We investigate whether the shift towards seemingly myopic behaviour in a sequential game, observed when the number of periods and participants increases, arises from limitations in backward reasoning or from strategic uncertainty. Building on our previous laboratory implementation of the Core–Periphery model in Economic Geography with four sequential players, we introduce a new treatment in which only the first decision is made by a human, while subsequent decisions are taken by expected payoff maximising artificial agents. As strategic uncertainty is removed in the new treatment, 71% of first movers choose the forward-looking migration decision, compared to 45% in the corresponding treatment with four human decision makers. The difficulty of applying backward induction for first movers is kept constant across the two treatments compared. Together with control for inequity aversion, the experimental design allows for isolating the increased strategic uncertainty faced by the first movers as the main driver behind the treatment effect. Moreover, the positive effect of economics training on sequentially rational play largely disappears once strategic uncertainty is eliminated. These results highlight the behavioural importance of strategic uncertainty in dynamic coordination environments.
In this paper, we introduce a psychological-game-inspired construct, termed an experimental game. This framework is intended for use in experimental analysis and departs from standard psychological games by being fully deterministic. The proposed model may be regarded as a Boolean analogue of a psychological game.
Voluntary contribution clubs have been increasingly proposed as a way to address social dilemmas of cooperation, which arise from free-riding and limited observability of individual actions. However, a club good does not necessarily ensure large scale cooperation, because of multiple equilibria. Our hypothesis is that a club good presents a social tipping point working as an endogenous threshold of cooperation. We use a discrete choice model in a population framework, the Garden Model, to describe and characterise contribution equilibria. The model provides a number of behavioural hypotheses that are tested in laboratory experiments. Aggregate experimental results show that subjects contribute more than they would if they were fully rational and contribution rates decrease on average, in agreement with the literature. However, disaggregate figures show equilibrium selection patterns: subjects can converge either to high or low rates of cooperation, which indicates an endogenous threshold of contribution. As the basin of attraction of high-contribution is smaller, early decisions drive the group to this desirable equilibrium less frequently.
Copy trading allows investors to replicate other traders’ transactions automatically. This study extends research on gender in finance from investors’ own trading behavior to their popularity among other investors. I use monthly observations on traders from a major social trading platform over 2013–2021 to describe popularity through the average number of copiers, its monthly net change, and variation in daily copier counts. A multiclass stochastic extreme gradient boosting model examines how these patterns relate to name-inferred gender after accounting for recorded performance, risk, and other trader characteristics. With these characteristics held fixed, a female rather than male classification lowers the predicted probability of having a small, low-visibility copier base by 5.7 percentage points. Conversely, it increases the likelihood of achieving a large, highly active copier base (characterized by significant growth or fluctuation) by 4.7 percentage points. By distinguishing the size of a copier base from changes in that base, the study provides a broader account of gender-related popularity than copier numbers alone. The findings also motivate monitoring gender-related patterns in copying alongside performance and risk when assessing how investor attention is distributed on social trading platforms.
