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

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

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

A Truthful Two-Stage Mechanism for Eliciting Probabilistic Estimates with Unknown Costs

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that motivates selfish rational agents to make a costly probabilistic estimate or forecast of a specified precision and report it truthfully to a centre. Our mechanism is applied in a setting where the centre is faced with multiple agents, and has no knowledge about their costs. Thus, in the first stage of the mechanism, the centre uses a reverse second price auction to allocate the estimation task to the agent who reveals the lowest cost. While, in the second stage, the centre issues a payment based on a strictly proper scoring rule. When taken together, the two stages motivate agents to reveal their true costs, and then to truthfully reveal their estimate. We prove that this mechanism is incentive compatible and individually rational, and then present empirical results comparing the performance of the well known quadratic, spherical and logarithmic scoring rules. We show that the quadratic and the logarithmic rules result in the centre making the highest and the lowest expected payment to agents respectively. At the same time, however, the payments of the latter rule are unbounded, and thus the spherical rule proves to be the best candidate in this setting

Mechanism Design for eliciting probabilistic estimates from multiple suppliers with unknown costs and limited precision

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly probabilistic estimate of some unknown parameter, by eliciting and fusing estimates from multiple suppliers. Each of these suppliers is capable of producing a probabilistic estimate of any precision, up to a privately known maximum, and by fusing several low precision estimates together the centre is able to obtain a single estimate with a specified minimum precision. Specifically, in the mechanism’s first stage M from N agents are pre-selected by eliciting their privately known costs. In the second stage, these M agents are sequentially approached in a random order and their private maximum precision is elicited. A payment rule, based on a strictly proper scoring rule, then incentivises them to make and truthfully report an estimate of this maximum precision, which the centre fuses with others until it achieves its specified precision. We formally prove that the mechanism is incentive compatible regarding the costs, maximum precisions and estimates, and that it is individually rational. We present empirical results showing that our mechanism describes a family of possible ways to perform the pre-selection in the first stage, and formally prove that there is one that dominates all others

Individualized Stabilization Criteria–Driven Ranibizumab versus Laser in Branch Retinal Vein Occlusion

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Individualized Ranibizumab Regimen Driven by Stabilization Criteria for Central Retinal Vein Occlusion

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A toolbox for the comprehensive analysis of small volume human intestinal samples that can be used with gastrointestinal sampling capsules

Detailed knowledge on the fate of dietary components inside the human intestinal tract is lacking. Access to this inner world of digestion is now possible through novel human gastrointestinal sampling capsules. Due to the novelty of such devices, no methodology has been published to stabilise and analyse the resulting samples. A complicating factor is that excretion of such capsules in faeces may take days, while degradation of the dietary components continues. Therefore a stabilising reagent should be pre-loaded in the capsule to ensure the measurement of a representative sample. Considering the small volume of recovered samples, analytical methods must be optimized to collect as many data as possible from little material. We present a complete workflow for stabilising and analysing the fermentation status of dietary fibres in such samples, including microbiota, fibre degradation, and short chain fatty acids. The final quenching reagent was designed based on safety and effectiveness to inhibit fructo- and galacto-oligosaccharides degradation and short chain fatty acids production by human ileostomy microbiota, and subsequently validated in faecal samples. The final composition of the stock quenching reagent is 175 mM Tris, 525 mM NaCl, 35 mM EDTA, 12% SDS, and 8 M urea at pH 8.5.</p

Spontaneous liver disease in wild-type C57BL/6JOlaHsd mice fed semisynthetic diet

Mouse models are frequently used to study mechanisms of human diseases. Recently, we observed a spontaneous bimodal variation in liver weight in C57BL/6JOlaHsd mice fed a semisynthetic diet. We now characterized the spontaneous variation in liver weight and its relationship with parameters of hepatic lipid and bile acid (BA) metabolism. In male C57BL/6JOlaHsd mice fed AIN-93G from birth to postnatal day (PN)70, we measured plasma BA, lipids, Very low-density lipoprotein (VLDL)-triglyceride (TG) secretion, and hepatic mRNA expression patterns. Mice were sacrificed at PN21, PN42, PN63 and PN70. Liver weight distribution was bimodal at PN70. Mice could be subdivided into two nonoverlapping groups based on liver weight: 0.6 SD 0.1 g (approximately one-third of mice, small liver; SL), and 1.0 SD 0.1 g (normal liver; NL; p<0.05). Liver histology showed a higher steatosis grade, inflammation score, more mitotic figures and more fibrosis in the SL versus the NL group. Plasma BA concentration was 14-fold higher in SL (p<0.001). VLDL-TG secretion rate was lower in SL mice, both absolutely (-66%, p<0.001) and upon correction for liver weight (-44%, p<0.001). Mice that would later have the SL-phenotype showed lower food efficiency ratios during PN21-28, suggesting the cause of the SL phenotype is present at weaning (PN21). Our data show that approximately one-third of C57BL/6JOlaHsd mice fed semisynthetic diet develop spontaneous liver disease with aberrant histology and parameters of hepatic lipid, bile acid and lipoprotein metabolism. Study designs involving this mouse strain on semisynthetic diets need to take the SL phenotype into account. Plasma lipids may serve as markers for the identification of the SL phenotype