複数の意思決定者を伴う線形確率システムにおけるH∞制約付き動的ゲーム

広島大学(Hiroshima University)博士(学術)Doctor of Philosophydoctora

Demand Estimation at Manufacturer-Retailer Duo: A Macro-Micro Approach

This dissertation is divided into two phases. The main objective of this phase is to use Bayesian MCMC technique, to attain (1) estimates, (2) predictions and (3) posterior probability of sales greater than certain amount for sampled regions and any random region selected from the population or sample. These regions are served by a single product manufacturer who is considered to be similar to newsvendor. The optimal estimates, predictions and posterior probabilities are obtained in presence of advertising expenditure set by the manufacturer, past historical sales data that contains both censored and exact observations and finally stochastic regional effects that cannot be quantified but are believed to strongly influence future demand. Knowledge of these optimal values is useful in eliminating stock-out and excess inventory holding situations while increasing the profitability across the entire supply chain. Subsequently, the second phase, examines the impact of Cournot and Stackelberg games in a supply-chain on shelf space allocation and pricing decisions. In particular, we consider two scenarios: (1) two manufacturers competing for shelf space allocation at a single retailer, and (2) two manufacturers competing for shelf space allocation at two competing retailers, whose pricing decisions influence their demand which in turn influences their shelf-space allocation. We obtain the optimal pricing and shelf-space allocation in these two scenarios by optimizing the profit functions for each of the players in the game. Our numerical results indicate that (1) Cournot games to be the most profitable along the whole supply chain whereas Stackelberg games and mixed games turn out to be least profitable, and (2) higher the shelf space elasticity, lower the wholesale price of the product; conversely, lower the retail price of the product, greater the shelf space allocated for that product

Investment Under Uncertainty, Market Evolution and Coalition Spillovers in a Game Theoretic Perspective.

The rationality assumption has been the center of neo-classical economics for more than half a century now. In recent years much research has focussed on models of bounded rationality. In this thesis it is argued that both full and bounded rationality can be used for different kind of problems. In the first part full rationality is assumed to analyse technology adoption by firms in a duopolistic and uncertain environment. In the second part, boundedly rational models are developed to study the evolution of market structure in oligopolistic markets as well as price formation on (possibly) incomplete financial markets. The third part of the thesis presents an alternative to the framework of Transferable Utility games in cooperative game theory. The model introduced here explicitly takes into account the outside options that players often have in real-life situations if they choose not to participate in a coalition.

Managerial flexibility and competitive interaction in investment decisions : a discrete-time agency theoretic perspective

JEL Classification System: C70; G31; G32.This work intends to incorporate into a unified perspective three different fields of corporate finance: the managerial flexibility present in investment projects through the so-called real options models; the existence of agency costs as a result of financing strategies that rely in a mix between equity and debt; the existence of competition between firms, in non-purely competitive markets, through game theory. The aim of this work is to produce an integrated perspective of analysis that constitutes a value added to the literature, bringing new angles of approach to these issues. Indeed, to date, the link between these different aspects that affect managerial decisions in competitive markets is not yet established in a unified point of view. As such, we believe that the connection established in the present research may contribute to an improved understanding of that decision-making process. The work comprises two fundamental components. First, a theoretical review of concepts and latest developments in each of the different themes which are later combined. After such review, a discrete-time model that makes the connection between these theories is developed. Such model departs from the analysis of Mauer and Ott (2000) and Smit and Trigeorgis (2004). Afterwards, a numerical simulation is performed and the findings from such analysis are described. The results, from the simulation performed show that the existence of competition in non-purely competitive markets does produce a significant impact in managerial decisions.Este trabalho pretende incorporar numa perspectiva unificada três diferentes temáticas das finanças empresariais: a flexibilidade operacional presente em projectos de investimento através dos denominados modelos de opções reais; a existência de custos de agência em consequência de estratégias de financiamento que recorrem a capitais próprios e a capitais alheios; a concorrência entre empresas, em mercados não puramente concorrenciais, analisada através da teoria de jogos. Pretende-se com este trabalho trazer uma perspectiva integradora de análise que constitua uma mais valia para a literatura e que permite trazer novos prismas de abordagem a estas questões. Com efeito, até ao presente, a ligação entre estes diferentes aspectos, que afectam a tomada de decisões empresariais em mercados concorrenciais, ainda não se encontra estabelecida de uma forma unificada. Como tal, julgamos que a ligação aqui estabelecida pode contribuir para uma melhoria na compreensão dessa mesma tomada de decisões. O trabalho desenvolvido inicia-se com uma revisão teórica dos principais conceitos e desenvolvimentos mais recentes em opções reais, teoria da agência e teoria dos jogos. A seguir, desenvolve-se um modelo a tempo discreto que unifica essas mesmas teorias. Tal modelo desenvolve-se a partir da análise de Mauer e Ott (2000) e da de Smit e Trigeorgis (2004). Posteriormente, procede-se a uma simulação e apresenta-se as conclusões da análise realizada. Os resultados encontrados demonstram que, na simulação realizada a existência de concorrência em mercados não puramente competitivos produz impactos significativos na tomada de decisões empresariais

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Curriculum learning for multilevel budgeted combinatorial problems

Learning heuristics for combinatorial optimization problems through graph neural networks have recently shown promising results on some classic NP-hard problems. These are single-level optimization problems with only one player. Multilevel combinatorial optimization problems are their generalization, encompassing situations with multiple players taking decisions sequentially. By framing them in a multi-agent reinforcement learning setting, we devise a value-based method to learn to solve multilevel budgeted combinatorial problems involving two players in a zero-sum game over a graph. Our framework is based on a simple curriculum: if an agent knows how to estimate the value of instances with budgets up to $B$, then solving instances with budget $B+1$ can be done in polynomial time regardless of the direction of the optimization by checking the value of every possible afterstate. Thus, in a bottom-up approach, we generate datasets of heuristically solved instances with increasingly larger budgets to train our agent. We report results close to optimality on graphs up to $100$ nodes and a $185 \times$ speedup on average compared to the quickest exact solver known for the Multilevel Critical Node problem, a max-min-max trilevel problem that has been shown to be at least $\Sigma_2^p$-hard

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2019-20 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

Corporate Investment under Uncertainty and Competition: A Real Options Approach.

Abstract: Corporate investment opportunities can be represented as a set of (real) options to acquire productive assets. Identification of the optimal exercise strategies for these options plays a crucial role in improving the quality of capital budgeting decisions and, as a consequence, in maximizing shareholders¿ wealth. Structural changes in the economic environment, imperfect product market competition and agency conflicts across different groups of the firm¿s stakeholders make the standard option theory alone often insufficient for analyzing real investment decisions. This thesis combines option theory with non-cooperative game theory to establish some new results concerning the impact of policy uncertainty, product market interactions, and debt financing on the firm¿s optimal investment strategy.