Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff

In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy

Market-based Recommendation: Agents that Compete for Consumer Attention

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains

Clostridium difficile infection among veterans health administration patients

OBJECTIVETo report on the prevalence and incidence of Clostridium difficile infection (CDI) from 2009 to 2013 among Veterans Healthcare Administration patientsDESIGNA retrospective descriptive analysis of data extracted from a large electronic medical record (EMR) databaseSETTINGData were acquired from VHA healthcare records from 2009 to 2013 that included outpatient clinical visits, long-term care, and hospitalized care as well as pharmacy and laboratory information.RESULTSIn 2009, there were 10,207 CDI episodes, and in 2013, there were 12,143 CDI episodes, an increase of 19.0%. The overall CDI rate increased by 8.4% from 193 episodes per 100,000 patient years in 2009 to 209 episodes per 100,000 patient years in 2013. Of the CDI episodes identified in 2009, 58% were identified during a hospitalization, and 42% were identified in an outpatient setting. In 2013, 44% of the CDI episodes were identified in an outpatient setting.CONCLUSIONThis is one of the largest studies that has utilized timely EMR data to describe the current CDI epidemiology at the VHA. Despite an aging population with greater burden of comorbidity than the general US population, our data show that VHA CDI rates stabilized between 2011 and 2013 following increases likely attributable to the introduction of the more sensitive nucleic acid amplification tests (NAATs). The findings in this report will help establish an accurate benchmark against which both current and future VA CDI prevention initiatives can be measured.Infect. Control Hosp. Epidemiol. 2015;36(9):1038–1045</jats:sec

Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs

High-throughput, quantitative analyses of genetic interactions in E. coli.

Large-scale genetic interaction studies provide the basis for defining gene function and pathway architecture. Recent advances in the ability to generate double mutants en masse in Saccharomyces cerevisiae have dramatically accelerated the acquisition of genetic interaction information and the biological inferences that follow. Here we describe a method based on F factor-driven conjugation, which allows for high-throughput generation of double mutants in Escherichia coli. This method, termed genetic interaction analysis technology for E. coli (GIANT-coli), permits us to systematically generate and array double-mutant cells on solid media in high-density arrays. We show that colony size provides a robust and quantitative output of cellular fitness and that GIANT-coli can recapitulate known synthetic interactions and identify previously unidentified negative (synthetic sickness or lethality) and positive (suppressive or epistatic) relationships. Finally, we describe a complementary strategy for genome-wide suppressor-mutant identification. Together, these methods permit rapid, large-scale genetic interaction studies in E. coli

LITOS - A new balloon-borne instrument for fine-scale turbulence soundings in the stratosphere

We have developed a new compact balloon payload called LITOS (Leibniz-Institute Turbulence Observations in the Stratosphere) for high resolution wind turbulence soundings in the stratosphere up to 35 km altitude. The wind measurements are performed using a constant temperature anemometer (CTA) with a vertical resolution of ∼2.5 mm, i.e. 2 kHz sampling rate at 5 m/s ascent speed. Thereby, for the first time, it is possible to study the entire turbulence spectrum down to the viscous subrange in the stratosphere. Including telemetry, housekeeping, batteries and recovery unit, the payload weighs less than 5 kg and can be launched from any radiosonde station. Since autumn 2007, LITOS has been successfully launched several times from the Leibniz-Institute of Atmospheric Physics (IAP) in Kühlungsborn, Germany (54° N, 12° E). Two additional soundings were carried out in 2008 and 2009 in Kiruna, Sweden (67° N, 21° E) as part of the BEXUS program (Balloon-borne EXperiments for University Students). We describe here the basic principle of CTA measurements and prove the validity of this method in the stratosphere. A first case study allows a clear distinction between non-turbulent regions and a turbulent layer with a thickness of some tens of meters. Since our measurements cover the transition between the inertial and viscous subrange, energy dissipation rates can be calculated with high reliability