Archivio per 25 aprile 2015

25
Apr
15

Chinese scientists genetically modify human embryos

See on Scoop.itcognition

In a world first, Chinese scientists have reported editing the genomes of human embryos. The results are published1 in the online journal Protein & Cell and confirm widespread rumours that such experiments had been conducted—rumours that sparked a high-profile debate last month2, 3 about the ethical implications of such work.

In the paper, researchers led by Junjiu Huang, a gene-function researcher at Sun Yat-sen University in Guangzhou, tried to head off such concerns by using ‘non-viable’ embryos, which cannot result in a live birth, that were obtained from local fertility clinics. The team attempted to modify the gene responsible for β-thalassaemia, a potentially fatal blood disorder, using a gene-editing technique known as CRISPR/Cas9. The researchers say that their results reveal serious obstacles to using the method in medical applications.

“I believe this is the first report of CRISPR/Cas9 applied to human pre-implantation embryos and as such the study is a landmark, as well as a cautionary tale,” says George Daley, a stem-cell biologist at Harvard Medical School in Boston. “Their study should be a stern warning to any practitioner who thinks the technology is ready for testing to eradicate disease genes.”

Some say that gene editing in embryos could have a bright future because it could eradicate devastating genetic diseases before a baby is born. Others say that such work crosses an ethical line: researchers warned in Nature2 in March that because the genetic changes to embryos, known as germline modification, are heritable, they could have an unpredictable effect on future generations. Researchers have also expressed concerns that any gene-editing research on human embryos could be a slippery slope towards unsafe or unethical uses of the technique.

The paper by Huang’s team looks set to reignite the debate on human-embryo editing — and there are reports that other groups in China are also experimenting on human embryos.

See on nature.com

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25
Apr
15

Tax Compliance and Public Goods Provision — An Agent-based Econophysics Approach

See on Scoop.itBounded Rationality and Beyond

We calculate the dynamics of tax evasion within a multi-agent econophysics model which is adopted from the theory of magnetism and previously has been shown to capture the main characteristics from agent-based based models which build on the standard Allingham and Sandmo approach. In particular, we implement a feedback of public goods provision on the decision-making of selfish agents which aim to pursue their self interest. Our results imply that such a feedback enhances the moral attitude of selfish agents thus reducing the percentage of tax evasion. Two parameters govern the behavior of selfish agents, (i) the rate of adaption to changes in public goods provision and (ii) the threshold of perception of public goods provision. Furtheron we analyze the tax evasion dynamics for different agent co mpositions and under the feedback of public goods provision. We conclude that policymakers may enhance tax compliance behavior via the threshold of perception by means of targeted public relations.

See on arxiv.org

25
Apr
15

Prospect Theory for Online Financial Trading

See on Scoop.itBounded Rationality and Beyond

Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are risk-averse with respect to gains and risk-seeking with respect to losses, a phenomenon called “loss aversion”. Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of them have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the loss aversion phenomenon, an essence in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the effect of loss aversion. Moreover, we introduce three risk-adjusted metrics inspired by prospect theory to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading, where traders are allowed to watch and follow the trading activities of others, by predicting potential winners statistically based on their historical trading behavior rather than their trading performance at any given point in time.

  
See on arxiv.org

25
Apr
15

Time-Inconsistent Planning: A Computational Problem in Behavioral Economics

See on Scoop.itBounded Rationality and Beyond

In many settings, people exhibit behavior that is inconsistent across time — we allocate a block of time to get work done and then procrastinate, or put effort into a project and then later fail to complete it. An active line of research in behavioral economics and related fields has developed and analyzed models for this type of time-inconsistent behavior. 
Here we propose a graph-theoretic model of tasks and goals, in which dependencies among actions are represented by a directed graph, and a time-inconsistent agent constructs a path through this graph. We first show how instances of this path-finding problem on different input graphs can reconstruct a wide range of qualitative phenomena observed in the literature on time-inconsistency, including procrastination, abandonment of long-range tasks, and the benefits of reduced sets of choices. We then explore a set of analyses that quantify over the set of all graphs; among other results, we find that in any graph, there can be only polynomially many distinct forms of time-inconsistent behavior; and any graph in which a time-inconsistent agent incurs significantly more cost than an optimal agent must contain a large “procrastination” structure as a minor. Finally, we use this graph-theoretic model to explore ways in which tasks can be designed to help motivate agents to reach designated goals.

See on arxiv.org

25
Apr
15

Quantum Information Biology: from information interpretation of quantum mechanics to applications in molecular biology and cognitive psychology

See on Scoop.itBounded Rationality and Beyond

We discuss foundational issues of quantum information biology (QIB) – one of the most successful applications of the quantum formalism outside of physics. QIB provides a multi-scale model of information processing in bio-systems: from proteins and cells to cognitive and social systems. This theory has to be sharply distinguished from “traditional quantum biophysics”. The latter is about quantum bio-physical processes, e.g., in cells or brains. QIB models the dynamics of information states of bio-systems. It is based on the quantum-like paradigm: complex bio-systems process information in accordance with the laws of quantum information and probability. This paradigm is supported by plenty of statistical bio-data collected at all scales, from molecular biology and genetics/epigenetics to cognitive psychology and behavioral economics. We argue that the information interpretation of quantum mechanics (its various forms were elaborated by Zeilinger and Brukner, Fuchs and Mermin, and D’ Ariano) is the most natural interpretation of QIB. We also point out that QBIsm (Quantum Bayesianism) can serve to find a proper interpretation of bio-quantum probabilities. Biologically QIB is based on two principles: a) adaptivity; b) openness (bio-systems are fundamentally open). These principles are mathematically represented in the framework of a novel formalism – quantum adaptive dynamics which, in particular, contains the standard theory of open quantum systems as a special case of adaptivity (to environment).

See on arxiv.org

25
Apr
15

Improving detection of influential nodes in complex networks

See on Scoop.itBounded Rationality and Beyond

Recently an increasing amount of research is devoted to the question of how the most influential nodes (seeds) can be found effectively in a complex network. There are a number of measures proposed for this purpose, for instance, high-degree centrality measure reflects the importance of the network topology and has a reasonable runtime performance to find a set of nodes with highest degree, but they do not have a satisfactory dissemination potentiality in the network due to having many common neighbors (CN(1)) and common neighbors of neighbors (CN(2)). This flaw holds in other measures as well. In this paper, we compare high-degree centrality measure with other well-known measures using ten datasets in order to find a proportion for the common seeds in the seed sets obtained by them. We, thereof, propose an improved high-degree centrality measure (named textit{DegreeDistance}) and improve it to enhance accuracy in two phases, FIDD and SIDD, by put a threshold on the number of common neighbors of already-selected seed nodes and a non-seed node which is under investigation to be selected as a seed as well as considering the influence score of seed nodes directly or through their common neighbors over the non-seed node. To evaluate the accuracy and runtime performance of DegreeDistance, FIDD, and SIDD, they are applied to eight large-scale networks and it finally turns out that SIDD dramatically outperforms other well-known measures and evinces comparatively more accurate performance in identifying the most influential nodes.

  
See on arxiv.org

25
Apr
15

Online Social Network Analysis: A Survey of Research Applications in Computer Science

See on Scoop.itBounded Rationality and Beyond

The emergence and popularization of online social networks suddenly made available a large amount of data from social organization, interaction and human behaviour. All this information opens new perspectives and challenges to the study of social systems, being of interest to many fields. Although most online social networks are recent (less than fifteen years old), a vast amount of scientific papers was already published on this topic, dealing with a broad range of analytical methods and applications. This work describes how computational researches have approached this subject and the methods used to analyse such systems. Founded on a wide though non-exaustive review of the literature, a taxonomy is proposed to classify and describe different categories of research. Each research category is described and the main works, discoveries and perspectives are highlighted.

  
See on arxiv.org




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