Hanh Tong

Address:

Department of Economics

Simon Fraser University

8888 University Drive

WMC 2700

Burnaby, BC V5A 1S6

Canada


Email:

hanht@sfu.ca


CV


I am a PhD candidate in Department of Economics at Simon Fraser University. My research interests are in Behavioral and Experimental Economics.

I am on the job market in 2018-2019. I plan to attend the AEA conference in Atlanta, GA.

Job Market Paper

Individual Heterogeneity in Traveler's Dilemma

We study individual heterogeneity in behaviors and in beliefs about a naive player (an anchor player) in the Traveler’s Dilemma game. In our within-subject design, subjects make choices in three blocks of ten of the Traveler’s Dilemma game without feedback. The novelty of our design lies in the treatment variation. We vary the game parameters - the upper bound, the lower bound, and the reward/penalty in a systematic way. In line with previous studies, we find a statistical negative effect of the reward/penalty on subject’s average claims. Second, there are three spikes in the relative frequency of choices at the upper bound, the middle and the lower bound, which account for around 50% of the total number of choices. Third, Quantal Response Equilibrium, some variations of Level-k and Noisy Introspection do not fit the aggregate data well, the best fitting models are Nash, Level-k and Noisy Introspection with the anchor as the top. Fourth, we compare the performance of leading models from behavioural game theory at the individual level. Half of our subjects are best fitted by Noisy Introspection with a naive player playing the top and a quarter of our subjects are best fitted by Nash.

Publication

A survey of instruction delivery and reinforcement methods in recent laboratory experiments reveals a wide and inconsistently reported variety of practices and limited research evaluating their effectiveness. Thus, we experimentally compare how methods of delivering and reinforcing experiment instructions impact subjects’ comprehension and retention of payoff-relevant information. We report a one-shot individual decision task in which non-money-maximizing behavior can be unambiguously identified and find that such behavior is prevalent in our baseline treatment which uses plain, but relatively standard experimental instructions. We find combinations of reinforcement methods that can eliminate half of non-money-maximizing behavior, and we find that we can induce a similar reduction via enhancements to the content of instructions. Residual non-money-maximizing behavior suggests that this may be an important source of noise in experimental studies.