Creativity in Environmental Systems Conflict Resolution

总体目标是开发创新技术，将创造力正式纳入冲突解决方案，以便在挑战性的环境争议中获得负责任、可持续和公平的结果。要操作的概念，创造力解决实际纠纷，两种开创性的决策技术将被设计：学习方法，发现如何决策者（DM）可能会认为冲突和创造力技术，以确定如何有目的地引导这种思维，以达到一个双赢的解决方案。在一个冲突中，其中可能存在的误解之间的DM和一个令人惊讶的结果可能会发生，学习方法将被构造为确定正确的偏好和意图的DM。例如，一个环境机构可能错误地认为一家化学公司对环境负责，并震惊地发现该公司由于财务问题而排放的污染物超过了允许的范围。当意外的结果确实发生时，DM从这种经验中学习，冲突进入一个建立在更明智的理解基础上的新阶段。此外，基于可观察到的行动DM可能采取的争端，其中偏好可能是很大程度上未知的，各种学习方法将被构造用于确定相关联的偏好。在尽可能多地了解特定争议后，将建立逆向工程技术来计算DM所需的偏好变化，以达到有吸引力的结果。这类信息在第三方干预中可提供大量信息，在这种干预中，调解人试图引导就某一问题发生争议的各方达成负责任的解决办法，例如，一家污染公司最终宁愿采用更先进的生产技术，大幅度减少废物的产生，从而使当地社区不受不利影响。 还将讨论在冲突中引入有吸引力的选择办法以形成更积极的态度的作用，例如为清理和开发受污染的土地提供税收奖励。在确定DM所需的行为类型方面也将取得重大进展，以导致特定结果在冲突中发生，因为DM在所谓的行为引擎问题中的偏好。 这项具有里程碑意义的研究的最终目的是使学习和创造性思维的根本进步，作为造福加拿大社会的长期目标的一部分，通过发现合理的解决方案，以挑战复杂的冲突，从系统的角度来看，社会，技术和环境的融合，重点是涉及水的污染争议，人类在能源生产、工业和农业方面的活动造成的土地和大气污染。

The overall objective is to develop innovative techniques for formally incorporating creativity into conflict resolution in order to obtain responsible outcomes to challenging environmental controversies, which are sustainable and fair. To operationalize the concept of creativity for resolving an actual dispute, two kinds of seminal decision technologies will be designed: learning methods for discovering how a decision maker (DM) may be thinking under conflict and creativity techniques for determining how this thinking can be purposefully directed in order to reach a win/win resolution. In a conflict in which misunderstandings may exist among DMs and for which a surprising result may occur, learning methods will be constructed for ascertaining the correct preferences and intentions of a DM. For example, an environmental agency may mistakenly think that a chemical company is environmentally responsible and is shocked to find out that the company has been discharging more pollutants than allowed into the environment due to financial concerns. When an unexpected outcome does occur, a DM learns from this experience and the conflict enters a new phase founded on a more informed understanding. Moreover, based on observable actions a DM may take in a dispute in which preferences may be largely unknown, various learning methods will be constructed for determining associated preferences. After learning as much as possible about a particular dispute, inverse engineering techniques will be built for calculating the preference changes needed by DMs to reach an attractive outcome. This type of information could be highly informative in a third party intervention in which a facilitator attempts to lead the parties in dispute over a certain issue towards a responsible resolution in which, for instance, a polluting company eventually prefers to cut back significantly in its generation of wastes by adopting more advanced production technologies so that the local community is not adversely affected. The role of introducing attractive options into a conflict to create a more positive attitude will also be addressed, such as offering tax incentives for cleansing and developing polluted land. Large strides will also be made in determining the kinds of behavior needed by DMs to cause specified outcomes to take place in a conflict given the preferences of the DMs in what is called the behavioral engine problem. The ultimate purpose of this landmark research is to make fundamental advancements in learning and creative thinking as part of the long-term goal of benefiting Canadian Society by discovering sensible resolutions to challenging complex conflicts lying at the confluence of society, technology and the environment from a systems perspective, with an emphasis on pollution controversies involving water, land and the atmosphere caused by human activities in energy production, industry and agriculture.