1,721,022 research outputs found
The Artificial Intelligence Paradigm Shift and Its Impact on Constitutional Law
No full textBig data and Artificial Intelligence are leading to radical changes in decision-making processes. In this paper, we aim to provide an overview of the paradigm shift that has occurred in the field of AI and its implications for law. Presenting the Compas case, we highlight some critical aspects of the application of AI on judicial decisions. We conclude the work by reflecting on the radical transformations that are proceeding at great speed, underlining the main challenges that only through interdisciplinary approaches can be effectively addressed and proposing some of the fundamental principles of a new constitutional law of the cybernetic era
Dimensionality reduction of complex dynamical systems
Full text linkNonlinear Dynamical Systems; Interdisciplinary Physics; Complex System
Critical slowing down associated with critical transition and risk of collapse in crypto-currency
Full text linkThe 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 cryptocurrency 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
The enduring relevance of simple machine learning models
No full textHopfield’s associative memory model and Hinton’s Boltzmann machines showcase the importance of simplicity and interpretability in AI. Their work urges modern AI to balance power with transparency, ensuring models remain comprehensible for research, education, and broader applications
Recent developments and future perspectives in statistical mechanics of ecological systems
No full textStatistical mechanics provides insights into linking microscopic details with macroscopic behavior, and this approach has extended to ecology with powerful results. In this perspective we review recent progress in the statistical mechanics pertaining to ecosystems, focusing on research directions which have the potential to uncover new important features of ecological communities across scales. These include the understanding of Damuth's and Kleiber's scaling laws, which suggest deep connections between body size, metabolism and population dynamics. Also, recent developments in microbial ecology are shifting attention towards functional dynamics, emphasizing gene function instead of species identity, which contributes to maintaining community stability amid taxonomic diversity. Finally, we argue that the interaction of ecological and evolutionary scales can enrich our understanding of biodiversity, resilience, and adaptability, linking community dynamics with evolutionary processes in an integrated ecological framewor
Delay effects on the stability of large ecosystems
Full text linkThe common intuition among the ecologists of the midtwentieth century was that large ecosystems should bemore stable than those with a smaller number of species.This view was challenged by Robert May, who found a stability bound for randomly assembled ecosystems; they become unstable for a sufficiently large number of species. In the present work, we show that May's bound greatly changes when the past population densities of a species affect its own current density. This is a common feature in real systems, where the effects of species' interactions may appear after a time lag rather than instantaneously.The local stability of these models with self-interaction is described by bounds, which we characterize in the parameter space.We find a critical delay curve that separates the region of stability fromthat of instability, and correspondingly, we identify a critical frequency curve that provides the characteristic frequencies of a system at the instability threshold. Finally, we calculate analytically the distributions of eigenvalues that generalizeWigner's aswell asGirko's laws. Interestingly,we find that, for sufficiently large delays, the eigenvalues of a randomly coupled system are complex even when the interactions are symmetric
EEG microstate transition cost correlates with task demands
Full text linkThe ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task, a cognitive test in which participants respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the framework's effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain.In our daily lives, our brains manage various tasks with different mental demands. Yet, quantifying how much mental effort each task demands is not always straightforward. To tackle this challenge, we developed a way to measure how much cognitive effort our brains use during tasks directly from electroencephalography (EEG) data, which is one of the most used tools to non-invasively measure brain activity. Our approach involved the identification of distinct patterns of synchronized neural activity across the brain, named EEG microstates. By employing optimal transport theory, we established a framework to quantify the cost associated with cognitive transitions based on modifications in EEG microstates. This allowed us to link changes in brain activity patterns to the cognitive effort required for task performance. To validate our framework, we applied it to EEG data collected during a commonly employed cognitive task known as the Stroop task. This task is recognized for challenging us with varying levels of cognitive demand. Our analysis revealed that as the task became more demanding, there were discernible shifts in the EEG microstates. Importantly, these shifts in neural activity patterns corresponded to higher costs associated with cognitive transitions. Our approach offers a promising methodology to assess cognitive effort using neural data, contributing to our comprehension of how the brain manages and adapts to varying cognitive challenges
Exact solution of dynamical mean-field theory for a linear system with annealed disorder
No full textWe investigate a disordered multi-dimensional linear system in which the interaction parameters are colored noises, varying stochastically in time with defined temporal correlations. We refer to this type of disorder as ‘annealed’, in contrast to quenched disorder in which couplings are fixed over time. Using generating functional methods, we extend dynamical mean-field theory to accommodate annealed disorder and employ it to find the exact solution of the linear model in the limit of a large number of degrees of freedom. Our analysis yields analytical results for the non-stationary autocorrelation, the stationary variance, the power spectral density, and the phase diagram of the model. Some unexpected features emerge upon changing the correlation time of the interactions. The stationary variance of the system and the critical variance of the disorder are generally found to be non-monotonic functions of the correlation time of the interactions. We also find that a re-entrant phase transition can take place when this correlation time is varied
Emergence of synchronised and amplified oscillations in neuromorphic networks with long-range interactions
No full textNeuromorphic networks can be described in terms of coarse-grained variables, where emergent sustained behaviours spontaneously arise if stochasticity is properly taken into account. For example it has been recently found that a directed linear chain of connected patch of neurons amplifies an input signal, also tuning its characteristic frequency. Here we study a generalization of such a simple model, introducing heterogeneity and variability in the parameter space and long-range interactions, breaking, in turn, the preferential direction of information transmission of a directed chain. On one hand, enlarging the region of parameters leads to a more complex state space that we analytically characterise; moreover, we explicitly link the strength distribution of the non-local interactions with the frequency distribution of the network oscillations. On the other hand, we found that adding long-range interactions can cause the onset of novel phenomena, as coherent and synchronous oscillations among all the interacting units, which can also coexist with the amplification of the signal
The emergence of cooperation from shared goals in the governance of common-pool resources
No full textSustainable use of common-pool resources is a major environmental governance challenge because of possible overexploitation. Communities devise self-governing institutions that avoid overuse and attain long-term benefits of cooperation. It is still unclear, however, what conditions allow cooperation to emerge, leading to greater long-term benefits. Until recently, studies of the sustainable governance of common-pool resources have overlooked feedback between user decisions and resource dynamics and failed to test the ability of shared goals to actually induce cooperation. Here we develop an online game to perform a set of experiments in which users of the same common-pool resource decide on their individual harvesting rates, which in turn are influenced by the resource dynamics. We show that if users share common goals, a high level of self-organized cooperation emerges, leading to long-term resource sustainability. Otherwise, selfish/individualistic behaviours lead to resource depletion. To explain these results, we develop a model of resource-decision dynamics based on optimal control theory and show how it is able to reproduce empirical results. We find that players self-organize and engage in collective action conducive to sustainable governance of common-pool resources by trade-off strategies that balance individual and collective payoff as well as short-term and long-term rewards
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