In several key industries, including semiconductors, biotechnology, computer software, and the Internet, our patent system is creating a patent thicket: an overlapping set of patent rights requiring that those seeking to commercialize new technology obtain licenses from multiple patentees. The patent thicket is especially thorny when combined with the risk of hold-up, namely the danger that new products will inadvertently infringe on patents issued after these products were designed. The need to navigate the patent thicket and hold-up is especially pronounced in industries such as telecommunications and computing in which formal standard-setting is a core part of bringing new technologies to market. Cross-licenses and patent pools are two natural and effective methods used by market participants to cut through the patent thicket, but each involves some transaction costs. Antitrust law and enforcement, with its historical hostility to cooperation among horizontal rivals, can easily add to these transaction costs. Yet a few relatively simple principles, such as the desirability package licensing for complementary patents but not for substitute patents, can go a long way towards insuring that antitrust will help solve the problems caused by the patent thicket and by hold-up rather than exacerbating them.

numerous seminars, and two anonymous referees for helpful comments. This project would not have been possible without the assistance of several individuals and organizations. MixMasterFlame and the FlameNap network shared P2P data with us, and BigChampagne LLC, the CMJ Network, Nathaniel Leibowitz, and Nevil Brownlee generously provided auxiliary data. We thank Keith Ross and David Weekly for assistance in understanding the KaZaA, OpenNap, and WinMX search protocols and database indices. Sarah Woolverton and Christina Hsiung Chen

Data is more than knowledge: implications of the reversed knowledge hierarchy for knowledge management and organizational memory In knowledge management literature it is often pointed out that it is important to distinguish between data, information and knowledge. The generally accepted view sees data as simple facts that become information as data is combined into meaningful structures, which subsequently become knowledge as meaningful information is put into a context and when it can be used to make predictions. This view sees data as a prerequisite for information, and information as a prerequisite for knowledge. In this paper, I will explore the conceptual hierarchy of data, information and knowledge, showing that data emerges only after we have information, and that information emerges only after we already have knowledge. The reversed hierarchy of knowledge is shown to lead to a different approach in developing information systems that support knowledge management and organizational memory. It is also argued that this difference may have major implications for organizational flexibility and renewal. 2

Introduction The task of deciding when and how to innovate is not an easy one. Consider the following managerial quandaries: . A CIO has joined a firm that lags in the adoption of emerging information technologies. He wonders: just how innovative should this firm be going forward, and what can be done to position it to be more willing and able to assume the challenge of early adoption? . A VP of marketing resides in a firm that generally leads in IT innovation, and must decide whether to endorse the immediate adoption of a particular innovation with major implications for marketing strategy. She wonders: are her firm's needs in this area and "readiness" to adopt sufficient to justify taking the lead with this specific innovation? If so, how should the assimilation process be managed? . A product manager must design a deployment strategy for an innovative software development tool. He wonders: how fast can this technology diffu

The fundamental goal of all good design and engineering is to create maximal value added for any given investment. There are many dimensions in which value can be assessed, from monetary profit to the solution of social problems. The benefits sought are often domain-specific, yet the logic is the same: design is an investment activity. Software economics is the field that seeks to enable significant improvements in software design and engineering through economic reasoning about product, process, program, and portfolio and policy issues. We summarize the state of the art and identify shortfalls in existing knowledge. Past work focuses largely on costs, not on benefits, thus not on value added; nor are current technical software design criteria linked clearly to value creation. We present a roadmap for research emphasizing the need for a strategic investment approach to software engineering. We discuss how software economics can lead to fundamental improvements in software design and engineering, in theory and practice. 1

Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework to implement versioning and price segmentation strategies. Bayesian updating is a natural tool for profiling users with clickstream data. I illustrate these four modeling techniques and discuss their potential for solving Internet marketing problems.

Media are explored as model to envision, to design, to formalize and to implement platforms for communities. We consider communities of both natural and artificial agents and aim at designing media which facilitate collaboration within such a community.

this paper. This paper was written while Haltiwanger served as Chief Economist of the Census Bureau. 1

We present a framework to measure empirically the size of indirect network effects in high-technology markets with competing incompatible technology standards. These indirect network effects arise due to inter-dependence in demand for hardware and compatible software. By modeling the joint determination of hardware sales and software availability in the market, we are able to describe the nature of demand inter-dependence and to measure the size of the indirect network effects. We apply the model to price and sales data from the industry for Personal Digital

The advent of the digital economy and the increasingly important role of various types of electronic market places provide unprecedented opportunity for suppliers of products and services to experiment with new pricing mechanisms and subsequent pricing models. Economic appropriateness, technical feasibility, and purposes of such pricing mechanisms depend mainly on product characteristics, customer groups, sales volume, and eventually the role of the three most important actors, ‘suppliers’, ‘intermediaries’, and ‘consumers’. New information technologies not only enable businesses to charge flexible prices on the Internet, but also allow for new price setting mechanisms in which consumers specify their interests and requirements. This paper provides some conceptual background for developing net-based pricing models and reflects first empirical evidences against it. 1