Time-to-birth prediction models and the influence of expert opinions

Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high--sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a predictive model, based on a new dataset containing information of 1,243 admissions, that predicts whether a patient will give birth within a given time after admission. Such a model could provide support in the clinical decision-making process. Predictions for birth within 48 h or 7 days after admission yield an Area Under the Curve of the Receiver Operating Characteristic (AUC) of 0.72 for both tasks. Furthermore, we show that by incorporating predictions made by experts at admission, which introduces a potential bias, the prediction effectiveness increases to an AUC score of 0.83 and 0.81 for these respective tasks

Autonomous vehicle decision-making: Should we be bio-inspired?

© Springer International Publishing AG 2017. On our crowded roads, drivers must compete for space but cooperate to avoid occupying the same space at the same time. Decision-making is strategic and requires mutual understanding of other’s choices. Fully autonomous vehicles (AVs) will need risk management software to make these types strategic decisions without human arbitration. Accidents will occur, and what constitutes rational and ‘safe’ decisions will be scrutinized by the legal system. It is far from clear how AV-Human and AV-AV interactions should be managed. Game Theory provides a framework for analyzing mutual ‘games’ with 2 or more players. It assumes that players mutually optimize their outcomes according to Nash equilibria (NE), but do humans follow Nash equilibria in Human-Human interactions? We implemented simple two-player competitive games to see whether people played rationally according to Nash equilibria. On each of 100 trials, each player was instructed to maximise their reward by pressing one of three buttons labelled “4”, “6”, and “12”, without knowing the other players choice. If players pressed different buttons, they received a reward of 4, 6, or 12 points accordingly. If players pressed the same button, the reward was reduced depending on the game type. Results showed that players did not follow NE, but played a probabilistic game that included the “4” button, even though pressing this button is always suboptimal. We suggest that this may be an evolutionary strategy, but it clearly shows that people do not follow the ‘rational’ Nash strategy. It seems that AV-human interactions will be probabilistic. In AV-AV interactions, software may be playing itself, and may also require probabilistic optimal evolutionary-type strategies. We doubt that the full implications of autonomous decision-making have been fully worked out. Whether probabilistic decisions will tolerated legally and actuarially is doubtful. One way to avoid them would be to allow regulated AV-AV communications, and force software decisions to be deterministic according to some protocol. However, AV-Human interactions seem likely to remain problematic

Reduced Normal Forms Are Not Extensive Forms

Fundamental results in the theory of extensive form games have singled out the reduced normal form as the key representation of a game in terms of strategic equivalence. In a precise sense, the reduced normal form contains all strategically relevant information. This note shows that a difficulty with the concept has been overlooked so far: given a reduced normal form alone, it may be impossible to reconstruct the game’s extensive form representation

To share or not to share: the optimal advertising effort with asymmetric advertising effectiveness

In this paper, we study a two-stage model in which a manufacturer expands to a new market through a local retailer and has private information on the advertising effectiveness. The manufacturer chooses the information sharing format with the retailer, either no information sharing or mandatory information sharing. Under no information sharing format, the manufacturer and the retailer play a signaling game. We derive both separating and pooling equilibria and conduct equilibrium refinements for the signaling game. Under mandatory information sharing format, the manufacturer simply informs the retailer the advertising effectiveness. We also establish the stylized model and derive the optimal advertising effort. By comparing the manufacturer’s ex ante profit under the two information sharing formats, we find that the manufacturer always prefers mandatory information sharing, under which both the advertising effort and profit can be higher. We also observe that unlike the common case that the channel members may have different preference over the information sharing formats, the manufacturer and the retailer can actually achieve alignment. While some previous studies suggest that the manufacturer and the retailer may have different preference over the information sharing formats, we find that they can actually achieve alignment with asymmetric information on advertising effectiveness

Price probabilities: A class of Bayesian and non-Bayesian prediction rules

This paper examines the implications of the market selection hypothesis on the accuracy of the probabilities implied by equilibrium prices and on the “learning” mechanism of markets. I use the standard machinery of dynamic general equilibrium models to generate a rich class of probabilities, price probabilities, and discuss their properties. This class includes the Bayes’ rule and known non-Bayesian rules. If the prior support is well-specified, I prove that all members of this class perform as well as Bayes’ rule in terms of likelihood. If the prior support is misspecified in that the Bayesian prior does not converge, I demonstrate that some members of price probabilities significantly outperform Bayes’. Because these members are never worse and sometimes better than Bayes, my result challenges the prevailing opinion that Bayes’ rule is the only rational way to learn