An alternating active-dormitive strategy enables disadvantaged prey to outcompete the perennially active prey through Parrondo's paradox

Parrondo's paradox reveals counterintuitive wins in biology and decision making in society

Parrondo's paradox refers to the paradoxical phenomenon of combining two losing strategies in a certain manner to obtain a winning outcome. It has been applied to uncover unexpected outcomes across various disciplines, particularly at different spatiotemporal scales within ecosystems. In this article, we provide a comprehensive review of recent developments in Parrondo's paradox within the interdisciplinary realm of the physics of life, focusing on its significant applications across biology and the broader life sciences. Specifically, we examine its relevance from genetic pathways and phenotypic regulation, to intercellular interaction within multicellular organisms, and finally to the competition between populations and species in ecosystems. This phenomenon, spanning multiple biological domains and scales, enhances our understanding of the unified characteristics of life and reveals that adaptability in a drastically changing environment, rather than the inherent excellence of a trait, underpins survival in the process of evolution. We conclude by summarizing our findings and discussing future research directions that hold promise for advancing the field.

Noise-induced Parrondo's paradox in discrete-time quantum walks

Parrondo's paradox refers to the apparently paradoxical effect whereby two or more dynamics in which a given quantity decreases are combined in such a way that the same quantity increases in the resulting dynamics. We show that noise can induce Parrondo's paradox in one-dimensional discrete-time quantum walks with deterministic periodic as well as aperiodic sequences of two-state quantum coins where this paradox does not occur in the absence of noise. Moreover, we show how the noise-induced Parrondo's paradox affects the time evolution of quantum entanglement for such quantum walks.

Visibility graph for time series prediction and image classification: a review

The analysis of time series and images is significant across different fields due to their widespread applications. In the past few decades, many approaches have been developed, including data-driven artificial intelligence methods, mechanism-driven physical methods, and hybrid mechanism and data-driven models. Complex networks have been used to model numerous complex systems due to its characteristics, including time series prediction and image classification. In order to map time series and images into complex networks, many visibility graph algorithms have been developed, such as horizontal visibility graph, limited penetrable visibility graph, multiplex visibility graph, and image visibility graph. The family of visibility graph algorithms will construct different types of complex networks, including (un-) weighted, (un-) directed, and (single-) multi-layered networks, thereby focusing on different kinds of properties. Different types of visibility graph algorithms will be reviewed in this paper. Through exploring the topological structure and information in the network based on statistical physics, the property of time series and images can be discovered. In order to forecast (multivariate) time series, several variations of local random walk algorithms and different information fusion approaches are applied to measure the similarity between nodes in the network. Different forecasting frameworks are also proposed to consider the information in the time series based on the similarity. In order to classify the image, several machine learning models (such as support vector machine and linear discriminant) are used to classify images based on global features, local features, and multiplex features. Through various simulations on a variety of datasets, researchers have found that the visibility graph algorithm outperformed existing algorithms, both in time series prediction and image classification. Clearly, complex networks are closely connected with time series and images by visibility graph algorithms, rendering complex networks to be an important tool for understanding the characteristics of time series and images. Finally, we conclude in the last section with future outlooks for the visibility graph.

A comprehensive framework for preference aggregation Parrondo's paradox

Individuals can make choices for themselves that are beneficial or detrimental to the entire group. Consider two losing choices that some individuals have to make on behalf of the group. Is it possible that the losing choices combine to give a winning outcome? We show that it is possible through a variant of Parrondo's paradox-the preference aggregation Parrondo's paradox (PAPP). This new variant of Parrondo's paradox makes use of an aggregate rule that combines with a decision-making heuristic that can be applied to individuals or parts of the social group. The aim of this work is to discuss this PAPP framework and exemplify it on a social network. This work enhances existing research by constructing a feedback loop that allows individuals in the social network to adapt its behavior according to the outcome of the Parrondo's games played.

Parrondo's paradox in quantum walks with three coins

Parrondo's paradox refers to the apparently paradoxical effect whereby a certain combination of biased random walks displays a counterintuitive reversal of the bias direction. We show that Parrondo's paradox can occur not only in the case of one-dimensional discrete-time quantum walks with a deterministic sequence of two quantum coins but also in the case of one-dimensional discrete-time quantum walks with a deterministic sequence of three quantum coins. Moreover, we show how Parrondo's paradox affects the time evolution of quantum entanglement for such quantum walks.