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Impact of incentive and selection strength on green technology innovation in Moran process
- Source :
- PLoS ONE, Vol 15, Iss 6, p e0235516 (2020), PLoS ONE
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- Methods of previous researches on green technology innovation will have difficulty in finite population. One solution is the use of stochastic evolutionary game dynamic-Moran process. In this paper we study stochastic dynamic games about green technology innovation with a two-stage free riding problem. Results illustrate the incentive and selection strength play positive roles in promoting participant to be more useful to society, but with threshold effect: too slighted strength makes no effect due to the randomness of the evolution process in finite population. Two-stage free riding problem can be solved with the use of inequality incentives, however, higher inequality can make policy achieves faster but more unstable, so there would be an optimal range. In this paper we provided the key variables of green technology innovation incentive and principles for the environmental regulation policy making. Also reminded that it’s difficult to formulate policies reasonably and make them achieve the expected results.
- Subjects :
- Behavioral Economics
Economics
Computer science
Evolutionary algorithm
Social Sciences
010501 environmental sciences
Behavioral economics
01 natural sciences
Cognition
Natural Selection
Psychology
Conservation Science
0303 health sciences
education.field_of_study
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Biological Evolution
Environmental Regulations
Free riding
Incentive
Physical Sciences
Medicine
Game theory
Algorithms
Research Article
Evolutionary Processes
Process (engineering)
Science
Decision Making
Population
Research and Analysis Methods
Models, Biological
Microeconomics
03 medical and health sciences
Environmental Law
Game Theory
Inventions
Computational Techniques
Moran process
Humans
Selection, Genetic
education
Regulations
030304 developmental biology
0105 earth and related environmental sciences
Stochastic Processes
Evolutionary Biology
Behavior
Ecology and Environmental Sciences
Cognitive Psychology
Biology and Life Sciences
Cognitive Science
Law and Legal Sciences
Evolutionary Algorithms
Evolutionary Computation
Mathematics
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....5e89750605e766bc1d94eed9efe81872
- Full Text :
- https://doi.org/10.1371/journal.pone.0235516