11,634 research outputs found
Rational Decision-Making in Business Organizations
Lecture to the memory of Alfred Nobel, December 8, 1978decision making;
Distribution of Mutual Information from Complete and Incomplete Data
Mutual information is widely used, in a descriptive way, to measure the
stochastic dependence of categorical random variables. In order to address
questions such as the reliability of the descriptive value, one must consider
sample-to-population inferential approaches. This paper deals with the
posterior distribution of mutual information, as obtained in a Bayesian
framework by a second-order Dirichlet prior distribution. The exact analytical
expression for the mean, and analytical approximations for the variance,
skewness and kurtosis are derived. These approximations have a guaranteed
accuracy level of the order O(1/n^3), where n is the sample size. Leading order
approximations for the mean and the variance are derived in the case of
incomplete samples. The derived analytical expressions allow the distribution
of mutual information to be approximated reliably and quickly. In fact, the
derived expressions can be computed with the same order of complexity needed
for descriptive mutual information. This makes the distribution of mutual
information become a concrete alternative to descriptive mutual information in
many applications which would benefit from moving to the inductive side. Some
of these prospective applications are discussed, and one of them, namely
feature selection, is shown to perform significantly better when inductive
mutual information is used.Comment: 26 pages, LaTeX, 5 figures, 4 table
Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets
In a "tipping" model, each node in a social network, representing an
individual, adopts a behavior if a certain number of his incoming neighbors
previously held that property. A key problem for viral marketers is to
determine an initial "seed" set in a network such that if given a property then
the entire network adopts the behavior. Here we introduce a method for quickly
finding seed sets that scales to very large networks. Our approach finds a set
of nodes that guarantees spreading to the entire network under the tipping
model. After experimentally evaluating 31 real-world networks, we found that
our approach often finds such sets that are several orders of magnitude smaller
than the population size. Our approach also scales well - on a Friendster
social network consisting of 5.6 million nodes and 28 million edges we found a
seed sets in under 3.6 hours. We also find that highly clustered local
neighborhoods and dense network-wide community structure together suppress the
ability of a trend to spread under the tipping model
The Underlying Return Generating Factors for REIT Returns: An Application of Independent Component Analysis
Multi-factor approaches to analysis of real estate returns have, since the pioneering work of Chan, Hendershott and Sanders (1990), emphasised a macro-variables approach in preference to the latent factor approach that formed the original basis of the arbitrage pricing theory. With increasing use of high frequency data and trading strategies and with a growing emphasis on the risks of extreme events, the macro-variable procedure has some deficiencies. This paper explores a third way, with the use of an alternative to the standard principal components approach â independent components analysis (ICA). ICA seeks higher moment independence and maximises in relation to a chosen risk parameter. We apply an ICA based on kurtosis maximisation to weekly US REIT data using a kurtosis maximising algorithm. The results show that ICA is successful in capturing the kurtosis characteristics of REIT returns, offering possibilities for the development of risk management strategies that are sensitive to extreme events and tail distributions.Real Estate Returns, REIT, ICA, Independent Component Analysis
Agricultural Risk Aversion Revisited: A Multicriteria Decision-Making Approach
In modelling farm systems it is widely accepted that risk plays a central role. Furthermore, farmers' risk aversion determines their decisions in both the short and the long run. This paper presents a methodology based on multiple criteria mathematical programming to obtain relative and absolute risk aversion coefficients. We rely on multiattribute utility theory (MAUT) to elicit a separable additive multiattribute utility function and then estimate the risk aversion coefficients and apply this methodology to an irrigated area of Northern Spain. The results show a wide variety of attitudes to risk among farmers, who mainly exhibit decreasing absolute risk aversion (DARA) and constant relative risk aversion (CRRA).Risk analysis, Agriculture, Utility theory, Multiple criteria analysis, Risk and Uncertainty,
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