The Role of Noise in the Spatial Public Goods Game

In this work we aim to analyze the role of noise in the spatial Public Goods Game, one of the most famous games in Evolutionary Game Theory. The dynamics of this game is affected by a number of parameters and processes, namely the topology of interactions among the agents, the synergy factor, and the strategy revision phase. The latter is a process that allows agents to change their strategy. Notably, rational agents tend to imitate richer neighbors, in order to increase the probability to maximize their payoff. By implementing a stochastic revision process, it is possible to control the level of noise in the system, so that even irrational updates may occur. In particular, in this work we study the effect of noise on the macroscopic behavior of a finite structured population playing the Public Goods Game. We consider both the case of a homogeneous population, where the noise in the system is controlled by tuning a parameter representing the level of stochasticity in the strategy revision phase, and a heterogeneous population composed of a variable proportion of rational and irrational agents. In both cases numerical investigations show that the Public Goods Game has a very rich behavior which strongly depends on the amount of noise in the system and on the value of the synergy factor. To conclude, our study sheds a new light on the relations between the microscopic dynamics of the Public Goods Game and its macroscopic behavior, strengthening the link between the field of Evolutionary Game Theory and statistical physics.Comment: 14 pages, 3 figure

Success and luck in creative careers

Luck is considered to be a crucial ingredient to achieve impact in all creative domains, despite their diversity. For instance, in science, the movie industry, music, and art, the occurrence of the highest impact work and of a hot streak within a creative career are very difficult to predict. Are there domains that are more prone to luck than others? Here, we provide new insights on the role of randomness in impact in creative careers in two ways: (i) we systematically untangle luck and individual ability to generate impact in the movie, music, and book industries, and in science, and compare the luck factor between these fields; (ii) we show the limited predictive power of collaboration networks to predict career hits. Taken together, our analysis suggests that luck consistently affects career impact across all considered sectors and improves our understanding in pinpointing the key elements in the prediction of success

An Information Theoretic approach to Post Randomization Methods under Differential Privacy

Post Randomization Methods (PRAM) are among the most popular disclosure limitation techniques for both categorical and continuous data. In the categorical case, given a stochastic matrix M and a specified variable, an individual belonging to category i is changed to category j with probability Mi,j . Every approach to choose the randomization matrix M has to balance between two desiderata: 1) preserving as much statistical information from the raw data as possible; 2) guaranteeing the privacy of individuals in the dataset. This trade-off has generally been shown to be very challenging to solve. In this work, we use recent tools from the computer science literature and propose to choose M as the solution of a constrained maximization problems. Specifically, M is chosen as the solution of a constrained maximization problem, where we maximize the Mutual Information between raw and transformed data, given the constraint that the transformation satisfies the notion of Differential Privacy. For the general Categorical model, it is shown how this maximization problem reduces to a convex linear programming and can be therefore solved with known optimization algorithms

Quantifying human performance in chess

From sports to science, the recent availability of large-scale data has allowed to gain insights on the drivers of human innovation and success in a variety of domains. Here we quantify human performance in the popular game of chess by leveraging a very large dataset comprising of over 120 million games between almost 1 million players. We find that individuals encounter hot streaks of repeated success, longer for beginners than for expert players, and even longer cold streaks of unsatisfying performance. Skilled players can be distinguished from the others based on their gaming behaviour. Differences appear from the very first moves of the game, with experts tending to specialize and repeat the same openings while beginners explore and diversify more. However, experts experience a broader response repertoire, and display a deeper understanding of different variations within the same line. Over time, the opening diversity of a player tends to decrease, hinting at the development of individual playing styles. Nevertheless, we find that players are often not able to recognize their most successful openings. Overall, our work contributes to quantifying human performance in competitive settings, providing a first large-scale quantitative analysis of individual careers in chess, helping unveil the determinants separating elite from beginner performance.Comment: 8 pages, 5 figure

Multiorder Laplacian for synchronization in higher-order networks

Traditionally, interaction systems have been described as networks, where links encode information on the pairwise influences among the nodes. Yet, in many systems, interactions take place in larger groups. Recent work has shown that higher-order interactions between oscillators can significantly affect synchronization. However, these early studies have mostly considered interactions up to 4 oscillators at time, and analytical treatments are limited to the all-to-all setting. Here, we propose a general framework that allows us to effectively study populations of oscillators where higher-order interactions of all possible orders are considered, for any complex topology described by arbitrary hypergraphs, and for general coupling functions. To this scope, we introduce a multi-order Laplacian whose spectrum determines the stability of the synchronized solution. Our framework is validated on three structures of interactions of increasing complexity. First, we study a population with all-to-all interactions at all orders, for which we can derive in a full analytical manner the Lyapunov exponents of the system, and for which we investigate the effect of including attractive and repulsive interactions. Second, we apply the multi-order Laplacian framework to synchronization on a synthetic model with heterogeneous higher-order interactions. Finally, we compare the dynamics of coupled oscillators with higher-order and pairwise couplings only, for a real dataset describing the macaque brain connectome, highlighting the importance of faithfully representing the complexity of interactions in real-world systems. Taken together, our multi-order Laplacian allows us to obtain a complete analytical characterization of the stability of synchrony in arbitrary higher-order networks, paving the way towards a general treatment of dynamical processes beyond pairwise interactions.Comment: Was "A multi-order Laplacian framework for the stability of higher-order synchronization

The structure and dynamics of multiplex networks

PhDNetwork science has provided useful answers to research questions in many fields, from biology to social science, from ecology to urban science. The first analyses of networked systems focused on binary networks, where only the topology of the connections were considered. Soon network scientists started considering weighted networks, to represent interactions with different strength, cost, or distance in space and time. Also, connections are not fixed but change over time. This is why in more recent years, a lot of attention has been devoted to temporal or time-varying networks. We now entered the era of multi-layer networks, or multiplex networks, relational systems whose units are connected by different relationships, with links of distinct types embedded in different layers. Multiplexity has been observed in many contexts, from social network analysis to economics, medicine and ecology. The new challenge consists in applying the new tools of multiplex theory to unveil the richness associated to this novel level of complexity. How do agents organise their interactions across layers? How does this affect the dynamics of the system? In the first part of the thesis, we provide a mathematical framework to deal with multiplex networks. We suggest metrics to unveil multiplexity from basic node, layer and edge properties to more complicated structure at the micro- and meso-scale, such as motifs, communities and cores. Measures are validated through the analysis of real-world systems such as social and collaboration networks, transportation systems and the human brain. In the second part of the thesis we focus on dynamical processes taking place on top of multiplex networks, namely biased random walks, opinion dynamics, cultural dynamics and evolutionary game theory. All these examples show how multiplexity is crucial to determine the emergence of unexpected and instrinsically multiplex collective behavior, opening novel perspectives for the field of non-linear dynamics on networks.European Union project LASAGN

Do higher-order interactions promote synchronization?

Understanding how nonpairwise interactions alter dynamical processes in networks is of fundamental importance to the characterization and control of many coupled systems. Recent discoveries of hyperedge-enhanced synchronization under various settings raised speculations that such enhancements might be a general phenomenon. Here, we demonstrate that even for simple systems such as Kuramoto oscillators, the effects of higher-order interactions are highly representation-dependent. Specifically, we show numerically and analytically that hyperedges typically enhance synchronization in random hypergraphs, but have the opposite effect in simplicial complexes. As an explanation, we identify higher-order degree heterogeneity as the key structural determinant of synchronization stability in systems with a fixed coupling budget. Our findings highlight the importance of appropriate representations in describing higher-order interactions. In particular, the choice of simplicial complexes or hypergraphs has significant ramifications and should not be purely motivated by technical conveniences.Comment: Comments welcome! Y.Z. and M.L. contributed equally to this work. Code available at https://github.com/maximelucas/higherorder_sync_promote