Current projects

Multi-agent insurance pricing using model-based bandits

This project is a part of my PhD research. The goal is to research the problem of pricing insurance premium using bandit algorithms in multi-agent environments. Within the project, we have developed a pricing environment using bottom-up approach. We have proved existence of Nash equilibrium for a single stage pricing competition. Furthermore, based on the analysis of the payoff functions, we have developed a set of assumptions that guarantee the uniqueness of Nash equilibrium in pure strategies.
Then, we perform numerical experiments with bandit algorithms competing within the environment. For that purpose, we have developed a logistic model of the environment that can be incorporated within Bayesian bandit algorithms.
The numerical experiments showed that while the model can accelerate the learning process when the environment and all opponents are stationary. However, when the opponents are learning, the model does not provide any advantage over the standard bandit algorithm.
Consequently, when any model of the environment, as perceived by the learning agent, it has to faithfully represent the truth, i.e. both the pricing environment and the possibly dynamic opponents.

Surycate project

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Personal instructions/frameworks and principles.

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Past projects

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