We can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we might infer whether it is likely to be challenging, fair, or fun simply from hearing the game rules, prior to deciding whether to invest time in learning the game or trying to play it well. Many studies of game play have focused on optimality and expertise, characterizing how people and computational models play based on moderate to extensive search and after playing a game dozens (if not thousands or millions) of times. Here, we study how people reason about a range of simple but novel connect-n style board games. We ask people to judge how fair and how fun the games are from very little experience: just thinking about the game for a minute or so, before they have ever actually played with anyone else, and we propose a resource-limited model that captures their judgments using only a small number of partial game simulations and almost no lookahead search.

翻译：我们能够在有效解决问题之前，就对其特征及潜在解决方案作出准确评估。以从未接触过的游戏为例，仅通过了解游戏规则，我们就能推断其是否具有挑战性、公平性或趣味性——这一切都发生在决定投入时间学习游戏或尝试精通之前。现有大量游戏研究聚焦于最优策略与专业技巧，通过中等到深度搜索分析人类与计算模型在数十次（甚至成千上万次）游戏后的表现。本研究探索人们对一系列简单而新颖的连线类棋盘游戏的推理机制。我们要求参与者在极有限经验下（仅通过一分钟左右的规则思考，未进行实际对战）评估游戏的公平性与趣味性，并提出一种资源受限的认知模型。该模型仅通过少量局部游戏模拟与近乎零前瞻搜索，即可准确捕捉人类的判断模式。