The growing interconnectivity of socioeconomic systems requires one to treat multiple relevant social and economic variables simultaneously as parts of a strongly interacting complex system. Here, we analyze and exploit correlations between the price fluctuations of selected cryptocurrencies and social media activities, and develop a predictive framework using noise-correlated stochastic differential equations. We employ the standard Geometric Brownian Motion to model cryptocurrency rates, while for social media activities and trading volume of cryptocurrencies we use the Geometric Ornstein-Uhlenbeck process. In our model, correlations between the different stochastic variables are introduced through the noise in the respective stochastic differential equation. Using a Maximum Likelihood Estimation on historical data of the corresponding cryptocurrencies and social media activities we estimate parameters, and using the observed correlations, forecast selected time series. We successfully analyze and predict cryptocurrency related social media and the cryptocurrency market itself with a reasonable degree of accuracy. In particular, we show that our method has impressive accuracy in predicting whether a cryptocurrency market will increase or decrease a day in the future, a significant result with regards to investing and trading cryptocurrencies.

Call Detail Record (CDR) datasets provide enough information about personal interactions of cell phone service customers to enable building detailed social networks. We take one such dataset and create a realistic social network to predict which customer will default on payments for the phone services, a complex behavior combining social, economic, and legal considerations. After extracting a large feature set from this network, we find that each feature poorly correlates with the default status. Hence, we develop a sophisticated model to enable reliable predictions. Our main contribution is a methodology for building complex behavior models from very large sets of diverse features and using different methods to choose those features that perform best for the final model. This approach enables us to identify the most efficient features for our problem which, unexpectedly, are based on the number of unique users with whom the given user communicates around the Christmas and New Year's Eve holidays. In general, features based on the number of close ties maintained by a user perform better than others. Our resulting models significantly outperform the methods * Corresponding author currently published in the literature. The paper contributes also a systematic analysis of properties of the network derived from CDR.

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We model a social-encounter network where linked nodes match for reproduction in a manner depending probabilistically on each node’s attractiveness. The developed model reveals that increasing either the network’s mean degree or the “choosiness” exercised during pair formation increases the strength of positive assortative mating. That is, we note that attractiveness is correlated among mated nodes. Their total number also increases with mean degree and selectivity during pair formation. By iterating over the model’s mapping of parents onto offspring across generations, we study the evolution of attractiveness. Selection mediated by exclusion from reproduction increases mean attractiveness, but is rapidly balanced by skew in the offspring distribution of highly attractive mated pairs.