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Found 7 papers, 3 papers with code
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning
Agent-based models (ABMs) have shown promise for modelling various real world phenomena incompatible with traditional equilibrium analysis.
Bounded rationality for relaxing best response and mutual consistency: The Quantal Hierarchy model of decision-making
We demonstrate the effectiveness of the Quantal Hierarchy model in several canonical economic games, {both simultaneous and sequential}, using out-of-sample modelling.
A maximum entropy model of bounded rational decision-making with prior beliefs and market feedback
Specifically, it allowed for the separation of past beliefs and utility maximisation behaviour of the agent as well as the analysis into the evolution of agent beliefs.
The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model
Housing markets are inherently spatial, yet many existing models fail to capture this spatial dimension.
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML
We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyperparameter values.
Improving generalisation of AutoML systems with dynamic fitness evaluations
A common problem machine learning developers are faced with is overfitting, that is, fitting a pipeline too closely to the training data that the performance degrades for unseen data.
Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image Classification
Image classification is an essential task in computer vision, which aims to categorise a set of images into different groups based on some visual criteria.
