li and Sch urhoff, 2019), but recent papers point out linear regressions with centrality measures can lead to biased estimation when the network is sparse (Cai, 2022).

A popular approach of automated mechanism design is to formulate a linear program (LP) whose solution gives a mechanism with desired properties. We analytically derive a class of optimal solutions for such an LP that gives mechanisms achieving standard properties of efficiency, incentive compatibility, strong budget balance (SBB), and individual rationality (IR), where SBB and IR are satisfied in expectation. Notably, our solutions are represented by an exponentially smaller number of essential variables than the original variables of LP. Our solutions, however, involve a term whose exact evaluation requires solving a certain optimization problem exponentially many times as the number of players, $N$, grows. We thus evaluate this term by modeling it as the problem of estimating the mean reward of the best arm in multi-armed bandit (MAB), propose a Probably and Approximately Correct estimator, and prove its asymptotic optimality by establishing a lower bound on its sample complexity. This MAB approach reduces the number of times the optimization problem is solved from exponential to $O(N\,\log N)$. Numerical experiments show that the proposed approach finds mechanisms that are guaranteed to achieve desired properties with high probability for environments with up to 128 players, which substantially improves upon the prior work.

We have developed a method for detecting real money traders (RMTers) to support the operators of massively multiplayer online role-playing games (MMORPGs). RMTers, who earn currency in the real world by selling properties in the virtual world, tend to form alliances and frequently exchange a huge volume of virtual currency within such a community. The proposed method exploits (1) the trading network, to identify the communities of characters, and (2) the volume of trades, to estimate the likelihood of communities and characters becoming engaged in real money trading. The results of an experiment using actual log data from a commercial MMORPG showed that using the trading network is more effective in detecting RMTers than conventional machine learning methods that assess individual character without referring to the trading network.

We have developed a method for detecting real money traders (RMTers) to support the operators of massively multiplayer online role-playing games (MMORPGs). RMTers, who earn currency in the real world by selling properties in the virtual world, tend to form alliances and frequently exchange a huge volume of virtual currency within such a community. The proposed method exploits (1) the trading network, to identify the communities of characters, and (2) the volume of trades, to estimate the likelihood of communities and characters becoming engaged in real money trading. The results of an experiment using actual log data from a commercial MMORPG showed that using the trading network is more effective in detecting RMTers than conventional machine learning methods that assess individual character without referring to the trading network.