Educação matemática pela arte
Gusmão, Lucimar Donizete
2013-08-28
Search results
8 records were found.
Comment: 49 pages, 20 figures
Fund for scientific research (FWO) Flanders; National ICT Australia
Commerce in information goods is one of the earliest emerging applications for intelligent agents in commerce. However, the fundamental characteristics of information goods mean that they can and likely will be offered in widely varying configurations. Participating agents will need to deal with uncertainty about both prices and location in multi-dimensional product space. Thus, studying the behavior of learning agents is central to understanding and designing for agent-based information economies. Since uncertainty will exist on both sides of transactions, and interactions between learning agents that are negotiating and transacting with other learning agents may lead to unexpected dynamics, it is important to study two-sided learning.
We present a simple but powerful model of an information bundling economy with a single producer an...
We introduce a novel power capping technique, IdleCap, that achieves higher effective server frequency for a given power constraint than existing techniques. IdleCap works by repeatedly alternating between the highest performance state and a low-power idle state, maintaining a fixed average power budget, while significantly increasing the average processor frequency. In experiments conducted on an IBM BladeCenter HS21 server across three representative workloads, IdleCap reduces the mean response time by up to a factor of 3 when compared to power capping using clock-throttling. Furthermore, we argue how IdleCap applies to next-generation servers using DVFS and advanced idle states.
Markets for digital information goods provide the possibility of exploring new and more complex pricing schemes, due to information goods' flexibility and negligible marginal cost. In this paper we compare the dynamic performance of price schedules of varying complexity under two different specifications of consumer demand shifts.
In an economy in which a producer must learn the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the producer has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule.
In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to...
We explore a scenario in which a monopolist producer of information goods seeks to maximize its profits in a market where consumer demand shifts frequently and unpredictably. The producer is free to set an arbitrarily complex price schedule-a function that maps the set of purchased items to a price-but without direct knowledge of consumer demand it cannot compute the optimal schedule. Instead, it must employ a form of optimization based on trial and error. By means of a simple model of consumer demand and a modified version of a simple nonlinear optimization routine, we study a variety of parameterizations of the price schedule and quantity some of the relationships among learnability, complexity, and profitability. In particular, we show that fixed pricing or simple two-parameter dynamic pricing schedules are preferred when consumer d...
In an automated market for electronic goods new problems arise that have not been well studied previously. For example, information goods are very flexible. Marginal costs are negligible and nearly limitless bundling and unbundling of these items are possible, in contrast to physical goods. Consequently, producers can offer complex pricing schemes. However, the profit-maximizing design of a complex pricing schedule depends on a producer's knowledge of the distribution of consumer preferences for the available information goods. Preferences are private and can only be gradually uncovered through market experience. In this paper we compare dynamic performance across price schedules of varying complexity. We provide the producer with two machine learning method producer that is performing a naive, knowledge-free form of leanings (function...
