USING MACROECONOMIC INDICATORS FOR MANIPULATING

The data of macroeconomics is very important for all actors of economic stage. Many times this data generates dissatisfaction, confusion, even revolt. We`ll try to identificate the limits of macroeconomic indicators, insisting on its manipulating character. For example the limits of the income per capita are quite numerous and may be organized by three main categories: the report variables related uncertainties, the effects of the exchange variations and the difficulties of a price system international comparison.Macroeconomic indicators, manipulate, limits, optimize

Theories of civil war onset: promises and pitfalls

Empirical research on civil war onset has been largely dominated by two approaches: a correlational or “correlates of civil war” approach which seeks to identify country-level characteristics associated with a higher likelihood of civil war outbreak, and a bargaining approach which starts from the assumption that warfare is costly and which views civil conflict as a by-product of bargaining failures. Correlational and bargaining studies of internal conflict onset have reached an analytical plateau because they fail to specify the precise mechanisms that yield civil warfare instead of a different type of violent or nonviolent outcome. An alternative, contentious framework is advanced for studying civil war onset. This framework situates the conflict event within a larger cycle of contention and specifies the mechanisms through which civil conflict is most likely to occur. According to this contentious perspective, civil wars are commonly produced by the combination of one structural condition—a state crisis of authority and/or legitimacy—and the interdependent effect of two mechanisms—radicalization and militarization. Through theory development and vignettes from a handful of civil war cases, the article makes the case that the contentious approach holds promise for elucidating how exactly civil conflicts break out. Despite holding initial explanatory power, the contentious theory of civil war onset advanced herein awaits more systematic empirical testing

Rebel governance in de facto states

De facto states, such as Somaliland (Somalia), are unrecognized separatist enclaves that display characteristics of statehood but lack an international legal status. To acquire domestic and external legitimacy, these actors engage in a wide range of governance practices: they set up military and police forces; executive, legislative, and judicial branches; hospitals; schools; banks; or social security networks. In spite of the obvious gains that can be accrued through the establishment of a complex governance architecture, de facto states exhibit great variation in the range of statelike institutions that they build: some, like Luhansk People’s Republic (Ukraine), put together a rudimentary governance apparatus, while others, like Transnistria (Moldova), manage to construct a complex system of rule. What explains the variation in governance practices across these separatist enclaves? Using original data on governance institutions across all de facto states (1945–2016), this study offers an empirical examination of the key factors that shape separatists’ incentives to supply governance. The findings reveal that de facto state separatists are less likely to provide governance when they have access to lootable mineral resources but are more likely to do so when they receive external military support, when peacekeepers are present, when they have access to relatively immobile assets, when they adopt a Marxist ideology, and when they control the territory for a long time. The findings help us better understand the conditions under which armed nonstate actors supplant sovereign states as de facto authorities and successfully institutionalize their rule

De facto states: survival and disappearance (1945-2011)

De facto states—polities, such as Abkhazia (Georgia) or the Donetsk People’s Republic (Ukraine), that appropriate many trappings of statehood without securing the status of full states—have been a constant presence in the postwar international order. Some de facto states, such as Northern Cyprus, survive for a long period of time. Others, including Tamil Eelam in Sri Lanka, are forcefully reintegrated into their parent states. Still others, such as Aceh in Indonesia, disappear as a result of peacemaking. A few, such as Eritrea, successfully transition to full statehood. What explains these very different outcomes? I argue that four factors account for much of this variation: the extent of military assistance that separatists receive from outside actors, the governance activities conducted by separatist insurgents, the fragmentation of the rebel movement, and the influence of government veto players. My analysis relies on an original dataset that includes all breakaway enclaves from 1945 to 2011. The findings enhance our understanding of separatist institutional outcomes, rebel governance, and the conditions that sustain nonstate territorial actors

THE ECONOMIC IMPLICATIONS OF THE GEOTHERMAL POTENTIAL OF WEST AND NORTHWEST REGION OF ROMANIA

The energy crises of the 70s led to the vigorous interventions of the industrialized states in the energy sector. On the European political agenda a new problem appeared, namely the one regarding the security of the energy supply. Romania is the third geothermal power in Europe, after Italy and Greece. The energy potential produced by means of geothermal resources of the West and North - West regions is approximately of 144 MWt. The production of a MWt of electricity through conventional sources (in our case study we chose diesel) emits into the atmosphere about 21,673 tons of CO2. If itâ€(tm)s used the entire installed capacity in these areas Romania reduces pollution by approximately 6,935,552 TCO2.durability, externalities, renewable, geothermal, energy independence, pollution

SOCIAL LIMITS OF THE ROMANIAN ECONOMICAL GROWTH

The phenomena and processes from the economical life have evolved with intensity and different results, determining the necessity of knowing the way in which the national economy evolves, as well as its dynamic approach. The existence and the dynamics o

Weighted Random Search for CNN Hyperparameter Optimization

Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). While the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. In previous work, we introduced the Weighted Random Search (WRS) method, a combination of Random Search (RS) and probabilistic greedy heuristic. In the current paper, we compare the WRS method with several state-of-the art hyperparameter optimization methods with respect to Convolutional Neural Network (CNN) hyperparameter optimization. The criterion is the classification accuracy achieved within the same number of tested combinations of hyperparameter values. According to our experiments, the WRS algorithm outperforms the other methods

Weighted Random Search for Hyperparameter Optimization

We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our method on a variation of one of the most commonly used objective function for this class of problems (the Grievank function) and for the hyperparameter optimization of a deep learning CNN architecture. Our results can be generalized to any optimization problem defined on a discrete domain