Search, Memory, and Choice Error: An Experiment

Multiple attribute search is a central feature of economic life: we consider much more than price when purchasing a home, and more than wage when choosing a job. An experiment is conducted in order to explore the effects of cognitive limitations on choice in these rich settings, in accordance with the predictions of a new model of search memory load. In each task, subjects are made to search the same information in one of two orders, which differ in predicted memory load. Despite standard models of choice treating such variations in order of acquisition as irrelevant, lower predicted memory load search orders are found to lead to substantially fewer choice errors. An implication of the result for search behavior, more generally, is that in order to reduce memory load (thus choice error) a limited memory searcher ought to deviate from the search path of an unlimited memory searcher in predictable ways-a mechanism that can explain the systematic deviations from optimal sequential search that have recently been discovered in peoples' behavior. Further, as cognitive load is induced endogenously (within the task), and found to affect choice behavior, this result contributes to the cognitive load literature (in which load is induced exogenously), as well as the cognitive ability literature (in which cognitive ability is measured in a separate task). In addition, while the information overload literature has focused on the detrimental effects of the quantity of information on choice, this result suggests that, holding quantity constant, the order that information is observed in is an essential determinant of choice failure.Financial support from the Spanish Ministerio de Ciencia y Tecnología and Feder Funds (SEJ-2007-62656) and the Spanish Ministry of Economics and Competition (ECO2012-34928, http://www.biodiversa.org/102) is gratefully acknowledged

Optimizing Queries to Remote Resources

One key property of the Semantic Web is its support for interoperability. Recent research in this area focuses on the integration of multiple data sources to facilitate tasks such as ontology learning, user query expansion and context recognition. The growing popularity of such machups and the rising number of Web APIs supporting links between heterogeneous data providers asks for intelligent methods to spare remote resources and minimize delays imposed by queries to external data sources. This paper suggests a cost and utility model for optimizing such queries by leveraging optimal stopping theory from business economics: applications are modeled as decision makers that look for optimal answer sets. Queries to remote resources cause additional cost but retrieve valuable information which improves the estimation of the answer set's utility. Optimal stopping optimizes the trade-off between query cost and answer utility yielding optimal query strategies for remote resources. These strategies are compared to conventional approaches in an extensive evaluation based on real world response times taken from seven popular Web services

Continuous Decision Support

Organizations are often faced with portfolio construction efforts that require them to select one or more alternatives, subject to resource constraints, with the aim of achieving the maximum value possible. This is a well-defined problem with a number of analytically defensible approaches, provided the entire set of alternatives is known when the decision event takes place. Less well treated in the literature is how to approach this problem when the entire set of alternatives is unknown, as when the alternatives arrive over time. This change in the availability of data shifts the problem from one of identifying an optimal subset to one in which a series of smaller decisions are undertaken regarding the acceptability of each alternative as it presents itself. This work expands upon a methodology known as the Triage Method. The original Triage Method provided a screening tool that could be applied to alternatives as they presented themselves to determine if they should be accepted for further study, rejected out of hand, or held pending until later date. This decision was made strictly upon the value of the alternative and with no consideration of its cost. Two extensions to the Triage Method are offered which provide a capability to consider cost and other resource requirements of the alternatives, thus allowing a move from simply screening to portfolio selection. Guidelines are presented as to when each of these extensions is best employed, a characterization of the performance tradeoff between these and more traditional methodologies is developed, and insight and techniques for setting the value of parameters required by the extensions are provided