In this paper, we investigate the effect of self-awareness (interlayer interaction) for information-epidemic dynamics with simplicial complexes both near and away from epidemic threshold. It is shown that, contrary to previous views, plays a key role small homogeneous networks, multiple susceptibility peaks can emerge in layer under combined self-awareness, even two types completely opposite mechanisms. This means that one needs be very careful when obtaining thresholds based on...

Cooperation is a self-organized collective behavior. It plays significant role in the evolution of both ecosystems and human society. Reinforcement learning different from imitation learning, offering new perspective for exploring cooperation mechanisms. However, most existing studies with public goods game (PGG) employ self-regarding setup or are on pairwise interaction networks. Players real world, however, optimize their policies based not only histories but also coplayers, played group...

Extensive behavioral experiments reveal that conditional cooperation is a prevalent phenomenon. Previous game-theoretical studies have predominantly relied on hard-manner models, where triggered only upon reaching specific threshold. However, this approach contrasts with the observed flexibility of human behavior, individuals adapt their strategies dynamically based surroundings. To capture adaptability, we introduce soft form by integrating Q-learning algorithm from reinforcement learning....

Abstract Behavioral experiments on the trust game have shown that and trustworthiness are commonly seen among human beings, contradicting prediction by assuming Homo economicus in orthodox Economics. This means some mechanism must be at work favors their emergence. Most previous explanations, however, need to resort exogenous factors based upon imitative learning, a simple version of social learning. Here, we turn paradigm reinforcement where individuals revise strategies evaluating...

Abstract Decision-making often overlooks the feedback between agents and environment. Reinforcement learning is widely employed through exploratory experimentation to address problems related states, actions, rewards, decision-making in various contexts. This work considers a new perspective, where individuals continually update their policies based on interactions with spatial environment, aiming maximize cumulative rewards learn optimal strategy. Specifically, we utilize Q-learning...

With the rapid development of sensor networks, machine vision faces problem storing and computing massive data. The human visual system has a very efficient information sense computation ability, which enlightening significance for solving above problems in vision. This review aims to comprehensively summarize latest advances bio-inspired image sensors that can be used improve machine-vision processing efficiency. After briefly introducing research background, relevant mechanisms systems are...

Punishment is a common tactic to sustain cooperation and has been extensively studied for long time. While most of previous game-theoretic work adopt the imitation learning framework where players imitate strategies those who are better off, logic in real world often much more complex. In this work, we turn reinforcement paradigm, individuals make their decisions based upon experience long-term returns. Specifically, investigate prisoners' dilemma game with Q-learning algorithm, cooperators...

Complex systems arising in a modern society typically have many resources and strategies available for their dynamical evolutions. To explore quantitatively the behaviors of such systems, we propose class models to investigate Minority Game (MG) dynamics with multiple strategies. In particular, agents tend choose least used based on local information. A striking finding is emergence grouping states defined terms distinct We develop an analytic theory mean-field framework understand...

Abstract Resource allocation takes place in various types of real-world complex systems such as urban traffic, social services institutions, economical and ecosystems. Mathematically, the dynamical process resource can be modeled minority games . Spontaneous evolution dynamics, however, often leads to a harmful herding behavior accompanied by strong fluctuations which large majority agents crowd temporarily for few resources, leaving many others unused. Developing effective control methods...

Collective behaviors by self-organization are ubiquitous in nature and human society extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing field is of great potential for these tasks. By combining reinforcement learning with evolutionary game (RLEG), we numerically discover rich spectrum collective behaviors---explosive events, oscillation, stable states, etc., that also often observed society. In this work, aim provide...

We study the evolution of two mutually interacting pairwise games on different topologies. On two-dimensional square lattices, we reveal that game-game interaction can promote cooperation prevalence in all cases, and cooperation-defection phase transitions even become absent fairly high is expected when becomes very strong. A mean-field theory developed points out dynamical routes arising therein. Detailed analysis shows indeed there are rich categories interactions either individual or bulk...

The remarkable adaptability of humans in response to complex environments is often demonstrated by the context-dependent adoption different behavioral modes. However, existing game-theoretic studies mostly focus on single-mode assumption, and impact this multimodality evolution cooperation remains largely unknown. Here, we study how evolves a population with two Specifically, incorporate Q-learning Tit-for-Tat (TFT) rules into our toy model investigate mode mixture cooperation. While players...

Resource allocation takes place in various kinds of real-world complex systems, such as traffic social services institutions or organizations, even ecosystems. The fundamental principle underlying resource-allocation dynamics is Boolean interactions associated with minority games, resources are generally limited and agents tend to choose the least used resource based on available information. A common but harmful dynamical behavior systems herding, where there time intervals during which a...

The phase transition of epidemic spreading model on networks is one the most important concerns physicists to theoretical epidemiology. In this paper, we present an analytical expression threshold for interplay between and human behavior multiplex networks. formula proposed in paper reveals relation single-layer that networks, which means conclusions can be used improve accuracy To verify how well our works different build a network with constant total number edges but gradually changing...

Decent social fairness is highly desired both for socio-economic activities and individuals, as it one of the cornerstones our welfare sustainability. How to effectively promote level thus becomes a significant issue be addressed. Here, by adopting pinning control procedure, we find that when very small fraction individuals are pinned fair players in Ultimatum Game, whole population unexpectedly evolves into full level. The basic observations quite robust homogeneous networks, but converging...