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Winning Team (winning + team)
Selected AbstractsRally racing: knowledge and learning requirements for a winning teamKNOWLEDGE AND PROCESS MANAGEMENT: THE JOURNAL OF CORPORATE TRANSFORMATION, Issue 2 2001Rafael Andreu In this paper we discuss how a winning rally racing team was developed from scratch in Seat Sport, the racing division within Seat, one of Volkswagen's divisions, which decided to enter the World Rally Cup in 1995. The discussion focuses on how different types of knowledge were literally ,grown up' from practically nothing to a winning team. We start by describing the knowledge requirements stemming from the goal of developing a World Rally Cup winning team. Defining them in the form of success factors, we classify them from different standpoints (explicit versus implicit, individual versus collective, mental models, coordination schemes, etc.). Next, we show how these different knowledge requirements were acquired and developed at Seat Sport. Finally, we derive the actual learning processes that were present at Seat Sport and relate them to the different kinds of needs and requirements. Copyright © 2001 John Wiley & Sons, Ltd. [source] Never change a winning team?PAPERS IN REGIONAL SCIENCE, Issue 1 2007Jouke Van Dijk Editor-in-Chief, also on behalf of the editorial team No abstract is available for this article. [source] A choice prediction competition: Choices from experience and from descriptionJOURNAL OF BEHAVIORAL DECISION MAKING, Issue 1 2010Ido Erev Abstract Erev, Ert, and Roth organized three choice prediction competitions focused on three related choice tasks: One shot decisions from description (decisions under risk), one shot decisions from experience, and repeated decisions from experience. Each competition was based on two experimental datasets: An estimation dataset, and a competition dataset. The studies that generated the two datasets used the same methods and subject pool, and examined decision problems randomly selected from the same distribution. After collecting the experimental data to be used for estimation, the organizers posted them on the Web, together with their fit with several baseline models, and challenged other researchers to compete to predict the results of the second (competition) set of experimental sessions. Fourteen teams responded to the challenge: The last seven authors of this paper are members of the winning teams. The results highlight the robustness of the difference between decisions from description and decisions from experience. The best predictions of decisions from descriptions were obtained with a stochastic variant of prospect theory assuming that the sensitivity to the weighted values decreases with the distance between the cumulative payoff functions. The best predictions of decisions from experience were obtained with models that assume reliance on small samples. Merits and limitations of the competition method are discussed. Copyright © 2009 John Wiley & Sons, Ltd. [source] | ||||||||||