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In rational-inattentiveness models agents update their information set only when the benefit outweighs the information cost.
We give a cooperative policy iteration algorithm for graphical games that converges to the best response when the neighbors of each agent do not update their policies, and to the cooperative Nash equilibrium when all agents update their policies simultaneously.
After each round, agents update their strategies based on the replicator dynamic.
The agents update their opinions as a result of interactions with their neighboring agents.
The simulation was run in a synchronous manner, in which all agents update their behavior simultaneously.
On the one hand, agents update their decision to provide the public good over time.
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Inserting or removing agents updates this list.
Firstly, the communication updating matrix W updates according to the participating agents updating method described in (6) to adapt to the plug-in operation.
And why should an ideal doxastic agent update her ranking function according to plain or Spohn Conditionalization?
A numerical algorithm is employed after each agent update to shift the cells to minimise edge overlap arising due to cell growth, division and migration.
Each agent carries five vectors: a winning bid list y i, a winning agent list z i, an agent update time s i, a bundle b i and the corresponding path p i.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com