@article{wang_wan_chen_2023, title={Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection}, ISSN={["0030-364X"]}, DOI={10.1287/opre.2023.2447}, abstractNote={The curse of dimensionality has long been one of the biggest challenges in solving large-scale simulation ranking and selection (R&S) problems. As the number of systems grows, existing approaches to R&S relying on the Bonferroni correction become increasingly conservative, rendering them overachieving in error control and consuming more computational resources than necessary. In “Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection,” Wang, Wan, and Chen develop Bonferroni-free and indifference-zone-optional ranking and selection procedures to deliver the prescribed probabilistic guarantee without overshooting. Their approach is to conduct always valid and fully sequential hypothesis tests that enable continuous monitoring of each candidate system and control the probability of correct selection. In addition, the indifference-zone parameter becomes dispensable in their procedures; however, when provided appropriately, it could improve the procedures’ computational and statistical efficiency.}, journal={OPERATIONS RESEARCH}, author={Wang, Wenyu and Wan, Hong and Chen, Xi}, year={2023}, month={Apr} }
 @article{wan_li_huang_2022, title={BLOCKCHAIN: A REVIEW FROM THE PERSPECTIVE OF OPERATIONS RESEARCHERS}, ISSN={["0891-7736"]}, DOI={10.1109/WSC57314.2022.10015500}, abstractNote={Blockchain is a distributed append-only digital ledger. The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in various businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.}, journal={2022 WINTER SIMULATION CONFERENCE (WSC)}, author={Wan, Hong and Li, Kejun and Huang, Yining}, year={2022}, pages={283–297} }
 @article{li_liu_wan_huang_2021, title={A discrete-event simulation model for the Bitcoin blockchain network with strategic miners and mining pool managers}, volume={134}, ISSN={["1873-765X"]}, DOI={10.1016/j.cor.2021.105365}, abstractNote={As the first and most famous cryptocurrency-based blockchain technology, Bitcoin has attracted tremendous attention from both academic and industrial communities in the past decade. A Bitcoin network is comprised of two interactive parties: individual miners and mining pool managers, each of which strives to maximize its own utility. In particular, individual miners choose which mining pool to join and decide on how much mining power to commit under limited constraints on the mining budget and mining power capacity; managers of mining pools determine how to allocate the mining reward and how to adjust the membership fee. In this work we investigate the miners’ and mining pool managers’ decisions in repeated Bitcoin mining competitions by building a Monte-Carlo discrete-event simulation model. Our simulation model (i) captures the behavior of these two parties and how their decisions affect each other, and (ii) characterizes the system-level dynamics of the blockchain in terms of the mining difficulty level and total mining power. In addition, we study the sensitivity of system performance metrics with respect to various control parameters. Our analysis may provide useful guidelines to mining activity participants in the Bitcoin network.}, journal={COMPUTERS & OPERATIONS RESEARCH}, author={Li, Kejun and Liu, Yunan and Wan, Hong and Huang, Yining}, year={2021}, month={Oct} }
 @article{chen_wan_cai_cheng_2021, title={Machine learning in/for blockchain: Future and challenges}, ISSN={["1708-945X"]}, DOI={10.1002/cjs.11623}, abstractNote={Machine learning and blockchain are two of the most notable technologies of recent years. The first is the foundation of artificial intelligence and big data analysis, and the second has significantly disrupted the financial industry. Both technologies are data‐driven, and thus there are rapidly growing interests in integrating both for more secure and efficient data sharing and analysis. In this article, we review existing research on combining machine learning and blockchain technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more research on deeper integration of these two promising technologies.}, journal={CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE}, author={Chen, Fang and Wan, Hong and Cai, Hua and Cheng, Guang}, year={2021}, month={Jun} }
 @article{garee_wan_ventresca_2021, title={Social Influence Network Simulation Design Affects Behavior of Aggregated Entropy}, ISSN={["2329-924X"]}, DOI={10.1109/TCSS.2021.3092694}, abstractNote={As agents interact and influence one another in a social network, the opinions they hold about some common topic can change over time. These changes may enable us to infer mechanisms of the network that control how interactions lead to opinion change. Inferring such mechanisms from opinion data could enable analysis of social influence in data-sparse scenarios. However, limited work has focused on this problem, despite its clear value. To address this gap, we create opinion data using agent-based simulation and experimental design. By viewing opinion changes as an information-generating process, opinion dynamics can be studied using entropy. This work explores the relationships between aggregated entropy and five simulation design factors. Three entropy measures are calculated on continuous-valued opinions and are analyzed using a main effects model and cluster analysis. Overall, the choices of influence model and error distribution are most important to the entropy measures, activation regime is important to some measures, and population size is unimportant. Also, design variation can be detected using time-series cluster analysis. These findings may support work in inferring properties about real-world social influence networks using opinion data collected from their members.}, journal={IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS}, author={Garee, Michael J. and Wan, Hong and Ventresca, Mario}, year={2021}, month={Jul} }
 @article{wan_li_huang_2020, title={BLOCKCHAIN: A REVIEW FROM THE PERSPECTIVE OF OPERATIONS RESEARCHERS}, ISSN={["0891-7736"]}, DOI={10.1109/WSC48552.2020.9383924}, abstractNote={Blockchain is a distributed, append-only digital ledger (database). The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in a wide variety of businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier operations-and-data-related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.}, journal={2020 WINTER SIMULATION CONFERENCE (WSC)}, author={Wan, Hong and Li, Kejun and Huang, Yining}, year={2020}, pages={75–89} }
 @article{li_liu_wan_zhang_2020, title={CAPTURING MINER AND MINING POOL DECISIONS IN A BITCOIN BLOCKCHAIN NETWORK: A TWO-LAYER SIMULATION MODEL}, ISSN={["0891-7736"]}, DOI={10.1109/WSC48552.2020.9383980}, abstractNote={Motivated by the growing interests in Bitcoin blockchain technology, we build a Monte-Carlo simulation model to study the miners’ and mining pool managers’ decisions in the Bitcoin blockchain network. Our simulation model aims to capture the dynamics of participants of these two different parties and how their decisions collectively affect the system dynamics. Given the limited amount of monetary budget and mining power capacity, individual miners decide on which mining pools to join and determine how much hashing power to invest. Mining pool managers need to determine how to appropriately allocate the mining reward and how to adjust the membership fee. In addition to the aforementioned miner and pool behavior, we also characterize the system-level dynamics of the blockchain in terms of mining difficulty level and total hashing power.}, journal={2020 WINTER SIMULATION CONFERENCE (WSC)}, author={Li, Kejun and Liu, Yunan and Wan, Hong and Zhang, Ling}, year={2020}, pages={3152–3163} }
 @article{sanchez_sanchez_wan_2020, title={WORK SMARTER, NOT HARDER: A TUTORIAL ON DESIGNING AND CONDUCTING SIMULATION EXPERIMENTS}, ISSN={["0891-7736"]}, DOI={10.1109/WSC48552.2020.9384057}, abstractNote={Simulation models are integral to modern scientific research, national defense, industry and manufacturing, and in public policy debates. These models tend to be extremely complex, often with thousands of factors and many sources of uncertainty. To understand the impact of these factors and their interactions on model outcomes requires efficient, high-dimensional design of experiments. Unfortunately, all too often, many large-scale simulation models continue to be explored in ad hoc ways. This suggests that more simulation researchers and practitioners need to be aware of the power of designed experiments in order to get the most from their simulation studies. In this tutorial, we demonstrate the basic concepts important for designing and conducting simulation experiments, and provide references to other resources for those wishing to learn more. This tutorial (an update of previous WSC tutorials) will prepare you to make your next simulation study a simulation experiment.}, journal={2020 WINTER SIMULATION CONFERENCE (WSC)}, author={Sanchez, Susan M. and Sanchez, Paul J. and Wan, Hong}, year={2020}, pages={1128–1142} }
 @article{ghosh_aggarwal_wan_2019, title={Strategic Prosumers: How to Set the Prices in a Tiered Market?}, volume={15}, ISSN={["1941-0050"]}, DOI={10.1109/TII.2018.2889301}, abstractNote={We consider users who may have renewable energy harvesting devices or distributed generators. Such users can behave as consumers or producers (hence, we denote them as prosumers) at different time instances. We consider a tiered market where the grid selects a price function, which reveals price in the real time based on the total demand to the grid. In the real time, a prosumer can buy from another prosumer in an exchange market knowing the price from the grid. The exchange price is set by a platform and can be different for different sellers. A prosumer is a selfish entity, which selects the amount of energy it wants to buy either from the grid or from other prosumers or the amount of excess energy it wants to sell to other prosumers by maximizing its own payoff. However, the strategy and the payoff of a prosumer inherently depend on the strategy of other prosumers as a prosumer can only buy if the other prosumers are willing to sell. We formulate the problem as a coupled constrained game and seek to obtain the generalized Nash equilibrium. We show that the game is a concave potential game and show that there exists a unique generalized Nash equilibrium. We propose a distributed algorithm that converges to the exchange price, which clears the market and achieves the generalized Nash equilibrium. We, finally, show how the grid should select the price function in a day-ahead scenario by computing the estimated demand from the history. Our numerical result shows that the tiered market can reduce the peak load and increase the prosumers’ total payoffs.}, number={8}, journal={IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, author={Ghosh, Arnob and Aggarwal, Vaneet and Wan, Hong}, year={2019}, month={Aug}, pages={4469–4480} }