Explanations of human technology often point to both its cumulative and combinatorial character. Using a novel computational framework, where individual agents attempt to solve problems by modifying, combining and transmitting technologies in an open-ended search space, this paper re-evaluates two prominent explanations for the cultural evolution of technology: that humans are equipped with (i) social learning mechanisms for minimizing information loss during transmission, and (ii) creative mechanisms for generating novel technologies via combinatorial innovation. Here, both information loss and combinatorial innovation are introduced as parameters in the model, and then manipulated to approximate situations where technological evolution is either more cumulative or combinatorial. Compared to existing models, which tend to marginalize the role of purposeful problem-solving, this approach allows for indefinite growth in complexity while directly simulating constraints from history and computation. The findings show that minimizing information loss is only required when the dynamics are strongly cumulative and characterised by incremental innovation. Contrary to previous findings, when agents are equipped with a capacity for combinatorial innovation, low levels of information loss are neither necessary nor sufficient for populations to solve increasingly complex problems. Instead, higher levels of information loss are advantageous for unmasking the potential for combinatorial innovation. This points to a parsimonious explanation for the cultural evolution of technology without invoking separate mechanisms of stability and creativity.
Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho & Ronaldo Menezes
Applied Network Science volume 5, Article number: 24 (2020)
Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction.
The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display the signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as the early warning signals of two major price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time-series’ autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.
AUTOMATA2020 in Stockholm, Sweden, August 10-12, 2020 Conference website: https://automata2020.weebly.com/ Submission deadline: March 30, 2020
Topics: cellular automata, dynamical system