Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments.

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies.Fil: De La Rosa, Josep Lluis. Universidad de Girona; EspañaFil: Hormazábal, Nicolás. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Lopardo, Gabriel Alejandro. Universidad de Girona; EspañaFil: Trias, Albert. Universidad de Girona; EspañaFil: Montaner, Miquel. No especifíca

Supporting Situation Awareness and Decision Making in Weather Forecasting

Weather forecasting is full of uncertainty, and as in domains such as air traffic control or medical decision making, decision support systems can affect a forecaster’s ability to make accurate and timely judgments. Well-designed decision aids can help forecasters build situation awareness (SA), a construct regarded as a component of decision making. SA involves the ability to perceive elements within a system, comprehend their significance, and project their meaning into the future in order to make a decision. However, how SA is affected by uncertainty within a system has received little attention. This tension between managing uncertainty, situation assessment, and the impact that technology has on the two, is the focus of this dissertation. To address this tension, this dissertation is centered on the evaluation of a set of coupled models that integrate rainfall observations and hydrologic simulations, coined “the FLASH system” (Flooded Locations and Simulated Hydrographs project). Prediction of flash flooding is unique from forecasting other weather-related threats due to its multi-disciplinary nature. In the United States, some weather forecasters have limited hydrologic forecasting experience. Unlike FLASH, current flash flood forecasting tools are based upon rainfall rates, and with the recent expansion into coupled rainfall and hydrologic models, forecasters have to learn quickly how to incorporate these new data sources into their work. New models may help forecasters to increase their prediction skill, but no matter how far the technology advances, forecasters must be able to accept and integrate the new tools into their work in order to gain any benefit. A focus on human factors principles in the design stage can help to ensure that by the time the product is transitioned into operational use, the decision support system addresses users’ needs while minimizing task time, workload, and attention constraints. This dissertation discusses three qualitative and quantitative studies designed to explore the relationship between flash flood forecasting, decision aid design, and SA. The first study assessed the effects of visual data aggregation methods on perception and comprehension of a flash flood threat. Next, a mixed methods approach described how forecasters acquire SA and mitigate situational uncertainty during real-time forecasting operations. Lastly, the third study used eye tracking assessment to identify the effects of an automated forecasting decision support tool on SA and information scanning behavior. Findings revealed that uncertainty management in forecasting involves individual, team, and organizational processes. We make several recommendations for future decision support systems to promote SA and performance in the weather forecasting domain

Bureaucracy as a Lens for Analyzing and Designing Algorithmic Systems

Scholarship on algorithms has drawn on the analogy between algorithmic systems and bureaucracies to diagnose shortcomings in algorithmic decision-making. We extend the analogy further by drawing on Michel Crozier’s theory of bureaucratic organizations to analyze the relationship between algorithmic and human decision-making power. We present algorithms as analogous to impartial bureaucratic rules for controlling action, and argue that discretionary decision-making power in algorithmic systems accumulates at locations where uncertainty about the operation of algorithms persists. This key point of our essay connects with Alkhatib and Bernstein’s theory of ’street-level algorithms’, and highlights that the role of human discretion in algorithmic systems is to accommodate uncertain situations which inflexible algorithms cannot handle. We conclude by discussing how the analysis and design of algorithmic systems could seek to identify and cultivate important sources of uncertainty, to enable the human discretionary work that enhances systemic resilience in the face of algorithmic errors.Peer reviewe

IFIP TC 13 Seminar: trends in HCI proceedings, March 26, 2007, Salamanca (Spain)

Actas del 13o. Seminario de la International Federation for Information Processing (IFIP), celebrado en Salamanca el 26 de marzo de 2007, sobre las nuevas líneas de investigación en la interacción hombre-máquina, gestión del conocimiento y enseñanza por la Web

A framework for informing consumers on the ecological impact of products at point of sale

The use of intelligent information technologies has the means to provide ecological information just-in-time, thus alleviating consumers' cognitive burden at the time of purchase. We propose a computational framework for supporting consumer awareness of the ecological impact of products they consider purchasing at point of sale. The proposed framework permits consulting multiple information sources through diverse access interfaces, combined with a recommendation engine to score product greenness. We evaluate our approach in terms of usability, performance, and user-influence tests through two conceptual prototypes: an online store and an augmented reality interface to use at physical stores. Our findings suggest that providing ecological information at the time of purchase is able to direct consumers' preference towards products that are ecological and away from products that are not; consumers also express willingness to pay slightly more for ecological products. The experimental results obtained with the interface prototypes are statistically significant

Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

A case study of intended versus actual experience of adaptivity in a tangible storytelling system

This article presents a case study of an adaptive, tangible storytelling sys- temcalled “The ReadingGlove”. The research addresses a gap in the field of adaptivity for ubiquitous systems by taking a critical look at the notion of “adaptivity” and how users experience it. The Reading Glove is an interactive storytelling system featur- ing a wearable, glove-based interface and a set of narratively rich objects. A tabletop display provides adaptive recommendations which highlight objects to select next, functioning as an expert storytelling system. The recommendation engine can be run in three different configurations to examine the effects of different adaptive methods. The study of the design process as well as the user experience of the Reading Glove allows us to develop a deeper understanding of the experience of adaptivity that is use- ful for designers of intelligent systems, particularly those with ubiquitous and tangible forms of interaction