AN INVESTIGATION OF MOTIVATIONAL BIAS IN SUBJECTIVE PREDICTIVE PROBABILITY DISTRIBUTIONS*

In this paper the problem of conscious motivational bias in the assessment of subjective predictive probability distributions is explored. The primary purpose of this research is to discover how subjects express their bias. Results of an experiment are reported in which subjective distributions were assessed by subjects, first in the absence of a biasing incentive and then in the presence of an incentive to bias upward coupled with an incentive to be “credible.” In the biased task, subjects reported manipulating up to five characteristics of their distribution, such as location, shape, and dispersion. Furthermore, subjects reported using combinations of the characteristics strategically. Dominant strategies included shifting the mode upward and redistributing probability to the left, shifting the mode upward a small amount and redistributing probability to the right, and shifting the mode upward and tightening the distribution. The location of the mode was mentioned first by nearly all subjects, suggesting that they first locate their biased distribution and then manipulate other characteristics to express their bias, need for credibility, and other concerns.     

论《弃儿汤姆·琼斯的历史》的时空体

时空体理论是俄国著名理论家巴赫金的核心理论之一,对理解和分析文学作品的艺术成就有非常重要的指导作用。运用巴赫金的\     

Computing first-order sensitivity indices with contribution to the sample mean plot

In this paper, we investigate the use of the contribution to the sample mean plot (CSM plot) as a graphical tool for sensitivity analysis (SA) of computational models. We first provide an exact formula that links, for each uncertain model input X_j, the CSM plot C_j(·) with the first-order variance-based sensitivity index S_j. We then build a new estimate for S_j using polynomial regression of the CSM plot. This estimation procedure allows the computation of S_j from given data, without any SA-specific design of experiment. Numerical results show that this new S_j estimate is efficient for large sample sizes, but that at small sample sizes it does not compare well with other S_j estimation techniques based on given data, such as the effective algorithm for computing global sensitivity indices method or metamodel-based approaches.     

Estimation of the Lognormal-Pareto Distribution Using Probability Weighted Moments and Maximum Likelihood

This article deals with the estimation of the lognormal-Pareto and the lognormal-generalized Pareto distributions, for which a general result concerning asymptotic optimality of maximum likelihood estimation cannot be proved. We develop a method based on probability weighted moments, showing that it can be applied straightforwardly to the first distribution only. In the lognormal-generalized Pareto case, we propose a mixed approach combining maximum likelihood and probability weighted moments. Extensive simulations analyze the relative efficiencies of the methods in various setups. Finally, the techniques are applied to two real datasets in the actuarial and operational risk management fields.     

Inference for the Birnbaum–Saunders Lifetime Regression Model with Applications

The Birnbaum–Saunders distribution is a positively skewed distribution that is frequently used for analyzing lifetime data. Regression analysis is widely used in this context when some covariates are involved in the life-test. In this article, we discuss the maximum likelihood estimation of the model parameters and associated inference. We discuss the likelihood-ratio tests for some hypotheses of interest as well as some interval estimation methods. A Monte Carlo simulation study is then carried out to examine the performance of the proposed estimators and the interval estimation methods. Finally, some numerical data analyses are done for illustrating all the inferential methods developed here.     

小议魔灵在《亚瑟王之死》中的叙事功能

魔灵是<亚瑟王之死>中的魔法师,他在叙事过程中发挥了很大的作用,主要体现在其预言推动情节、强化作品叙事的逻辑性等方面.     

Temporal distance reduces the attractiveness of p-bets compared to $-bets

Although people normally prefer a more certain option over a riskier option of equal expected value, sometimes they are tempted to choose the riskier, but more rewarding one. Such temptation is even stronger when people decide for the distant future as compared with the near future. In Experiments 1 and 2 we showed that increasing temporal distance makes people more likely to choose a high risk $-bet (€400, 0.02;0) over a low risk p-bet (€14, 0.60;0). Furthermore, the risk aversion shift increased proportionally to the time delay and persisted even for long delays (6 months). In Experiment 3, we showed that this temporal effect is associated with a decrease in positive feelings towards the p-bet, and with a decrease in the positive evaluation of the high-probability (60%) of the p-bet, but not with an increase of the positive evaluation of the high-payoff (400 euro) of the p-bet. In Experiment 4, we showed that increasing the salience of the probability feature tended to decrease the temporal effect, while increasing the salience of the prize did not vary the strength of the effect. Results are in line with an affect-based explanation of the temporal effect.     

Overconfidence bias and conjunction fallacy in predicting outcomes of football matches

The aim of this study was to explore the occurrence of the overconfidence bias and the conjunction fallacy in betting behavior among frequent and sporadic bettors and to test whether it was influenced by the task format (probability vs. frequencies). Frequent bettors (N = 67) and sporadic bettors (N = 63) estimated whether the bets on football games presented to them via an on-line questionnaire would be successful. The bets consisted of singles (one match outcomes) and conjunctions (two matches outcomes), and were presented either in probability or frequency terms. Both frequent and sporadic bettors showed similar levels of the overconfidence bias. However, the frequent bettors made the conjunction fallacy more often than the sporadic bettors. The presentation of the task in the frequency terms significantly reduced the overconfidence bias in comparison to the evaluations in probability terms, but left the conjunction fallacy unaffected.     

De Finetti was Right: Probability Does Not Exist

De Finetti's treatise on the theory of probability begins with the provocative statement PROBABILITY DOES NOT EXIST, meaning that probability does not exist in an objective sense. Rather, probability exists only subjectively within the minds of individuals. De Finetti defined subjective probabilities in terms of the rates at which individuals are willing to bet money on events, even though, in principle, such betting rates could depend on state-dependent marginal utility for money as well as on beliefs. Most later authors, from Savage onward, have attempted to disentangle beliefs from values by introducing hypothetical bets whose payoffs are abstract consequences that are assumed to have state-independent utility. In this paper, I argue that de Finetti was right all along: PROBABILITY, considered as a numerical measure of pure belief uncontaminated by attitudes toward money, does not exist. Rather, what exist are de Finetti's `previsions', or betting rates for money, otherwise known in the literature as `risk neutral probabilities'. But the fact that previsions are not measures of pure belief turns out not to be problematic for statistical inference, decision analysis, or economic modeling. This revised version was published online in June 2006 with corrections to the Cover Date.     

A comparative evaluation of the estimators of the three-parameter generalized pareto distribution

The parameters and quantiles of the three-parameter generalized Pareto distribution (GPD3) were estimated using six methods for Monte Carlo generated samples. The parameter estimators were the moment estimator and its two variants, probability-weighted moment estimator, maximum likelihood estimator, and entropy estimator. Parameters were investigated using a factorial experiment. The performance of these estimators was statistically compared, with the objective of identifying the most robust estimator from amongst them.