In the problem of aggregation of rankings or preferences of several agents, there is a well-known result that reasonable social ranking is not strategy-proof. In other words, there are some situations when at least one agent can submit insincere ranking and change the final result in a way beneficial to him. We call this situation manipulable and using computer modelling we study 10 majority relation-based collective decision rules and compare them by their degree of manipulability, i.e. by the share of the situation in which manipulation is possible. We found that there is no rule that is best for all possible cases but some rules like Fishburn rule, Minimal undominated set and Uncovered set II are among the least manipulable ones.
Modern neuroimaging studies begin to explore neurobiological mechanisms of social norms enforcement. Several regions of frontal lobes and temporo-parieto-occipital cortex play a key role in decision making of social punishment of fairness’ norm violation. The cutting–edge methods of brain stimulation allow to change a frequency and intensity of social punishment in different economic tasks (games). The analysis of modern studies show that brain mechanisms of decision making to punish non–cooperative individual requires further investigation with brain stimulation methods to differentiate a role of frontal and temporo-parieto-occipital regions and clarify its interaction.
The Arctic region is one of the most sensitive and vulnerable to climate change. The dramatic melting of Arctic ice has several negative consequences for the whole ecosystem as well as for a way of life of native people but it also creates new opportunities for the region. First, it opens up potential for exploitation of large deposits of natural resources such oil and gas. Second, it shrinks Arctic shipping routes which offer significant economic savings for many countries. These benefits has already attracted many countries, both Arctic and non-Arctic, thus resulting in potential conflict of interests. In our paper we present a mathematical approach to the problem of conflict resolution in the Arctic. First, we propose an approach how the level of interest in each part of the region should be evaluated with respect to main resources - oil, gas, fish and maritime routes. Second, we present several models of areas allocation to resolve the problem of conflict resolution. As a result, we applied several scenarios of areas allocation, evaluated their efficiency based on the total satisfaction level and identified conflict zones in the Arctic.
Trading processes is a vital part of human life and any unstable situation results in the change of living conditions of individuals. We study the power of each country in terms of produce trade. Trade relations between countries are represented as a network, where vertices are territories and edges are export flows. As flows of products between participants are heterogeneous we consider various groups of substitute goods (cereals, fish, vegetables). We detect key participants affecting food retail with the use of classical centrality measures. We also perform clustering procedure in order to find communities in networks.
This paper systematizes the empirical results on efficiency concepts applied to higher education institutions, data envelopment analysis (DEA) adjusted to heterogeneous samples, inputs and outputs chosen for these institutions and factors tended to make universities efficient. Special attention is paid to the consistency of results yielded by different models.
Our study employs the network approach to the problem of international migration. During the last years, migration has attracted a lot of attention and has been examined from many points of view. However, very few studies considered it from the network perspective. The international migration can be represented as a network (or weighted directed graph) where the nodes correspond to countries and the edges correspond to migration flows. The main focus of our study is to reveal a set of critical or central elements in the network. To do it, we calculated different existing and new centrality measures. In our research the United Nations International Migration Flows Database (version 2015) was used. As a result, we obtained information on critical elements for the migration process in 2013.
The oil and gas industry growth has increased rapidly in the Barents Sea during the last few years. The Arctic zone is considered to be a relatively clean area. However, there is a certain number of “hot spots” in the Arctic due to the activities of extracting companies.
We studied the problems connected with production of these two types of fossil fuel and carried out simulation model. This model shows the results of oil or gas flowing accident related to drilling complex, taking into account sea currents. By using this model, we can highlight areas in the Barents Sea with the highest potential of the disaster so that preventive measures could be taken. In addition, this model helps to organize elimination of fossil fuel flowing consequences.
We consider an application of power indices, which take into account preferences of agents for coalition formation proposed for an analysis of power distribution in elected bodies to reveal most powerful (central) nodes in networks. These indices take into account the parameters of the nodes in networks, a possibility of group influence from the subset of nodes to single nodes, and intensity of short and long interactions among the nodes.
Nontransitivity of winningness of chess arrangements (i.e., their relations like in the rock-paper-scissors game) is recently found property of chess environment. The concept of interactivity of a game, i.e., extent of its parties’ interaction (interpenetration) is introduced. Games of different interactivity are analyzed, and nontransitivity is considered as a consequence of high interactivity. Consequences of nontransitivity of superiority (domination) for cognition of complex systems and mastery of them are discussed.
The work is related to the detection of key international and Russian economic journals in cross-citation networks. A list of international journals and information on their cross-citations were taken from Web of Science (WoS) database while information on Russian journals was taken from Russian Science Citation Index (RSCI). We calculated classical centrality measures, which are used for key elements detection in networks, and proposed new indices based on short-range and long-range interactions. A distinct feature of the proposed methods is that they consider individual attributes of each journal and take into account only the most significant links between them. An analysis of 100 main international and 29 Russian economic journals was conducted. As a result, we detected journals with large number of citations to important journals and also journals where the observed rate of selfcitation is a dominant in the total level of citation. The obtained results can be used as a guidance for researchers planning to publish a new paper and as a measure of importance of scientific journals.
We consider a model of regions’ ranking in terms of their vulnerability to natural and technological disasters. Regions are different in terms of their resistance to different disasters, by their population, by the distribution of the sources of potential disasters, etc. We consider different models of a data envelopment analysis (DEA) approach taking into account the risks of the implementation of different measures, their cost as well as the heterogeneity of regions. The numerical examples demonstrate the application of the constructed model for the regions of Russian Federation.
Despite the growth of negative attitudes to homosexuals in Russia the research into this topic has been extremely scarce. Based on the analysis of social discourse, we have created a pool of items and undertaken three empirical studies aimed to develop and validate the Russian Attitudes to Homosexuals Inventory (RAHI) and investigate the associations of homophobic attitudes with a range of demographic and psychological variables. In Study 1 we used an online sample (N = 1,007) and explored the structure of the item pool, finding 8 factors, 5 of which referred to different dimensions of perceived threat of homosexuals (to individuals, morals, society, Russian culture, and heterosexual lifestyle) and 3 described social strategies directed at homosexuals (criminal punishment, medical treatment, and discrimination vs. protection). The scales were highly reliable (α = .82-.91) and formed a single second-order dimension, labelled general index of homophobia. Negative attitudes to homosexuals were stronger in males, religious respondents, and those heterosexuals who denied having experienced any feelings of same-sex attraction in their life. In Study 2 (paper-based sample, N = 292) we cross-validated the second-order structure of the RAHI. Using hierarchical multiple regression we found that homophobia was positively predicted by authoritarianism and negatively predicted by experience of same-sex attraction and social contact with homosexuals as friends. We also found weaker positive associations of homophobia with religiosity, social identification with gender, masculinity, extraversion, and social desirability, as well as a negative association with openness. In Study 3 we used contrast groups of neutral and anti-homosexual online community members (N = 330 and N = 107) to check the criterion validity of the RAHI. The findings are in line with the existing body of research from other countries, but reveal the culturally-specific features of the content of Russian homophobia (e.g., homosexuality is viewed as a result of Western influence). The RAHI emerged as a valid and reliable tool, which can be used for future Russian-language studies.
The problem of quick detection of central nodes in large networks is studied. There are many measures that allow to evaluate a topological importance of nodes of the network. Unfortunately, most of them cannot be applied to large networks due to their high computational complexity. However, if we narrow the initial network and apply these centrality measures to the sparse network, it is possible that the obtained set of central nodes will be similar to the set of central nodes in large networks. If these sets are similar, the centrality measures with a high computational complexity can be used for central nodes detection in large networks. To check the idea, several random networks were generated and different techniques of network reduction were considered. We also adapted some rules from social choice theory for the key nodes detection. As a result, we show how the initial network should be narrowed in order to apply centrality measures with a high computational complexity and maintain the set of key nodes of a large network.