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- Creators: Arizona State University
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This dissertation presents three essays in economics. Firstly, I study the problem of allocating an indivisible good between two agents under incomplete information. I provide a characterization of mechanisms that maximize the sum of the expected utilities of the agents among all feasible strategy-proof mechanisms: Any optimal mechanism must be a convex combination of two fixed price mechanisms and two option mechanisms. Secondly, I study the problem of allocating a non-excludable public good between two agents under incomplete information. An equal-cost sharing mechanism which maximizes the sum of the expected utilities of the agents among all feasible strategy-proof mechanisms is proved to be optimal. Under the equal-cost sharing mechanism, when the built cost is low, the public good is provided whenever one of the agents is willing to fund it at half cost; when the cost is high, the public good is provided only if both agents are willing to fund it. Thirdly, I analyze the problem of matching two heterogeneous populations. If the payoff from a match exhibits complementarities, it is well known that absent any friction positive assortative matching is optimal. Coarse matching refers to a situation in which the populations into a finite number of classes, then randomly matched within these classes. The focus of this essay is the performance of coarse matching schemes with a finite number of classes. The main results of this essay are the following ones. First, assuming a multiplicative match payoff function, I derive a lower bound on the performance of n-class coarse matching under mild conditions on the distributions of agents' characteristics. Second, I prove that this result generalizes to a large class of match payoff functions. Third, I show that these results are applicable to a broad class of applications, including a monopoly pricing problem with incomplete information, as well as to a cost-sharing problem with incomplete information. In these problems, standard models predict that optimal contracts sort types completely. The third result implies that a monopolist can capture a large fraction of the second-best profits by offering pooling contracts with a small number of qualities.
ContributorsShao, Ran (Author) / Manelli, Alejandro (Thesis advisor) / Chade, Hector (Thesis advisor) / Schlee, Edward (Committee member) / Kovrijnykh, Natalia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Data privacy is emerging as one of the most serious concerns of big data analytics, particularly with the growing use of personal data and the ever-improving capability of data analysis. This dissertation first investigates the relation between different privacy notions, and then puts the main focus on developing economic foundations for a market model of trading private data.
The first part characterizes differential privacy, identifiability and mutual-information privacy by their privacy--distortion functions, which is the optimal achievable privacy level as a function of the maximum allowable distortion. The results show that these notions are fundamentally related and exhibit certain consistency: (1) The gap between the privacy--distortion functions of identifiability and differential privacy is upper bounded by a constant determined by the prior. (2) Identifiability and mutual-information privacy share the same optimal mechanism. (3) The mutual-information optimal mechanism satisfies differential privacy with a level at most a constant away from the optimal level.
The second part studies a market model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The value of epsilon units of privacy is measured by the minimum payment such that an individual's equilibrium strategy is to report data in an epsilon-differentially private manner. For the setting with binary private data that represents individuals' knowledge about a common underlying state, asymptotically tight lower and upper bounds on the value of privacy are established as the number of individuals becomes large, and the payment--accuracy tradeoff for learning the state is obtained. The lower bound assures the impossibility of using lower payment to buy epsilon units of privacy, and the upper bound is given by a designed reward mechanism. When the individuals' valuations of privacy are unknown to the data collector, mechanisms with possible negative payments (aiming to penalize individuals with "unacceptably" high privacy valuations) are designed to fulfill the accuracy goal and drive the total payment to zero. For the setting with binary private data following a general joint probability distribution with some symmetry, asymptotically optimal mechanisms are designed in the high data quality regime.
The first part characterizes differential privacy, identifiability and mutual-information privacy by their privacy--distortion functions, which is the optimal achievable privacy level as a function of the maximum allowable distortion. The results show that these notions are fundamentally related and exhibit certain consistency: (1) The gap between the privacy--distortion functions of identifiability and differential privacy is upper bounded by a constant determined by the prior. (2) Identifiability and mutual-information privacy share the same optimal mechanism. (3) The mutual-information optimal mechanism satisfies differential privacy with a level at most a constant away from the optimal level.
The second part studies a market model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The value of epsilon units of privacy is measured by the minimum payment such that an individual's equilibrium strategy is to report data in an epsilon-differentially private manner. For the setting with binary private data that represents individuals' knowledge about a common underlying state, asymptotically tight lower and upper bounds on the value of privacy are established as the number of individuals becomes large, and the payment--accuracy tradeoff for learning the state is obtained. The lower bound assures the impossibility of using lower payment to buy epsilon units of privacy, and the upper bound is given by a designed reward mechanism. When the individuals' valuations of privacy are unknown to the data collector, mechanisms with possible negative payments (aiming to penalize individuals with "unacceptably" high privacy valuations) are designed to fulfill the accuracy goal and drive the total payment to zero. For the setting with binary private data following a general joint probability distribution with some symmetry, asymptotically optimal mechanisms are designed in the high data quality regime.
ContributorsWang, Weina (Author) / Ying, Lei (Thesis advisor) / Zhang, Junshan (Thesis advisor) / Scaglione, Anna (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2016
Description
In this dissertation, I study strategic communication, in which a sender strategically discloses information to persuade a receiver to take an action favorable to the sender. I study two models of constrained communication. The first one generalizes the standard Bayesian Persuasion model to allow for the receiver's strategic behavior. The second one, joint work with Nour Chalhoub, studies a dynamic information disclosure under the assumption that the sender can only lie in one direction, by under-reporting the state, but never over-reporting it. The constraints in each model are intuitive for real-life application and lead to results that are of substantial difference from the results of the standard settings.
ContributorsEscobar, Marco Eugenio (Author) / Manelli, Alejandro M (Thesis advisor) / Chade, Hector A (Committee member) / Kleiner, Andreas (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation introduces and examines Soft Curved Reconfigurable Anisotropic Mechanisms (SCRAMs) as a solution to address actuation, manufacturing, and modeling challenges in the field of soft robotics, with the aim of facilitating the broader implementation of soft robots in various industries. SCRAM systems utilize the curved geometry of thin elastic structures to tackle these challenges in soft robots. SCRAM devices can modify their dynamic behavior by incorporating reconfigurable anisotropic stiffness, thereby enabling tailored locomotion patterns for specific tasks. This approach simplifies the actuation of robots, resulting in lighter, more flexible, cost-effective, and safer soft robotic systems. This dissertation demonstrates the potential of SCRAM devices through several case studies. These studies investigate virtual joints and shape change propagation in tubes, as well as anisotropic dynamic behavior in vibrational soft twisted beams, effectively demonstrating interesting locomotion patterns that are achievable using simple actuation mechanisms. The dissertation also addresses modeling and simulation challenges by introducing a reduced-order modeling approach. This approach enables fast and accurate simulations of soft robots and is compatible with existing rigid body simulators. Additionally, this dissertation investigates the prototyping processes of SCRAM devices and offers a comprehensive framework for the development of these devices. Overall, this dissertation demonstrates the potential of SCRAM devices to overcome actuation, modeling, and manufacturing challenges in soft robotics. The innovative concepts and approaches presented have implications for various industries that require cost-effective, adaptable, and safe robotic systems. SCRAM devices pave the way for the widespread application of soft robots in diverse domains.
ContributorsJiang, Yuhao (Author) / Aukes, Daniel (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2023
Description
Robotic technology can be broadly categorized into two main approaches based on the compliance of the robot's materials and structure: hard and soft. Hard, traditional robots, with mechanisms to transmit forces, provide high degrees of freedom (DoFs) and precise manipulation, making them commonly used in industry and academic research. The field of soft robotics, on the other hand, is a new trend from the past three decades of robotics that uses soft materials such as silicone or textiles as the body or material base instead of the rigid bodies used in traditional robots. Soft robots are typically pre-programmed with specific geometries, and perform well at tasks such as human-robot interaction, locomotion in complex environments, and adaptive reconfiguration to the environment, which reduces the cost of future programming and control. However, full soft robotic systems are often less mobile due to their actuation --pneumatics, high-voltage electricity or magnetics -- even if the robot itself is at a millimeter or centimeter scale. Rigid or hard robots, on the other hand, can often carry the weight of their own power, but with a higher burden of cost for control and sensing. A middle ground is thus sought, to combine soft robotics technologies with rigid robots, by implementing mechanism design principles with soft robots to embed functionalities or utilize soft robots as the actuator on a rigid robotic system towards an affordable robotic system design. This dissertation showcases five examples of this design principle with two main research branches: locomotion and wearable robotics. In the first research case, an example of how a miniature swimming robot can navigate through a granular environment using compliant plates is presented, compared to other robots that change their shape or use high DoF mechanisms. In the second pipeline, mechanism design is implemented using soft robotics concepts in a wearable robot. An origami-inspired, soft "exo-shell", that can change its stiffness on demand, is introduced. As a follow-up to this wearable origami-inspired robot, a geometry-based, ``near" self-locking modular brake is then presented. Finally, upon combining the origami-inspired wearable robot and brake design, a concept of a modular wearable robot is showcased for the purpose of answering a series of biomechanics questions.
ContributorsLi, Dongting (Author) / Aukes, Daniel M (Thesis advisor) / Sugar, Thomas G (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2023
Description
Agricultural supply chains are complex systems which pose significant challenges beyond those of traditional supply chains. These challenges include: long lead times, stochastic yields, short shelf lives and a highly distributed supply base. This complexity makes coordination critical to prevent food waste and other inefficiencies. Yet, supply chains of fresh produce suffer from high levels of food waste; moreover, their high fragmentation places a great economic burden on small and medium sized farms.
This research develops planning tools tailored to the production/consolidation level in the supply chain, taking the perspective of an agricultural cooperative—a business model which presents unique coordination challenges. These institutions are prone to internal conflict brought about by strategic behavior, internal competition and the distributed nature of production information, which members keep private.
A mechanism is designed to coordinate agricultural production in a distributed manner with asymmetrically distributed information. Coordination is achieved by varying the prices of goods in an auction like format and allowing participants to choose their supply quantities; the auction terminates when production commitments match desired supply.
In order to prevent participants from misrepresenting their information, strategic bidding is formulated from the farmer’s perspective as an optimization problem; thereafter, optimal bidding strategies are formulated to refine the structure of the coordination mechanism in order to minimize the negative impact of strategic bidding. The coordination mechanism is shown to be robust against strategic behavior and to provide solutions with a small optimality gap. Additional information and managerial insights are obtained from bidding data collected throughout the mechanism. It is shown that, through hierarchical clustering, farmers can be effectively classified according to their cost structures.
Finally, considerations of stochastic yields as they pertain to coordination are addressed. Here, the farmer’s decision of how much to plant in order to meet contracted supply is modeled as a newsvendor with stochastic yields; furthermore, options contracts are made available to the farmer as tools for enhancing coordination. It is shown that the use of option contracts reduces the gap between expected harvest quantities and the contracted supply, thus facilitating coordination.
This research develops planning tools tailored to the production/consolidation level in the supply chain, taking the perspective of an agricultural cooperative—a business model which presents unique coordination challenges. These institutions are prone to internal conflict brought about by strategic behavior, internal competition and the distributed nature of production information, which members keep private.
A mechanism is designed to coordinate agricultural production in a distributed manner with asymmetrically distributed information. Coordination is achieved by varying the prices of goods in an auction like format and allowing participants to choose their supply quantities; the auction terminates when production commitments match desired supply.
In order to prevent participants from misrepresenting their information, strategic bidding is formulated from the farmer’s perspective as an optimization problem; thereafter, optimal bidding strategies are formulated to refine the structure of the coordination mechanism in order to minimize the negative impact of strategic bidding. The coordination mechanism is shown to be robust against strategic behavior and to provide solutions with a small optimality gap. Additional information and managerial insights are obtained from bidding data collected throughout the mechanism. It is shown that, through hierarchical clustering, farmers can be effectively classified according to their cost structures.
Finally, considerations of stochastic yields as they pertain to coordination are addressed. Here, the farmer’s decision of how much to plant in order to meet contracted supply is modeled as a newsvendor with stochastic yields; furthermore, options contracts are made available to the farmer as tools for enhancing coordination. It is shown that the use of option contracts reduces the gap between expected harvest quantities and the contracted supply, thus facilitating coordination.
ContributorsMason De Rada, Andrew Nicholas (Author) / Villalobos, Jesus R (Thesis advisor) / Griffin, Paul (Committee member) / Kempf, Karl (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2015
Description
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.
ContributorsGonzalez Sanchez, Eric Patricio (Author) / Manelli, Alejandro (Thesis advisor) / Chade, Hector (Committee member) / Schlee, Edward (Committee member) / Arizona State University (Publisher)
Created2020