Library of Congres§ Cataloging-in-Publication Data. Shapiro, Carl. Information rules: a strategic guide to the network economy /. Carl Shapiro and Hal R. Varian . PDF | On Jan 1, , Carl Shapiro and others published Information Rules: A Strategic Guide to The Network Economy. Shapiro, Carl and Varian, Hal R. Information Rules,. Harvard Business School Press. Page 2. Page 3. Page 4. Page 5. Page 6. Page 7. Page 8. Page 9. Page
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Library of Congress Cataloging-in-Publication Data. Shapiro, Carl. Information rules: a strategic guide to the network economy /. Carl Shapiro and Hal R. Varian . The main thesis of this book is that while technology may change, economic laws do not. The authors, Carl Shapiro and Hal Varian point out that the information. peypredkoefritlec.cf: Information Rules: A Strategic Guide to the Network Economy ( ): Carl Shapiro, Hal R. Varian: Books.
For example, we ask why some network architectures favor mass market vs.
There are some additional mathematical examples, and a short section on the effects of architecture on content creation that we did not include in the published version. I outline various circumstances under which such sharing may increase or decrease producer profits. If a rental market is present, more copies will be sold at a lower price; I derive conditions that illustrate when this is more or less profitable than a sales-only market.
When content is viewed only a few times and transactions costs of rental are low, rental may be more attractive than sales to both producers and consumers. Finally, when users have heterogeneous tastes, a rental market provides a nice way to segment high-value and low value users.
These effects tend to suggest that rental markets may often increase profits, contrary to widespread views to the contrary. Mechanism Design for Computerized Agents [PDF] The field of economic mechanism design has been an active area of research in economics for at least 20 years.
In this paper I provide an overview of this subject for an audience interested in applications to electronic commerce and discuss some special problems that arise in this context. Pricing Information Goods [PDF] [PostScript] I describe some of the issues involved in pricing information goods such as computer software, databases, electronic journals and so on.
In particular I discuss the incentives to engage in differential pricing and examine some of the forms such differential pricing may take.
We describe the technology and costs of the Internet, then discuss how to design efficient pricing in order to allocate scarce Internet resources. We offer a "smart market" as a device to efficiently price congestion.
This paper overlaps substantially with the paper above "Pricing". We describe the history, technology and costs of the Internet at greater length than in "Pricing". We describe a "smart market" for pricing Internet congestion.
There is more attention to the smart market, and less to other pricing considerations, than in "Pricing". Written with Jeff MacKie-Mason. Written for WWW '94 Chicago , which answers some frequently asked questions about usage-sensitive pricing for Internet resources. We describe the basic economic theory of pricing a congestible resource such as an ftp server, a router, a Web site, etc. We explore the implications of flat pricing and congestion pricing for capacity expansion in centrally planned, competitive, and monopolistic environments.
However, the causal mechanism for this phenomenon is far from clear. In this paper I investigate three models of how entry may cause cost reduction: managerial incentives, survival of the fittest, and imitation. The models have quite different implications for social welfare.
Information goods vs. Old economy examples are books and movies. There is a high fixed cost of writing a book, but it only costs a few dollars to make copies of the book.
The internet and digitization of documents, videos and music have simply taken this to the next level. Information is now nearly free to reproduce. How do you price something that is free to reproduce?
Information Rules argues to price information according to its value to the end user instead of its cost. Different versions of varying quality can be easily created that have different values to different segments. Segments with low price sensitivity can be given a higher value version for a higher value price. Meanwhile, virtually the same information can be recast in a lower value format and given away or sold inexpensively to those who are price sensitive.
Standards, lock-in and switching costs Standards wars are everywhere in the new economy and how to deal with different standards and how to understand the impact of competing standards on customers and competition is important. But this is not new.
Logical rules can be applied to deduce new information from existing information. For example, given rules about the structure of a class membership hierarchy, a traditional logic-based system can determine class membership. SNePS is one such system, but also provides a number of other useful representational and functional facilities that can aid information fusion and reasoning in the cyber domain.
These include the capabilities to: represent and distinguish co-referential terms; represent meta-knowledge; detect contradictions.
The SNePS system was designed to handle co-referential terms in a way that provides each term its own unique intentional denotation, but allows for these terms to co-refer using equivalence relationships.
Such a representation technique can provide a sense for an entity that is unique to each information source.
SNePS can also represent knowledge about knowledge. Such a feature is capable of representing what SNePS knows about the knowledge contents of the external information sources.
Apart from representational features, one functional feature of the SNePS system is its 2 capability for belief revision. The system can automatically detect contradictory information and present it to the user of the system.
After the user selects the information to reject, an automatic repair propagation procedure is invoked. Such a feature is desired in information fusion as different external sources may contain contradictory information. A description of these and how they are represented follows: 3.
In order to accomplish this we're taking our task one step at a time, analyzing the questions we want answered at the current step, and then asking the SME how they'd go about it. Through this process we have identified a number of external information tools to aid us, as well as constructed reasoning axioms that can answer the current questions under consideration.
Presently, the process under consideration is determining if an INFERD attack track for a particular host is a false positive, or true-positive. We have determined with the help of the SME this can be done by examining the vulnerabilities of the system using the Nessus security scanning tool Section 2. Apart from the above reasoning rules, some general rules are provided to the system for the task at hand as well as in anticipation for future reasoning tasks.
For example, if host h1 is part of network n1, and port p1 is part of h1, it can be concluded p1 is part of the network n1. The second rule is specifically tailored for properties the system believes are subsumed by parent parts expressed as SumbsumedProperty.
This rule was selected as Nessus reports only that ports have a specific vulnerability on them, but our desired goal is to know if certain hosts have vulnerability. By asserting that CVE, BID, and SID vulnerability identifiers are a subsumed property the system can know which hosts have a vulnerability, if it knows one of its ports has that same vulnerability. The first rule asserts that if some entity is a member of some class represented by the Isa predicate , and that class is a subclass, or a kind of, another class represented by the Ako predicate , then that entity is a member of the superclass as well.
The second rule provides a the transitivity rule to the Ako relationship. Reports on these vulnerabilities are generated as an XML file, which details ports on the scanned hosts and CVE identifiers for vulnerabilities detected on those ports. Additional information can include the operating systems running on the hosts, network trace routes, host aliases, and severity of the vulnerability. An example of the Nessus output is presented in Fig.
These become facts in our knowledge base. The following proposition-valued terms are required to represent the 5 Nessus report: PropertyValue x,y,z - Entity x has a property y with value z Isa x,y - Entity x is a member of the category of y PartOf x,y - Object x is a part of object y About x,y - x contains information about y As are the following terms: Network - the category of networks Host - the category of hosts.
Alert - the category of alerts.