Footballonomics: The Anatomy of American Football; Evidence from 7 years of NFL game data

Do NFL teams make rational decisions? What factors potentially affect the probability of wining a game in NFL? How can a team come back from a demoralizing interception? In this study we begin by examining the hypothesis of rational coaching, that is, coaching decisions are always rational with respect to the maximization of the expected points scored. We reject this hypothesis by analyzing the decisions made in the past 7 NFL seasons for two particular plays; (i) the Point(s) After Touchdown (PAT) and (ii) the fourth down decisions. Having rejected the rational coaching hypothesis we move on to examine how the detailed game data collected can potentially inform game-day decisions. While NFL teams personnel definitely have an intuition on which factors are crucial for winning a game, in this work we take a data-driven approach and provide quantifiable evidence using a large dataset of NFL games for the 7-year period between 2009 and 2015. In particular, we use a logistic regression model to identify the impact and the corresponding statistical significance of factors such as possession time, number of penalty yards, balance between passing and rushing offense etc. Our results clearly imply that avoiding turnovers is the best strategy for winning a game but turnovers can be overcome with letting the offense on the field for more time. Finally we combine our descriptive model with statistical bootstrap in order to provide a prediction engine for upcoming NFL games. Our evaluations indicate that even by only considering a small number of (straightforward) factors, we can achieve a very good prediction accuracy. In particular, the average accuracy during seasons 2014 and 2015 is approximately 63%. This performance is comparable to the more complicated state-of-the-art prediction systems, while it outperforms expert analysts 60% of the time.Comment: Working study - Papers has been presented at the Machine Learning and Data Mining for Sports Analytics 2016 workshop and accepted at PLOS ON

Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper

Finance and Growth: What We Know and What We Need To Know

The paper reviews recent literature on the relationship between finance and growth, highlighting areas where we need to know more. The paper argues that institutions, such as financial regulation, have a first-order effect on financial development and growth, and that their effectiveness could determine the success or failure of policies like bank privatisation or financial liberalisation. It concludes that a better understanding of the obstacles to financial development, which include institutional, legal and political economy constraints, is needed.

Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

Quantum Ontologies and Mind-Matter Synthesis

Aspects of a quantum mechanical theory of a world containing efficacious mental aspects that are closely tied to brains, but that are not identical to brains.Comment: 69 pages. Invited contribution to Xth Max Born Symposium: "Quantum Future". Published in "Quantum Future", eds. P. Blanchard and A. Jadczyk, Springer-Verlag, 1999, ISBN 3-540-65218-3. LBNL 4072