Opinion Polarization by Learning from Social Feedback

We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment, is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017

Monetary policy transmission in the euro area: what do aggregate and national structural models tell us?

This paper analyses the monetary transmission mechanism in the euro area through the use of large scale macroeconomic models at the disposal of the European Central Bank and the National Central Banks of the Eurosystem. The results reported are based on a carefully designed common simulation experiment involving a 100 basis point rise in the policy interest rate for two years accompanied by common assumptions regarding the path of longterm interest rates and the exchange rate. Aggregating the country level results, the fall in output is found to reach a maximum of 0.4 per cent after 2 years. The maximum aggregate fall in prices is also 0.4 per cent, but it occurs 2 years later. The dominant channel of transmission in the first two years is the exchange rate channel, but in terms of the impact on output, the user cost of capital channel becomes dominant from the third year of the simulation onwards.monetary policy transmission mechanism, macroeconomic models

Local News And Event Detection In Twitter

Twitter, one of the most popular micro-blogging services, allows users to publish short messages on a wide variety of subjects such as news, events, stories, ideas, and opinions, called tweets. The popularity of Twitter, to some extent, arises from its capability of letting users promptly and conveniently contribute tweets to convey diverse information. Specifically, with people discussing what is happening outside in the real world by posting tweets, Twitter captures invaluable information about real-world news and events, spanning a wide scale from large national or international stories like a presidential election to small local stories such as a local farmers market. Detecting and extracting small news and events for a local place is a challenging problem and is the focus of this thesis. In particular, we explore several directions to extract and detect local news and events using tweets in Twitter: a) how to identify local influential people on Twitter for potential news seeders; b) how to recognize unusualness in tweet volume as signals of potential local events; c) how to overcome the data sparsity of local tweets to detect more and smaller undergoing local news and events. Additionally, we also try to uncover implicit correlations between location, time, and text in tweets by learning embeddings for them using a universal representation under the same semantic space. In the first part, we investigate how to measure the spatial influence of Twitter users by their interactions and thereby identify the locally influential users, which we found are usually good news and event seeders in practice. In order to do this, we built a large-scale directed interaction graph of Twitter users. Such a graph allows us to exploit PageRank based ranking procedures to select top local influential people after innovatively incorporating in geographical distance to the transition matrix used for the random walking. In the second part, we study how to recognize the unusualness in tweet volume at a local place as signals of potential ongoing local events. The intuition is that if there is suddenly an abnormal change in the number of tweets at a location (e.g., a significant increase), it may imply a potential local event. We, therefore, present DeLLe, a methodology for automatically Detecting Latest Local Events from geotagged tweet streams (i.e., tweets that contain GPS points). With the help of novel spatiotemporal tweet count prediction models, DeLLe first finds unusual locations which have aggregated an unexpected number of tweets in the latest time period and then calculates, for each such unusual location, a ranking score to identify the ones most likely to have ongoing local events by addressing the temporal burstiness, spatial business, and topical coherence. In the third part, we explore how to overcome the data sparsity of local tweets when trying to discover more and smaller local news or events. Local tweets are those whose locations fall inside a local place. They are very sparse in Twitter, which hinders the detection of small local news or events that have only a handful of tweets. A system, called Firefly, is proposed to enhance the local live tweet stream by tracking the tweets of a large body of local people, and further perform a locality-aware keyword based clustering for event detection. The intuition is that local tweets are published by local people, and tracking their tweets naturally yields a source of local tweets. However, in practice, only 20% Twitter users provide information about where they come from. Thus, a social network-based geotagging procedure is subsequently proposed to estimate locations for Twitter users whose locations are missing. Finally, in order to discover correlations between location, time and text in geotagged tweets, e.g., “find which locations are mostly related to the given topics“ and “find which locations are similar to a given location“, we present LeGo, a methodology for Learning embeddings of Geotagged tweets with respect to entities such as locations, time units (hour-of-day and day-of-week) and textual words in tweets. The resulting compact vector representations of these entities hence make it easy to measure the relatedness between locations, time and words in tweets. LeGo comprises two working modes: crossmodal search (LeGo-CM) and location-similarity search (LeGo-LS), to answer these two types of queries accordingly. In LeGo-CM, we first build a graph of entities extracted from tweets in which each edge carries the weight of co-occurrences between two entities. The embeddings of graph nodes are then learned in the same latent space under the guidance of approximating stationary residing probabilities between nodes which are computed using personalized random walk procedures. In comparison, we supplement edges between locations in LeGo-LS to address their underlying spatial proximity and topic likeliness to support location-similarity search queries

An Analysis on Applications of Contemporary Financial Accounting Topics

This thesis is composed of twelve independent case studies, each focusing on a different topic in financial accounting. These cases were completed over the course of a year under the instruction of Dr. Dickinson through the Honors Accountancy Independent Study course. To develop this thesis, I examined the information provided in every case study and performed independent research to analyze each unique accounting topic. In addition to independent analysis, the cases were reviewed in collaboration with other students in the Independent Study course, promoting discussion of varying opinions on contemporary accounting theories. The subject matter of the cases ranged from real world scenarios to more abstract accounting principles and trends. While writing this thesis, I integrated knowledge from my intermediate accounting courses and was also challenged to think and research beyond the information taught in the classroom. Many of the case studies provided a deeper insight into more relevant topics in the modern field of accounting than the principles taught in a standard accounting class. Through the process of creating this thesis and my participation in the Independent Study course, I enhanced my critical thinking and problem-solving abilities while also developing skills in professionalism. The knowledge I gained during the composition of this thesis will be beneficial both to my success in graduate school and my future career in public accounting

The Student Loan Debt Market: The role of federal student loan allocation on undergraduate student default rates

This study examines the effectiveness of federal student loan allocation and its role in student loan default rates. The loan allocation method is represented by program loan limits imposed by Congress. In addition to program loan limits, the study considers other educational and economic institutional drivers that education research has been linked to student loan default rates. The relationship between loan limits, other variables, and default rates is tested through a multivariable regression analysis. The analysis suggests that program loan limits do not have a significant effect on student loan default rates, but student loans are an effective means to help students finance education. To conclude the study, I assess three potential loan allocation methods as well as discuss my experience with the study and working with limited public data.Bachelor of Art

Measuring European Economic Integration

The three essays of this dissertation contribute to the measurement of European economic integration and investigate the welfare effects of the European countries. The first study presents a newly developed index – the EU Index – which measures the extent of economic integration into the European Union for the EU-15 member states over the period 1999–2010. The principal component analysis assigns accurate weights to the 25 indicators used in the index. Large heterogeneities are found between the member states with respect to overall integration and to various sub-indices. By using cluster analysis, it is also shown that the prevailing economic heterogeneities have produced a ‘core group’ of countries and a ‘multi-speed Europe’, which challenges the present and future steps of European integration. The second study uses the EU Index for an empirical assessment and analyzes whether European citizens have become more or less satisfied with life due to increased economic integration. With more than 180,000 observations, ordered logit estimations as well as a two-stage OLS procedure reveal significant positive impacts on reported well-being. Especially increased economic activity in the EU single market and increased economic homogeneity among the member states show the largest marginal effects. The ‘love-of-variety’ approach and the existence of a high inequality aversion among the European citizens are some possible explanations, which imply that the EU should take further action in that regard. The effect of the size of a country on economic growth is investigated in the third study. By analyzing panel data of the EU-27 countries over the period 1993–2012, the first regressions suggest that the size effect seems negligible and that mostly the standard neoclassical growth variables are important in determining economic growth. However, size does matter when the old and new member states are explored separately, though, the effect of country size decreases as market integration increases. Particularly in the light of the large and increasing number of small member states, further completion of the EU single market should stay at the forefront of the future European integration process

Inference to the best explanation in science

This thesis defends inference to the best explanation (IBE) by giving an account of explanatory 'loveliness' in science. I begin by presenting IBE in generic form and showing how it out-performs rival accounts of induction. I then trace a path through the early literature which emphasises the role of background belief in determining loveliness. I then introduce crucial features of Lipton's account of IBE. I argue that Lipton's remarks on loveliness, through minimal, support the background-dependent view and that, appropriately construed, the view does not trivialise IBE.EThOS - Electronic Theses Online ServiceGBUnited Kingdo