TAKING THE PERSPECTIVE OF A SELLER AND A BUYER: IMPLICATIONS FOR PRICE ELICITATION AND PRICE FRAMING

This dissertation consists of two essays which investigate how assuming the role of a seller or a buyer affects valuations in a price elicitation task (essay I) and how different presentations of an equivalent price affect evaluations when a consumer plays the dual roles of a buyer and a seller in transactions involving trade-ins (essay II). Sellers’ willingness to accept (WTA) to give up a good is typically higher than buyers' willingness to pay (WTP) to obtain the good. Essay I proposes that valuation processes of sellers and buyers are guided by a motivational orientation of “getting the best.” For a seller (buyer) indicating WTA (WTP), getting the best implies receiving as much as possible to give up a specific good (giving up as little as possible to get the specific good). Results of six studies suggest that the WTA-WTP elicitation task activates different directional goals, leading to the WTA-WTP disparity. The different directional goals lead sellers and buyers to focus on different aspects and bias their cognitive reasoning and interpretation of information. By connecting the valuation process to the general motivation of getting the best, this research provides a unifying framework to explain the disparate interpretations of the WTA-WTP disparity. Many new purchases and replacement decisions involve consumers’ trading in their old products. In such transactions, the overall exchange may be priced either as separate transactions (partitioned) with price tags for the payment and the receipt or as a single net price (consolidated) which takes into account the value of the trade-in. Essay II examines whether consumers prefer a partitioned price versus a consolidated price presentation. The findings suggest that when consumers are trading in a product which has a low value relative to the price of a new product, they prefer a consolidated price. In contrast, when trading in a product which has high value, they prefer a partitioned price. The results suggest that consumers use the price of the new product as an anchor to evaluate the trade-in value, and the perception of the trade-in value influences the overall evaluation especially when the transaction is partitioned.

ANALYSIS AND SIMULATION OF ENERGY USE AND COST AT A MUNICIPAL WASTEWATER TREATMENT PLANT

The cost of electricity, a major operating cost of municipal wastewater treatment plants, is related to influent flow rate, power price, and power load. With knowledge of inflow and price patterns, plant operators can manage processes to reduce electricity costs. Records of influent flow, power price, and load are evaluated for Blue Plains Advanced Wastewater Treatment Plant. Diurnal and seasonal trends are analyzed. Power usage is broken down among treatment processes. A simulation model of influent pumping, a large power user, is developed. It predicts pump discharge and power usage based on wet-well level. Individual pump characteristics are tested in the plant. The model accurately simulates plant inflow and power use for two pumping stations [R2 = 0.68, 0.93 (inflow), R2 =0.94, 0.91(power)]. Wet-well stage-storage relationship is estimated from data. Time-varying wet-well level is added to the model. A synthetic example demonstrates application in managing pumps to reduce electricity cost.

Mathematical Programming Models for Influence Maximization on Social Networks

In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.