Radha Mookerjee

When Hackers Talk: Managing Information Security Under Variable Attack Rates and Knowledge Dissemination

This paper analyzes interactions between a firm that seeks to discriminate between normal users and hackers that try to penetrate and compromise the firms information assets. We develop an analytical model in which a variety of factors are balanced to best manage the detection component within information security management. The approach not only considers conventional factors such as detection rate and false-positive rate, but also factors associated with hacker behavior that occur in response to improvements in the detection system made by the firm. Detection can be improved by increasing the systems discrimination ability (i.e., the ability to distinguish between attacks and normal usage) through the application of maintenance effort. The discrimination ability deteriorates over time due to changes in the environment. Also, there is the possibility of sudden shocks that can sharply degrade the discrimination ability. The firms cost increases as hackers become more knowledgeable by disseminating security knowledge within the hacker population. The problem is solved to reveal the presence of a steady-state solution in which the level of system discrimination ability and maintenance effort are held constant. We find an interesting result where, under certain conditions, hackers do not benefit from disseminating security knowledge among one another. In other situations, we find that hackers benefit because the firm must lower its detection rate in the presence of knowledge dissemination. Other insights into managing detection systems are provided. For example, the presence of security shocks can increase or decrease the optimal discrimination level as compared to the optimal level without shocks.

Leveraging philanthropic behavior for customer support: the case of user support forums

Online user forums for technical support are being widely adopted by IT firms to supplement traditional customer support channels. Customers benefit from having an additional means of product support, while firms benefit by lowering the costs of supporting a large customer base. Typically these forums are populated with content generated by users, consisting of questioners (solution seekers) and solvers (solution providers). While questioners can be expected to keep returning as long as they can find answers, firms must employ different means in order to recognize and encourage the contributions of solvers. We identify and compare the impact of two widely adopted recognition mechanisms on the philanthropic behavior of solvers. In the first mechanism, feedback-based recognition, solver contribution is evaluated by questioners. In the second mechanism, quantity-based recognition, all contributions are weighted equally regardless of questioner feedback. We draw on the pro-social behavior literature to identify four drivers of solver contribution: (1) peer recognition, (2) image motivation, (3) social comparison, and (4) social exposure. We show that the choice of recognition mechanism strongly influences a solvers problem-solving behavior, highlighting the importance of the firms decision in this regard. We address issues of solvers self-selecting a type of recognition mechanism by using propensity score analysis in order to show that solver behavior is a result of forum conditioning. We also study the impact of the recognition mechanism on forum quality and the effectiveness of support to draw comparative analytics.

Maintaining enterprise software applications

The problem of maintaining integrated applications is by no means a simple one and requires an interdisciplinary approach.

Integrated Modeling Environments in Organizations: An Empirical Study

Considerable attention in the information systems and management science literature has focused on computer-based modeling environments, sometimes called integrated modeling environments or model management systems. This research has been primarily concerned with suggesting features/components of modeling environments such as improved executable modeling languages for model creation, integration, and data representation; specialized database systems for managing model data; and customized model-solver software. However, there has been little (if any) empirical guidance offered in the literature about the specific needs of business and industry for computer-based integrated modeling environments. Using a data set compiled from a national survey of modelers (analysts) and model users (decision makers), we empirically investigate the validity of several of the key assumptions of modeling environment research reported in the literature, and examine the relationships between the modeling factors: data complexity, model complexity, modeling intensity, modeler/user requirements, and need for computer-based integrated modeling environments in organizations. Our empirical analysis of the data set shows that practitioners rank automated access to model data and automated error checking (e.g., model syntax and semantics checking) high as desirable components in modeling environments. We find that users prefer to have modeling environments linked to their current modeling and modeling-support software systems. Our findings further suggest that a high percentage of modelers and users are dissatisfied with the software systems they are currently using to support their modeling activities. Finally, a covariance structure analysis of the modeling environment factors clearly shows that: (a) model complexity has a direct positive effect on modeling intensity; (b) data complexity has an insignificant direct effect on modeling intensity, but has a negative effect on modeler/user requirements; and (c) modeler/user requirements have a direct positive effect on need for computer-based integrated modeling environments in organizations.

To Show or Not Show: Using User Profiling to Manage Internet Advertisement Campaigns at Chitika

We study the problem of an Internet advertising firm that wishes to maximize advertisement (ad) revenue, subject to click-through rate restrictions imposed by the publisher who controls the website on which the ads are displayed. The problem is directly motivated by Chitika, an Internet advertising firm that operates in the Boston area. Chitika contracts with publishers to place relevant ads over a specified period, usually one month, on publisher websites. We develop a predictive model of a visitor clicking on a given ad. Using this prediction of the probability of a click, we develop a decision model that uses a varying threshold to decide whether or not to show an ad to the visitor. We vary the threshold depending on (1) the cumulative number of times an ad has been shown and (2) the cumulative number of clicks on the ad. The decision models objective is to maximize the advertising firms revenue subject to a click-through rate constraint. The implemented models work in real time in Chitikas advertising network. We also discuss the implementation challenges and business impact.

Maintaining Diagnostic Knowledge-Based Systems: A Control-Theoretic Approach

Diagnostic knowledge-based systems are used in a variety of application domains to support classification decisions. The effectiveness of such systems often decreases as the application environment or user preferences change over time. Hence, frequent adjustments to the system knowledge by a human expert become necessary. We study the problem of determining the optimal amount of effort that should be exerted to maintain the system over a planning horizon (finite or infinite). Using the receiver operating characteristic curve to derive a measure for system performance, we maximize system value by balancing system benefits with maintenance costs. The problem is cast as an optimal control model in which the goal is to choose the timing and extent of maintenance that must be expended to maximize system value. We find that the optimal solution usually possesses a steady-state component. The maintenance problem is also solved as a discrete, impulse control problem, as well as one where maintenance effort has a nonlinear impact on system performance.

Optimizing Performance-Based Internet Advertisement Campaigns

This study provides an approach to manage an ongoing Internet ad campaign that substantially improves the number of clicks and the revenue earned from clicks. The problem we study is faced by an Internet advertising firm (Chitika) that operates in the Boston area. Chitika contracts with publishers to place relevant advertisements (ads) over a specified period on publisher websites. Ad revenue accrues to the firm and the publisher only if a visitor clicks on an ad (i.e., we are considering the cost-per-click model in this study). This might imply that all visitors to the publisher’s website be shown ads. However, this is not the case if the publisher imposes a click-through-rate constraint on the advertising firm. This performance constraint captures the publisher’s desire to limit ad clutter on the website and hold the advertising firm responsible for the publisher’s opportunity cost of showing an ad that did not result in a click. We develop a predictive model of a visitor clicking on a given ad. Using this prediction of the probability of a click, we develop a decision model that uses a threshold to decide whether or not to show an ad to the visitor. The decision model’s objective is to maximize the advertising firm’s revenue subject to a click-through-rate constraint. A key contribution of this paper is to characterize the structure of the optimal solution. We study and contrast two competing solutions: (1) a static solution, and (2) a rolling-horizon solution that resolves the problem at certain points in the planning horizon. The static solution is shown to be optimal when accurate information on the input parameters to the problem is known. However, when the parameters to the model can only be estimated with some error, the rolling-horizon solution can perform better than the static solution. When using the rolling-horizon solution, it becomes important to choose the appropriate resolving frequency. The implemented models operate in real time in Chitika’s advertising network. Implementation challenges and the business impact of our solution are discussed. To present a head-to-head comparison of our implemented approach with the past practice at Chitika, we implemented our solution in parallel to the past practice.

OR/SM: A Prototype Integrated Modeling Environment Based on Structured Modeling

This article describes the design and implementation of (OR/SM), a computerized modeling environment based on Structured Modeling. The uniqueness of OR/SM is in the following: (1) the use of oracle Tools and Database as the delivery platform; (2) automatic and interactive links to sas, a powerful and widely used commercial statistical analysis software system and optimization solver; and (3) an interactive link to qs (Quantitative Systems)—a commercial software package for solving a wide range of operations management models. Some other key features are: (1) automatic generation of relational database tables for model data; (2) interactive checking of model syntax and semantics; and (3) automatic generation of several reference documents. Examples from blending, inventory control, and marketing mix management are used to illustrate the capabilities of OR/SM.

The design and implementation of OR/SM: A prototype integrated modeling environment

This paper describes the design, implementation, and interaction of the processes of OR/SM, a computerized modeling environment built on ORACLE Tools and Database (OR) using Structured Modeling (SM) as the conceptual framework. Some of the key features of OR/SM include: (a) interactive checking of model syntax and semantics; (b) automatic gen-eration of relational database tables for model data; (c) automatic generation of several reference documents; (d) automatic and interactive links to SAS, a powerful and widely used commercial statistical analysis software system and optimization solver; and (e) an interactive link to QS (Quantitative Systems) - a commercial software package for solving a wide range of operations management models.