Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

The vicious cycle: fundraising and perceived visibility in US presidential primaries

Scholars of presidential primaries have long posited a dynamic positive feedback loop between fundraising and electoral success. Yet existing work on both directions of this feedback remains inconclusive and is often explicitly cross-sectional, ignoring the dynamic aspect of the hypothesis. Pairing high-frequency FEC data on contributions and expenditures with Iowa Electronic Markets data on perceived probability of victory, we examine the bidirectional feedback between contributions and viability. We find robust, significant positive feedback in both directions. This might suggest multiple equilibria: a candidate initially anointed as the front-runner able to sustain such status solely by the fundraising advantage conferred despite possessing no advantage in quality. However, simulations suggest the feedback loop cannot, by itself, sustain advantage. Given the observed durability of front-runners, it would thus seem there is either some other feedback at work and/or the process by which the initial front-runner is identified is informative of candidate quality

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Understanding Markups in the Open Economy under Bertrand Competition

The purpose of this paper is to understand the effects of endogenous markups and trade costs on the pricing behavior of exporters when firms are heterogeneous in productivity. Using new analytical distributions for markups under Bertrand competition, we uncover Ricardian patterns of export pricing that generate higher markups and export price volatility when industrialized countries sell to developing countries. These Ricardian patterns dissipate when developing countries move from bilateral to multilateral trade liberalization. The results arise from a form of price rigidity for exports that arises endogenously due to cut-throat competition, even though prices are otherwise perfectly flexible.

Estimating Risk Preferences in the Field

We survey the literature on estimating risk preferences using field data. We concentrate our attention on studies in which risk preferences are the focal object and estimating their structure is the core enterprise. We review a number of models of risk preferences—including both expected utility (EU) theory and non-EU models—that have been estimated using field data, and we highlight issues related to identification and estimation of such models using field data. We then survey the literature, giving separate treatment to research that uses individual-level data (e.g., property insurance data) and research that uses aggregate data (e.g., betting market data). We conclude by discussing directions for future research

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Preemption, Leapfrogging, and Competition in Patent Races

This paper investigates when patent races will be characterized by vigorous competition and when they will degenerate into a monopoly. Under some conditions, a firm with an arbitrarily small headstart can preempt its rivals. Such 'e-preemption' is shown to depend on whether a firm that is behind in the patent race, as measured by the expected time remaining until discovery, can't 'leapfrog' the competition and become the new leader. An example of an R and D game with random discovery illustrates how e-preemption can occur when leapfrogging is impossible. A multi-stage R and D process allows leapfrogging and thus permits competition. A similar conclusion emerges in a model of a deterministic patent race with imperfect monitoring of rival firms' R and D investment activities