This paper describes Community Connections, an ongoing service-learning project at the University of San Francisco aimed at bridging the digital divide. We discuss Community Connections' work both in San Francisco and in Peru, the advantages and learning outcomes of incorporating service-learning into a traditional computer science curriculum, outline some of the pitfalls and challenges in implementing this program, and provide advice for implementing technology-oriented service-learning programs at other institutions based on our experiences.

Tags have recently become popular as a means of annotating and organizing Web pages and blog entries. Advocates of tagging argue that the use of tags produces a 'folksonomy', a system in which the meaning of a tag is determined by its use among the community as a whole. We analyze the effectiveness of tags for classifying blog entries by gathering the top 350 tags from Technorati and measuring the similarity of all articles that share a tag. We find that tags are useful for grouping articles into broad categories, but less effective in indicating the particular content of an article. We then show that automatically extracting words deemed to be highly relevant can produce a more focused categorization of articles. We also show that clustering algorithms can be used to reconstruct a topical hierarchy among tags, and suggest that these approaches may be used to address some of the weaknesses in current tagging systems.

No abstract available.

As online markets for the exchange of goods and services become more common, the study of markets composed, at least in part, of autonomous agents has taken on increasing importance. In contrast to traditional complete–information economic scenarios, agents that are operating in an electronic marketplace often do so under considerable uncertainty. In order to reduce their uncertainty, these agents must learn about the world around them. When an agent producer is engaged in a learning task in which data collection is costly, such as learning the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the agent 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 learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one–shot decision and show that moderate complexity schedules, in particular two–part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision–theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period. By explicitly considering both the learnability and the profit extracted by different price schedules, a producer can extract more profit as it learns than if it naively chose models that are accurate once learned.

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 may set an arbitrarily complex price schedule---a function that maps the set of purchased items to a price. However, lacking direct knowledge of consumer demand, it cannotcompute the optimal schedule. Instead, it attempts to optimize profits via 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 parametrizations of the price schedule and quantify 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 demand shifts frequently, but that dynamic pricing based on more complex schedules tends to be most profitable when demand shifts very infrequently.

Electronic goods are flexible and have negligible marginal costs. These features allow a producer of electronic goods to explore pricing schemes, and in particular bundling, that would not be feasible with physical goods. However, they can also make it more difficult for a producer to differentiate itself from competitors offering identical goods. Previous research in this area indicates that in markets where producers compete over the sale of identical information goods, cyclical price wars often develop. In this paper, we provide a characterization of the conditions that result in price wars and show analytically how the existence of niches within the consumer population can lead duopolist producers to each target separate niches and avoid price wars. In situations where producers have incomplete information about consumer preferences, and so must learn a strategy, producers will be concerned not only with the relative benefits of niche targeting as opposed to a price war, but also with th...

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 methods producer that is performing a naive, knowledge-free form of leanings (function approximation and hill-climbing) which implement a strategy that balances exploitation to maximize current profits against exploration of the profit landscape to improve future profits. We find that the tradeoff between exploitation and exploration is different depending on the learning algorithms employed, and in particular depending on the complexity of the price schedule that if offered. In general, simpler price schedules are more robust and give up less profit during the learning periods even though in our stationary environment learning eventually is complete and the more complex schedules have high long-run profits. These results hold for both learning methods, even though the relative performance of the methods is quite sensitive to choice of initial conditions and differences in the smoothness of the profit landscape for different price schedules. Our results have implications for automated learning and strategic pricing in non-stationary environments, which arise when the consumer population changes, individuals change their preferences, or competing firms change their strategies.