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-Agent Motion Planning with B\'ezier Curve Optimization under Kinodynamic Constraints

Multi-Agent Motion Planning (MAMP) is a problem that seeks collision-free dynamically-feasible trajectories for multiple moving agents in a known environment while minimizing their travel time. MAMP is closely related to the well-studied Multi-Agent Path-Finding (MAPF) problem. Recently, MAPF methods have achieved great success in finding collision-free paths for a substantial number of agents. However, those methods often overlook the kinodynamic constraints of the agents, assuming instantaneous movement, which limits their practicality and realism. In this paper, we present a three-level MAPF-based planner called PSB to address the challenges posed by MAMP. PSB fully considers the kinodynamic capability of the agents and produces solutions with smooth speed profiles that can be directly executed by the controller. Empirically, we evaluate PSB within the domains of traffic intersection coordination for autonomous vehicles and obstacle-rich grid map navigation for mobile robots. PSB shows up to 49.79% improvements in solution cost compared to existing methods

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

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

Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution

The Multi-Agent Path Finding (MAPF) problem involves planning collision-free paths for multiple agents in a shared environment. The majority of MAPF solvers rely on the assumption that an agent can arrive at a specific location at a specific timestep. However, real-world execution uncertainties can cause agents to deviate from this assumption, leading to collisions and deadlocks. Prior research solves this problem by having agents follow a Temporal Plan Graph (TPG), enforcing a consistent passing order at every location as defined in the MAPF plan. However, we show that TPGs are overly strict because, in some circumstances, satisfying the passing order requires agents to wait unnecessarily, leading to longer execution time. To overcome this issue, we introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG), which allows switching passing orders during execution to avoid unnecessary waiting time. We design two anytime algorithms for constructing a BTPG: BTPG-na\"ive and BTPG-optimized. Experimental results show that following BTPGs consistently outperforms following TPGs, reducing unnecessary waits by 8-20%.Comment: Accepted by AAAI-202

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

ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem

Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, plays a critical role in many applications. Sometimes, assigning a target to each agent also presents a challenge. The Combined Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires one to simultaneously assign targets to agents and plan collision-free paths for agents. Several algorithms, including CBM, CBS-TA, and ITA-CBS, optimally solve the TAPF problem, with ITA-CBS being the leading algorithm for minimizing flowtime. However, the only existing bounded-suboptimal algorithm ECBS-TA is derived from CBS-TA rather than ITA-CBS. So, it faces the same issues as CBS-TA, such as searching through multiple constraint trees and spending too much time on finding the next-best target assignment. We introduce ITA-ECBS, the first bounded-suboptimal variant of ITA-CBS. Transforming ITA-CBS to its bounded-suboptimal variant is challenging because different constraint tree nodes can have different assignments of targets to agents. ITA-ECBS uses focal search to achieve efficiency and determines target assignments based on a new lower bound matrix. We show that it runs faster than ECBS-TA in 87.42% of 54,033 test cases