Rate of Arbitrage and Reconciled Beliefs

Any group of risk neutral agents who hold differing beliefs is vulnerable to money pumps (arbitrage). Thus, the agents may wish to reconcile their beliefs into a new joint belief. We propose a criterion for the choice of reconciled belief based on the notion of ``rate of arbitrage.''' It is shown that there exists a unique belief (probability distribution) that minimizes the maximal expected rate of arbitrage, and an explicit formula for this belief is given.

Non-Bayesian Testing of a Stochastic Prediction

We propose a method to test a prediction of the distribution of a stochastic process. In a non-Bayesian non-parametric setting, a predicted distribution is tested using a realization of the stochastic process. A test associates a set of realizations for each predicted distribution, on which the prediction passes. So that there are no type I errors, a prediction assigns probability 1 to its test set. Nevertheless, these sets are small, in the sense that "most" distributions assign it probability 0, and hence there are few type II errors. It is also shown that there exists such a test that cannot be manipulated, in the sense that an uninformed predictor who is pretending to know the true distribution is guaranteed to fail on an uncountable number of realizations, no matter what randomized prediction he employs. The notion of a small set we use is category I, described in more detail in the paper.

A True Expert Knows which Question Should be Asked.

We suggest a test for discovering whether a potential expert is informed of the distribution of a stochastic process. In a non-Bayesian non-parametric setting, the expert is asked to make a prediction which is tested against a single realization of the stochastic process. It is shown that by asking the expert to predict a “small” set of sequences, the test will assure that any informed expert can pass the test with probability one with respect to the actual distribution. Moreover, for the uninformed non-expert it is impossible to pass this test, in the sense that for any choice of a “small” set of sequences, only a “small” set of measures will assign a positive probability to the given set. Hence for “most” measures, the non-expert will surely fail the test. We define small as category 1 sets, described in more detail in the paper.

Non-Bayesian testing of a stochastic prediction

We propose a method to test a prediction of the distribution of a stochastic process. In a non-Bayesian non-parametric setting, a predicted distribution is tested using a realization of the stochastic process. A test associates a set of realizations for each predicted distribution, on which the prediction passes. So that there are no type I errors, a prediction assigns probability 1 to its test set. Nevertheless, these sets are .small., in the sense that .most.distributions assign it probability 0, and hence there are .few. type II errors. It is also shown that there exists such a test that cannot be manipulated, in the sense that an uninformed predictor who is pretending to know the true distribution is guaranteed to fail on an uncountable number of realizations, no matter what randomized prediction he employs. The notion of a small set we use is category I, described in more detail in the paper

Citizenship Education and Liberalism: A State of the Debate Analysis 1990–2010

What kind of citizenship education, if any, should schools in liberal societies promote? And what ends is such education supposed to serve? Over the last decades a respectable body of literature has emerged to address these and related issues. In this state of the debate analysis we examine a sample of journal articles dealing with these very issues spanning a twenty-year period with the aim to analyse debate patterns and developments in the research field. We first carry out a qualitative analysis where we design a two-dimensional theoretical framework in order to systematise the various liberal debate positions, and make us able to study their justifications, internal tensions and engagements with other positions. In the ensuing quantitative leg of the study we carry out a quantitative bibliometric analysis where we weigh the importance of specific scholars. We finally discuss possible merits and flaws in the research field, as evidenced in and by the analysis