Pay-per-click (PPC) is a common pricing model used to pay for ads on the Web and is open to the possibility for click fraud, where clicks are not from a legitimate user. Identifying click fraud is generally done in a three-stage process: the service provider (SP) first classifies clicks as fraudulent or not, then the advertiser does the same with a different technology, and if there is a disagreement, the SP examines further and his conclusions are considered binding. The advertiser pays for clicks that are identified as valid in the first two stages or confirmed as valid in the last stage. We model the choice of the identification technologies as a double moral hazard problem. We analyze the case where the PPC is incentive compatible to overcome the moral hazard problem, and examine the question of whether the incentive compatible PPC is sufficient to incentivize the two parties to unilaterally make further improvements to their identification technologies and simultaneously increase their profits. We show that when the cost of the third-stage identification technology is large, which is likely to be the case because of its complexity and use of expensive human experts, the incentive compatible PPC does not support unilateral technological improvements. We then examine a setting where the third-stage identification is delegated to a third party and find that this arrangement can induce unilateral improvements to the identification technologies in the first two stages. Collectively our results show that although the PPC model itself may not induce improvements in the first two stages of click fraud identification, a common arrangement espoused of having a third party resolve disagreements helps make PPC support unilateral technological improvements. Accordingly, we show an indirect benefit to the third-party arrangement.
Online advertising has transformed the advertising industry with its measurability and accountability. Online software and services supported by online advertising is becoming a reality as evidenced by the success of Google and its initiatives. Therefore, the choice of a pricing model for advertising becomes a critical issue for these firms. We present a formal model of pricing models in online advertising using the principal-agent framework to study the two most popular pricing models: input-based cost per thousand impressions (CPM) and performance-based cost per click-through (CPC). We identify four important factors that affect the preference of CPM to the CPC model, and vice versa. In particular, we highlight the interplay between uncertainty in the decision environment, value of advertising, cost of mistargeting advertisements, and alignment of incentives. These factors shed light on the preferred online-advertising pricing model for publishers and advertisers under different market conditions.
Prior work on software release policy implicitly assumes that testing stops at the time of software release. In this research, we propose an alternative release policy for custom-built enterprise-level software projects that allows testing to continue for an additional period after the software product is released. Our analytical results show that the software release policy with postrelease testing has several important advantages over the policy without postrelease testing. First, the total expected cost is lower. Second, even though the optimal time to release the software is shortened, the reliability of the software is improved throughout its lifecycle. Third, although the expected number of undetected bugs is higher at the time of release, the expected number of software failures in the field is reduced. We also analyze the impact of market uncertainty on the release policy and find that all our prior findings remain valid. Finally, we examine a comprehensive scenario where in addition to uncertain market opportunity cost, testing resources allocated to the focal project can change before the end of testing. Interestingly, the software should be released earlier when testing resources are to be reduced after release.
We study agency problems that arise when prototypes are used for requirements assessment. The precision with which the prototype helps a client assess his requirements depends on (a) the type of prototype provided by the vendor and (b) the client's feedback effort. The vendor can provide either a neutral or nonneutral prototype: The nonneutral prototype influences the client towards one particular set of requirements that may not be the true requirement, and the neutral prototype allows the client to assess his true requirements. This leads to the vendor's moral hazard problem. The client chooses to exert either the high or low feedback effort after the vendor provides the prototype. Because the effort is unobservable to the vendor, it can lead to the client exerting the low feedback effort: the client's commitment problem. In this paper we develop and discuss the role of the contract payment to provide the vendor with incentives to supply the neutral prototype, as well as for the client to commit to the high feedback effort. In this setting, we also examine the "anchoring" effect, wherein even a high-feedback effort can influence the client more toward a particular set of requirements with the nonneutral prototype. Our results highlight the interplay among the feedback effort, anchoring, and vendor payments.
Information derived from relational databases is routinely used for decision making. However, little thought is usually given to the quality of the source data, its impact on the quality of the derived information, and how this in turn affects decisions. To assess quality, one needs a framework that defines relevant metrics that constitute the quality profile of a relation, and provides mechanisms for their evaluation. We build on a quality framework proposed in prior work, and develop quality profiles for the result of the primitive relational operations Difference and Union. These operations have nuances that make both the classification of the resulting records as well as the estimation of the different classes quite difficult to address, and very different from that for other operations. We first determine how tuples appearing in the results of these operations should be classified as accurate, inaccurate or mismember, and when tuples that should appear do not (called incomplete) in the result. Although estimating the cardinalities of these subsets directly is difficult, we resolve this by decomposing the problem into a sequence of drawing processes, each of which follows a hyper-geometric distribution. Finally, we discuss how decisions would be influenced based on the resulting quality profiles.
Investments in information technology (IT) have become crucial for firms to improve the quality of their products and services. Typically, IT cost for the same performance level declines over time. In a competitive market, a decline in IT cost over time provides a cost advantage to the later entrant, making the early entrant's investment decision problem challenging. In this paper, we study the problem of strategic IT investments in the declining cost scenario using a sequential duopoly model. Our results show that declining IT cost intensifies or relaxes competition between firms depending on whether they are serving quality- or price-sensitive markets. In both cases, the average price per unit quality decreases when the IT cost declines, which benefits consumers. We also show that if the first entrant is uncertain about the extent of its cost disadvantage, the first entrant overinvests (underinvests) in a price-sensitive (quality-sensitive) market as the degree of uncertainty increases.
In this paper, we consider the problem of generating effective information- gathering, communication, and decision-making (lCD) strategies for a distributed expert problem-solving (DEPS) system. We focus on the special case of a dual-processor DEPS system and present a decision-theoretic model that enables the characterization of feasible, efficient, and optimal lCD strategies. In view of the tremendous amount of computing needed to generate optimal strategies for problems of practical size, we develop useful heuristic procedures for constructing high-quality efficient lCD strategies. We illustrate the use of the model and the solution procedure through an example.