AurelienNioche/DuopolyAnalysis

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Created almost 7 years ago
Updated over 5 years ago

AurelienNioche/DuopolyAnalysis

AurelienNioche

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Analysis part of Duopoly project

ActiveTeachingModel

How selecting carefully the next item based on a cognitive model of the user in an associative learning task can improve the learning process?

Jupyter Notebook

InteractiveInference

Jupyter Notebook

Sugarscape

Reproduction of Epstein & Axtell (1996)

SpatialCompetition

Variation of Hotelling's model (1929) with not fully-informed consumers

EvoProspect

MoneyApp

Client part for an experiment about money emergence. Goes with https://github.com/AurelienNioche/MoneyServer

AdaptiveDesignOptimization

Jupyter Notebook

Hotelling

[disrupted; see 'SpatialCompetition' project: https://github.com/AurelienNioche/SpatialCompetition] Agent-based model freely inspired from Hotelling (1929), with customers having difficulties for accessing information.

DuopolyDjango

Server part of Duopoly Game. Based on Django framework.

KW

Replication of Kiyotaki & Wright 1989 paper with RL agents.

NormalizingFlowsTutorial

Jupyter Notebook

LectureUserResearch

Jupyter Notebook

KalterKriegDerKulturen

Culture evolution with genetic algorithm

ActiveTeachingServer

Server part of the Active Teaching project

Jupyter Notebook

DuopolyAssets

Client part of the Duopoly game (Unity assets)

CNN

Following tutorial: https://www.datacamp.com/community/tutorials/convolutional-neural-networks-python