Risky Choices in Social and Cultural Contexts

关于替代选择的决定往往涉及风险和不确定性。我们是应该买洪水保险，还是宁愿存钱？我们更喜欢高风险还是低风险的股市投资？我们什么时候应该选择手术而不是化疗来治疗癌症？我们是应该选择一个高风险的选择，试图拯救所有有三分之一成功机会的人，还是应该规避风险，肯定地拯救三分之一的人？这项研究项目考察了人们如何在现实世界的约束下自动搜索、选择和使用社交和交流线索来确定他们的风险行为。我们以前的发现表明，人们对亲属关系、匿名性、群体规模、群体构成和选择选项的享乐语言框架/短语等线索很敏感，并对这些决定线索给予不同的优先级。这些嵌入决策问题的信息决定了最低可接受的结果，这被称为特定任务的“最低要求”(MR)。根据提出的风险选择的均值-方差模型，决策者试图最大化达到风险的机会假设有两个选择：肯定获得100美元，而赌博有50%的机会赢得300美元，有50%的机会失去100美元。也就是说，这两个选项的平均结果相同，但差异不同。均值-方差模型考虑了期望值、期权的期望值和期望值之间的关系，当期望值低于期望值时，决策者倾向于选择高方差(赌博)期权而不是确定性期望值，因为较高的方差会增加到达期望值的机会；反之，当期望值高于期望值时，参与者会厌恶风险，并且为了避免可能的伤害(即跌破期望值)，他们会倾向于肯定的事而不是赌博。该模型还预测，当MR高于均值加方差或当MRIS极低时，均值-方差启发式将被逆转。这些预测将在不同的任务域(例如，LIFEV)中进行测试。不同的预期价值(例如，600条生命与6条生命；6000美元与600美元)、不同的回报(例如，寿命与预期寿命的数量)和参与者的不同样本(例如，学生、当地居民、专业人士)、在不同的任务条件下(例如，计算机显示器、彩票游戏的反馈)、在积极和消极的框架条件下以及跨文化。

Decisions on alternative options often involve risks and uncertainties. Should we buy flood insurance or would we rather save the money? Do we prefer a high-risk or a low-risk stock market investment?When do we choose surgery rather than chemotherapy for cancer treatment?Should we take a risky option and try to save all the people with a one-third chance of success or should we avoid the risk and save one-third of them for certain? This research project examines how people automatically search,select, and use social and communicational cues under real world constraints to determine their risk behavior. Our previous findings have shown that people are sensitive to cues of kinship, anonymity, group size, group constitution, and hedonic verbal framing/phrasing of choice options and place different priorities upon these decision cues. These pieces of information embedded in a decision problem determine the minimally acceptable outcome, which is called the "minimum requirement" (MR) for a particular task. According to the proposed mean-variance model of risky choice, a decision maker tries to maximize the chance of reaching the MR. Suppose there are two choice options: a sure gain of $100 vs. a gamble with a 50% chance of wining $300 and a 50% chance of losing $100. That is, the two options have the same mean outcome but differentvariances. The mean-variance model takes into account the relationship between theMR, the expected mean values of choice options, and the variances in those choices.When the expected mean value of the options is below the MR, a decision maker is expected to favor a high variance (gamble) option over its sure-thing equivalent, because higher variance increases the chance of reaching the MR. In contrast, when the mean value is above the MR, the participants are expected to be risk averse and favor the sure thing overthe gamble to avoid possible harms (i.e., falling below the MR). The model also predicts that the mean-variance heuristic willbe reversed either when MR is higher than the mean plus variance or when MRis extremely low. The predictions will be tested in different task domains (e.g., lifevs. money), with different expected values (e.g., 600 lives vs. 6 lives; $6000 vs. $600), different payoffs (e.g., the number of lives vs. life-span expectancy) and different samples of the participants (e.g., students, local residents, professionals), in different task conditions (e.g.,feedback by computer displays, lottery games), under both positive and negative framing conditions, and cross-culturally.