Archivio per 16 aprile 2015

16
Apr
15

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16
Apr
15

15 Lessons from Behavioural Economics

See on Scoop.itBounded Rationality and Beyond

The following deck was used by @tjalve in our internal #teachme session. It covers 15 lessons from Behavioural Economics you can apply to your ongoing projects. 
The concepts covered are: 
1. The Endowment Effect 
2. Hyperbolic Discounting 
3. The IKEA effect 
4. Anchoring Bias 
5. The Von Restorff Effect 
6. Loss Aversion 
7. Hedonic Adaption 
8. The Bandwagon Effect 
9. The Inaction inertia effect 
10. The Zeigarnik Effect 
11. The Framing Effect 
12. The Goal Gradient Effect 
13. The Choice Paradox 
14. Round Pricing Preference 
15. Reciprocity

See on slideshare.net

16
Apr
15

[cs/0405018] Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms

See on Scoop.itBounded Rationality and Beyond

The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketS and the S&P CNX NIFTY stock index. We analyzed 7 year’s Nasdaq 100 main index values and 4 year’s NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately.

  
See on arxiv.org

16
Apr
15

[1504.02656] A complex network approach to cloud computing

See on Scoop.itBounded Rationality and Beyond

Cloud computing has become an important means to speed up computing. One problem influencing heavily the performance of such systems is the choice of nodes as servers responsible for executing the users’ tasks. In this article we report how complex networks can be used to model such a problem. More specifically, we investigate the performance of the processing respectively to cloud systems underlain by Erdos-Renyi and Barabasi-Albert topology containing two servers. Cloud networks involving two communities not necessarily of the same size are also considered in our analysis. The performance of each configuration is quantified in terms of two indices: the cost of communication between the user and the nearest server, and the balance of the distribution of tasks between the two servers. Regarding the latter index, the ER topology provides better performance than the BA case for smaller average degrees and opposite behavior for larger average degrees. With respect to the cost, smaller values are found in the BA topology irrespective of the average degree. In addition, we also verified that it is easier to find good servers in the ER than in BA. Surprisingly, balance and cost are not too much affected by the presence of communities. However, for a well-defined community network, we found that it is important to assign each server to a different community so as to achieve better performance.

See on arxiv.org

16
Apr
15

A meta-analysis of state-of-the-art electoral prediction from Twitter data

See on Scoop.itBounded Rationality and Beyond

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.

  
See on arxiv.org

16
Apr
15

Web Spam, Social Propaganda an the Evolution of Search Engine Rankings

See on Scoop.itBounded Rationality and Beyond

Search Engines have greatly influenced the way we experience the web. Since the early days of the web, users have been relying on them to get informed and make decisions. When the web was relatively small, web directories were built and maintained using human experts to screen and categorize pages according to their characteristics. By the mid 1990’s, however, it was apparent that the human expert model of categorizing web pages does not scale. The first search engines appeared and they have been evolving ever since, taking over the role that web directories used to play. But what need makes a search engine evolve? Beyond the financial objectives, there is a need for quality in search results. Search engines know that the quality of their ranking will determine how successful they are. Search results, however, are not simply based on well-designed scientific principles, but they are influenced by web spammers. Web spamming, the practice of introducing artificial text and links into web pages to affect the results of web searches, has been recognized as a major search engine problem. It is also a serious users problem because they are not aware of it and they tend to confuse trusting the search engine with trusting the results of a search. In this paper, we analyze the influence that web spam has on the evolution of the search engines and we identify the strong relationship of spamming methods on the web to propagandistic techniques in society. Our analysis provides a foundation for understanding why spamming works and offers new insight on how to address it. In particular, it suggests that one could use social anti-propagandistic techniques to recognize web spam.

See on cs.wellesley.edu

16
Apr
15

Alzheimer’s study finds possible cause of disease

See on Scoop.itSocial Neuroscience Advances

A study using mice has uncovered a possible cause of Alzheimer’s disease, and suggests that a drug currently being investigated in human clinical trials to treat cancer could prevent the illness.

The research has been heralded as offering hope of finding new treatments for dementia.

The findings, by Duke University in America and published in the Journal of Neuroscience, are surprising, according to one of the authors, as they contradict current thinking on the disease.

See on theguardian.com




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