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

Institutions and cultures usually evolve in response to environmental incentives. However, sometimes institutional change occurs due to stochastic drivers beyond current fitness, including drift, path dependency, blind imitation, and complementary cooperation in fluctuating environments. Disentangling the selective and stochastic components of social system change enables us to identify the key features of long-term organizational development. Evolutionary approaches provide organizational science with abundant theories to demonstrate organizational evolution by tracking beneficial or harmful features. In this study, focusing on 20,000 Minecraft communities, we measure these drivers empirically using two of the most widely applied evolutionary models: the Price equation and the bet-hedging model. As a result, we find strong selection pressure on administrative and information rules, suggesting that their positive correlation with community fitness is the main reason for their frequency change. We also find that stochastic drivers decrease the average frequency of administrative rules. The result makes sense when viewed in the context of evolutionary bet-hedging. We show through the bet-hedging result that institutional diversity contributes to the growth and stability of rules related to information, communication, and economic behaviors.

Information Theory for Human and Social Processes.

Ever since its earliest years, information theory has enjoyed both a promising and complicated relationship with the social sciences [...].

How Complexity and Uncertainty Grew with Algorithmic Trading.

The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007-2017) and show that increased algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first sight. On the micro-level, traders employ algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This entails more predictable structure and more complexity. On the macro-level, the increased overall complexity implies more combinatorial possibilities, and therefore, more uncertainty about the future. The chain rule of entropy reveals that uncertainty has been reduced when trading on the level of the fourth digit behind the dollar, while new uncertainty started to arise at the fifth digit behind the dollar (aka 'pip-trading'). In short, our information theoretic analysis helps us to clarify that the seeming contradiction between decreased uncertainty on the micro-level and increased uncertainty on the macro-level is the result of the inherent relationship between complexity and uncertainty.

Digital technology and social change: the digital transformation of society from a historical perspective
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Digital technology, including its omnipresent connectedness and its powerful artificial intelligence, is the most recent long wave of humanity's socioeconomic evolution. The first technological revolutions go all the way back to the Stone, Bronze, and Iron Ages, when the transformation of material was the driving force in the Schumpeterian process of creative destruction. A second metaparadigm of societal modernization was dedicated to the transformation of energy (aka the "industrial revolutions"), including water, steam, electric, and combustion power. The current metaparadigm focuses on the transformation of information. Less than 1% of the world's technologically stored information was in digital format in the late 1980s, surpassing more than 99% by 2012. Every 2.5 to 3 years, humanity is able to store more information than since the beginning of civilization. The current age focuses on algorithms that automate the conversion of data into actionable knowledge. This article reviews the underlying theoretical framework and some accompanying data from the perspective of innovation theory.
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La tecnología digital, que incluye una altísima conectividad y una poderosa inteligencia artificial, constituye el desarrollo más reciente y significativo en la evolución socioeconómica de la humanidad. Las primeras revoluciones tecnológicas se remontan a las Edades de Piedra, Bronce y Hierro, cuando la transformación del material fue la fuerza impulsora en el proceso Schumpeteriano de destrucción creativa. Un segundo metaparadigma de modernización social fue el que ocurrió con la transformación de la energía (también conocida como "revoluciones industriales"), incluyendo el agua, el vapor, la electricidad y la energía de combustión. El metaparadigma actual se centra en la transformación de la información. A fines de la década de 1980, menos del 1% de la información almacenada tecnológicamente en el mundo estaba en formato digital y ha llegado a más del 99% en 2012. Cada 2,5 a 3 años, la humanidad puede almacenar más información que desde el comienzo de la civilización. La era actual se centra en algoritmos que automatizan la conversión de datos en conocimiento procesable. Desde la perspectiva de la teoría de la innovación, este artículo revisa el marco teórico subyacente y algunos datos inherentes a él.

La technologie numérique, sa connectivité omniprésente et la puissance de l'intelligence artificielle font vivre à l'humanité sa phase d'évolution la plus longue sur un plan socio-économique. Les premières révolutions technologiques remontent à l'âge de pierre, du bronze et du fer, lorsque la transformation de la matière était le moteur du processus schumpétérien de destruction créatrice. La transformation de l'énergie qu'elle soit hydraulique, à vapeur, électrique ou par combustion (aussi appelée "révolutions industrielles") est à l'origine d'un deuxième méta-modèle de modernisation sociétale basée sur le changement technologique. La transformation de l'information est au centre du méta-modèle actuel. Moins de 1 % de l'information était stockée en format numérique à la fin des années 80, contre plus de 99 % en 2012Tous les 2,5 à 3 ans, l'humanité est capable d'archiver plus d'informations que celles créées depuis le début des civilisations. Nous sommes maintenant entrés dans l'ère des algorithmes qui automatisent la conversion des données en connaissances exploitables. Dans cet article, nous nous plaçons du point de vue de l'innovation pour analyser le cadre théorique de cette transformation et certaines données qui y sont inhérentes.

Information in the Biosphere: Biological and Digital Worlds.

Evolution has transformed life through key innovations in information storage and replication, including RNA, DNA, multicellularity, and culture and language. We argue that the carbon-based biosphere has generated a cognitive system (humans) capable of creating technology that will result in a comparable evolutionary transition. Digital information has reached a similar magnitude to information in the biosphere. It increases exponentially, exhibits high-fidelity replication, evolves through differential fitness, is expressed through artificial intelligence (AI), and has facility for virtually limitless recombination. Like previous evolutionary transitions, the potential symbiosis between biological and digital information will reach a critical point where these codes could compete via natural selection. Alternatively, this fusion could create a higher-level superorganism employing a low-conflict division of labor in performing informational tasks.

Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making.

A single coherent framework is proposed to synthesize long-standing research on 8 seemingly unrelated cognitive decision-making biases. During the past 6 decades, hundreds of empirical studies have resulted in a variety of rules of thumb that specify how humans systematically deviate from what is normatively expected from their decisions. Several complementary generative mechanisms have been proposed to explain those cognitive biases. Here it is suggested that (at least) 8 of these empirically detected decision-making biases can be produced by simply assuming noisy deviations in the memory-based information processes that convert objective evidence (observations) into subjective estimates (decisions). An integrative framework is presented to show how similar noise-based mechanisms can lead to conservatism, the Bayesian likelihood bias, illusory correlations, biased self-other placement, subadditivity, exaggerated expectation, the confidence bias, and the hard-easy effect. Analytical tools from information theory are used to explore the nature and limitations that characterize such information processes for binary and multiary decision-making exercises. The ensuing synthesis offers formal mathematical definitions of the biases and their underlying generative mechanism, which permits a consolidated analysis of how they are related. This synthesis contributes to the larger goal of creating a coherent picture that explains the relations among the myriad of seemingly unrelated biases and their potential psychological generative mechanisms. Limitations and research questions are discussed.

The world's technological capacity to store, communicate, and compute information.

We estimated the world's technological capacity to store, communicate, and compute information, tracking 60 analog and digital technologies during the period from 1986 to 2007. In 2007, humankind was able to store 2.9 × 10(20) optimally compressed bytes, communicate almost 2 × 10(21) bytes, and carry out 6.4 × 10(18) instructions per second on general-purpose computers. General-purpose computing capacity grew at an annual rate of 58%. The world's capacity for bidirectional telecommunication grew at 28% per year, closely followed by the increase in globally stored information (23%). Humankind's capacity for unidirectional information diffusion through broadcasting channels has experienced comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in digital format in 2007), and the majority of our technological memory has been in digital format since the early 2000s (94% digital in 2007).