Mathematical Economic Models
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Mathematical models out-perform doctors in predicting cancer patients' responses to treatment

Mathematical prediction models are better than doctors at predicting the outcomes and responses of lung cancer patients to treatment, according to new research presented today (Saturday) at the 2nd Forum of the European Society for Radiotherapy and Oncology (ESTRO).


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SFI MOOC: Introduction to Dynamical Systems and Chaos

SFI MOOC: Introduction to Dynamical Systems and Chaos | Mathematical Economic Models | Scoop.it

This course will begin on January 6, 2014.  If you are enrolled, you will receive email notification that the course has started. 
In this course you'll gain an introduction to the modern study of dynamical systems, the interdisciplinary field of applied mathematics that studies systems that change over time. 
Topics to be covered include: phase space, bifurcations, chaos, the butterfly effect, strange attractors, and pattern formation.

 

Introduction to Dynamical Systems and Chaos (Winter, 2014)
Instructor: David Feldman

http://www.complexityexplorer.org/online-courses/4


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Orbital Sciences estaciona la segunda nave comercial en la ... - FayerWayer

Orbital Sciences estaciona la segunda nave comercial en la ... - FayerWayer | Mathematical Economic Models | Scoop.it
El Nuevo Herald
Orbital Sciences estaciona la segunda nave comercial en la ...
FayerWayer
... la competencia en el mercado del transporte espacial.
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Interpreting probabilities: THe Art of Mathematical Modelling

Interpreting probabilities: THe Art of Mathematical Modelling | Mathematical Economic Models | Scoop.it
The question is how should we interpret a probability. So for example, if I want to estimate the probability that a coin will land heads on a single toss how should I construct the experiment? My professors had said that there was no non-circular real world interpretation of what a probability is. At the time, this bothered me because I think of distributions like the Binomial distribution as the simplest types of mathematical models; the mathematical models with the best predictive abilities and with the most reasonable assumptions. Models in mathematical biology, on the other hand, are usually quite intricate with assumptions that are a lot less tractable. My thinking was that if it was impossible to estimate the probability that a coin lands heads on solid philosophical grounds then there was no hope for me, trying to estimate parameters for mathematical models in biology.

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Great Marchigiani: Vito Volterra - Italian mathematician

Great Marchigiani: Vito Volterra - Italian mathematician | Mathematical Economic Models | Scoop.it

Vito Volterra, (born May 3, 1860, Ancona, Papal States [Italy]—died October 11, 1940, Rome), Italian mathematician who strongly influenced the modern development of calculus.
Volterra’s later work in analysis and mathematical physics was influenced by Enrico Betti while the former attended the University of Pisa (1878–82). Volterra was appointed professor of rational mechanics at Pisa in 1883, the year he began devising a general theory of functionals (functions that depend on a continuous set of values of another function). This concept led to the development of new fields of analysis, including important applications to the solution of integral and differential equations. The important idea of harmonic integrals derives essentially from his functional calculus. He also applied his analytic methods with good results to optics, electromagnetism, and elasticity and to the theory of distortions.
In 1892 Volterra became professor of mechanics at the University of Turin, and eight years later he accepted the chair of mathematical physics at the University of Rome. In 1905 he became a senator of the Kingdom of Italy. Although he was more than 55 years old, he joined the Italian air force during World War I and helped develop dirigibles as weapons of war. The first to propose using helium in the place of hydrogen in airships, he helped organize helium manufacture in Italy.
After the war Volterra devoted his attention to mathematical biology. Unknown to him, much of his work duplicated that of previous researchers, but it drew the attention of mathematicians to the field. His abstract mathematical models of biological processes (such as predator-prey systems) found many analogies in physical science.

Volterra opposed fascism from the outset. In 1931 he refused to take the required oath of loyalty to the government of Benito Mussolini and was forced to leave the University of Rome. The following year he was required to resign from all Italian scientific academies. Thereafter he lived mainly outside Italy. His collected works, Opere matematiche; memorie e note (“Mathematical Works: Memories and Notes”), were published in five volumes between l954 and 1962.


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Tecnología española para el control de riesgos de las finanzas - El Confidencial

Tecnología española para el control de riesgos de las finanzas - El Confidencial | Mathematical Economic Models | Scoop.it
Tecnología española para el control de riesgos de las finanzasEl ConfidencialPiense en unir tecnología y matemáticas…¿suena complicado verdad?

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Adjunct, Online Mathematics

Adjunct, Online Mathematics | Mathematical Economic Models | Scoop.it

Duties: Adjunct Faculty sought for undergraduate and graduate level math courses that are included in the online business programs. Classes include topics such as:
Intermediate Algebra, Finite Mathematics, Basic & Applied Statistics, Advanced Statistics and Advanced Business Mathematics.


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La "Fondation Sciences Mathématiques de Paris" ofrece becas y ... - Embajada de Francia en España

La "Fondation Sciences Mathématiques de Paris" ofrece becas y ... - Embajada de Francia en España | Mathematical Economic Models | Scoop.it
La "Fondation Sciences Mathématiques de Paris" ofrece becas y ...
Embajada de Francia en España
La Fondation Sciences Mathématiques de Paris es una red creada por instituciones de investigación de la capital francesa.
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Probabilidad y Procesos Estocásticos

Esta página es un "Centro de Conocimiento" dedicado al curso "Probabilidad y Procesos Estocásticos" y cursos similares, creado por Alianza Superior. Los recursos son curados por docentes expertos en la materia. Postúlese para ser curador y aumente su prestigio como docente experto.


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Prof. Steve Keen on Debunking Economics

Steve Keen is Associate Professor of Economics & Finance at the University of Western Sydney. His main academic research interest is in developing mathematical models of Hyman Minsky's Financial Instability Hypothesis. Prof. Keen's best-selling book, Debunking Economics, delivers a powerful critique of modern neoclassical macroeconomics. He maintains a website dedicated to 'analysing the global debt bubble' at www.debtdeflation.com.


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Una critica de la moderna macroeconomia neoclasica

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Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model | Mathematical Economic Models | Scoop.it

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.


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Mathematicians Predict the Future With Data From the Past

Mathematicians Predict the Future With Data From the Past | Mathematical Economic Models | Scoop.it

In Issac Asimov's classic science fiction saga Foundation, mathematics professor Hari Seldon predicts the future using what he calls psychohistory.

Drawing on mathematical models that describe what happened in the past, he anticipates what will happen next, including the fall of the Galactic Empire.

That may seem like fanciful stuff. But Peter Turchin is turning himself into a real-life Hari Seldon — and he’s not alone.

Turchin — a professor at the University of Connecticut — is the driving force behind a field called “cliodynamics,” where scientists and mathematicians analyze history in the hopes of finding patterns they can then use to predict the future. It’s named after Clio, the Greek muse of history.

These academics have the same goals as other historians — “We start with questions that historians have asked for all of history,” Turchin says. “For example: Why do civilizations collapse?” — but they seek to answer these questions quite differently. They use math rather than mere language, and according to Turchin, the prognosis isn’t that far removed from the empire-crushing predictions laid down by Hari Seldon in the Foundation saga. Unless something changes, he says, we’re due for a wave of widespread violence in about 2020, including riots and terrorism.


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Christophe CESETTI's curator insight, April 11, 2013 5:34 AM

it's said...but not expected "Unless something changes, we’re due for a wave of widespread violence in about 2020, including riots and terrorism"

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Predicting Human Preferences Using the Block Structure of Complex Social Networks

Predicting Human Preferences Using the Block Structure of Complex Social Networks | Mathematical Economic Models | Scoop.it

With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a “new” computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups.


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