Defensive forecasting for optimal prediction with expert advice

The method of defensive forecasting is applied to the problem of prediction with expert advice for binary outcomes. It turns out that defensive forecasting is not only competitive with the Aggregating Algorithm but also handles the case of "second-guessing" experts, whose advice depends on the learner's prediction; this paper assumes that the dependence on the learner's prediction is continuous.Comment: 14 page

Universal Learning of Repeated Matrix Games

We study and compare the learning dynamics of two universal learning algorithms, one based on Bayesian learning and the other on prediction with expert advice. Both approaches have strong asymptotic performance guarantees. When confronted with the task of finding good long-term strategies in repeated 2x2 matrix games, they behave quite differently.Comment: 16 LaTeX pages, 8 eps figure

The truth, but not yet: Avoiding naïve skepticism via explicit communication of metadisciplinary aims

Introductory students regularly endorse naïve skepticism—unsupported or uncritical doubt about the existence and universality of truth—for a variety of reasons. Though some of the reasons for students’ skepticism can be traced back to the student—for example, a desire to avoid engaging with controversial material or a desire to avoid offense—naïve skepticism is also the result of how introductory courses are taught, deemphasizing truth to promote students’ abilities to develop basic disciplinary skills. While this strategy has a number of pedagogical benefits, it prevents students in early stages of intellectual development from understanding truth as a threshold concept. Using philosophy as a case study, I argue that we can make progress against naïve skepticism by clearly discussing how metadisciplinary aims differ at the disciplinary and course levels in a way that is meaningful, reinforced, and accessible

On-line regression competitive with reproducing kernel Hilbert spaces

We consider the problem of on-line prediction of real-valued labels, assumed bounded in absolute value by a known constant, of new objects from known labeled objects. The prediction algorithm's performance is measured by the squared deviation of the predictions from the actual labels. No stochastic assumptions are made about the way the labels and objects are generated. Instead, we are given a benchmark class of prediction rules some of which are hoped to produce good predictions. We show that for a wide range of infinite-dimensional benchmark classes one can construct a prediction algorithm whose cumulative loss over the first N examples does not exceed the cumulative loss of any prediction rule in the class plus O(sqrt(N)); the main differences from the known results are that we do not impose any upper bound on the norm of the considered prediction rules and that we achieve an optimal leading term in the excess loss of our algorithm. If the benchmark class is "universal" (dense in the class of continuous functions on each compact set), this provides an on-line non-stochastic analogue of universally consistent prediction in non-parametric statistics. We use two proof techniques: one is based on the Aggregating Algorithm and the other on the recently developed method of defensive forecasting.Comment: 37 pages, 1 figur

Prediction and Situational Option Generation in Soccer

Paul Ward, Michigan Technological University Naturalistic models of decision making, such as the Recognition- Primed Decision (RPD) model (e.g., Klein, Calderwood, & Clinton-Cirocco, 1986; Klein, 1997), suggest that as individuals become more experienced within a domain they automatically recognize situational patterns as familiar which, in turn, activates an associated situational response. Typically, this results in a workable course of action being generated first, and subsequent options generated only if the initial option proves ineffective

Administrative Compensation for Medical Injuries: Lessons From Three Foreign Systems

Examines "no-fault" systems in New Zealand, Sweden, and Denmark, in which patients injured by medical negligence can file for compensation through governmental or private adjudicating organizations. Considers lessons for U.S. medical malpractice reform

Competitive on-line learning with a convex loss function

We consider the problem of sequential decision making under uncertainty in which the loss caused by a decision depends on the following binary observation. In competitive on-line learning, the goal is to design decision algorithms that are almost as good as the best decision rules in a wide benchmark class, without making any assumptions about the way the observations are generated. However, standard algorithms in this area can only deal with finite-dimensional (often countable) benchmark classes. In this paper we give similar results for decision rules ranging over an arbitrary reproducing kernel Hilbert space. For example, it is shown that for a wide class of loss functions (including the standard square, absolute, and log loss functions) the average loss of the master algorithm, over the first $N$ observations, does not exceed the average loss of the best decision rule with a bounded norm plus $O(N^{-1/2})$. Our proof technique is very different from the standard ones and is based on recent results about defensive forecasting. Given the probabilities produced by a defensive forecasting algorithm, which are known to be well calibrated and to have good resolution in the long run, we use the expected loss minimization principle to find a suitable decision.Comment: 26 page

Online Learning in Case of Unbounded Losses Using the Follow Perturbed Leader Algorithm

In this paper the sequential prediction problem with expert advice is considered for the case where losses of experts suffered at each step cannot be bounded in advance. We present some modification of Kalai and Vempala algorithm of following the perturbed leader where weights depend on past losses of the experts. New notions of a volume and a scaled fluctuation of a game are introduced. We present a probabilistic algorithm protected from unrestrictedly large one-step losses. This algorithm has the optimal performance in the case when the scaled fluctuations of one-step losses of experts of the pool tend to zero.Comment: 31 pages, 3 figure

Introduction: food relocalisation and knowledge dynamics for sustainability in rural areas

The chapter presents the literature on local food and local knowledge and introduces the case studies analysed in the volum