Inherent causes of language change

The inevitable causes of language change are considered in this article. It gives an up-to-date view of the phonetic natural tendencies which are the predictable result of a human’s anatomical, physiological and psychological make-up. В статті розглядаються неминучі причини зміни мови. Вона пропонує сучасний погляд на природні тенденції у фонетиці, які є передбачуваним результатом анатомічної, фізіологічної та психологічної будови людини

AGN Feedback Causes Downsizing

We study the impact of outflows driven by active galactic nuclei (AGN) on galaxy formation. Outflows move into the surrounding intergalactic medium (IGM) and heat it sufficiently to prevent it from condensing onto galaxies. In the dense, high-redshift IGM, such feedback requires highly energetic outflows, driven by a large AGN. However, in the more tenuous low-redshift IGM, equivalently strong feedback can be achieved by less energetic winds (and thus smaller galaxies). Using a simple analytic model, we show that this leads to the anti-hierarchical quenching of star-formation in large galaxies, consistent with current observations. At redshifts prior to the formation of large AGN, galaxy formation is hierarchical and follows the growth of dark-matter halos. The transition between the two regimes lies at the z ~ 2 peak of AGN activity.Comment: 6 pages, 2 figures, ApJL in pres

Pluralism without Genic Causes?

Since the fundamental challenge that I laid at the doorstep of the pluralists was to defend, with nonderivative models, a strong notion of genic cause, it is fatal that Waters has failed to meet that challenge. Waters agrees with me that there is only a single cause operating in these models, but he argues for a notion of causal ‘parsing’ to sustain the viability of some form of pluralism. Waters and his colleagues have some very interesting and important ideas about the sciences, involving pluralism and parsing or partitioning causes, but they are ideas in search of an example. He thinks he has found an example in the case of hierarchical and genic selection. I think he has not

Impairment losses: causes and impacts

Purpose - To analyze recognition of impairment losses in tangible and intangible assets, and their relevance to investors in companies listed in the Lisbon and Madrid Stock Exchange (2007-2011).Methodology - Quantitative analysis of a panel data sample of 80 companies listed in the Lisbon and Madrid Stock Exchange (2007-2011) was carried out. Panel data linear and non-linear regression models were estimated.Findings - We found that the amount of impairment losses showed an upward trend, and that these losses are most significant among intangibles, especially goodwill (GW). We also found that the probability of recognition of impairment losses is positively influenced by the dimension of entities and negatively by market value (p < 0.10). Portuguese export-oriented companies have a higher probability of not recognizing impairments. However, Portuguese companies with higher market values have greater probability of recognizing impairment losses, contrary to the sample as a whole, in which the relationship is negative (p < 0.10). The results also suggest that there is a smoothing effect on results because of impairments, especially in IBEX35 companies. As to the relevance of impairment losses to market value, we confirm a significant negative relationship, in line with conclusions from previous studies.Originality/value - This study contributes to the introduction of the cultural factor in this analysis, highlighting the differentiated behaviors between Portuguese and Spanish companies

Causes of the Financial Crisis

[Excerpt] The current financial crisis began in August 2007, when financial stability replaced inflation as the Federal Reserve’s chief concern. The roots of the crisis go back much further, and there are various views on the fundamental causes. It is generally accepted that credit standards in U.S. mortgage lending were relaxed in the early 2000s, and that rising rates of delinquency and foreclosures delivered a sharp shock to a range of U.S. financial institutions. Beyond that point of agreement, however, there are many questions that will be debated by policymakers and academics for decades. Why did the financial shock from the housing market downturn prove so difficult to contain? Why did the tools the Fed used successfully to limit damage to the financial system from previous shocks (the Asian crises of 1997-1998, the stock market crashes of 1987 and 2000-2001, the junk bond debacle in 1989, the savings and loan crisis, 9/11, and so on) fail to work this time? If we accept that the origins are in the United States, why were so many financial systems around the world swept up in the panic? To what extent were long-term developments in financial markets to blame for the instability? Derivatives markets, for example, were long described as a way to spread financial risk more efficiently, so that market participants could bear only those risks they understood. Did derivatives, and other risk management techniques, actually increase risk and instability under crisis conditions? Was there too much reliance on computer models of market performance? Did those models reflect only the post-WWII period, which may now come to be viewed not as a typical 60-year period, suitable for use as a baseline for financial forecasts, but rather as an unusually favorable period that may not recur? Did government actions inadvertently create the conditions for crisis? Did regulators fail to use their authority to prevent excessive risk-taking, or was their jurisdiction too limited and/or compartmentalized? While some may insist that there is a single cause, and thus a simple remedy, the sheer number of causal factors that have been identified tends to suggest that the current financial situation is not yet fully understood in its full complexity. This report consists of a table that summarizes very briefly some of the arguments for particular causes, presents equally brief rejoinders, and includes a reference or two for further reading. It will be updated as required by market developments

What causes terrorism?

Popular beliefs link terrorism to economic, political and social under- development. In this contribution, we comprehensively review the related, most relevant cross-country analyses to ascertain the true determinants of terrorism. The related theoretical underpinnings are presented and com- mon analytical and methodological objections are discussed. In general, we nd that terrorism is closely linked to political instability, sharp divides within the populace, country size and further demographic, institutional and international factors. Sound counter-terrorism policies should work on these prominent root causes of terrorism. Evidence is only marginal that economic performance, structural economic conditions, democrati- zation, education or religious aliation signicantly interact with terror- ism. Thus, we are skeptical towards popular policy advice that focuses on poverty alleviation, a promotion of economic development, democratiza- tion, education or the like.Determinants of Terrorism, Political Violence, Counter-Terrorism Policies

The Bayesian sampler : generic Bayesian inference causes incoherence in human probability

Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample