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This study first replicates, then perturbs, the centipede game as implemented by McKelvey and Palfrey (1992). It is thus both a replication study and an original research study. We use controlled laboratory experiments, with computer interfaces for each treatment, anonymous round-robin matching among the subjects across rounds, multiple (10) rounds within each treatment, and incremental changes between adjacent treatments allowing for an assessment of effects at the margin of different game configurations. We find unraveling to the subgame perfect equilibrium somewhat faster than did McKelvey and Palfrey (1992), when using their exact design. Perturbations to that design show that setting non-taker payoffs to zero induces earlier unraveling, as does the use of higher stakes (as in Murphy, Rapoport, and Parco (2006), and Rapoport, Stein, Parco, and Nicholas (2003), respectively). Other, subsequent perturbations show: that there is at most a subtle effect associated with using a 10-second timer with a default move, relative to untimed active moves; and that clock format versus tree format has a minimal effect in common information, unchanging payoff-parameterization environments. We verify the robustness of some key past findings in real-time games. We also explore in a common information environment, the effect of design features previously used in independent private values settings; here we find new evidence that features which might modulate information acquisition and/or processing in an independent private values setting may not restrict behavior in a common information setting.

The authors propose an Markov Chain Monte Carlo (MCMC) method for estimating a class of linear sum assignment problems (LSAP; the discrete case of the optimal transport problems). Prominent examples include multi-item auctions and mergers in industrial organizations. This contribution is to decompose the joint likelihood of the allocation and prices by exploiting the primal and dual linear programming formulation of the underlying LSAP. Our decomposition, coupled with the data augmentation technique, leads to an MCMC sampler without a repeated model-solving phase.