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，以获得负责任的结果，以挑战环境争议，这是可持续的和公平的。为了将解决实际争端的创造力概念付诸实践，将设计两种开创性的决策技术：用于发现决策者（DM）在冲突中可能如何思考的学习方法，以及用于确定如何有目的地指导这种思维以达成双赢解决方案的创造力技术。在DM之间可能存在误解并可能产生令人惊讶的结果的冲突中，将构建学习方法以确定DM的正确偏好和意图。例如，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向环境排放的污染物超过了允许的数量。当一个意想不到的结果确实发生时，DM从这个经验中学习，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DM在争论中可能采取的可观察到的行动，其中偏好可能在很大程度上是未知的，将构建各种学习方法来确定相关偏好。在尽可能多地了解一个特定的争议之后，逆向工程技术将被用来计算dm需要的偏好变化，以达到一个有吸引力的结果。这种类型的信息在第三方干预中可能是非常有用的，在这种干预中，调解人试图引导对某一问题有争议的各方走向负责任的解决办法，例如，一家污染公司最终倾向于通过采用更先进的生产技术来大幅减少其产生的废物，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以创造更积极态度的作用，例如为清洁和开发受污染的土地提供税收激励。在所谓的行为引擎问题中，在给定dm的偏好的情况下，确定dm所需要的导致冲突中特定结果的行为类型也将取得重大进展。这项具有里程碑意义的研究的最终目的是在学习和创造性思维方面取得根本性的进步，作为造福加拿大社会的长期目标的一部分，从系统的角度发现明智的解决方案，以挑战社会，技术和环境交汇处的复杂冲突，重点是能源生产中人类活动引起的涉及水，土地和大气的污染争议。工业和农业。

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.