Dynamic capacity provision for wireless sensors connectivity: A profit optimization approach

[EN] We model a wireless sensors' connectivity scenario mathematically and analyze it using capacity provision mechanisms, with the objective of maximizing the profits of a network operator. The scenario has several sensors' clusters with each one having one sink node, which uploads the sensing data gathered in the cluster through the wireless connectivity of a network operator. The scenario is analyzed both as a static game and as a dynamic game, each one with two stages, using game theory. The sinks' behavior is characterized with a utility function related to the mean service time and the price paid to the operator for the service. The objective of the operator is to maximize its profits by optimizing the network capacity. In the static game, the sinks' subscription decision is modeled using a population game. In the dynamic game, the sinks' behavior is modeled using an evolutionary game and the replicator dynamic, while the operator optimal capacity is obtained solving an optimal control problem. The scenario is shown feasible from an economic point of view. In addition, the dynamic capacity provision optimization is shown as a valid mechanism for maximizing the operator profits, as well as a useful tool to analyze evolving scenarios. Finally, the dynamic analysis opens the possibility to study more complex scenarios using the differential game extension.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness through project TIN2013-47272-C2-1-R; AEI/FEDER, UE through project TEC2017-85830-C2-1-P; and co-supported by the European Social Fund BES-2014-068998.Sanchis-Cano, Á.; Guijarro, L.; Condoluci, M. (2018). Dynamic capacity provision for wireless sensors connectivity: A profit optimization approach. International Journal of Distributed Sensor Networks (Online). 14(4):1-14. https://doi.org/10.1177/1550147718772544S114144Weiser, M. (1991). The Computer for the 21st Century. Scientific American, 265(3), 94-104. doi:10.1038/scientificamerican0991-94Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. doi:10.1016/j.future.2013.01.010Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2013). Sensing as a service model for smart cities supported by Internet of Things. Transactions on Emerging Telecommunications Technologies, 25(1), 81-93. doi:10.1002/ett.2704Wang, N., Hossain, E., & Bhargava, V. K. (2016). Joint Downlink Cell Association and Bandwidth Allocation for Wireless Backhauling in Two-Tier HetNets With Large-Scale Antenna Arrays. IEEE Transactions on Wireless Communications, 15(5), 3251-3268. doi:10.1109/twc.2016.2519401Chowdhury, M. Z., Jang, Y. M., & Haas, Z. J. (2013). Call admission control based on adaptive bandwidth allocation for wireless networks. Journal of Communications and Networks, 15(1), 15-24. doi:10.1109/jcn.2013.000005Nan, G., Mao, Z., Yu, M., Li, M., Wang, H., & Zhang, Y. (2014). Stackelberg Game for Bandwidth Allocation in Cloud-Based Wireless Live-Streaming Social Networks. IEEE Systems Journal, 8(1), 256-267. doi:10.1109/jsyst.2013.2253420Zhu, K., Niyato, D., Wang, P., & Han, Z. (2012). Dynamic Spectrum Leasing and Service Selection in Spectrum Secondary Market of Cognitive Radio Networks. IEEE Transactions on Wireless Communications, 11(3), 1136-1145. doi:10.1109/twc.2012.010312.110732Vamvakas, P., Tsiropoulou, E. E., & Papavassiliou, S. (2017). Dynamic Provider Selection & Power Resource Management in Competitive Wireless Communication Markets. Mobile Networks and Applications, 23(1), 86-99. doi:10.1007/s11036-017-0885-yNiyato, D., Hoang, D. T., Luong, N. C., Wang, P., Kim, D. I., & Han, Z. (2016). Smart data pricing models for the internet of things: a bundling strategy approach. IEEE Network, 30(2), 18-25. doi:10.1109/mnet.2016.7437020Guijarro, L., Pla, V., Vidal, J. R., & Naldi, M. (2016). Maximum-Profit Two-Sided Pricing in Service Platforms Based on Wireless Sensor Networks. IEEE Wireless Communications Letters, 5(1), 8-11. doi:10.1109/lwc.2015.2487259Romero, J., Guijarro, L., Pla, V., & Vidal, J. R. (2017). Price competition between a macrocell and a small-cell service provider with limited resources and optimal bandwidth user subscription: a game-theoretical model. Telecommunication Systems, 67(2), 195-209. doi:10.1007/s11235-017-0331-2Al Daoud, A., Alanyali, M., & Starobinski, D. (2010). Pricing Strategies for Spectrum Lease in Secondary Markets. IEEE/ACM Transactions on Networking, 18(2), 462-475. doi:10.1109/tnet.2009.2031176Do, C. T., Tran, N. H., Huh, E.-N., Hong, C. S., Niyato, D., & Han, Z. (2016). Dynamics of service selection and provider pricing game in heterogeneous cloud market. Journal of Network and Computer Applications, 69, 152-165. doi:10.1016/j.jnca.2016.04.012Tsiropoulou, E. E., Vamvakas, P., & Papavassiliou, S. (2017). Joint Customized Price and Power Control for Energy-Efficient Multi-Service Wireless Networks via S-Modular Theory. IEEE Transactions on Green Communications and Networking, 1(1), 17-28. doi:10.1109/tgcn.2017.2678207Sanchis-Cano, A., Romero, J., Sacoto-Cabrera, E., & Guijarro, L. (2017). Economic Feasibility of Wireless Sensor Network-Based Service Provision in a Duopoly Setting with a Monopolist Operator. Sensors, 17(12), 2727. doi:10.3390/s17122727Weber, T. A. (2011). Optimal Control Theory with Applications in Economics. doi:10.7551/mitpress/9780262015738.001.0001Mandjes, M. (2003). Pricing strategies under heterogeneous service requirements. Computer Networks, 42(2), 231-249. doi:10.1016/s1389-1286(03)00191-9Shariatmadari, H., Ratasuk, R., Iraji, S., Laya, A., Taleb, T., Jäntti, R., & Ghosh, A. (2015). Machine-type communications: current status and future perspectives toward 5G systems. IEEE Communications Magazine, 53(9), 10-17. doi:10.1109/mcom.2015.7263367Ng, C.-H., & Soong, B.-H. (2008). Queueing Modelling Fundamentals. doi:10.1002/9780470994672Mendelson, H. (1985). Pricing computer services: queueing effects. Communications of the ACM, 28(3), 312-321. doi:10.1145/3166.3171Altman, E., Boulogne, T., El-Azouzi, R., Jiménez, T., & Wynter, L. (2006). A survey on networking games in telecommunications. Computers & Operations Research, 33(2), 286-311. doi:10.1016/j.cor.2004.06.005Belleflamme, P., & Peitz, M. (2015). Industrial Organization. doi:10.1017/cbo9781107707139Reynolds, S. S. (1987). Capacity Investment, Preemption and Commitment in an Infinite Horizon Model. International Economic Review, 28(1), 69. doi:10.2307/2526860Barron, E. N. (2013). Game Theory. doi:10.1002/9781118547168Sandholm, W. (2009). Pairwise Comparison Dynamics and Evolutionary Foundations for Nash Equilibrium. Games, 1(1), 3-17. doi:10.3390/g1010003Schlag, K. H. (1998). Why Imitate, and If So, How? Journal of Economic Theory, 78(1), 130-156. doi:10.1006/jeth.1997.234

Agent-based models of competing population.

Yip Kin Fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 101-104).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- The Distribution of Fluctuations in Financial Data --- p.5Chapter 2.1 --- Empirical Statistics --- p.5Chapter 2.2 --- Data analyzed --- p.8Chapter 2.3 --- Levy Distribution --- p.10Chapter 2.4 --- Returns Distribution and Scaling Properties --- p.12Chapter 2.5 --- Volatility Clustering --- p.19Chapter 2.6 --- Conclusion --- p.21Chapter 3 --- Models of Herd behaviour in Financial Markets --- p.22Chapter 3.1 --- Cont and Bouchaud's model --- p.22Chapter 3.2 --- The Model of Egiuluz and Zimmerman --- p.24Chapter 3.3 --- EZ Model with Size-Dependent Dissociation Rates --- p.28Chapter 3.4 --- Democratic and Dictatorship Self-Organized Model --- p.31Chapter 3.5 --- Effect of Size-Dependent Fragmentation and Coagulation Prob- abilities --- p.33Chapter 3.6 --- Extensions of EZ model --- p.35Chapter 3.7 --- Conclusion --- p.39Chapter 4 --- Review on the Minority Game(MG) --- p.42Chapter 4.1 --- The Model and Results --- p.42Chapter 4.2 --- Crowd-anticrowd Theory and Phase Transition --- p.46Chapter 4.3 --- Market Efficiency --- p.48Chapter 5 --- MG with biased strategy pool --- p.52Chapter 5.1 --- The Model --- p.53Chapter 5.2 --- Numerical Results and Discussion --- p.53Chapter 5.3 --- Theory: MG with Biased Strategy Pool --- p.61Chapter 5.4 --- Conclusion --- p.69Chapter 6 --- MG with Randomly Participating Agents --- p.71Chapter 6.1 --- The Model with One RPA --- p.71Chapter 6.2 --- Results for q = 0.5 --- p.72Chapter 6.3 --- Inefficiency and Success Rate --- p.76Chapter 6.4 --- Results for q ≠ 0.5 --- p.82Chapter 6.5 --- Many RPAs --- p.85Chapter 6.6 --- Conclusion --- p.86Chapter 7 --- A Model on Coupled Minority Games --- p.88Chapter 7.1 --- The Model --- p.89Chapter 7.2 --- Results and Discussion。 --- p.90Chapter 7.3 --- Conclusion --- p.95Chapter 8 --- Conclusion --- p.97Bibliography --- p.101Chapter A --- Solving Cluster Size distribution in EZ model --- p.10

An Abstract Framework for Non-Cooperative Multi-Agent Planning

[EN] In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents¿ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.This work was partially funded by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by Universitat Politecnica de Valencia (UPV) PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana Fondo Social Europeo.Jordán, J.; Bajo, J.; Botti, V.; Julian Inglada, VJ. (2019). An Abstract Framework for Non-Cooperative Multi-Agent Planning. Applied Sciences. 9(23):1-18. https://doi.org/10.3390/app9235180S118923De Weerdt, M., & Clement, B. (2009). Introduction to planning in multiagent systems. Multiagent and Grid Systems, 5(4), 345-355. doi:10.3233/mgs-2009-0133Dunne, P. E., Kraus, S., Manisterski, E., & Wooldridge, M. (2010). Solving coalitional resource games. Artificial Intelligence, 174(1), 20-50. doi:10.1016/j.artint.2009.09.005Torreño, A., Onaindia, E., Komenda, A., & Štolba, M. (2018). Cooperative Multi-Agent Planning. ACM Computing Surveys, 50(6), 1-32. doi:10.1145/3128584Fikes, R. E., & Nilsson, N. J. (1971). Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3-4), 189-208. doi:10.1016/0004-3702(71)90010-5Hoffmann, J., & Nebel, B. (2001). The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 14, 253-302. doi:10.1613/jair.855Dukeman, A., & Adams, J. A. (2017). Hybrid mission planning with coalition formation. Autonomous Agents and Multi-Agent Systems, 31(6), 1424-1466. doi:10.1007/s10458-017-9367-7Hadad, M., Kraus, S., Ben-Arroyo Hartman, I., & Rosenfeld, A. (2013). Group planning with time constraints. Annals of Mathematics and Artificial Intelligence, 69(3), 243-291. doi:10.1007/s10472-013-9363-9Guo, Y., Pan, Q., Sun, Q., Zhao, C., Wang, D., & Feng, M. (2019). Cooperative Game-based Multi-Agent Path Planning with Obstacle Avoidance*. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). doi:10.1109/isie.2019.8781205v. Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100(1), 295-320. doi:10.1007/bf01448847Mookherjee, D., & Sopher, B. (1994). Learning Behavior in an Experimental Matching Pennies Game. Games and Economic Behavior, 7(1), 62-91. doi:10.1006/game.1994.1037Ochs, J. (1995). Games with Unique, Mixed Strategy Equilibria: An Experimental Study. Games and Economic Behavior, 10(1), 202-217. doi:10.1006/game.1995.1030Applegate, C., Elsaesser, C., & Sanborn, J. (1990). An architecture for adversarial planning. IEEE Transactions on Systems, Man, and Cybernetics, 20(1), 186-194. doi:10.1109/21.47820Sailer, F., Buro, M., & Lanctot, M. (2007). Adversarial Planning Through Strategy Simulation. 2007 IEEE Symposium on Computational Intelligence and Games. doi:10.1109/cig.2007.368082Willmott, S., Richardson, J., Bundy, A., & Levine, J. (2001). Applying adversarial planning techniques to Go. Theoretical Computer Science, 252(1-2), 45-82. doi:10.1016/s0304-3975(00)00076-1Nau, D. S., Au, T. C., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D., & Yaman, F. (2003). SHOP2: An HTN Planning System. Journal of Artificial Intelligence Research, 20, 379-404. doi:10.1613/jair.1141Knuth, D. E., & Moore, R. W. (1975). An analysis of alpha-beta pruning. Artificial Intelligence, 6(4), 293-326. doi:10.1016/0004-3702(75)90019-3Vickrey, W. (1961). COUNTERSPECULATION, AUCTIONS, AND COMPETITIVE SEALED TENDERS. The Journal of Finance, 16(1), 8-37. doi:10.1111/j.1540-6261.1961.tb02789.xClarke, E. H. (1971). Multipart pricing of public goods. Public Choice, 11(1), 17-33. doi:10.1007/bf01726210Groves, T. (1973). Incentives in Teams. Econometrica, 41(4), 617. doi:10.2307/1914085Savaux, J., Vion, J., Piechowiak, S., Mandiau, R., Matsui, T., Hirayama, K., … Silaghi, M. (2016). DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). doi:10.1109/wi.2016.0057Buzing, P., Mors, A. ter, Valk, J., & Witteveen, C. (2006). Coordinating Self-interested Planning Agents. Autonomous Agents and Multi-Agent Systems, 12(2), 199-218. doi:10.1007/s10458-005-6104-4Ter Mors, A., & Witteveen, C. (s. f.). Coordinating Non Cooperative Planning Agents: Complexity Results. IEEE/WIC/ACM International Conference on Intelligent Agent Technology. doi:10.1109/iat.2005.60Hrnčíř, J., Rovatsos, M., & Jakob, M. (2015). Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach. Journal of Intelligent Transportation Systems, 19(1), 89-105. doi:10.1080/15472450.2014.941759Galuszka, A., & Swierniak, A. (2009). Planning in Multi-agent Environment Using Strips Representation and Non-cooperative Equilibrium Strategy. Journal of Intelligent and Robotic Systems, 58(3-4), 239-251. doi:10.1007/s10846-009-9364-4Rosenthal, R. W. (1973). A class of games possessing pure-strategy Nash equilibria. International Journal of Game Theory, 2(1), 65-67. doi:10.1007/bf01737559Jordán, J., Torreño, A., de Weerdt, M., & Onaindia, E. (2017). A better-response strategy for self-interested planning agents. Applied Intelligence, 48(4), 1020-1040. doi:10.1007/s10489-017-1046-5Veloso, M., Muñoz-Avila, H., & Bergmann, R. (1996). Case-based planning: selected methods and systems. AI Communications, 9(3), 128-137. doi:10.3233/aic-1996-9305VOORNEVELD, M., BORM, P., VAN MEGEN, F., TIJS, S., & FACCHINI, G. (1999). CONGESTION GAMES AND POTENTIALS RECONSIDERED. International Game Theory Review, 01(03n04), 283-299. doi:10.1142/s0219198999000219Han-Lim Choi, Brunet, L., & How, J. P. (2009). Consensus-Based Decentralized Auctions for Robust Task Allocation. IEEE Transactions on Robotics, 25(4), 912-926. doi:10.1109/tro.2009.2022423Monderer, D., & Shapley, L. S. (1996). Potential Games. Games and Economic Behavior, 14(1), 124-143. doi:10.1006/game.1996.0044Friedman, J. W., & Mezzetti, C. (2001). Learning in Games by Random Sampling. Journal of Economic Theory, 98(1), 55-84. doi:10.1006/jeth.2000.2694Aamodt, A., & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1), 39-59. doi:10.3233/aic-1994-7104Bertsekas, D. P. (1988). The auction algorithm: A distributed relaxation method for the assignment problem. Annals of Operations Research, 14(1), 105-123. doi:10.1007/bf02186476Bertsekas, D. P., & Castanon, D. A. (1989). The auction algorithm for the transportation problem. Annals of Operations Research, 20(1), 67-96. doi:10.1007/bf0221692

The impact of learner metacognition and goal orientation on problem-solving in a serious game environment

To understand the impact of two learner characteristics—metacognition and goal orientation—on problem-solving, this study investigated 159 undergraduate learners’ metacognition, goal orientations, and problem-solving performances and processes in a laboratory setting using a Serious Game (SG) environment—Alien Rescue (AR)—that adopts Problem-based Learning (PBL) pedagogy for teaching space science. Utilizing multiple data sources, including computer log data and problem-solving solution scores within the SG, survey data, gameplay screencast videos, and interview data, this study combined a sequential mixed method design and serious games analytics techniques to answer the following two questions: (a) To what extent are learner problem-solving performance differences based on learner characteristics, and why? (b) To what extent are learner problem-solving process differences based on learner characteristics, and why? The results indicated that (a) learner metacognition affected problem-solving. Specifically, there were statistically significant differences in learner problem-solving performances based on metacognition, and learners also demonstrated different problem-solving processes based on metacognition. (b) Learner goal orientation impacted problem-solving. Particularly, learners in different goal orientation groups had different problem-solving processes. (c) The interaction between metacognition and goal orientations had an impact on learner problem-solving performances. Specifically, learners were clustered into three groups based on these two characteristics, including (a) high metacognition and high multiple goal orientations, (b) low metacognition and medium multiple goal orientations, and (c) medium metacognition and low multiple goal orientations. Learner problem-solving performances were statistically significant based on these three clusters. In addition, learner metacognition and goal orientations together could predict learner problem-solving performances. (d) The interaction between metacognition and goal orientations also had an impact on learner problem-solving processes. These differences in learner problem-solving performances and processes can be explained by learner characteristic differences, the problem complexity, SG design, and Dunning-Kruger effects (i.e., the cognitive bias that people of low metacognitive ability might mistakenly assess their metacognitive level as higher than it is). In addition, this study summarized 10 steps of how to be a successful and efficient problem solver in AR. These steps are as follows: 1) identify the problem correctly; 2) explore the 3D environment by visiting all rooms in AR and look over all tools; 3) discover what one alien species needs to survive in Alien Database; 4) search the Solar System Database for possible planets; 5) develop hypotheses about where this alien species can live; 6) figure out if there is any missing information needed for making a decision; 7) launch probes to gather information in the Probe Design room; 8) check the data from the probe in the Mission Control room; 9) decide whether the selected planet is a good choice for the selected alien species; 10) if so, write a recommendation message with the justification in the Communication Center—if not, go back to step 4. This research offers additional understanding of learner characteristic impacts on problem-solving in SG environments with PBL pedagogy. It can also contribute to future designs of these environments to benefit learners based on their metacognitive levels. In addition, the study limitations and further research in this area are discussed.Curriculum and Instructio

Complexity of the gale string problem for equilibrium computation in games

This thesis presents a report on original research, extending a result published as joint work with Merschen and von Stengel in Electronic Notes in Discrete Mathematics [4]. We present a polynomial time algorithm for two problems on labeled Gale strings, a combinatorial structure introduced by Gale [11] that can be used in the representation of a particular class of games. These games were used by Savani and von Stengel [25] as an example of exponential running time for the classical Lemke-Howson algorithm to find a Nash equilibrium of a bimatrix game [16]. It was therefore conjectured that solving these games was a complete problem in the class PPAD (Polynomial Parity Argument, Directed version, see Papadimitriou [24]). In turn, a major motivation for the definition of PPAD was the study of complementary pivoting methods, such as the Lemke-Howson algorithm. Our result, unexpectedly, sets apart this class of games as a case where a Nash equilibrium can be found in polynomial time. Since Daskalakis, Goldberg and Papaditrimiou [6] and Chen and Deng [5] proved that finding a Nash equilibrium in general normal-form games is PPAD-complete, we have a special class of games, unless PPAD = P. Our proof exploits two results. As seen in Savani and von Stengel [25] [26], we represent the Nash equilibria of these special games as Gale strings. We then give a graph where the perfect matchings correspond to Nash equilibria via Gale strings, and we exploit Edmonds’ polynomial-time algorithm for a perfect matching in a graph [7]. The proof given in Casetti, Merschen and von Stengel [4] covered only the case of even-dimensional Gale strings; here we extend the result to the general case. Merschen [19] and V´egh and von Stengel [28] expanded on our ideas, proving further results on the index of Nash equilibria (see Shapley [27]) in the framework of “oiks” introduced by Edmonds [8] and Edmonds and Sanit`a [9]

플랫폼 경제에서 네트워크 외부효과가 이용자의 선호에 미치는 영향

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2018. 8. 황준석.In our society, the industrial economy has been mainstreamChapter 1. Introduction 1 1.1 Research motivation 1 1.2 Research purpose and outline 4 Chapter 2. Network externality in the platform economy: A literature review and research framework 7 2.1 Introduction 7 2.2 Importance of solving information asymmetry 9 2.3 Network externality 12 2.3.1 Social learning theory in the concern of network externality 12 2.3.2 Network externality 15 2.4 Platform economy 18 2.4.1 Characteristics of platform economy 18 2.4.2 Firm centered platform economy 26 2.4.3 Proposed consumer centered platform economy 30 2.5 Empirical studies of network externality in platform economy 34 2.5.1 Low degree of interaction with people and low degree of functional integration 35 2.5.2 High degree of interaction with people and low degree of function integration 36 2.5.3 Medium degree of interaction with people and degree of functional integration 43 2.5.4 Low degree of interaction with people and high degree of functional integration 47 2.6 Conclusions and future research agenda 50 Chapter 3. The impact of number of users as network externality in online game 52 3.1 Introduction 52 3.2 Background and theoretical foundation 55 3.2.1 Network externality measurement 55 3.2.2 User gratification theory and self determination theory 57 3.3 Research model & hypotheses 59 3.4 Survey and estimation results 64 3.4.1 Survey and data 64 3.4.2 Estimation results 68 3.5 Discussion 79 Chapter 4. The efficiency change of sellers across the diffusion of transaction platform securing the customers 83 4.1 Introduction 83 4.2 Network externality on platforms 86 4.3 Hotel industry and its platforms 88 4.4 Methodology 92 4.4.1 Stochastic frontier analysis 92 4.4.2 Meta-frontier analysis 94 4.5 Data and results 97 4.5.1 Data 97 4.5.2 Estimation results 99 4.6 Conclusion 103 Chapter 5. How do potential consumers assuage uncertainties of emerging technology Consumer preference and acceptance on an autonomous vehicle 107 5.1 Introduction 107 5.2 Literature review 109 5.2.1 Network externality based on social learning theory 109 5.2.2 Consumers attitudes toward an autonomous vehicle 111 5.3 Methodology and data 113 5.3.1 Survey design 113 5.3.2 Model specification 121 5.3.3 Data description 123 5.4 Results 125 5.4.1 Estimated results 125 5.4.2 Market simulation 129 5.5 Discussion 132 Chapter 6. Overall conclusion 136 6.1 Summary and policy Implications 136 6.2 Contribution and limitations 140 Bibliography 145 Abstract (Korean) 187 Docto

Zielonka's Recursive Algorithm: dull, weak and solitaire games and tighter bounds

Dull, weak and nested solitaire games are important classes of parity games, capturing, among others, alternation-free mu-calculus and ECTL* model checking problems. These classes can be solved in polynomial time using dedicated algorithms. We investigate the complexity of Zielonka's Recursive algorithm for solving these special games, showing that the algorithm runs in O(d (n + m)) on weak games, and, somewhat surprisingly, that it requires exponential time to solve dull games and (nested) solitaire games. For the latter classes, we provide a family of games G, allowing us to establish a lower bound of 2^(n/3). We show that an optimisation of Zielonka's algorithm permits solving games from all three classes in polynomial time. Moreover, we show that there is a family of (non-special) games M that permits us to establish a lower bound of 2^(n/3), improving on the previous lower bound for the algorithm.Comment: In Proceedings GandALF 2013, arXiv:1307.416

A Comparison of BDD-Based Parity Game Solvers

Parity games are two player games with omega-winning conditions, played on finite graphs. Such games play an important role in verification, satisfiability and synthesis. It is therefore important to identify algorithms that can efficiently deal with large games that arise from such applications. In this paper, we describe our experiments with BDD-based implementations of four parity game solving algorithms, viz. Zielonka's recursive algorithm, the more recent Priority Promotion algorithm, the Fixpoint-Iteration algorithm and the automata based APT algorithm. We compare their performance on several types of random games and on a number of cases taken from the Keiren benchmark set.Comment: In Proceedings GandALF 2018, arXiv:1809.0241

Local Strategy Improvement for Parity Game Solving

The problem of solving a parity game is at the core of many problems in model checking, satisfiability checking and program synthesis. Some of the best algorithms for solving parity game are strategy improvement algorithms. These are global in nature since they require the entire parity game to be present at the beginning. This is a distinct disadvantage because in many applications one only needs to know which winning region a particular node belongs to, and a witnessing winning strategy may cover only a fractional part of the entire game graph. We present a local strategy improvement algorithm which explores the game graph on-the-fly whilst performing the improvement steps. We also compare it empirically with existing global strategy improvement algorithms and the currently only other local algorithm for solving parity games. It turns out that local strategy improvement can outperform these others by several orders of magnitude