Some relevant aspects of dynamics and control of heavy-duty hydraulic machines in a teleoperated mode were investigated. These machines, such as excavators and forest harvesters, are mostly used in primary industries. They have a manipulator-like structure with a nonlinear and coupled actuating system. The aim of the project is to investigate different approaches towards converting such machines, with minimum changes, into task-oriented human-supervisory control systems. This provides the opportunity to use both human supervision and robotic power in hazardous environments and for tasks for which human decision is necessary. A methodology was developed for fast and accurate simulations. Analytical, steady-state and numerical techniques were combined using Large-Scale Systems analysis. The inclusion of nonlinearities in the form of discontinuities (e.g., gear backlash and stick-slip friction) in the model was investigated. Numerical simplifications of the structural dynamics and alternative solutions for the hydraulic part were also studied. The model describing the performance of the machine has been written in ACSL (Advanced Continuous Simulation Language) on a VAX computer system. A modified version of the program is at present running close to real-time on a single processor in conjunction with high speed graphics in a manner similar to a flight simulator used for human interface studies and training. The model also evaluates the performance of the machine in a teleoperated mode and under different control strategies. As a result a velocity control algorithm has been developed which is applied in conjunction with the closed-loop components for teleoperation of heavy-duty hydraulic machines; it is basically a feedforward compensation which uses the measured hydraulic line pressures along with fluid-flow equations as criteria to control the joint velocities as well as to uncouple the interconnected actuating system. The control algorithm has been written in C language and is running on an IRONICS computer system, interfaced between the human operator and the machine. The simulation results are supported by the experimental evidence. The experiments were performed on a Caterpillar 215B excavator. Improved operator safety, extension of human capability, job quality and productivity increase are the advantages of a successful implementation of robotic technology to these industrial machines. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

abstract: I study split-pie bargaining problems between two agents. In chapter two, the types of both agents determine the value of outside options -- I refer to these as interdependent outside options. Since a direct mechanism stipulates outcomes as functions of agents' types, a player can update beliefs about another player’s type upon receiving a recommended outcome. I term this phenomenon as information leakage. I discuss binding arbitration, where players must stay with a recommended outcome, and non-binding arbitration, where players are not obliged to stay with an allocation. The total pie is reduced if the outcome is an outside option. With respect to efficiency, I derive a necessary and sufficient condition for first best mechanisms. These are mechanisms that assign zero probability to outside options for every report received. The condition describes balanced forces in conflict (outside options) and is the same in the cases of binding and non-binding arbitration. I also show a strong link between conflict and information: when conflict exists, information leakage occurs. Hence, non-binding arbitration may seem more restrictive than binding arbitration. To analyze why this is the case, I solve for second best mechanisms with binding arbitration and find a condition under which they can be implemented under non-binding arbitration. Thus, I show that non-binding arbitration can be as effective as binding arbitration in terms of efficiency. I also examine whether the equivalence between binding and non-binding arbitration can cease to hold, and provide analysis of why this happens. In chapter three, the bargaining problem entails no uncertainty but rather envy. Players can feel envy about the allocation of the other player. The Nash Bargaining solution is obtained in this context and some comparative statics are shown. The introduction of envy makes the more envious party a tougher negotiator. / Dissertation/Thesis / Doctoral Dissertation Economics 2020

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第22227号 / 経博第615号 / 新制||経||293(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 関口 格, 教授 原 千秋, 准教授 NEWTON Jonathan Charles Scott / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

<p>Technological systems contain complex elements and processes with a diverse set of agents and problem-solving arrangements. They often interact with and influence multi-lateral stakeholders with varying interests and incentives. Recent technological developments and engineering advancements such as digital marketplaces and high-tech networks create both new challenges and opportunities to understand further about effective mechanism designs. This dissertation attempts to answer corporate-level mechanism design issues in two different technological systems: high-tech biopharmaceutical networks and the online peer-to-peer lending industry.</p><p><br></p><p>The first part of the dissertation focuses on identifying the emergence and evolution of near decomposable systems in interorganizational relationships. To do so, first I conceptually discuss how near decomposable systems can emerge in interfirm relationships. Second, leveraging advancements in network science, I empirically analyze a detailed biopharmaceutical alliance data set and find that strategic alliance networks of biopharmaceutical firms exhibit near decomposable characteristics. I identify an emerging evolutionary pattern with smaller networks of subcommunities organizing hierarchically over time into a larger network structure, with the subcommunities generally exhibiting local clustering. A salient finding, compared to previous studies in the field of strategic management, is the identification of nested clusters formed in hierarchical fashion within this interfirm network. I find the potential for simultaneous evolutionary processes to be in play in various subnetworks within the overall industry-level network. The accrual of local changes impacting the structural processes of the subnetworks slowly diffuses to the larger, less integrated modules of the network. Finally, with the help of a simulation model, I identify how fitness heterogeneity among firms, fitness heterogeneity among partnerships and the rate of growth of partnerships impact the emergence of near decomposability in varying degrees.</p><p><br></p><p>The second study focuses on understanding an important market access control mechanism: platform owners granting priority access to a subset of supply-side complementors to grow the marketplace and remove potential demand-side bottlenecks. Platform governance mechanisms, such as market access control, help to align all market players towards a specific value proposition. I study the interplay between priority access and the variation in expertise of the complementors. Leveraging a randomized priority access given to expert institutional investors in the online peer-to-peer lending industry, I show that it creates negative spillover effects on the performance of crowd retail investors. I provide evidence in support of two mechanisms in driving the impact of priority access, the intensity of priority access and cream skimming by institutional complementors, on the retail crowd market. Again using simulation to extend the analysis, I find that the brunt of negative impacts is likely borne by more risk-averse retail investors.</p>

This dissertation consists of three essays on dynamic contracts. Chapter One studies a dynamic principal-agent model in which the agent continuously works on a project which may yield a success. The principal cannot observe the success, but she observes imperfect signals over time after the agent stops working. The principal is more patient than the agent and both are risk neutral. In the optimal contract where the agent observes the success, the agent is induced to exert full effort until success and report it truthfully. The optimal payment scheme features a combination of wage and deferred bonus. When the agent does not observe the success, the optimal contract features a stochastic deadline and a deferred bonus payment. Chapter Two studies a discrete time principal-agent model where the agent's effort and ability are both private information. The wage is exogenously fixed and the principal designs a firing policy to incentivize the agent to work. In each period, the agent works on a project with binary outcomes. The high type has a higher probability of getting a good outcome than the low type conditional on high effort. The outcome in each period is publicly observed. In the optimal contract, the principal hires the high type for sure and hires the low type with some probability. Conditional on being hired, the high type faces a higher standard of performance. Chapter Three studies a dynamic model of delegated decision making with adverse selection and imperfect monitoring. In each period, a principal may delegate to a biased agent who has better information. The quality of the agent's information depends on his ability. In the optimal mechanism where the agent's ability is publicly observable, the principal delegates to the agent at the beginning of their relationship and the agent behaves in the principal's interest. Depending on the history, the principal either commits to delegating forever or stops delegating eventually. When the agent's ability is private information, the optimal mechanism features pooling at the top. The principal offers the same mechanism to the agent if his ability is known to be above a cutoff.

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In this work, we propose a PRivAcy-Diversity-Aware Team Formation framework, namely PRADA-TF, that can be deployed based on the trust relationships between users in online social networks (OSNs). Our proposed PRADA-TF is mainly designed to reflect team members' domain expertise and privacy preserving preferences when a task requires a wide range of diverse domain expertise for its successful completion. The proposed PRADA-TF aims to form a team for maximizing its productivity based on members' characteristics in their diversity, privacy preserving, and information sharing. We leveraged a game theory called Mechanism Design in order for a mechanism designer as a team leader to select team members that can maximize the team's social welfare, which is the sum of all team members' utilities considering team productivity, members' privacy preserving, and potential privacy loss caused by information sharing. To screen a set of candidate teams in the OSN, we built an expert social network based on real co-authorship datasets (i.e., Netscience) with 1,590 scientists, used the semi-synthetic datasets to construct a trust network based on a belief model called Subjective Logic, and identified trustworthy users as candidate team members. Via our extensive simulation experiments, we compared the seven different TF schemes, including our proposed and existing TF algorithms, and analyzed the key factors that can significantly impact the expected and actual social welfare, expected and actual potential privacy leakout, and team diversity of a selected team. / Master of Science / In this work, we propose a PRivAcy-Diversity-Aware Team Formation framework, namely PRADA-TF, that can be deployed based on the trust relationships between users in online social networks (OSNs). Our proposed PRADA-TF is mainly designed to reflect team members' domain expertise and privacy preserving preferences when a task requires a wide range of diverse domain expertise for its successful completion. The proposed PRADA-TF aims to form a team based on members' characteristics in their diversity, privacy preserving, and information sharing so as to maximize the performance of the team. We leveraged a game theory called Mechanism Design in order for a mechanism designer as a team leader to select team members that can maximize the team's social welfare, which is the sum of all team members' utilities considering team productivity, members' privacy preserving, and potential privacy loss caused by information sharing. To screen a set of candidate teams in the OSN, we built an expert social network based on real co-authorship datasets with 1,590 scientists, used the semi-synthetic datasets to construct a trust network representing the trust relationship between the users in OSNs, and identified trustworthy users as candidate team members. Via our extensive simulation experiments, we compared the seven different team formation (TF) schemes, including our proposed and existing TF algorithms, and analyzed the key factors that can significantly impact the expected and actual social welfare, expected and actual potential privacy leakout, and team diversity of a selected team.

This paper presents a novel approach for designing and optimizing maintenance service contracts through the application of mechanism design theory. When offering a contract to its customer, the maintenance service provider seeks to specify contract terms - such as price, service features and incentives - that maximize the provider's profit, satisfy customer needs, allocate risks effectively and mitigate moral hazards. Optimal contract design has to account for asymmetric information and uncertainties associated with customer characteristics and behaviors. We illustrate our mechanism design approach by applying it to the contract design challenge of a gas turbine manufacturer, which also provides maintenance services for its aircraft engines. In our solution approach, we compute an optimal set of contracts. The entire set is presented to the customer and is designed such that the customer will accept one of the contract alternatives without negotiations. In addition to eliminating the costs and delays associated with negotiations, this approach also reveals the customer's private information to the service provider, which the provider can use to its benefit in maintenance management and future contract renewals. Furthermore, we design and incorporate win-win incentive mechanisms into the contracts, which reward the customer for actions that reduces maintenance costs. We present a deterministic and a stochastic mechanism design model, the latter accounting for uncertainties associated with customer actions, engine performance, and maintenance costs during the contract execution phase. / Master of Science