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In addition, by incorporating the notion of a reward into our negotiation model, the agents can sometimes reach agreements that would not otherwise be possible.
In the standard "herding" model, the agents learn from each other's decisions, which can lead to cascades on a given decision when later agents' private information is swamped.
In a spatial agent-based model the agents are situated in a spatial environment and are typically assumed to be able to move in various ways across this environment.
In this paper we propose a design's of the expert's decision-making process trough the use of a cognitive model, and fuzzy sets to model the agents' reactive deliberative process.
In this opinion dynamics model, the agents express their opinions in numerical intervals, and the bounded confidences vary in a specified interval as time varies (i.e., dynamic bounded confidence).
Agent model: The agents execute the roles of the protocol.
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While learning the model, the agent explores the domain in a developing and curious way, progressively learning more complex skills.
As in the classical principal-agent setting with hidden action, in our model the agent is induced to deploy unobservable effort by linking her pay to the firm's performance.
In their model, the agent can receive compensation at two points in time.
The considered agent-based model is a stochastic, spatially-explicit, discrete-time, simulation model where the agents represent human individuals.
In our EABM we implemented the expression of the rpoS gene by modeling the agents to spend less energy and stop cellular division under starvation conditions.
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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