Essays on the Impact of Social and Psychological Factors on Strategic Firm Decisions

Traditional economic analysis assumes that consumers are fully rational and consumer preferences are independent of consumers’ social context. Research has shown ample evidence that consumer preferences may vary by the social context of consumption, and social and psychological factors influence consumers’ decision making. This dissertation examines the effects of social and psychological factors on consumers’ decision making and how firms make strategic product and pricing decisions to respond to these effects. In the first chapter of the dissertation, I examine how firms selling repeated-purchased products price discriminate consumers based on consumers’ purchase history data, given that consumers are concerned about price fairness. In the second chapter, I examine how firms selling durable goods introduce product upgrades, given that consumers’ utility from consuming a product depends on the relative standing of the product in the marketplace. In the third chapter, I examine how firms selling status products make the design differentiation decision for their product lines, given that product design reveals consumers’ social group and consumers have status considerations. In the above research, I provide qualitatively new insights on the impact of psychological and social factors on firms’ strategic decisions and offer important implications for managers and public policy makers

Wind Sensor

Wind measurement is needed in many practical and scientific research situations. Some specific applications require to precisely measuring both wind direction and wind speed at the same time. Current commercial sensors for wind direction and wind speed measurement usually use ultrasonic technology and the sensors are very expensive (\u3e $1500). In addition, the sensors are large in dimension and cannot measure airflow patterns in high spatial resolution. Therefore new and low cost wind speed and direction sensors that can satisfy the specific requirements are needed. This research project will develop a low cost and compact anemometer to measure the wind speed as well as three dimensional wind directions. Four prototypes are built and tested with a better improvement on each prototype. Data are collected by using LabVIEW and analyzed by using Matlab and Excel. The last prototype is tested successfully to verify the concepts that we would expect and qualitatively analyzed. Some improvements can be implemented to this wind sensor for commercial usage

User Engagement with Mobile Technologies: A Multi-Dimensional Conceptualization of Technology Use

Our study conceptualizes user engagement – a form of technology use targeting the emerging ubiquitous mobile technology generation such as mobile health (mHealth) and social network applications. User engagement manifests in three dimensions, including behavioral, cognitive, and emotional engagement. We validated the measures (in both objective and subjective forms) for the three-dimension user engagement in two different mobile technology contexts, i.e., an e-nursing mobile application and a question-and-answer social network application. We further delineated the relationships among the three dimensions: 1) prior behavioral engagement contributed to both emotional and cognitive engagement, 2) emotional engagement lead to post behavioral engagement, and 3) emotional engagement, compared with prior behavioral engagement and cognitive engagement, exerted a stronger influence predicting post behavioral engagement. Our study enriches both technology use and engagement literature

EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal. In this paper, we study how to decrease its runtime even further using inadmissible heuristics. Motivated by Explicit Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS, that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high-level node to expand next. We also investigate recent improvements of CBS and adapt them to EECBS. We find that EECBS with the improvements runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances. We hope that the scalability of EECBS enables additional applications for bounded-suboptimal MAPF algorithms.Comment: Published at AAAI 202

Essays on the Impact of Social and Psychological Factors on Strategic Firm Decisions

Traditional economic analysis assumes that consumers are fully rational and consumer preferences are independent of consumers’ social context. Research has shown ample evidence that consumer preferences may vary by the social context of consumption, and social and psychological factors influence consumers’ decision making. This dissertation examines the effects of social and psychological factors on consumers’ decision making and how firms make strategic product and pricing decisions to respond to these effects. In the first chapter of the dissertation, I examine how firms selling repeated-purchased products price discriminate consumers based on consumers’ purchase history data, given that consumers are concerned about price fairness. In the second chapter, I examine how firms selling durable goods introduce product upgrades, given that consumers’ utility from consuming a product depends on the relative standing of the product in the marketplace. In the third chapter, I examine how firms selling status products make the design differentiation decision for their product lines, given that product design reveals consumers’ social group and consumers have status considerations. In the above research, I provide qualitatively new insights on the impact of psychological and social factors on firms’ strategic decisions and offer important implications for managers and public policy makers

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.Comment: Published at AAAI 202

Multi-Goal Multi-Agent Pickup and Delivery

In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where agents constantly engage with new tasks and need to plan collision-free paths to execute them. To execute a task, an agent needs to visit a pair of goal locations, consisting of a pickup location and a delivery location. We propose two variants of an algorithm that assigns a sequence of tasks to each agent using the anytime algorithm Large Neighborhood Search (LNS) and plans paths using the Multi-Agent Path Finding (MAPF) algorithm Priority-Based Search (PBS). LNS-PBS is complete for well-formed MAPD instances, a realistic subclass of MAPD instances, and empirically more effective than the existing complete MAPD algorithm CENTRAL. LNS-wPBS provides no completeness guarantee but is empirically more efficient and stable than LNS-PBS. It scales to thousands of agents and thousands of tasks in a large warehouse and is empirically more effective than the existing scalable MAPD algorithm HBH+MLA*. LNS-PBS and LNS-wPBS also apply to a more general variant of MAPD, namely the Multi-Goal MAPD (MG-MAPD) problem, where tasks can have different numbers of goal locations.Comment: IROS 202

Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree

Combined Target-Assignment and Path-Finding problem (TAPF) requires simultaneously assigning targets to agents and planning collision-free paths for agents from their start locations to their assigned targets. As a leading approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages both K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each search tree. While being able to find an optimal solution, CBS-TA suffers from scalability due to the duplicated collision resolution in multiple trees and the expensive computation of K-best assignments. We therefore develop Incremental Target Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS generates only a single search tree and avoids computing K-best assignments by incrementally computing new 1-best assignments during the search. We show that, in theory, ITA-CBS is guaranteed to find an optimal solution and, in practice, is computationally efficient