Television advertising is a billion-dollar industry in the United States. Currently, advertisers place their messages in television programs that are estimated to have a high proportion of their target demographic viewers. The advertising spots are usually purchased months in advance at set list prices or at negotiated prices. Technologies that can place advertisements at the cable box level, instead of the program level, will provide advertisers with the ability to target any demographic group directly and in real-time. This thesis explores the new decision-making required by this new technology and how the television advertisement space can be sold more effectively. In particular, it compares a list price system to a number of new auction models. The structure of the auctions for the new targeted television advertising system is unique and has not been previously studied in the literature. This thesis explores new auction models that can capture these unique features and lead to desirable results for the seller of the advertisement space. A simplified analytical model shows how these features impact advertisers’ bidding behavior and how a list price system compares to the auction models in the ability to raise revenue for the seller of the advertising space. These issues are then explored under various market settings with differing numbers of advertisers and value distributions that these advertisers have for the advertising space. Since sequential first price auctions have undesirable consequences such as strong price fluctuations, this work focuses on second price auctions. The Vickrey-Clarke-Groves (VCG) mechanism is customized for this problem by developing an optimization formulation that determines the best set of advertisers for a particular advertisement space. Because execution time may be an issue, other auction models are developed that lead to similar outcomes as the VCG mechanism but require less computational effort. This thesis provides guidance on when a list price system will lead to higher expected revenue than an auction model and vice versa in a targeted television advertising system. It also demonstrates why some of the standard auction models cannot be applied to this problem and what type of new models are required to lead to desirable advertising outcomes.

Television advertising is a billion-dollar industry in the United States. Currently, advertisers place their messages in television programs that are estimated to have a high proportion of their target demographic viewers. The advertising spots are usually purchased months in advance at set list prices or at negotiated prices. Technologies that can place advertisements at the cable box level, instead of the program level, will provide advertisers with the ability to target any demographic group directly and in real-time. This thesis explores the new decision-making required by this new technology and how the television advertisement space can be sold more effectively. In particular, it compares a list price system to a number of new auction models. The structure of the auctions for the new targeted television advertising system is unique and has not been previously studied in the literature. This thesis explores new auction models that can capture these unique features and lead to desirable results for the seller of the advertisement space. A simplified analytical model shows how these features impact advertisers’ bidding behavior and how a list price system compares to the auction models in the ability to raise revenue for the seller of the advertising space. These issues are then explored under various market settings with differing numbers of advertisers and value distributions that these advertisers have for the advertising space. Since sequential first price auctions have undesirable consequences such as strong price fluctuations, this work focuses on second price auctions. The Vickrey-Clarke-Groves (VCG) mechanism is customized for this problem by developing an optimization formulation that determines the best set of advertisers for a particular advertisement space. Because execution time may be an issue, other auction models are developed that lead to similar outcomes as the VCG mechanism but require less computational effort. This thesis provides guidance on when a list price system will lead to higher expected revenue than an auction model and vice versa in a targeted television advertising system. It also demonstrates why some of the standard auction models cannot be applied to this problem and what type of new models are required to lead to desirable advertising outcomes.

Nitrate discharges from diffuse agricultural sources have long term effects on groundwater and surface water quality. Market-based instruments have been proposed as a means of balancing the demand for nitrate intensive farming and the capacity of the natural water bodies to dilute nitrates. Trading is complicated by the dispersed, delayed, and protracted effects of diffuse sources. Market mechanisms proposed to date have failed to incorporate these physical characteristics of nitrate pollution correctly. We propose a new market mechanism for allocating and pricing nitrate discharge permits, based on the design of modern electricity markets which use LP models to find optimal prices and dispatch schedules. The system operates as a centralized auction. The sources submit bids to the auction indicating the benefits gained from each unit of nitrate discharge. The auction operator runs an LP which maximises the benefits from trade, subject to a set of environmental and operational constraints. The LP solution produces the optimal prices and allocations relative to the economic values indicated in the bids. Our contributions include alternative LP models to suit different hydro-geological and socio-economic conditions. We present a generalized LP model which can include constraints that describe nitrate residence and transport in groundwater and surface water, the ability of water bodies to accept nitrates, and the operational limitations of the commercial sources. We show how to adapt available methods to incorporate the complex physical systems into an optimisation model. We present a double-sided market model which allows the polluters to buy permits, and environmental agents to lease out the ability of the natural water resources to accept nitrates. The model allows the providers of environmental services to participate in the market as sellers. We build up and prove the concepts by explaining the prices and allocations produced by the LP models. Based on the theory of nodal pricing applied in electricity markets, we discuss the price structures and relationships and show how the prices would reflect the spatial and temporal effects of diffuse nitrate discharges. We interpret the information generated from the outcomes of trading and discuss how the available tools and information can be used by the market participants to optimize their bids. We expand the proposed market model to include point sources, and identify the factors that determine the extent to which the point and nonpoint sources can trade with each other. In addition, we develop measures of the extent to which the diffuse sources themselves can trade with each other. We demonstrate the models and the resulting prices and allocations, using a catchment nitrate transport model.