Premiere: Thursday 20 June, starting at 3pm BST//10am EDT

Presenters:

Shuffle, Play, Learn: Experiential Learning in Game Theory Using “Love Letter”

Yang Zhang (National University of Singapore)

Although game theory is widely applicable to many real-world decision-making problems, the necessary mathematical abstraction to teach the subject creates barriers for students to appreciate its relevance. Dixit (2005) suggests using innovative in-class activities, such as classroom games and movie clips, to make learning game theory more enjoyable and relatable. Adding to this approach, I experimented with a group project in an intermediate-level game theory class where students play and analyze a card game using game theory.

I designed the group project following Kolb (1984)’s experiential learning framework, emphasizing hands-on activities and reflection. In groups of three or four, students were tasked to play the card game “Love Letter” multiple times and select one round of play they found most intriguing, inspiring, or surprising. Each group was asked to document the round and analyze it by explaining how knowledge of game theory is used (or could be used) in their selected round, using specific examples from their gameplay.

The game is selected for its simple yet strategic structure. As an incomplete-information sequential game, there are rich possibilities for strategic behaviors. Nevertheless, with only 21 cards, the game is not overly complicated. Each round lasts approximately 10 minutes, making recording a round’s playthrough feasible. The project can also be implemented with other strategic card or board games.

The project is not only fun but also enables students to apply game theory in analyzing their own decisions within a relatable context. Moreover, it helps measure student learning. For example, some concepts are shown to be more challenging to apply. There is also considerable variation in the quality of analysis across groups, from misapplications of concepts to reflective analysis on how backward induction is applied to correct mistakes in their gameplay. Thus, their projects serve as an opportunity to correct theoretical misconceptions.

Diversity in Group Composition, Performance, and Learning: Evidence from Economics

Tim Burnett (Aston University) and Stefania Paredes Fuentes (University of Southampton)

Most university programmes in economics and adjacent subjects will, at some stage, ask students to work in groups, as part of their learning and/or their assessment. There are numerous reasons why group work is considered important, such as the chance to engage in more-authentic teaching and learning tasks, the development of skills such as communication and negotiation, and the opportunity to assess a different range of competencies not normally covered under individual assessment.

Despite the accepted benefits of group work, there is little consensus on how group composition affects performance in group tasks. There is also very little research which has considered such composition in the context of group-work for learning, and the role that group-work can play in students’ more general academic performance (outside of the immediate group task).

This research considers a large introductory Macroeconomics module at a leading UK university where students were exogenously allocated to persistent study groups and asked to complete several group assignments throughout the year. We consider a range of student characteristics in the analysis of both in-group performance, and individual performance in Macroeconomics and in other modules.

While we find some evidence linking diversity of nationality to group task performance, the main highlight findings concern the role of group composition in supporting students’ broader learning and academic application–suggesting that female students benefit from the presence of other females in their groups, and that the emergence of single dominant nationalities within groups can harm wider learning.

The findings in this study have implications for the design of learning and assessment activities, especially where these concern group-work.

Industrial Organization group projects: Identifying price discrimination and price dispersion in the real world

Timothy Wong (National University of Singapore)

Like many economics elective courses, undergraduate Industrial Organization courses typically emphasize theoretical models of firm strategy. Usually, real world connections are made through examples and case studies.

I design two group projects that allow students to make more hands-on connections with the real world. These projects require students to investigate price discrimination and price dispersion in markets in which they are already consumers. The projects also introduce students to experimental design. Students must collect and interpret data with the goal of making causal inference.

The first assignment requires students to investigate price discrimination within the ride-hail market. First, students make hypotheses on the market segments to whom the ride-hail provider price discriminates. Then they must design a data collection process which will allow them to analyze ride-hail prices in order to identify their hypothesized price discrimination practices. With this assignment, students learn about confounding variables, which they must control for in their study.

The second project is assigned after students have seen a summary of an empirical paper on the effect of price search on price dispersion. Students must identify homogeneous products which they hypothesize vary in price across locations due to differences in price search. Importantly, they must identify variables that proxy for the extent of price search. Students must then collect data on the relevant variables in order to demonstrate that price dispersion is caused by the proxy for search that they have identified.

With both projects, students present their results to the rest of the class.

These project give students an early flavor of empirical research. Econometrics is not a pre-requisite for this course. Nevertheless, through these projects, students learn important empirical lessons, like the challenges of identification in causal inference and the value of large datasets. These projects get students excited for more sophisticated real world analysis that they can conduct once they have more econometric training.