The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

Abstract

In our information age, information alone has become a driver of social growth. Information is the fuel of "big data" companies, and the decision-making compass of policy makers. Can we quantify how much information leads to how much social growth potential? Information theory is used to show that information (in bits) is effectively a quantifiable ingredient of growth. The article presents a single equation that allows both to describe hands-off natural selection of evolving populations and to optimize population fitness in uncertain environments through intervention. The setup analyzes the communication channel between the growing population and its uncertain environment. The role of information in population growth can be thought of as the optimization of information flow over this (more or less) noisy channel. Optimized growth implies that the population absorbs all communicated environmental structure during evolutionary updating (measured by their mutual information). This is achieved by endogenously adjusting the population structure to the exogenous environmental pattern (through bet-hedging/portfolio management). The setup can be applied to decompose the growth of any discrete population in stationary, stochastic environments (economic, cultural, or biological). Two empirical examples from the information economy reveal inherent trade-offs among the involved information quantities during growth optimization.

Quantifying the Selective, Stochastic, and Complementary Drivers of Institutional Evolution in Online Communities

“…At best, a perfectly informative cue would precisely reveal the state of the environment: the remaining uncertainty about the environment would be zero and the population could be adjusted to grow optimally. This can be formalized using information that is common to the environment and the evolving population: the more you know (about the environment), the more you can grow (the population) [ 63 ].…”

“…It uses information about the environment to maximize long-term increase rates [ 64 ]. Built upon Kelly’s idea and subsequent expansions [ 65 , 66 , 67 , 68 ], Hilbert derived a measure to establish a cooperative resource redistribution strategy to maximize socioeconomic growth [ 63 ]. By establishing fitness matrices of rules for different environmental states, we can determine the most efficient rule distribution strategy for the sustainability and incrementation of this type of rule.…”

Twitter, Presidential Debates and Attention Economy: A Symbiosis between Television Audience and Social Media Users during Campaign Season

“…In the 2016 elections, the technological sophistication increased to such a level that this time the surprise was Trump's team. During the third presidential debate held against Clinton, he could take one of the arguments raised on television and create, by using algorithms, 175 thousand different versions of said message, which were sent massively, and at the same time, hypersegmentedly, with variations and details according to the profile of each user (Hilbert, 2017). If the tools that played a core role in the political communication of the campaigns were Twitter in 2008 and Facebook in 2016, in 2018 it was the instant messaging Santander, P., Elórtegui, C. & Buzzo, C.…”

Redes sociales, inteligencia computacional y predicción electoral: el caso de las primarias presidenciales de Chile 2017

“…Cuatro años después, en 2012, la campaña de Obama trabajó con una base de datos de 16 millones de perfiles de indecisos que permitió clasificarlos individualmente, para luego operar comunicacionalmente sobre ellos. En las elecciones de 2016, la sofisticación tecnológica aumentó y el equipo electoral de Trump pudo, durante el tercer debate televisivo que mantuvo con Clinton, tomar uno de los argumentos planteados en televisión y, por medio del uso de algoritmos, sus asesores crearon 175 mil versiones de dicho mensaje, los que fueron enviados con variaciones y matices de acuerdo al perfil de cada usuario (Hilbert, 2017).…”