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In group anagram games, players cooperate to form words by sharing letters that they are initially given. The aim is to form as many words as possible as a group, within five minutes. Players take several different actions: requesting letters from their neighbors, replying to letter requests, and forming words. Agent-based models (ABMs) for the game compute likelihoods of each player’s next action, which contain uncertainty, as they are estimated from experimental data. We adopt a Bayesian approach as a natural means of quantifying uncertainty, to enhance the ABM for the group anagram game. Specifically, a Bayesian nonparametric clustering method is used to group player behaviors into different clusters without pre-specifying the number of clusters. Bayesian multi nominal regression is adopted to model the transition probabilities among different actions of the players in the ABM. We describe the methodology and the benefits of it, and perform agent-based simulations of the game.
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Protocol for an agent-based model of recombination in bacteria playing a public goods game
Agent-based models are composed of individual agents coded for traits, such as cooperation and cheating, that interact in a virtual world based on defined rules. Here, we describe the use of an agent-based model of homologous recombina- tion in bacteria playing a public goods game. We describe steps for software installation, setting model parameters, running and testing models, and visuali- zation and statistical analysis. This protocol is useful in analyses of horizontal gene transfer, bacterial sociobiology, and game theory.
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- PAR ID:
- 10474985
- Publisher / Repository:
- Cell Press
- Date Published:
- Journal Name:
- STAR Protocols
- Volume:
- 4
- Issue:
- 4
- ISSN:
- 2666-1667
- Page Range / eLocation ID:
- 102733
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.xpro.2023.102733
