The success of a computer system depends upon a user choosing it, but the field of Human-Computer Interaction has little ability to predict this user choice. We present a new method that measures user choice, and quantifies it as a measure of utility. Our method has two core features. First, it introduces an economic definition of utility, one that we can operationalize through economic experiments. Second, we employ a novel method of crowdsourcing that enables the collection of thousands of economic judgments from real users. ACM Classification: H5.m. Information interfaces and presentation: User Interfaces.

An effective Decision Support System (DSS) should help its users improve decision-making in complex, information-rich, dynamic environments. We present a feature gap analysis of current decision support technologies, and we identify a set of DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. We show that there is a gap between the features provided by current DSS technologies and the DSS Desiderata we aim for. We present a design-science approach that extends the boundaries of human decisionmakers by creating a new and innovative artifact called “evaluator service networks ” at the confluence of people, organizations, and technology. Our artifact enables users to compose decision behaviors from separate, configurable components, and allows dynamic construction of analysis and modeling tools from small, single-purpose evaluator services. The result is a network that can easily be configured to test hypotheses and analyze the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management. We present an example of an evaluator service network that determines sales prices in a rich, dynamic trading environment. Additionally we describe visual interface elements that allow users to see and manipulate the configuration of the network, and to construct economic dashboards that can display the current and historical state of any node in the network.

Web search engines respond to a query by returning more results than can be reasonably reviewed. These results typically include the title, link, and snippet of content from the target link. Each result has the potential to be useful or useless and thus reviewing it has a cost and potential benefit. This paper studies the behavior of a rational agent in this setting, whose objective is to maximize the probability of finding a satisfying result while minimizing cost. We propose two similar agents with different capabilities: one that only compares result snippets relatively and one that predicts from the result snippet whether the result will be satisfying. We prove that the optimal strategy for both agents is a stopping rule: the agent reviews a fixed number of results until the marginal cost is greater than the marginal expected benefit, maximizing the overall expected utility. Finally, we discuss the relationship between rational agents and search users and how our findings help us understand reviewing behaviors.

Abstract –The advances of modeling and simulation as well as agent systems have been phenomenal. The premise of the agent paradigm, its related theory and methodologies together with advances in multilevel modeling of complex systems are opening new frontiers for advancing the studies of the physical, natural, social, military, and information sciences and engineering. This survey paper provides a comprehensive analysis of the evolution of the synergy of modeling and simulation and agent systems and their applications to various fields. The analysis framework explores the evolution of the application and technology of agent-directed simulation. We present a unified and comprehensive perspective regarding the broad range for the synergy of simulation and agents. In drawing the trace of the evolution of agents and simulation to a close, we offer comments on three aspects. The first involves the breadth and extent of agent simulation applications; the second relates to the role of agent simulation in computational experimentation and exploratory analysis; and the third pertains to the future of the use of agents in simulation, as well as simulation for agents. Index Terms – agent-directed simulation, agent simulation, agentbased simulation, agent-supported simulation, agent technology 1.1 Background 1.

A recent letter to the journal Nature claimed integration of auditory and tactile information in speech perception. Although multisensory integration of auditory and visible speech has been repeatedly documented, neither perception nor integration was sufficiently formalized, operationalized, and tested to support this claim about auditory and tactile information. In a recent letter to the journal Nature by Gick and Derrick (2009) called Aero-Tactile Integration in Speech Perception, the authors concluded that “perceivers integrate naturalistic tactile information during auditory speech perception without previous training. ” This conclusion was made based on their findings that inaudible air puffs on the skin increased the accuracy of distinguishing between speech sounds that differed in aspiration (/p / versus /b / and /t / versus /d/) (Derrick, Anderson, Gick, & Green, (2009). Notwithstanding the fact that the integration of audible and visible information has been repeatedly and conclusively demonstrated (Massaro, 1998), this note questions the authors ’ conclusion that “These results demonstrate that perceivers 1 Author to whom correspondence should be addressed. Electronic mail:

lunch and NBER’s Higher Education Working Group meeting for their many helpful comments and

Risk assessment methods based on scoring methods that rate the severity of each risk factor on an ordinal scale are widely used and frequently perceived by users to have value. We argue that this perceived benefit is probably illusory in most cases. We begin by describing a number of common scoring methods currently used to assess risk in a variety of different domains. We then review the literature on the use of ordinal scales in risk analysis, the use of "verbal scales" for eliciting estimates of risks and probabilities, and the extensive research about peculiar human errors when assessing risks. We also supplement this overview with some data of our own. When these diverse kinds of evidence are combined, the case against scoring methods is difficult to deny. In addition to the evidence against the value of scoring methods, there is also a lack of good evidence in their favor. We conclude our overview by reviewing the reasons why risk assessment approaches should describe risk in terms of mathematical probabilities.

∗The authors acknowledge helpful discussions and exchanges with H. Gersbach, Y. Malevergne, M. Marsili and R. Woodard. All remaining errorsare ours. PS and JAH were supported byEuropeanCOSTActionMP0801(PhysicsofCompetition andConflicts)and by the Polish Ministry of Science and Education, Grant No 578/N-COST/2009/0. DS acknowledges financial support from the ETH Competence Center “Coping with Crises in Complex Socio-Economic Systems ” (CCSS) through ETH Research Grant CH1-01-08-2

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lunch and NBER’s Higher Education Working Group meeting for their many helpful comments and