A maximum entropy framework for non-exponential distributions

Abstract

Probability distributions having power-law tails are observed in a broad range of social, economic, and biological systems. We describe here a potentially useful common framework. We derive distribution functions $\{p_k\}$ for situations in which a `joiner particle' $k$ pays some form of price to enter a `community' of size $k-1$, where costs are subject to economies-of-scale (EOS). Maximizing the Boltzmann-Gibbs-Shannon entropy subject to this energy-like constraint predicts a distribution having a power-law tail; it reduces to the Boltzmann distribution in the absence of EOS. We show that the predicted function gives excellent fits to 13 different distribution functions, ranging from friendship links in social networks, to protein-protein interactions, to the severity of terrorist attacks. This approach may give useful insights into when to expect power-law distributions in the natural and social sciences.

9 pages, 4 figures, 1 table

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• Statistical laws in linguistics
• Predictability of extreme events in social media
• Stationary properties of maximum entropy random walks
• The Burr 12 Distribution Family and the Maximum Entropy Principle: Power-Law Phenomena are not necessarily Nonextensive
• The Theory of Thermodynamic Relativity
• From Boltzmann to Zipf through Shannon and Jaynes
• A complete data frame work for fitting power law distributions
• Inferring transition rates on networks with incomplete knowledge
• Maximum Multiscale Entropy and Neural Network Regularization
• Detecting temperature fluctuations at equilibrium