Contributions in Merlin

We study a new equilibrium concept in non-cooperative games, where players follow a behavioral rule called best-reply matching. Under this rule a player matches the probability of playing a pure strategy to the probability that this strategy is a best reply. Kosfeld, Droste, and Voorneveld [Games and Economic Behavior 40 (2002) 270] show that best-reply matching equilibria are stationary states in a simple model of social learning, where newborns adopt a best-reply to recent observations of play. In this paper we analyze best-reply matching in more detail and illustrate the concept by means of well-known examples. For example in the centipede game it is shown that players will continue with large probability.

Given that overconsumption in industrial countries is a main cause of environmental degradation, a shift toward more sustainable consumption patterns is required. This study attempts to uncover personal and contextual barriers to consumers' purchases of green food and to strengthen knowledge about fostering green purchases. Survey data are used to examine the influence of distinct categories of personal factors (such as attitudes, personal norms, perceived behavior barriers, knowledge) and contextual factors (such as socioeconomic characteristics, living conditions, and store characteristics) on green purchases of Swiss consumers. Results from regression analysis suggest that green food purchases are facilitated by positive attitudes of consumers toward (a) environmental protection, (b) fair trade, (c) local products, and (d) availability of action-related knowledge. In turn, green behavior is negatively associated with (e) perceived time barriers and (f) frequency of shopping in supermarkets. Surprisingly, green purchases are not significantly related to moral thinking, monetary barriers, or the socioeconomic characteristics of the consumers. Implications for policy makers and for companies and marketers engaged in the promotion and commercialization of green products are discussed.

Most of the thrust in the semantic web movement comes from the observation that existing NLP tools are not sophisticated or efficient enough to process the full richness of Natural Language, and therefore Machine Understandable annotations need to be added to Web Resources in order to make them accessible by remote agents. However, when the target application is not required to handle a huge amount of documents, but more limited sets, it is conceivable and practical to take advantage of NLP tools to pre-process textual documents in order to generate annotations (to be verified by human editors). We discuss an approach based on a combination of various Natural Language Processing techniques that addresses this issue. Documents are analized fully automatically and converted into a semantic annotation, which can then be stored together with the original documents. It is this annotation that constitutes the machine understandable resource that remote agents can query.

Extracts of this paper were presented at the conference "Work Beyond 60: Preparing for the Demographic Shock", 6–7 March 2003 in Vienna organized by The Geneva Association, The Club of Rome, and The Risk Institute. Parts were also presented at the Bertelsmann Foundation conference "Strategien gegen den Fachkräftemangel" in Berlin, 2 July 2002 and at the Bertelsmann Foundation conference "Reformen zur Steigerung der Beschäftigungsfähigkeit älterer Arbeitskräfte" in Berlin, 26 October 2001. The authors would like to thank the participants as well as Jaap van Dam, Thomas Liebig, Fred Henneberger, and Geneviève Reday-Mulvey for their valuable comments and discussions. Alfonso Sousa-Poza would like to thank the Swiss National Science Foundation for financial assistance. The usual disclaimer applies.

Large amounts of technical documentation are available in machine readable format, however there is a lack of effective ways to access them. In this paper we propose an approach based on linguistic techniques, geared towards the creation of a domain-specific Knowledge Base, starting from the available technical documentation. We then discuss an effective way to access the information encoded in the Knowledge Base. Given a user question phrased in natural language the system is capable of retrieving the encoded semantic information that most closely matches the user input, and present it by highlighting the textual elements that were used to deduct it.

This article develops a theory of multiunit auctions where short squeezes can occur in the secondary market. Both uniform and discriminatory auctions are studied and bidders can submit multiple bids. We show that bidders with short and long preauction positions have different valuations in an otherwise common value setting. Discriminatory auctions lead to more short squeezing and higher revenue than uniform auctions, ceteris paribus. Asymptotically, as the auction size approaches infinity, the two formats lead to equivalent outcomes. Shorts employ more aggressive equilibrium bidding strategies. Most longs strategically choose to be passive. Free riding on a squeeze by small, long players has no impact on these results, but affects revenue in discriminatory auctions.

This paper addresses the classification of linked entities. We introduce a relational vector-space (VS) model (in analogy to the VS model used in information retrieval) that abstracts the linked structure, representing entities by vectors of weights. Given labeled data as background knowledge/training data, classification procedures can be defined for this model, including a straightforward, “direct” model using weighted adjacency vectors. Using a large set of tasks from the domain of company affiliation identification, we demonstrate that such classification procedures can be effective. We then examine the method in more detail, showing that as expected the classification performance correlates with the relational autocorrelation of the data set. We then turn the tables and use the relational VS scores as a way to analyze/visualize the relational autocorrelation present in a complex linked structure. The main contribution of the paper is to introduce the relational VS model as a potentially useful addition to the toolkit for relational data mining. It could provide useful constructed features for domains with low to moderate relational autocorrelation; it may be effective by itself for domains with high levels of relational autocorrelation, and it provides a useful abstraction for analyzing the properties of linked data.

This paper describes a novel theoretical and empirical approach to tasks such as business process redesign and knowledge management. The project involves collecting examples of how different organizations perform similar processes, and organizing these examples in an on-line ìprocess handbook"". The handbook is intended to help people: (1) redesign existing organizational processes, (2) invent new organizational processes (especially ones that take advantage of information technology), and (3) share ideas about organizational practices. A key element of the work is an approach to analyzing processes at various levels of abstraction, thus capturing both the details of specific processes as well as the ""deep structure"" of their similarities. This approach uses ideas from computer science about inheritance and from coordination theory about managing dependencies. A primary advantage of the approach is that it allows people to explicitly represent the similarities (and differences) among related processes and to easily find or generate sensible alternatives for how a given process could be performed. In addition to describing this new approach, the work reported here demonstrates the basic technical feasibility of these ideas and gives one example of their use in a field study.

In the past, most collaboration support systems have focused on either automating fixed work processes or simply supporting communication in ad-hoc processes. This results in systems that are usually inflexible and difficult to change or that provide no specific support to help users decide what to do next. This paper describes a new kind of tool that bridges the gap between these two approaches by flexibly supporting processes at many points along the spectrum: from highly specified to highly unspecified. The development of this approach was strongly based on social science theory about collaborative work.

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