Expertise in Ultra-Endurance Triathletes Early Sport Involvement, Training Structure, and the Theory of Deliberate Practice

The theory of deliberate practice (Ericsson, Krampe, & Tesch-Römer, 1993) is predicated on the concept that the engagement in specific forms of practice is necessary for the attainment of expertise. The purpose of this paper was to examine the quantity and type of training performed by expert UE triathletes. Twenty-eight UE triathletes were stratified into expert, middle of the pack, and back of the pack groups based on previous finishing times. All participants provided detailed information regarding their involvement in sports in general and the three triathlon sports in particular. Results illustrated that experts performed more training than non-experts but that the relationship between training and performance was not monotonic as suggested by Ericsson et al. Further, experts' training was designed so periods of high training stress were followed by periods of low stress. However, early specialization was not a requirement for expertise. This work indicates that the theory of deliberate practice does not fully explain expertise development in UE triathlon.

A Review on Repeated Games and Reputations with Incomplete Information

In this project we review the effects of reputation within the context of game theory. This is done through a study of two key papers. First, we examine a paper from Fudenberg and Levine: Reputation and Equilibrium Selection in Games with a Patient Player (1989). We add to this a review Gossner’s Simple Bounds on the Value of a Reputation (2011). We look specifically at scenarios in which a long-run player faces a series of short-run opponents, and how the former may develop a reputation. In turn, we show how reputation leads directly to both lower and upper bounds on the long-run player’s payoffs.

Keeping Variables within Bounds: Using Information between Observations

This research develops an econometric framework to analyze time series processes with bounds. The framework is general enough that it can incorporate several different kinds of bounding information that constrain continuous-time stochastic processes between discretely-sampled observations. It applies to situations in which the process is known to remain within an interval between observations, by way of either a known constraint or through the observation of extreme realizations of the process. The main statistical technique employs the theory of maximum likelihood estimation. This approach leads to the development of the asymptotic distribution theory for the estimation of the parameters in bounded diffusion models. The results of this analysis present several implications for empirical research. The advantages are realized in the form of efficiency gains, bias reduction and in the flexibility of model specification. A bias arises in the presence of bounding information that is ignored, while it is mitigated within this framework. An efficiency gain arises, in the sense that the statistical methods make use of conditioning information, as revealed by the bounds. Further, the specification of an econometric model can be uncoupled from the restriction to the bounds, leaving the researcher free to model the process near the bound in a way that avoids bias from misspecification. One byproduct of the improvements in model specification is that the more precise model estimation exposes other sources of misspecification. Some processes reveal themselves to be unlikely candidates for a given diffusion model, once the observations are analyzed in combination with the bounding information. A closer inspection of the theoretical foundation behind diffusion models leads to a more general specification of the model. This approach is used to produce a set of algorithms to make the model computationally feasible and more widely applicable. Finally, the modeling framework is applied to a series of interest rates, which, for several years, have been constrained by the lower bound of zero. The estimates from a series of diffusion models suggest a substantial difference in estimation results between models that ignore bounds and the framework that takes bounding information into consideration.