Project duration:

10 weeks

 

Description:

The project is a multifaceted analysis of a large dataset of chess games (>300 million observations in total). There are two main research arms:

  • Evidence of discrimination and biases (such as gender) in a competitive environment
  • Analysis of decision-making processes, especially the influence of fatigue, complexity and competitive stakes

Chess databases are idea environments in order to test questions about biases and decision-making. They provide huge amounts of clean, well identified data from a competitive environment, and in which the usually hidden variable of ability is controlled for. Several academic publications in recent years have made use of this sort of data.

Specific hypotheses for testing with the data include:

  • Do players perform differently when paired with opponents of different genders? Do these gender biases depend on whether the opponent is physically present?
  • Do players exhibit regret aversion?
  • Do players have a psychological bias towards a colour, and if so, why?
  • How do competitive stakes affect decision-making, such as risk-taking?
  • How does fatigue affect risk-taking and performance?
  • Is player time management inefficient and bias-prone, or rationalizable?

The results of the data analysis may motivate the design of an experiment to be conducted on chess players in order to further identify effects.

 

Expected outcomes and deliverables:

The project offers an interesting introduction into both applied economics and behavioural economics through the lens of a novel testing environment. The student will gain exposure and skills in both of these disciplines. Moreover, the student will gain experience in large-scale data manipulation and in identifying causal relationships. There is the possibility that continued collaboration will develop into scientific journal publications.

 

Suitable for:

Applicants should already possess the skills to confidently manipulate large data sets. In particular, competence with Python, Stata, R or other software capable of large data manipulation is mandatory. Familiarity with statistical or applied economics techniques for causal analysis is desirable. Understanding of chess and tournament databases is advantageous, but not essential.

 

Primary Supervisor:

 

Dr David Smerdon

 

Further info:

Contact: Dr David Smerdon

07 334 67047

d.smerdon@uq.edu.au