摘要：People tend to behave inconsistently over time due to an inherent present bias. As this may impair performance, social and economic settings need to be adapted accordingly. Common tools to reduce the impact of time-inconsistent behavior are penalties and prohibition. Such tools are called commitment devices. In recent work Kleinberg and Oren [EC, 2014] connect the design of a prohibition-based commitment device to a combinatorial problem in which edges are removed from a task graph G with n nodes. However, this problem is NP-hard to approximate within a ratio less than n^(1/2)/3 [Albers and Kraft, WINE, 2016]. To address this issue, we propose a penalty-based commitment device that does not delete edges, but raises their cost. The benefits of our approach are twofold. On the conceptual side, we show that penalties are up to 1/beta times more efficient than prohibition, where 0 < beta <= 1 parameterizes the present bias. On the computational side, we improve approximability by presenting a 2-approximation algorithm for allocating penalties. To complement this result, we prove that optimal penalties are NP-hard to approximate within a ratio of 1.08192.
