Nan Fang Nie,Xin Xing Zhang,Chu Shan Fang,Qiu Yan Zhu,Jiao Yang Lu,Fu Rui Zhang,Qing Feng Yao,Wei Tao Huang,Xue Zhi Ding,Li Qiu Xia
Game theory—the scientific study of interactive, rational decision making—describes the interaction of two or more players from macroscopic organisms to microscopic cellular and subcellular levels. Life based on molecules is the highest and most complex expression of molecular interactions. However, using simple molecules to expand game theory for molecular decision-making remains challenging. Herein, we demonstrate a proof-of-concept molecular game-theoretical system (molecular prisoner’s dilemma) that relies on formation of the thymine–Hg2+–thymine hairpin structure specifically induced by Hg2+and fluorescence quenching and molecular adsorption capacities of cobalt oxyhydroxide (CoOOH) nanosheets, resulting in fluorescence intensity and distribution change of polythymine oligonucleotide 33-repeat thymines (T33). The “bait” molecule, T33, interacted with two molecular players, CoOOH and Hg2+, in different states (absence = silence and presence = betrayal), regarded as strategies. We created conflicts (sharing or self-interest) of fluorescence distribution of T33, quantifiable in a 2 × 2 payoff matrix. In addition, the molecular game-theoretical-system based on T33 and CoOOH was used for sensing Hg2+over the range of 20 to 600 nM with the detection limit of 7.94 nM (3σ) and for determination of Hg2+in pond water. Inspired by the proof-of-concept for molecular game theory, various molecular decision-making systems could be developed, which would help promote molecular information processing and generating novel molecular intelligent decision systems for environmental monitoring and molecular diagnosis and therapy.Keywords:cobalt oxyhydroxide nanosheets;fluorescent sensing;game theory;Hg2+;thymine–Hg2+–thymine
In the context of an eco-responsible production and distribution of electrical energy at the local scale of an urban territory, we consider a smart grid as a system interconnecting different prosumers, which all retain their decision-making autonomy and defend their own interests in a comprehensive system where the rules, accepted by all, encourage virtuous behavior. In this paper, we present and analyze a model and a management method for smart grids that is shared between different kinds of independent actors, who respect their own interests, and that encourages each actor to behavior that allows, as much as possible, an energy independence of the smart grid from external energy suppliers. We consider here a game theory model, in which each actor of the smart grid is a player, and we investigate distributed machine-learning algorithms to allow decision-making, thus, leading the game to converge to stable situations, in particular to a Nash equilibrium. We propose a Linear Reward Inaction algorithm that achieves Nash equilibria most of the time, both for a single time slot and across time, allowing the smart grid to maximize its energy independence from external energy suppliers.
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