Statistical modelling of games

Gao, Yu (2016) Statistical modelling of games. PhD thesis, University of Nottingham.

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This thesis mainly focuses on the statistical modelling of a selection of games, namely, the minority game, the urn model and the Hawk-Dove game. Chapters 1 and 2 give a brief introduction and survey of the field. In Chapter 3, the key characteristics of the minority game are reproduced. In addition, the minority game is extended to include wealth distribution and leverage effect. By assuming that each player has initial wealth which rises and falls according to profit and loss, with the potential of borrowing and bankruptcy, we find that modelled wealth distribution may be power law distributed and leverage increases the instability of the system. In Chapter 4, to explore the effects of memory, we construct a model where agents with memories of different lengths compete for finite resources. Using analytical and numerical approaches, our research demonstrates that an instability exists at a critical memory length; and players with different memory lengths are able to compete with each other and achieve a state of co-existence. The analytical solution is found to be connected to the well-known urn model. Additionally, our findings reveal that the temperature is related to the agent's memory. Due to its general nature, this memory model could potentially be relevant for a variety of other game models. In Chapter 5, our main finding is extended to the Hawk-Dove game, by introducing the memory parameter to each agent playing the game. An assumption is made that agents try to maximise their profits by learning from past experiences, stored in their finite memories. We show that the analytical results obtained from these two games are in agreement with the results from our simulations. It is concluded that the instability occurs when agents' memory lengths reach the critical value. Finally, Chapter 6 provides some concluding remarks and outlines some potential future work.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Mao, Yong
Owers-Bradley, J.R.
Subjects: Q Science > QA Mathematics > QA273 Probabilities
T Technology > T Technology (General)
Faculties/Schools: UK Campuses > Faculty of Science > School of Physics and Astronomy
Item ID: 33298
Depositing User: Gao, Yu
Date Deposited: 20 Jul 2016 06:40
Last Modified: 16 Dec 2017 00:32

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