Nomination contests: theory and empirical evidence from professional soccer

This paper develops a theory of contests based on perceived abilities, and provides evidence for the predictions of this theory using panel data from professional soccer. We examine how soccer players perform in club matches during the informal)nomination contests for national teams prior to an important international Cup, the Euro 2008. Our differences-in-differences analysis uses players from nonqualified nations who play in the same league as a control group. We find a large positive effect of nomination contest participation on several output measures for players with intermediate chances of being nominated, as proxied by past national team participations. For players with no prior national team experience there is no significant effect. We also find support for the theory that players whose nomination is close to certain reduce their effort in order to avoid injuries or exhaustion prior to the Cup. Finally, any positive reaction is strongest for young players. --

Consumer welfare and unobserved heterogeneity in discrete choice models : the value of alpine road tunnels

We investigate the sensitivity of consumer surplus estimates to parametric assumptions on individual preference heterogeneity in a discrete choice framework. We compare results from a parametric random coefficients logit model and a recently proposed nonparametric sieve estimator. In particular, we provide an assessment of the direct economic value of crossing the Alps for the European road freight sector. Using revealed preference data from a detailed survey on transalpine road freight traffic, we estimate the yearly cost of closing the Mont-Blanc Tunnel, which was closed for 3 years following a large accident in early 1999. Ultimately, our results permit the economic evaluation of security and transport policy measures affecting transalpine traffic. Our findings suggest that the way we model unobserved heterogeneity significantly affects our welfare results

Entry and competition in freight transport : the case of a prospective transalpine rail link between France and Italy

We analyze the expected effects of building a rail tunnel between Lyon and Turin on i) the market shares of the established and the new suppliers, and ii) consumer surplus. The prospective project consists of a 53km rail tunnel providing freight shippers with a new alpine path. We calibrate an equilibrium model where freight shippers choose a mode and alpine path to ship goods from a given origin to a given destination. Freight carriers strategically set prices for the differentiated products they supply. Deriving the market equilibrium, we simulate the entry of a quality-improved product and test its competitive viability. The prospective alpine path proves both competitive and welfare-enhancing on the regional market, loses its competitive edge on the wider North-South market, and leads to a modal shift on the West-East market. We argue that the new infrastructure is only one tool out of a global modal shift-oriented policy toolbox

Nomination contests : theory and empirical evidence from professional soccer

This paper develops a theory of contests based on perceived abilities, and provides evidence for the predictions of this theory using panel data from professional soccer. We examine how soccer players perform in club matches during the informal)nomination contests for national teams prior to an important international Cup, the Euro 2008. Our differences-in-differences analysis uses players from nonqualified nations who play in the same league as a control group. We find a large positive effect of nomination contest participation on several output measures for players with intermediate chances of being nominated, as proxied by past national team participations. For players with no prior national team experience there is no significant effect. We also find support for the theory that players whose nomination is close to certain reduce their effort in order to avoid injuries or exhaustion prior to the Cup. Finally, any positive reaction is strongest for young players

Considerations on partially identified regression models

Motivated by Manski and Tamer (2002) and especially their partial identification analysis of the regression model where one covariate is only interval-measured, we offer several contributions. Manski and Tamer (2002) propose two estimation approaches in this context, focussing on general results. The modified minimum distance (MMD) estimates the true identified set and the modified method of moments (MMM) a superset. Our first contribution is to characterize the true identified set and the superset. Second, we complete and extend the Monte Carlo study of Manski and Tamer (2002). We present benchmark results using the exact functional form for the expectation of the dependent variable conditional on observables to compare with results using its nonparametric estimates, and illustrate the superiority of MMD over MMM. For MMD, we propose a simple shortcut for estimation

A Note on Regressions with Interval Data on a Regressor

Motivated by Manski and Tamer (2002) and especially their partial identification analysis of the regression model where one covariate is only interval-measured, we present two extensions. Manski and Tamer (2002) propose two estimation approaches in this context, focussing on general results. The modified minimum distance (MMD) estimates the true identified set and the modified method of moments (MMM) a superset. Our first contribution is to characterize the true identified set and the superset. Second, we complete and extend the Monte Carlo study of Manski and Tamer (2002). We present benchmark results using the exact functional form for the expectation of the dependent variable conditional on observables to compare with results using its nonparametric estimate, and illustrate the superiority of MMD over MMM

Effort in Nomination Contests: Evidence from Professional Soccer

In most promotion and hiring situations several agents compete for a limited number of attractive positions, assigned on the basis of the agents' relative reputations. Economic theory predicts that agents' effort incentives in such contests depend non-monotonically on their anticipated winning chances, but empirical evidence is lacking. We use panel data to study soccer players' responses to the (informal) nomination contests for being on a national team participating in the 2008 Euro Cup. The control group consists of players who work for the same clubs but are nationals of countries that did not participate in the Euro Cup. We fi�nd that nomination contest participation has substantial positive effects on the performances of players with intermediate chances of being nominated for their national team. Players whose nomination is close to certain perform worse than otherwise, particularly in duels that carry a high injury risk. For players without any recent national team appearances, we fi�nd no signifi�cant effects

Out of distribution detection for intra-operative functional imaging

Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.Comment: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32689-0_

A learning robot for cognitive camera control in minimally invasive surgery

Background!#!We demonstrate the first self-learning, context-sensitive, autonomous camera-guiding robot applicable to minimally invasive surgery. The majority of surgical robots nowadays are telemanipulators without autonomous capabilities. Autonomous systems have been developed for laparoscopic camera guidance, however following simple rules and not adapting their behavior to specific tasks, procedures, or surgeons.!##!Methods!#!The herein presented methodology allows different robot kinematics to perceive their environment, interpret it according to a knowledge base and perform context-aware actions. For training, twenty operations were conducted with human camera guidance by a single surgeon. Subsequently, we experimentally evaluated the cognitive robotic camera control. A VIKY EP system and a KUKA LWR 4 robot were trained on data from manual camera guidance after completion of the surgeon's learning curve. Second, only data from VIKY EP were used to train the LWR and finally data from training with the LWR were used to re-train the LWR.!##!Results!#!The duration of each operation decreased with the robot's increasing experience from 1704 s ± 244 s to 1406 s ± 112 s, and 1197 s. Camera guidance quality (good/neutral/poor) improved from 38.6/53.4/7.9 to 49.4/46.3/4.1% and 56.2/41.0/2.8%.!##!Conclusions!#!The cognitive camera robot improved its performance with experience, laying the foundation for a new generation of cognitive surgical robots that adapt to a surgeon's needs

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. Methods: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. Results: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). Conclusion: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery