Constructing Probability Models for Large Corpora of Well-Informed but Probabilistically Incoherent Judgments

这个跨学科团队（心理学，计算和应用数学，统计学，经济学，计算机科学）的目标是克服专家系统，决策支持系统或知识发现系统中推理引擎的弱点，这些系统通常在具有不确定特征的环境中工作。目前的技术依赖于贝叶斯理论，并且在不能保证条件独立性或专家提供的概率可能不合理的情况下不能很好地执行。由于基于概率计算的推理提供了对机会的合理评估的最佳保证，因此已经开发了用于计算复杂事件空间上的概率的有效方案。所有这些算法的基础是一个“概率模型”，即，各种事件组合的概率表示。反过来，概率模型是从一组关于环境中不确定性的初始事实构建的。这些事实有时可以从数据库中提取，使用相对频率作为概率。然而，通常所需的概率必须从专家那里获得，专家凭直觉做出反应。对专家的依赖引起了不连贯的幽灵，即，这些判断根本无法与任何概率模型相协调。事实上，在一个大的判断集合中保持一致性在计算和心理上都是繁重的，而且很少实现。当希望综合几位法官的意见时，一位法官的不一致判决就更加复杂了。为了利用潜在的不连贯和不一致的判断，使用特殊的优化算法来构建一个紧凑的概率模型，最好地近似所有的判断。通过将它们应用于一些复杂领域的专家意见的机构的算法进行测试。这些算法的发展将促进人工专家系统的自动构造。每当一个专家判断的身体可以组装，算法可以应用于创建一个紧凑的代表性的集体智慧的法官。概率模型的理论研究成果将应用于休斯顿空气质量政策的分析。将通过挑选城市周围空气质量控制站的测量结果、环境科学和医学专家的判断以及该地区计量经济学和环境模型的输出，建立一个大型概率数据库。该项目有可能在概率，应用学习和数据挖掘研究社区中产生重大的智力影响，并为环境研究人员和休斯顿提供有用的工具。decision-makers.http://www.ruf.rice.edu/~osherson

This interdisciplinary team (Psychology, Computational and Applied Mathematics, Statistics, Economics, Computer Science) has a goal of overcoming the weaknesses in inference engines in expert systems, decision support systems or in knowledge discovery systems that often work in environments with uncertain characteristics. The current techniques rely on Baysian theory and do not perform well in situations in which conditional independence cannot be guaranteed, or the probabilities provided by experts may not be sound. Since inferences based on probability calculations offer the best guarantee of sensible assessments of chance, efficient schemes have been developed for computing probabilities over complex event spaces. Underlying all such algorithms is a "probability model," i.e., a representation of the chances of various combinations of events. In turn, probability models are constructed from an initial set of facts about uncertainty in the environment. These facts can sometimes be extracted from databases, using relative frequency as probability. Often, however, the needed probabilities must be obtained from an expert, who responds intuitively. Reliance upon experts raises the specter of incoherence, i.e., judgments that cannot be reconciled with any probability model at all. Indeed, maintaining coherence across a large set of judgments is both computationally and psychologically taxing, and seldom achieved. Incoherent judgment on the part of a single judge is compounded when it is desired to integrate the opinions of several judges. To exploit potentially incoherent and inconsistent judgments, special optimization algorithms are used to construct a compact probability model that best approximates all the judgments in play. The algorithms are tested by applying them to a body of expert opinion in some complex domain. Development of the algorithms will facilitate the automatic construction of artificial expert systems. Whenever a body of expert judgment can be assembled, the algorithms can be applied in view of creating a compact representation of the collective wisdom of the judges. The results of the theoretical research on the probability models will be applied to the analysis of air quality policy in Houston. A large probabilistic database will be established by culling measurements from air quality control stations around the city, expert judgements in environmental science and medicine, and the output of econometric and environmental models of the region. The project has the potential to have a significant intellectual impact in probability, applied learning, and datamining research communities and also provide a useful tool to environmental researchers and Houston decision-makers.http://www.ruf.rice.edu/~osherson