We study flexible information acquisition in a coordination game. "Flexible" acquisition means that players choose not only how much but also what kind of information to acquire. Information acquisition has a cost proportional to reduction of entropy. Hence, players will collect the information most relevant to their welfare but can be rationally inattentive to other aspects of the fundamental. When information is cheap, this flexibility enables players to acquire information that makes efficient coordination possible, which also leads to multiple equilibria. This result contrasts with the global game literature, where information structure is less flexible and cheap information leads to a unique equilibrium with inefficient coordination. We then go beyond the entropic information cost to set out the key aspects of flexibility and examine the way in which they drive our results.
We study flexible information acquisition in a coordination game. "Flexible" acquisition means that players choose not only how much but also what kind of information to acquire. Information acquisition has a cost proportional to reduction of entropy. Hence, players will collect the information most relevant to their welfare but can be rationally inattentive to other aspects of the fundamental. When information is cheap, this flexibility enables players to acquire information that makes efficient coordination possible, which also leads to multiple equilibria. This result contrasts with the global game literature, where information structure is less flexible and cheap information leads to a unique equilibrium with inefficient coordination.
Combustion is the main source of energy and environmental pollution. The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions. In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field. In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed. Combustion studies combined with ML can be divided into theoretical and industrial aspects. Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel. ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation. ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation. ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields. ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants. In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines. Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters. The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.
Players receive a return to investment that is increasing in the proportion of others who invest and the state, and incur a small cost for acquiring information about the state. Their information is reflected in a stochastic choice rule, specifying the probability of a signal leading to investment. If discontinuous stochastic choice rules are infinitely costly, there is a unique equilibrium as costs become small, in which actions are a best response to a uniform (Laplacian) belief over the proportion of others investing. Infeasibility of discontinuous stochastic choice rules captures the idea that it is impossible to perfectly distinguish states that are arbitrarily close together and is both empirically documented and satisfied by many natural micro-founded cost functionals on information. Our results generalize global game selection results (Carlsson and van Damme (1993) and Morris and Shin (2003)), and establish that they do not depend on the specific additive noise information structure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.