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AbstractA formal, generic description of decision support systems is introduced. This description views a decision support system as having three principal components: a language system, a knowledge system, and a problem processing system. Several systems that fit the generic decision support system idea, but are (for the most part) not the customary kinds of systems encountered in business applications, are described. The concepts and techniques employed in these systems can make important contributions to the emergence of more powerful business‐oriented decision support systems.

AbstractAn important aspect of decision support systems (DSS) is their utilization of computational models. To illustrate the evolving roles of models in decision support systems, several representative systems are reviewed. These illustrations reveal three major interfaces that must be considered by a decision support system's designer. Successful treatment of these DSS interfaces depends upon two types of languages: one for directing computations and one for directing data manipulation. Each language type is presented as forming a spectrum of languages. Combined, the two spectra provide a classification scheme for decision support systems. This scheme is both useful as a framework for comparative study of decision support systems and also suggestive of directions for future research and developments in the decision support field.

ABSTRACTThe central issue of this research is the extent to which computer facilities can be used to support organizational decision‐making processes beyond mere performance of information retrieval. This depends upon the extent to which computers can be made to emulate human perceptual and judgmental processes. We present a framework for understanding these cognitive processes and examine how it applies to organizational decisions. Moreover, the framework furnishes a basis for the design of a generalized, intelligent problem processor. This processor is general in the sense of its ability to support a decision maker's activities, regardless of the decision maker's application area (e.g., urban planning, water‐quality planning, etc.). It is intelligent in the sense of its ability to comprehend English‐like queries and subsequently formulate models, interface appropriate data with those models, and execute the models to produce some facts or expectations about the problem under consideration.

ABSTRACTThis paper examines the attributes of a generalized data base management system with respect to its impact on managerial decision making. The discussion focuses upon two primary considerations: 1) the organization of data within a data base such that all intricate relationships are represented; and 2) the utilization of a facile method for non‐programming users to interrogate the data base. Examples drawn from the field of material requirements planning are used to illustrate the concepts and potential of the generalized data base management system.

AbstractThis paper considers the problem of computing, by iterative methods, optimal policies for Markov decision processes. The policies computed are optimal for all sufficiently small interest rates.

Cyber-insecurity is a serious threat in the digital world. In the present paper, we argue that a suboptimal cybersecurity environment is partly due to organizations' underinvestment on security and a lack of suitable policies. The motivation for this paper stems from a related policy question: how to design policies for governments and other organizations that can ensure a sufficient level of cybersecurity. We address the question by exploring a policy devised to alleviate information asymmetry and to achieve transparency in cybersecurity information sharing practice. We propose a cybersecurity evaluation agency along with regulations on information disclosure. To empirically evaluate the effectiveness of such an institution, we conduct a large-scale randomized field experiment on 7919 US organizations. Specifically, we generate organizations' security reports based on their outbound spam relative to the industry peers, then share the reports with the subjects in either private or public ways. Using models for heterogeneous treatment effects and machine learning techniques, we find evidence from this experiment that the security information sharing combined with publicity treatment has significant effects on spam reduction for original large spammers. Moreover, significant peer effects are observed among industry peers after the experiment.

ABSTRACTThe scenario of established business sellers utilizing online auction markets to reach consumers and sell new products is becoming increasingly common. We propose a class of risk management tools, loosely based on the concept of financial options that can be employed by such sellers. While conceptually similar to options in financial markets, we empirically demonstrate that option instruments within auction markets cannot be developed employing similar methodologies, because the fundamental tenets of extant option pricing models do not hold within online auction markets. We provide a framework to analyze the value proposition of options to potential sellers, option‐holder behavior implications on auction processes, and seller strategies to write and price options that maximize potential revenues. We then develop an approach that enables a seller to assess the demand for options under different option price and volume scenarios. We compare option prices derived from our approach with those derived from the Black‐Scholes model (Black & Scholes, 1973) and discuss the implications of the price differences. Experiments based on actual auction data suggest that options can provide significant benefits under a variety of option‐holder behavioral patterns.

Cybersecurity is a national priority in this big data era. Because of negative externalities and the resulting lack of economic incentives, companies often underinvest in security controls, despite government and industry recommendations. Although many existing studies on security have explored technical solutions, only a few have looked at the economic motivations. To fill the gap, we propose an approach to increase the incentives of organizations to address security problems. Specifically, we utilize and process existing security vulnerability data, derive explicit security performance information, and disclose the information as feedback to organizations and the public. We regularly release information on the organizations with the worst security behaviors, imposing reputation loss on them. The information is also used by organizations for self-evaluation in comparison to others. Therefore, additional incentives are solicited out of reputation concern and social comparison. To test the effectiveness of our approach, we conducted a field quasi-experiment for outgoing spam for 1,718 autonomous systems in eight countries and published SpamRankings.net, the website we created to release information. We found that the treatment group subject to information disclosure reduced outgoing spam approximately by 16%. We also found that the more observed outgoing spam from the top spammer, the less likely an organization would be to reduce its own outgoing spam, consistent with the prediction by social comparison theory. Our results suggest that social information and social comparison can be effectively leveraged to encourage desirable behavior. Our study contributes to both information architecture design and public policy by suggesting how information can be used as intervention to impose economic incentives. The usual disclaimers apply for NSF grants 1228990 and 0831338.

Cybersecurity is a national priority in this big data era. Because of negative externalities and the resulting lack of economic incentives, companies often underinvest in security controls, despite government and industry recommendations. Although many existing studies on security have explored technical solutions, only a few have looked at the economic motivations. To fill the gap, we propose an approach to increase the incentives of organizations to address security problems. Specifically, we utilize and process existing security vulnerability data, derive explicit security performance information, and disclose the information as feedback to organizations and the public. We regularly release information on the organizations with the worst security behaviors, imposing reputation loss on them. The information is also used by organizations for self-evaluation in comparison to others. Therefore, additional incentives are solicited out of reputation concern and social comparison. To test the effectiveness of our approach, we conducted a field quasi-experiment for outgoing spam for 1,718 autonomous systems in eight countries and published SpamRankings.net, the website we created to release information. We found that the treatment group subject to information disclosure reduced outgoing spam approximately by 16%. We also found that the more observed outgoing spam from the top spammer, the less likely an organization would be to reduce its own outgoing spam, consistent with the prediction by social comparison theory. Our results suggest that social information and social comparison can be effectively leveraged to encourage desirable behavior. Our study contributes to both information architecture design and public policy by suggesting how information can be used as intervention to impose economic incentives.