| Subject pool or game | Source$^a$ | Group size | Sample size | Nash equil'm | Pred'n error | Data Mean | Data Std dev | Data Mode | $\tau$ | Fit of CH model Mean | Fit of CH model Error | Fit of CH model Std dev | Fit of CH model Mode | B…a. HCW (98) is Ho, Camerer, Weigelt AER 98; CHC are new data from Camerer, Ho, and Chong; CHW is Camerer, Ho, Weigelt (unpublished); Kovalchik is data reported by Kovalchik et al. [in press].
values derived from simple axioms mentioned abo…A sensible intuition is that when stakes are higher, subjects will use more steps of reasoning (and may think others will think harder too). Rows 3 and 6 compare low stakes ($1 per person per period) and high stakes ($4) in games with an in…
Kovalchik
other · 3 mentions across 1 reading
In this course
Kovalchik appears as a dataset contributor in experimental game theory research on strategic reasoning and equilibrium prediction, cited alongside Camerer and Ho's work on how human players deviate from Nash equilibrium play. The reference indicates Kovalchik et al.'s empirical findings were used to test models of iterated reasoning in games, helping establish the behavioral patterns that motivate computational approaches to modeling bounded rationality. This work fits the course's interest in how human cognition constrains decision-making in strategic environments—a foundational problem for understanding both human behavior and the AI systems meant to interact with or predict it.
Mentioned in 1 reading
Appears alongside
People mentioned in the same passages — sorted by co-occurrence weight.