Aggregation of Expert Opinions

Conflicts of interest arise between a decision maker and agents who have information pertinent to the problem because of differences in their preferences over outcomes. We show how the decision maker can extract the information by distorting the decisions that will be taken, and show that only slight distortions will be necessary when agents are "informationally small." We further show that as the number of informed agents becomes large the necessary distortion goes to zero. We argue that the particular mechanisms analyzed are substantially less demanding informationally than those typically employed in implementation and virtual implementation. In particular, the equilibria we analyze are "conditionally" dominant strategy in a precise sense. Further, the mechanisms are immune to manipulation by small groups of agents.Information Aggregation, Mechanism Design, Incomplete Information

Aggregation of Expert Opinions

Conflicts of interest arise between a decision maker and agents who have information pertinent to the problem because of differences in their preferences over outcomes. We show how the decision maker can extract the information by distorting the decisions that will be taken, and show that only slight distortions will be necessary when agents are informationally small. We further show that as the number of informed agents becomes large the necessary distortion goes to zero. We argue that the particular mechanisms analyzed are substantially less demanding informationally than those typically employed in implementation and virtual implementation. In particular, the equilibria we analyze are conditionally dominant strategy in a precise sense. Further, the mechanisms are immune to manipulation by small groups of agents.Information aggregation, Asymmetric information, Cheap talk, Experts

Modeling Expert Opinions on Food Healthiness: A Nutrition Metric

Background Research over the last several decades indicates the failure of existing nutritional labels to substantially improve the healthiness of consumers' food and beverage choices. The difficulty for policy-makers is to encapsulate a wide body of scientific knowledge in a labeling scheme that is comprehensible to the average shopper. Here, we describe our method of developing a nutrition metric to fill this void. Methods We asked leading nutrition experts to rate the healthiness of 205 sample foods and beverages, and after verifying the similarity of their responses, we generated a model that calculates the expected average healthiness rating that experts would give to any other product based on its nutrient content. Results The form of the model is a linear regression that places weights on 12 nutritional components (total fat, saturated fat, cholesterol, sodium, total carbohydrate, dietary fiber, sugars, protein, vitamin A, vitamin C, calcium, and iron) to predict the average healthiness rating that experts would give to any food or beverage. We provide sample predictions for other items in our database. Conclusions Major benefits of the model include its basis in expert judgment, its straightforward application, the flexibility of transforming its output ratings to any linear scale, and its ease of interpretation. This metric serves the purpose of distilling expert knowledge into a form usable by consumers so that they are empowered to make healthier decisions.

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

Key perennial weeds in arable crops in the Nordic countries

Our review on the most common perennial weeds in the Nordic countries draws on 1) a Nordic/Baltic joint desk-top study done in 1997-99, 2) information from national weed surveys and 3) expert opinions from Denmark, Finland, Norway and Sweden

Expert Opinions and Logarithmic Utility Maximization in a Market with Gaussian Drift

This paper investigates optimal portfolio strategies in a financial market where the drift of the stock returns is driven by an unobserved Gaussian mean reverting process. Information on this process is obtained from observing stock returns and expert opinions. The latter provide at discrete time points an unbiased estimate of the current state of the drift. Nevertheless, the drift can only be observed partially and the best estimate is given by the conditional expectation given the available information, i.e., by the filter. We provide the filter equations in the model with expert opinion and derive in detail properties of the conditional variance. For an investor who maximizes expected logarithmic utility of his portfolio, we derive the optimal strategy explicitly in different settings for the available information. The optimal expected utility, the value function of the control problem, depends on the conditional variance. The bounds and asymptotic results for the conditional variances are used to derive bounds and asymptotic properties for the value functions. The results are illustrated with numerical examples.Comment: 21 page