Journal of Physics: Complexity

Abstract This is the English version of my inaugural lecture at Collège de France in 2021. I reflect on the difficulty of multi-disciplinary research, which often hinges on unexpected epistemological and methodological differences, for example about the scientific status of models . What is the purpose of a model? What are we ultimately trying to establish: rigorous theorems or ad-hoc calculation recipes; absolute truth, or heuristic representations of the world? I argue that the main contribution of statistical physics to social and economic sciences is to make us realise that unexpected behaviour can emerge at the aggregate level, that isolated individuals would never experience. Crises, panics, opinion reversals, the spread of rumours or beliefs, fashion effects and the zeitgeist , but also the existence of money, lasting institutions, social norms and stable societies, must be understood in terms of collective belief and/or trust, self-sustained by interactions, or on the contrary, the rapid collapse of this belief or trust. The appendix contains my opening remarks to the workshop ‘More is Different’, as a tribute to Phil Anderson.

Jean-Philippe Bouchaud

Journal of Physics Complexity

From Statistical Physics to Social Sciences: The Pitfalls of Multi-disciplinarity

从统计物理学到社会科学：多学科交叉的陷阱

Abstract Recommender systems are vital for shaping user online experiences. While some believe they may limit new content exploration and promote opinion polarization, a systematic analysis is still lacking. In this paper we present a model that explores the influence of recommender systems on novel content discovery. Surprisingly, analytical and numerical findings reveal that these techniques can enhance novelty discovery rates. Also, distinct algorithms with similar discovery rates yield different outcomes, with the matrix factorization algorithm producing opinion polarization. Our approach shed light on the interplay between algorithmic recommendations and novelties discovery, offering a framework to enhance recommendation techniques beyond accuracy metrics.

Giordano De Marzo

Pietro Gravino

Vittorio Loreto

Recommender systems may enhance the discovery of novelties

Mikhail Prokopenko

Paul C W Davies

Michael Harré

Marcus Heisler

Zdenka Kuncic

Geraint Lewis

Ori Livson

Joseph Lizier

Fernando Rosas

Biological arrow of time: Emergence of tangled information hierarchies and self-modelling dynamics

生物时间之箭：纠缠信息层次的涌现与自建模动力学

Abstract The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept–representing highly nonlinear dynamical systems by infinite-dimensional linear operators–has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians.

Stefan Klus

Nataša Djurdjevac Conrad

Dynamical systems and complex networks: A Koopman operator perspective

动力系统与复杂网络：Koopman算子视角

Abstract Security issues in cyber-physical systems are often the Achilles heel for cooperative control and coordination. This paper addresses resilient consensus problems for multiagent systems under heterogenous time-varying delays and mobile Byzantine adversaries. We propose two types of mobile attack models for continuous-time dynamical agents with or without infection detection capabilities. Delayed consensus protocols are presented where the agents are able to adjust their behavior during the latency period after recovery from an attack. It is shown that resilient consensus can be achieved under bounded delays and robust communication topologies. As a further contribution, resilient vector consensus has been solved within our framework. Simulations regarding the mobile patterns of adversaries indicate the irrelevance of global and local mobility in terms of convergence rate over Erős-Rényi random networks. In contrast, local mobility of adversaries leads to longer convergence time compared to global mobility in power-law random networks due to their characteristic topology.&amp;#xD;

Yilun Shang

Resilient consensus for multiagent networks with time-varying delay and mobile adversaries

多智能体网络在时变延迟和移动对手情况下的鲁棒一致性

Abstract Swarming is central to many problems in physics, biology, and engineering &amp;#xD;where collective motion and cooperation emerge through interactions of many agents. As a result, inferring network connections underlying swarms from time series data is an important problem with broad application. In this work, we propose a method based on phase-space regression of the observed dynamics with random forest models, from which relative interactions are clustered according to their Gini importance with respect to a given agent. Network connections are inferred by comparing how the statistics of the strongly and weakly important clusters overlap. Because the method entails fitting the dynamics and finding the most important interactions for each agent individually with general swarming assumptions, high accuracy can be maintained with relatively modest amounts of observation data and utilizing only a small number of generalizable hyperparameters across a variety of behavioral patterns, diverse physical ingredients, and heterogeneous network topologies.

Abbreviation	J. Phys. Complex.
Journal Impact	2.44
Quartiles(Global)	PHYSICS, MATHEMATICAL(Q1)

ISSN	2632-072X
h-index	15

Publication Information	Publisher: IOP Publishing Ltd.，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2020，Proportion of original research papers: 100.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Journal Fees	$2385Details
Average review cycle	网友分享经验：14 Weeks

Journal of Physics: Complexity

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From Statistical Physics to Social Sciences: The Pitfalls of Multi-disciplinarity

Recommender systems may enhance the discovery of novelties

Biological arrow of time: Emergence of tangled information hierarchies and self-modelling dynamics

Dynamical systems and complex networks: A Koopman operator perspective

Resilient consensus for multiagent networks with time-varying delay and mobile adversaries

Resilient consensus for multiagent networks with time-varying delay and mobile adversaries

Dynamical systems and complex networks: A Koopman operator perspective

Recommender systems may enhance the discovery of novelties

Biological arrow of time: Emergence of tangled information hierarchies and self-modelling dynamics

Swarming network inference with importance clustering of relative interactions