Understanding Norm Change: An Evolutionary Game-Theoretic Approach (Extended Version)
April 16, 2017 Β· Declared Dead Β· π Adaptive Agents and Multi-Agent Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Soham De, Dana S. Nau, Michele J. Gelfand
arXiv ID
1704.04720
Category
cs.CY: Computers & Society
Cross-listed
cs.GT,
cs.MA,
cs.SI
Citations
34
Venue
Adaptive Agents and Multi-Agent Systems
Last Checked
6 months ago
Abstract
Human societies around the world interact with each other by developing and maintaining social norms, and it is critically important to understand how such norms emerge and change. In this work, we define an evolutionary game-theoretic model to study how norms change in a society, based on the idea that different strength of norms in societies translate to different game-theoretic interaction structures and incentives. We use this model to study, both analytically and with extensive agent-based simulations, the evolutionary relationships of the need for coordination in a society (which is related to its norm strength) with two key aspects of norm change: cultural inertia (whether or how quickly the population responds when faced with conditions that make a norm change desirable), and exploration rate (the willingness of agents to try out new strategies). Our results show that a high need for coordination leads to both high cultural inertia and a low exploration rate, while a low need for coordination leads to low cultural inertia and high exploration rate. This is the first work, to our knowledge, on understanding the evolutionary causal relationships among these factors.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computers & Society
π
π
The Cartographer
R.I.P.
π»
Ghosted
Artificial Intelligence: the global landscape of ethics guidelines
R.I.P.
π»
Ghosted
The role of artificial intelligence in achieving the Sustainable Development Goals
R.I.P.
π»
Ghosted
Green AI
R.I.P.
π»
Ghosted
Principles alone cannot guarantee ethical AI
R.I.P.
π»
Ghosted
Tackling Climate Change with Machine Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted