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The two most fundamental notions in mechanism design are truthfulness and efficiency. In many market settings, such as the classic one-sided matching/assignment setting, these two properties partially conflict, creating a trade-off which is rarely examined in the real-world. In this article, we investigate this trade-off through the high-stakes Israeli medical internship market. This market used to employ a standard truthful yet sub-optimal mechanism and it has recently transitioned to an “almost” truthful, more efficient mechanism. Through this in-the-field study, spanning over two years, we study the interns’ behavior using both official data and targeted surveys. We first identify that substantial strategic behaviors are exercised by the participants, virtually eliminating any efficiency gains from the transition. In order to mitigate the above, we performed an intervention in which conclusive evidence was provided showing that, for most of the interns, reporting truthfully was much better than what they actually did. Unfortunately, a re-examination of the market reveals that our intervention had only minor effects. These results combine to question the practical benefits of “almost” truthfulness in real-world market settings and shed new light on the typical truthfulness-efficiency trade-off.
Reinforcement learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort and expertise on the human designer’s part. To date, human factors are generally not considered in the development and evaluation of possible RL approaches. In this article, we set out to investigate how different methods for injecting human knowledge are applied, in practice, by human designers of varying levels of knowledge and skill. We perform the first empirical evaluation of several methods, including a newly proposed method named State Action Similarity Solutions (SASS) which is based on the notion of similarities in the agent’s state–action space. Through this human study, consisting of 51 human participants, we shed new light on the human factors that play a key role in RL. We find that the classical reward shaping technique seems to be the most natural method for most designers, both expert and non-expert, to speed up RL. However, we further find that our proposed method SASS can be effectively and efficiently combined with reward shaping, and provides a beneficial alternative to using only a single-speedup method with minimal human designer effort overhead.
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