Artigos de revistas sobre o tema "Coarse correlated equilibria"

Coarse correlation models strategic interactions of rational agents complemented by a correlation device which is a mediator that can recommend behavior but not enforce it. Despite being a classical concept in the theory of normal-form games since 1978, not much is known about the merits of coarse correlation in extensive-form settings. In this paper, we consider two instantiations of the idea of coarse correlation in extensive-form games: normal-form coarse-correlated equilibrium (NFCCE), already defined in the literature, and extensive-form coarse-correlated equilibrium (EFCCE), a new solution concept that we introduce. We show that EFCCEs are a subset of NFCCEs and a superset of the related extensive-form correlated equilibria. We also show that, in n-player extensive-form games, social-welfare-maximizing EFCCEs and NFCCEs are bilinear saddle points, and give new efficient algorithms for the special case of two-player games with no chance moves. Experimentally, our proposed algorithm for NFCCE is two to four orders of magnitude faster than the prior state of the art.

We study the role of communication in repeated games with private monitoring. We first show that without communication, the set of Nash equilibrium payoffs in such games is a subset of the set ofε‐coarse correlated equilibrium payoffs (ε‐CCE) of the underlying one‐shot game. The value ofεdepends on the discount factor and the quality of monitoring. We then identify conditions under which there are equilibria with “cheap talk” that result in nearly efficient payoffs outside the setε‐CCE. Thus, in our model, communication is necessary for cooperation.

Policy gradient methods enjoy strong practical performance in numerous tasks in reinforcement learning. Their theoretical understanding in multiagent settings, however, remains limited, especially beyond two-player competitive and potential Markov games. In this paper, we develop a new framework to characterize optimistic policy gradient methods in multi-player Markov games with a single controller. Specifically, under the further assumption that the game exhibits an equilibrium collapse, in that the marginals of coarse correlated equilibria (CCE) induce Nash equilibria (NE), we show convergence to stationary epsilon-NE in O(1/epsilon^2) iterations, where O suppresses polynomial factors in the natural parameters of the game. Such an equilibrium collapse is well-known to manifest itself in two-player zero-sum Markov games, but also occurs even in a class of multi-player Markov games with separable interactions, as established by recent work. As a result, we bypass known complexity barriers for computing stationary NE when either of our assumptions fails. Our approach relies on a natural generalization of the classical Minty property that we introduce, which we anticipate to have further applications beyond Markov games.

The connection between games and no-regret algorithms has been widely studied in the literature. A fundamental result is that when all players play no-regret strategies, this produces a sequence of actions whose time-average is a coarse-correlated equilibrium of the game. However, much less is known about equilibrium selection in the case that multiple equilibria exist. In this work, we study the convergence of no-regret bidding algorithms in auctions. Besides being of theoretical interest, bidding dynamics in auctions is an important question from a practical viewpoint as well. We study the repeated game between bidders in which a single item is sold at each time step and the bidder's value is drawn from an unknown distribution. We show that if the bidders use any mean-based learning rule then the bidders converge with high probability to the truthful pure Nash Equilibrium in a second price auction, in VCG auction in the multi-slot setting and to the Bayesian Nash equilibrium in a first price auction. We note mean-based algorithms cover a wide variety of known no-regret algorithms such as Exp3, UCB, \epsilon-Greedy etc. Also, we analyze the convergence of the individual iterates produced by such learning algorithms, as opposed to the time-average of the sequence. Our experiments corroborate our theoretical findings and also find a similar convergence when we use other strategies such as Deep Q-Learning.

How can we design mechanisms to promote efficient use of shared resources? Here, we answer this question in relation to the well-studied class of atomic congestion games, used to model a variety of problems, including traffic routing. Within this context, a methodology for designing tolling mechanisms that minimize the system inefficiency (price of anarchy) exploiting solely local information is so far missing in spite of the scientific interest. In this article, we resolve this problem through a tractable linear programming formulation that applies to and beyond polynomial congestion games. When specializing our approach to the polynomial case, we obtain tight values for the optimal price of anarchy and corresponding tolls, uncovering an unexpected link with load balancing games. We also derive optimal tolling mechanisms that are constant with the congestion level, generalizing the results of Caragiannis et al. [8] to polynomial congestion games and beyond. Finally, we apply our techniques to compute the efficiency of the marginal cost mechanism. Surprisingly, optimal tolling mechanism using only local information perform closely to existing mechanism that utilize global information, e.g., Bilò and Vinci [6], while the marginal cost mechanism, known to be optimal in the continuous-flow model, has lower efficiency than that encountered levying no toll. All results are tight for pure Nash equilibria and extend to coarse correlated equilibria.

We study the price of anarchy (PoA) of simultaneous 2nd price auctions (S2PA) under a new natural condition of no underbidding, meaning that agents never bid on items less than their marginal values. We establish improved (mostly tight) bounds on the PoA of S2PA under no underbidding for different valuation classes (including unit demand, submodular, XOS, subadditive, and general monotone valuations), in both full information and incomplete information settings. To derive our results, we introduce a new parameterized property of auctions, termed (gamma,delta) revenue guaranteed, which implies a PoA of at least gamma/(1+delta). Via extension theorems, this guarantee extends to coarse correlated equilibria (CCE) in full information settings, and to Bayesian PoA (BPoA) in settings with incomplete information and arbitrary (correlated) distributions. We then show that S2PA are (1,1) revenue guaranteed with respect to bids satisfying no underbidding. This implies a PoA of at least 1/2 for general monotone valuation, which extends to BPOA with arbitrary correlated distributions. Moreover, we show that (lambda,mu) smoothness combined with (gamma,delta) revenue guaranteed guarantees a PoA of at least (gamma+lambda)/(1+delta+mu). This implies a host of results, such as a tight PoA of 2/3 for S2PA with submodular (or XOS) valuations, under no overbidding and no underbidding. Beyond establishing improved bounds for S2PA, the no underbidding assumption sheds new light on the performance of S2PA relative to simultaneous 1st price auctions.

Abstract We run a laboratory experiment to test the concept of coarse correlated equilibrium (Moulin and Vial in Int J Game Theory 7:201–221, 1978), with a two-person game with unique pure Nash equilibrium which is also the solution of iterative elimination of strictly dominated strategies. The subjects are asked to commit to a device that randomly picks one of three symmetric outcomes (including the Nash point) with higher ex-ante expected payoff than the Nash equilibrium payoff. We find that the subjects do not accept this lottery (which is a coarse correlated equilibrium); instead, they choose to play the game and coordinate on the Nash equilibrium. However, given an individual choice between a lottery with equal probabilities of the same outcomes and the sure payoff as in the Nash point, the lottery is chosen by the subjects. This result is robust against a few variations. We explain our result as selecting risk-dominance over payoff dominance in equilibrium.

Abstract We consider a situation in which a decision-maker gathers information from imperfectly informed experts, receiving coarse signals about a uniform state of the world. Private information is (conditionally) correlated across players, and communication is cheap talk. We show that with two experts correlation unambiguously tightens the conditions on preferences for a truth-telling equilibrium. However, with multiple experts the effect of correlation on the incentives to report information truthfully can be non-monotonic: while little and large levels of correlation hinder truth-telling, intermediate levels may discipline experts’ equilibrium behaviour and foster truthful communication. We discuss the implications of our results for the political discussion in the presence of ‘selective exposure' to media, where similarity in preferences comes with higher correlation, and a trade-off between truth-telling incentives and informational content arises.

The adsorption of atrazine and alachlor was studied on samples of three horizons from soils with different textures and organic carbon contents. Soils were equilibrated with five concentrations of atrazine and alachlor using batch techniques. Adsorption affinity for atrazine and alachlor was approximated by the Freundlich constant (Kf), distribution coefficient (Kd), and the normalizedKdbased on organic carbon (Koc). Adsorption was not significantly correlated with soil depth, clay content, or organic carbon. Atrazine adsorption was a linear function of equilibrium concentration for nearly all soil horizons but was nonlinear in most horizons for alachlor. The extent of atrazine adsorption was greater in all horizons of the fine-textured soils (Kd= 1.5 to 5.5) compared to coarse-textured soils (Kd= 0.40 to 0.87). The same general trends with texture were not apparent for alachlor. Conversion ofKdtoKocfailed to reduce the variability in the linear adsorption coefficient for atrazine and alachlor in the different soils of this study.

Regret minimization has proved to be a versatile tool for tree-form sequential decision making and extensive-form games. In large two-player zero-sum imperfect-information games, modern extensions of counterfactual regret minimization (CFR) are currently the practical state of the art for computing a Nash equilibrium. Most regret-minimization algorithms for tree-form sequential decision making, including CFR, require (i) an exact model of the player’s decision nodes, observation nodes, and how they are linked, and (ii) full knowledge, at all times t, about the payoffs—even in parts of the decision space that are not encountered at time t. Recently, there has been growing interest towards relaxing some of those restrictions and making regret minimization applicable to settings for which reinforcement learning methods have traditionally been used—for example, those in which only black-box access to the environment is available. We give the first, to our knowledge, regret-minimization algorithm that guarantees sublinear regret with high probability even when requirement (i)—and thus also (ii)—is dropped. We formalize an online learning setting in which the strategy space is not known to the agent and gets revealed incrementally whenever the agent encounters new decision points. We give an efficient algorithm that achieves O(T^3/4) regret with high probability for that setting, even when the agent faces an adversarial environment. Our experiments show it significantly outperforms the prior algorithms for the problem, which do not have such guarantees. It can be used in any application for which regret minimization is useful: approximating Nash equilibrium or quantal response equilibrium, approximating coarse correlated equilibrium in multi-player games, learning a best response, learning safe opponent exploitation, and online play against an unknown opponent/environment.

Human populations tend to grow steadily, because of the ability of people to make innovations, and thus overcome and extend the limits imposed by natural resources. It is therefore questionable whether traditional concepts of population ecology, including environmental carrying capacity, can be applied to human societies. The existence of carrying capacity cannot be simply inferred from population time-series, but it can be indicated by the tendency of populations to return to a previous state after a disturbance. So far only indirect evidence at a coarse-grained scale has indicated the historical existence of human carrying capacity. We analysed unique historical population data on 88 settlements before and after the Thirty Years War (1618–1648), one the longest and most destructive conflicts in European history, which reduced the population of Central Europe by 30–50%. The recovery rate of individual settlements after the war was positively correlated with the extent of the disturbance, so that the population size of the settlements after a period of regeneration was similar to the pre-war situation, indicating an equilibrium population size (i.e. carrying capacity). The carrying capacity of individual settlements was positively determined mostly by the fertility of the soil and the area of the cadastre, and negatively by the number of other settlements in the surroundings. Pre-industrial human population sizes were thus probably controlled by negative density dependence mediated by soil fertility, which could not increase due to limited agricultural technologies.

Classical models of electric double layer (EDL) are mostly concerned with ion distributions and their responses to the surface potential that can be regulated by changing either the voltage or the extent of surface ionization. With the help of adjustable parameters, classical EDL models are able to describe diverse physical phenomena including EDL capacitance and ion transport in good agreement with experimental measurements. However, major challenges remain in understanding chemical reactions within EDL important for the rational design and optimization of electrochemical processes such as energy storage and electrocatalysis. In this talk, I will present a molecular-thermodynamic model to predict the charging behavior of ionizable surfaces under various solution conditions. If a solid is non-conductive, the surface reactions can be reliably described with a Langmuir-type model coupled with liquid-state methods to account for the local thermodynamic non-ideality. When a solid is conductive, however, the electronic properties of different surface sites are intrinsically coupled with each other leading to highly correlated surface reactions. While the brute-force prediction of chemical equilibrium at an electrified surface is computationally prohibitive, a combination of the first-principles calculations and coarse-grained modeling provides complemental information that may be useful for diverse electrochemical applications.

Abstract. The inorganic aerosol composition was measured in the southeastern United States, a region that exhibits high aerosol mass loading during the summer, as part of the 1 June to 15 July 2013 Southern Oxidant and Aerosol Study (SOAS) campaign. Measurements using a Monitor for AeRosols and GAses (MARGA), an ion chromatograph coupled with a wet rotating denuder and a steam-jet aerosol collector for monitoring of ambient inorganic gas and aerosol species, revealed two periods of high aerosol nitrate (NO3−) concentrations during the campaign. These periods of high nitrate were correlated with increased concentrations of coarse mode mineral or sea spray aerosol species, particularly Na+ and Ca2+, and with a shift towards aerosol with larger (1 to 2.5 μm) diameters. We suggest this nitrate aerosol forms by multiphase reactions of HNO3 and particles, reactions that are facilitated by transport of mineral dust and sea spray aerosol from a source within the United States. The observed high aerosol acidity prevents the formation of NH4NO3, the inorganic nitrogen species often dominant in fine-mode aerosol at higher pH. Calculation of the rate of the heterogeneous uptake of HNO3 on mineral aerosol supports the conclusion that aerosol NO3− is produced primarily by this process, and is likely limited by the availability of mineral dust surface area. Modeling of NO3− and HNO3 by thermodynamic equilibrium models (ISORROPIA II and E-AIM) reveals the importance of including mineral cations in the southeastern United States to accurately balance ion species and predict gas/aerosol phase partitioning.

Abstract Domain walls in magnets, vortex lattices in superconductors, contact lines at depinning, and many other systems can be modeled as an elastic system subject to quenched disorder. The ensuing field theory possesses a well-controlled perturbative expansion around its upper critical dimension. Contrary to standard field theory, the renormalization group (RG) flow involves a function, the disorder correlator Δ(w), and is therefore termed the functional RG. Δ(w) is a physical observable, the auto-correlation function of the center of mass of the elastic manifold. In this review, we give a pedagogical introduction into its phenomenology and techniques. This allows us to treat both equilibrium (statics), and depinning (dynamics). Building on these techniques, avalanche observables are accessible: distributions of size, duration, and velocity, as well as the spatial and temporal shape. Various equivalences between disordered elastic manifolds, and sandpile models exist: an elastic string driven at a point and the Oslo model; disordered elastic manifolds and Manna sandpiles; charge density waves and Abelian sandpiles or loop-erased random walks. Each of the mappings between these systems requires specific techniques, which we develop, including modeling of discrete stochastic systems via coarse-grained stochastic equations of motion, super-symmetry techniques, and cellular automata. Stronger than quadratic nearest-neighbor interactions lead to directed percolation, and non-linear surface growth with additional Kardar–Parisi–Zhang (KPZ) terms. On the other hand, KPZ without disorder can be mapped back to disordered elastic manifolds, either on the directed polymer for its steady state, or a single particle for its decay. Other topics covered are the relation between functional RG and replica symmetry breaking, and random-field magnets. Emphasis is given to numerical and experimental tests of the theory.

The purpose of this paper is to document the influence of depositional environments on shallow-water, low-relief clinoforms from the description of five ancient carbonate platforms: the Neoproterozoic (Namibia), Middle Jurassic (France), Lower Cretaceous (France), Upper Cretaceous (Oman) and Miocene (Turkey). These examples have been investigated on the basis of field observations. The clinoforms are described with reference to geometric and compositional attributes: declivity, shape, height, sedimentary structures, sediment fabric and components. The results show great variability in stratal geometry, declivity and facies distribution: (1) depositional profiles vary from exponential, to sigmoidal, to oblique; (2) maximal slope angles range from 3 to 25°, most of them being grouped between 10 and 18°; (3) facies differentiation identified from lateral facies successions along beds, and vertical facies successions through beds, is pronounced to subtle. This study documents linkages between depositional environments and clinoform attributes. Proximal/shallow clinoforms display round-edged exponential profiles. Sediment deposition has resulted from unidirectional currents in the upper convex section, and storm-generated oscillatory currents in the lower concave part. The sediment fabric changes gradually along this type of clinoform. There is little vertical facies differentiation through these clinobeds which have formed from a continuous amalgamation of deposits. By contrast, distal clinoforms (shelf break, distally steepened ramp settings) yield a much broader spectrum of profiles and are generally shorter and steeper. Sedimentary structures in gravel-sized deposits of the upper slope indicate pure traction by unidirectional currents. Conversely, marks of oscillatory flows (undular, wavy top bounding surfaces of clinobeds) are common in the lower slope. Intercalation of massive, fine-grained deposits suggests offshore transport of carbonate mud by suspension. Each distal clinobed represents a single flow event. Accordingly, facies differentiation is weak laterally but may be pronounced through the clinobeds. Our study suggests that low-relief forms of proximal/shallow environments, which contain coarse-grained and photo-independently produced debris, record hydrodynamic equilibrium profiles, whereas the higher-relief forms of this setting rather reflect a high differential production rate of carbonate sediment with water depth. The carbonate sediment of the distal clinobeds mainly derives from skeletal production by oligophotic and photo-independent biota of the middle shelf/ramp and upper portion of the clinoforms. The contribution by in situ skeletal biota only becomes significant on the lower slope, indicating that the distal, submerged slopes of carbonate platforms are not organically but hydrodynamically generated. Our compilation shows that the slope angles of shallow marine, low-relief clinoforms do not simply correlate to the sediment grain size and fabric, in contrast to what has been documented for the high, linear slope profiles. This difference stems from the depositional settings, namely the involved transport mechanisms. Low-relief clinoform accretion seems to be dominantly influenced by wave-induced sediment transport, in contrast to linear flanks of high-relief clinoforms that build to the angle of repose, and for which gravity is the primary transport process.

The kinematic of the Cretaceous deformation and the relationship between deformation and metamorphism of the Pelagonian crystalline was studied in Voras Mt (northern Greece). The Pelagonian crystalline in this area has been subdivided into a lower, core part, consisting mainly of gneissic rocks and schists and an upper, cover part, consistine of schists and quartzites with marble intercalations. Intensely deformed granitic rocks of Upper Paleozoic age intrude the Pelagonian crystalline basement. An S j foliation is the oldest fabric recognized in the Pelagonian crystalline. Sj is mainly defined by syn-Sjgarnet(Gr1), biotite(Btj), white mica (Wnij), chloritoid, kyanite and plagioclase in the metapelitic rocks and green amphibole, epidote, plagioclase, and biotiteiBtj) in the amphibolite. Garnet grows also in some cases post-kinematically. Ilmenite and tourmaline are often found in the pelitic rocks as well. S is overprinted by an S2 foliation that developed as a crenulation cleavage. In most places, however, S2 has destroyed all earlier fabrics and a single S2 fabric is present related to, isoclinal or sheath folds intrafolial in places. S2 in the metapelitic rocks is characterized by the syn-S2 development of chlorite, white mica(Wm2) and plagioclase. In the amphibolite S2 is mainly defined by the syn-kinematic development of actinolite, plagioclase, biotite(Bt2), white mica(Wm2) and chlorite. During D2 garnet(Gr1) and biotiteiBtj) are partially replaced by chlorite, while green amphibole is replaced by actinolite and chlorite. Chloritoid remains generally stable along the S2-planes but in some places transforms to chlorite and sericite. Furthermore, D2 was locally followed by a static post-kinematic annealing indicated by polygonal quartz microfabrics with equilibrated grain boundaries and triple points. The overall orientation of S2 is dome shaped with a gentle SW-ward and NE-ward dip in the southwestern and northeastern flanks of the dome respectively. Syn-S2 minerals defined a very well exposed NW-SE trending stretching lineation. Kinematic indicators show a main top to the SE sense of movement. An S3 crenulation cleavage associated with asymmetric NW-SE trending folds is also present in most parts of the core and cover rocks, possibly, related to a constrictional type of deformation. A well developed, S4 shear band cleavage is mainly present in the upper parts of the metamorphic dome and formed under cooler conditions. S4 shear bands are associated with a NW-SE developed stretching lineation defined by elongated and dynamically recrystallized quartz grains and a preferred orientation of white mica and chlorite. Along the S4 shear bands a transformation of garnet, biotite, chloritoid and amphibole into chlorite is always observed. S4 shear bands indicate a main top to the SE sense of movement. The P-T metamorphic conditions were derived from textural equilibria and mineral assemblages, as well as from the spatial distribution of the metamorphic minerals. Syn-Dj metamorphism reached the conditions of the boundaries between greenschist and amphibolite facies. Syn-D2 retrogression took place under greenschist facie conditions. K/Ar radiometric datings on coarse-grained syn-St and younger fine-grained syn-S2 micas define an Early Cretaceous cooling age ('135Ma) for the older event and a Mid- to Late Cretaceous age ('90-80Ma) for the second event. A white mica age of ca. 65Ma correlates with S4 shear band clevage. Furthermore, the intrusion age of a granitic body into the Pelagonian crystalline is dated using the Pb/Pb single zircon evaporation method. The estimated intrusion age of 300±3Ma suggests that the Pelagonian crystalline was affected by a pre-kinematic magmatic activity relative to its Cretaceous deformation.

The random first order transition theory (RFOT) describing the transition from a supercooled liquid to an ideal glass has been actively developed over the last twenty years. This theory is formulated in a way that allows a description of the transition from the initial equilibrium state to the final metastable state without considering any kinetic processes. The RFOT and its applications for real molecular systems (multicomponent liquids with various intermolecular potentials, gel systems, etc.) are widely represented in English-language sources. However, these studies are practically not described in any Russian sources. This paper presents an overview of the studies carried out in this field. REFERENCES 1. Sanditov D. S., Ojovan M. I. Relaxation aspectsof the liquid—glass transition. Uspekhi FizicheskihNauk. 2019;189(2): 113–133. DOI: https://doi.org/10.3367/ufnr.2018.04.0383192. Tsydypov Sh. B., Parfenov A. N., Sanditov D. S.,Agrafonov Yu. V., Nesterov A. S. 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AbstractAs in public good provisions, in a public bad situation such as abatement, the non-cooperative interplay of the participants typically results in low levels of quantities (provision or abatement). In a simple class of n-person quadratic games, we show how Coarse correlated equilibria, using simple mediation devices, can significantly outperform Nash equilibrium outcomes in terms of a stated policy objective.

AbstractI consider a generalizarion of Vanderschraaf's correlated conventions to Quasi-Conventions, using the concept of coarse correlated equilibria. I discuss the possibility of improved payoffs and the question of learnability by simple uncoupled learning dynamics. Laboratory experiments are surveyed. The generalization introduces strains of commitment, which can be see from different points of view. I conclude that the strains of commitment preclude using the generalization as a stand-alone definition of convention, but that in certain settings Quasi-Conventions can be important modules within larger true conventions.

We consider a strategic variant of the knapsack problem: the items are owned by agents, and agents can misrepresent their sets of items---either by hiding items (understating), or by reporting fake ones (overstating). Each agent's utility equals the total value of her items included in the knapsack. We wish to maximize social welfare, and attempt to design mechanisms that lead to small worst-case approximation ratios at equilibrium. We provide a randomized mechanism with attractive strategic properties: it has a price of anarchy of 2 for Bayes-Nash and coarse correlated equilibria. For overstating-only agents, it becomes strategyproof, and has a matching lower bound. For the case of two understating-only agents, we provide a specialized randomized strategyproof 1.522-approximate mechanism, and a lower bound of 1.09. When all agents but one are honest, we provide a deterministic strategyproof 1.618-approximate mechanism with a matching lower bound. The latter two mechanisms are also useful in problems beyond the one in consideration.

A major challenge of multiagent reinforcement learning (MARL) is the curse of multiagents, where the size of the joint action space scales exponentially with the number of agents. This remains to be a bottleneck for designing efficient MARL algorithms, even in a basic scenario with finitely many states and actions. This paper resolves this challenge for the model of episodic Markov games. We design a new class of fully decentralized algorithms—V-learning, which provably learns Nash equilibria (in the two-player zero-sum setting), correlated equilibria, and coarse correlated equilibria (in the multiplayer general-sum setting) in a number of samples that only scales with [Formula: see text], where Ai is the number of actions for the ith player. This is in sharp contrast to the size of the joint action space, which is [Formula: see text]. V-learning (in its basic form) is a new class of single-agent reinforcement learning (RL) algorithms that convert any adversarial bandit algorithm with suitable regret guarantees into an RL algorithm. Similar to the classical Q-learning algorithm, it performs incremental updates to the value functions. Different from Q-learning, it only maintains the estimates of V-values instead of Q-values. This key difference allows V-learning to achieve the claimed guarantees in the MARL setting by simply letting all agents run V-learning independently. Funding: This work was partially supported by Office of Naval Research Grant N00014-22-1-2253.

AbstractWe study the limit behavior and performance of no-regret dynamics in general game theoretic settings. We design protocols that achieve both good regret and equilibration guarantees in general games. We also establish a strong equivalence between them and coarse correlated equilibria (CCE). We examine structured game settings where stronger properties can be established for no-regret dynamics and CCE. In congestion games with non-atomic agents (each contributing a fraction of the flow), as we decrease the individual flow of agents, CCE become closely concentrated around the unique equilibrium flow of the non-atomic game. Moreover, we compare best/worst case no-regret learning behavior to best/worst case Nash equilibrium (NE) in small games. We prove analytical bounds on these inefficiency ratios for 2×2 games and unboundedness for larger games. Experimentally, we sample normal form games and compute their measures of inefficiency. We show that the ratio distribution has sharp decay, in the sense that most generated games have small ratios. They also exhibit strong anti-correlation between each other, that is games with large improvements from the best NE to the best CCE present small degradation from the worst NE to the worst CCE.

AbstractActive matter may sometimes behave almost indistinguishably from equilibrium matter. This is particularly evident for some particle-based models and active field-theories close to a critical point which falls in the Ising universality class. Here we show however that, even when critical, active particles strongly violate the equilibrium fluctuation-dissipation in the high-wave-vector and high-frequency regime. Conversely, at larger spatiotemporal scales the theorem is progressively restored and the critical dynamics is in effective equilibrium. We develop a field-theoretical description of this scenario employing a space-time correlated noise field finding that the theory qualitatively captures the numerical results already at the Gaussian level. Moreover a dynamic renormalization group analysis shows that the correlated noise does not change the equilibrium critical exponents. Our results demonstrate that a correlated noise field is a fundamental ingredient to describe critical active matter at the coarse-grained level.

Simulations of soft materials often adopt low-resolution coarse-grained (CG) models. However, the CG representation is not unique and its impact upon simulated properties is poorly understood. In this work, we investigate the space of CG representations for ubiquitin, which is a typical globular protein with 72 amino acids. We employ Monte Carlo methods to ergodically sample this space and to characterize its landscape. By adopting the Gaussian network model as an analytically tractable atomistic model for equilibrium fluctuations, we exactly assess the intrinsic quality of each CG representation without introducing any approximations in sampling configurations or in modeling interactions. We focus on two metrics, the spectral quality and the information content, that quantify the extent to which the CG representation preserves low-frequency, large-amplitude motions and configurational information, respectively. The spectral quality and information content are weakly correlated among high-resolution representations but become strongly anticorrelated among low-resolution representations. Representations with maximal spectral quality appear consistent with physical intuition, while low-resolution representations with maximal information content do not. Interestingly, quenching studies indicate that the energy landscape of mapping space is very smooth and highly connected. Moreover, our study suggests a critical resolution below which a “phase transition” qualitatively distinguishes good and bad representations.

AbstractIn order to gain insights on the conditions of aqueous alteration on asteroid Ryugu and the origin of water in the outer solar system, we developed the measurement of water content in magnetite at the micrometer scale by secondary ion mass spectrometry (NanoSIMS) and determined the H and Si content of coarse‐grained euhedral magnetite grains (polyhedral magnetite) and coarse‐grained fibrous (spherulitic) magnetite from the Ryugu polished section A0058‐C1001. The hydrogen content in magnetite ranges between ~900 and ~3300 wt ppm equivalent water and is correlated with the Si content. Polyhedral magnetite has low and homogenous silicon and water content, whereas fibrous magnetite shows correlated Si and water excesses. These excesses can be explained by the presence of hydrous Si‐rich amorphous nanoinclusions trapped during the precipitation of fibrous magnetite away from equilibrium and testify that fibrous magnetite formed from a hydrous gel with possibly more than 20 wt% water. An attempt to determine the water content in sub‐μm framboids indicates that additional calibration and contamination issues must be addressed before a safe conclusion can be drawn, but hints at elevated water content as well. The high water content in fibrous magnetite, expected to be among the first minerals to crystallize at low water–rock ratio, points to the control of water content by local conditions of magnetite precipitation rather than large‐scale alteration conditions. Systematic lithological variations associated with water‐rich and water‐poor magnetite suggest that the global context of alteration may be better understood if local water concentrations are compared with millimeter‐scale distribution of the various morphologies of magnetite. Finally, the high water content in the magnetite precursor gel indicates that the initial O isotopic composition in alteration water must not have been very different from that of the earliest magnetite crystals.

Agriculture-based Internet of Things (Ag-IoT) can generate near-real-time quantitative data to analyze crop growth. Unmanned Aerial Vehicle (UAV) based mobile edge computing systems is widely used in Ag-IoT due to the low cost, fast movement and ease of operation of UAVs. Nevertheless, sensors in agriculture may be under a tall and dense crop canopy or under the soil where they have difficulty harvesting a steady of energy and have too worse a channel condition to transmit more data. This paper considers a UAV-Sensor Collaboration Wireless Network (USCWN) in which each UAV carries a computation module and a wireless power transfer module to provide service and energy to sensors. Then, we propose an online resource management framework with two sub-algorithms to maximize the network throughput of USCWN. The first is the sensor fuzzy selection algorithm, which determines which UAV the sensor transmits data to, considering the different resource properties between sensors and UAVs without prior knowledge. The other is a semi-distributed dynamic resource allocation algorithm in which the sensors compete with UAV’s energy, computation, and communication resources in a stochastic game. The UAV acts as a game manager to manage sensor resources. The stochastic game is solved using Lyapunov optimization, which yields a coarse correlated equilibrium that is better than the nash equilibrium. Performance evaluation shows that our proposed framework has a higher network throughput than other agricultural works and maintains stable sensor energy.

In this paper, we are concerned about the nature of information and how to gather and compose data with the help of so called FAIR digital objects (FDOs) in order to transform them to knowledge. FDOs are digital surrogates of real objects. The nature of information is intrinsically linked to the kind of questions one is asking. One might not ask a question or get philosophical about it. Answers depend on the data different disciplines gather about their objects of study. In Statistical Physics, classical Shannon entropy measures system order which in equilibrium just equals the heat exchanged with the environment. In cell biology, each protein carries certain functions which create specific information. Cognitive science describes how organisms perceive their environment via functional sensors and control behavior accordingly. Note that one can have function and control without meaning. In contrast, psychology is concerned with the assessment of our perceptions by assigning meaning and ensuing actions. Finally, philosophy builds logical constructs and formulates principles, in effect transforming facts into complex knowledge. All these statements make sense, but there is an even more concise way. Indeed, Luciano Floridi provides a precise and thorough classification of information in his central oeuvre On the Philosophy of Information (Floridi 2013). Especially, he performs a sequential construction to develop the attributes which data need to have in order to count as knowledge. Semantic information is necessarily well-formed, meaningful and truthful. Well-formed data becomes meaningful by action based-semantics of an autonomous-agent solving the symbol grounding problem (Taddeo and Floridi 2005) interacting with the environment. Knowledge is created then by being informed through relevant data accounted for. We notice that the notion of agency is crucial for defining meaning. The apparent gap between Sciences and Humanities (Bawden and Robinson 2020) is created by the very existence of meaning. Further, meaning depends on interactions & connotations which are commensurate with the effective complexity of the environment of a particular agent resulting in an array of possible definitions. In his classical paper More is different (Anderson 1972) discussed verbatim the hierarchical nature of science. Each level is made of and obeys the laws of its constituents from one level below with the higher-level exhibiting emergent properties like wetness of water assignable only to the whole system. As we rise through the hierarchies, there is a branch of science for each level of complexity; on each complexity level there are objects for which it is appropriate and fitting to build up vocabulary for the respective levels of description leading to formation of disciplinary languages. It is the central idea of causal emergence that on each level there is an optimal degree of coarse graining to define those objects in such a way that causality becomes maximal between them. This means there is emergence of informative higher scales in complex materials extending to biological systems and into the brain with its neural networks representing our thoughts in a hierarchy of neural correlates. A computational toolkit for optimal level prediction and control has been developed (Hoel and Levin 2020) which was conceptually extended to integrated information theory of consciousness (Albantakis et al. 2019). The large gap between sciences and humanities discussed above exhibits itself in a series of small gaps connected to the emergence of informative higher scales. It has been suggested that the origin of life may be identified as a transition in causal structure and information flow (Walker 2014). Integrated information measures globally how much the causal mechanisms of a system reduce the uncertainty about the possible causes for a given state. A measure of “information flow” that accurately captures causal effects has been proposed (Ay and Polani 2008). The state of the art is presented in (Ay et al. 2022) where the link between information and complexity is discussed. Ay et al single out hierarchical systems and interlevel causation. Even further, (Rosas et al. 2020) reconcile conflicting views of emergence via an exact information-theoretic approach to identify causal emergence in multivariate data. As information becomes differentially richer one eventually needs complexity measures beyond {Rn}. One may define generalized metrices on these spaces (Pirr&oacute 2009) measuring information complexity on ever higher hierarchical levels of information. As one rises through hierarchies, information on higher scale is usually gained by coarse graining to arrive at an effective, nevertheless exact description, on the higher scale. It is repeated coarse graining of syntactically well-ordered information layers which eventually leads to semantic information in a process which I conjecture to be reminiscent of renormalization group flow leading to a universal classification scheme. Thus, we identify scientific disciplines and their corresponding data sets as dual universality classes of physical and epistemic structure formation, respectively. Above the semantic gap, we may call this process quantification of the qualitative by semantic metrics. Indeed, (Kolchinsky and Wolpert 2018) explored for the first time quantitative semantic concepts in Physics in their 2018 seminal paper entitled Semantic information, autonomous agency and non-equilibrium statistical physics. Their measures are numeric variants of entropy. Semantic information is identified with ‘the information that a physical system has about its environment that is causally necessary for the system to maintain its own existence over time’. FDOs are employed in these processes in two fundamental ways. For practical implementations of FDO technology, see accompanying abstract (Wittenburg et al. 2022). First, the FAIR principles (Wilkinson et al. 2016) ensure that unconnected pieces of data may be percolated into an integrated data space. Percolation creates the information density needed to feed AI-driven built up of semantic space. Without FDOs we wouldn't have the gravity for this to occur. Second, the very structure of FDOs, capable of symmetry preserving or breaking fusion events into composed entities, makes them homologous to mathematical categories. This will proof to be a powerful tool to unravel the nature of information via analyzing its topological structure algebraically, especially when considering our conjecture concerning universality, classes of information and their possible instantiations on vastly different length and time scales, in effect explaining analogous structure formation.