Contributions in Merlin

In this paper, we investigate the optimal timing for deep geological disposal of nuclear waste. Our model is based on the real options approach to investment under uncertainty. In this context, the problem is similar to the optimal exercise policy for a perpetual American spread option. The potential usefulness of such a model for actual decision-making on a sensitive issue is illustrated by some numerical simulations.

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We show that Gul and Pesendorfer’s [Econometrica 69 (2001) 1403] representation result for preferences with temptation and self-control can be reexpressed in terms of a costly intrapersonal conflict between a Planner and Doer, as in Thaler and Shefrin [J. Political Econ. 89 (1981) 392] and psychologists’ standard view of self-control problems.

In this paper we analyze the long‐run dynamics of the market selection process among simple trading strategies in an incomplete asset market with endogenous prices. We identify a unique surviving financial trading strategy. Investors following this strategy asymptotically gather total market wealth. This result generalizes findings by Blume and Easlcy (1992) to any complete or incomplete asset market.

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We analyze a myopic strategy adjustment process in strategic-form games. It is shown that the steady states of the continuous time limit, which is constructed assuming frequent play and slow adjustment of strategies, are exactly the best-reply matching equilibria, as discussed by Droste, Kosfeld, and Voorneveld (2000. Mimeo, Tilburg University). In a best-reply matching equilibrium every player ‘matches’ the probability of playing a pure strategy to the probability that this pure strategy is a best reply to the pure-strategy profile played by his opponents. We derive stability results for the steady states of the continuous time limit in 2×2 bimatrix games and coordination games. Analyzing the asymptotic behavior of the stochastic adjustment process in discrete time shows convergence to minimal curb sets of the game. Moreover, absorbing states of the process correspond to best-reply matching equilibria of the game.

The ability to rapidly locate useful on-line services (e.g. software applications, software components, process models, or service organizations), as opposed to simply useful documents, is becoming increasingly critical in many domains. As the sheer number of such services increases it will become increasingly more important to provide tools that allow people (and software) to quickly find the services they need, while minimizing the burden for those who wish to list their services with these search engines. This can be viewed as a critical enabler of the ‘friction-free’ markets of the ‘new economy’. Current service retrieval technology is, however, seriously deficient in this regard. The information retrieval community has focused on the retrieval of documents, not services per se, and has as a result emphasized keyword-based approaches. Those approaches achieve fairly high recall but low precision. The software agents and distributed computing communities have developed simple ‘frame-based’ approaches for ‘matchmaking’ between tasks and on-line services increasing precision at the substantial cost of requiring all services to be modeled as frames and only supporting perfect matches. This paper proposes a novel, ontology-based approach that employs the characteristics of a process-taxonomy to increase recall without sacrificing precision and computational complexity of the service retrieval process.

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companies’ interrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as classifying companies by industry membership.

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