Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

Migrant Networks and the Spread of Information

Diaspora networks provide information to future migrants, which affects their success in the host country. While the existing literature explains the effect of networks on the outcomes of migrants through the size of the migrant community, we show that the quality of the network is an equally important determinant. We argue that networks that are more integrated in the society of the host country can provide more accurate information to future migrants about job prospects. In a decision model with imperfect signalling, we show that migrants with access to a better network are more likely to make the right decision, that is, they migrate only if they gain. We test these predictions empirically using data on recent Mexican migrants to the United States. To instrument for the quality of networks, we exploit the settlement of immigrants who came during the Bracero program in the 1950s. The results are consistent with the model predictions, providing evidence that connections to a better integrated network lead to better outcomes after migration

Modelling the dynamics of industry populations

This paper examines four models which might be used to account for variations in the number of producers who operate in a particular market over the lifetime of that market. Two of these are standard economics textbook models, one is a non-standard model and one is a textbook model derived from the literature on organizational ecology. The four models have several observable differences and this opens up the possibility of testing any one against the others. We apply these four models to 93 years of data on the population of domestic car producers in the US car industry. The salient feature of this population is the very large rise and fall in the number of firms operating in the very early years of the industry, a phenomena which seems hard to account for using any of the three textbook models that we consider here

Equal Size, Equal Role? Interest Rate Interdependence Between the Euro Area and the United States

This article investigates whether the degree of interdependence between the US and the euro area has changed with EMU by analysing the effects of monetary policy and macroeconomic news on daily interest rates in the two economies. The article finds a strongly increased interdependence of money markets around EMU. Spillover effects from the US to the euro area remain stronger than in the opposite direction, but US markets have started reacting to euro area developments. In recent years certain US macroeconomic news affect euro area money markets and have become good leading indicators for the euro area. Copyright 2005 Royal Economic Society.