NETWORK: People-centred Computational Environments for Design and Decision-making

当前的计算机辅助设计和决策支持工具支持较晚的、定义良好的问题解决阶段，在这些阶段，产品或目标是有形的、有形的和可理解的。然而，更抽象的概念的制定和发展得不到支持，特别是在不确定性是固有特征的情况下。此外，计算机辅助设计和决策支持往往是特定领域的。很少或根本没有利用跨域体验。该网络建议调查、制定和实施有可能纠正这两种不平衡的研究和发展议程。在任何设计或决策活动的早期阶段，人都扮演着重要的共同角色。需要同时考虑不同的方面。例如，与多种选择、限制和目标有关的问题；以人为本的知识和经验；我们为之设计的环境以及受我们的设计/决策影响的人。固有的不确定性和糟糕的问题定义最初加剧了这种情况。初步的基于计算机的问题表示可能包括模糊概念和稀疏信息。此外，随着信息和知识从最初的搜索和探索以及相关的人类同化中积累起来，这种表征也会发生变化。人类的经验起着重要作用。因此，需要以人为中心的系统，将这些知识与基于计算机的模拟、搜索和探索、数据处理和数据可视化结合起来。决策者应沉浸在数据生成、评价和分析、随后修改以计算机为基础的表示法和进一步探索重新定义的空间的迭代过程中。将根据目前的使用情况重新评估相对成熟的计算智能技术的效用。网络调查将延伸到目前相关的研究领域和新的新兴技术的潜力。这些不会被认为是独立的，但将与一系列成熟的使能计算技术一起进行调查，以确保成功的整合。该领域的复杂性使得需要更多的协作工作来确定这些主要的研究和开发领域，这些领域将导致用于早期设计和决策过程的通用、以人为中心的计算系统。非常显著的跨学科兴趣是显而易见的。以人为中心的计算在不同的决策过程中的潜力现在正在被发现。业务/组织结构、财务系统和资源分配/调度只是可从设想的系统中受益的领域的一个小例子。我们打算发展网络成员，以确保这些领域复盖良好，并能够从集体推理、合作研究、讲习班和培训活动中受益。还必须包括代表与统计、智能系统和处理决策过程中的不确定性和风险的更具确定性的方法有关的大量研究的成员。将既定的技术和战略与新兴的以人为本的制度相融合，可能会产生许多互利。该网络的优势将在于看似不同的认知学科之间的协作，这些学科需要共同的核心专门知识来支持决策过程。其结果将是相互共生的设计和决策环境的确定，这些环境为捕捉和实现发现和创新创造了新的潜在接口。

Current computer-aided design and decision support tools support the later, well defined stages of problem solving where a product or objective is physical, tangible, and comprehensible. However, more abstract concept formulation and development is poorly supported, especially where uncertainty is an inherent characteristic. Furthermore, computer-aided design and decision-support tends to be domain specific. There is little or no exploitation of cross-domain experience. The Network proposes to investigate, develop and implement research and development agendas that have the potential to redress both these imbalances. During the early stages of any design or decision-making activity people play a major common role. Differing aspects need to be concurrently considered. For instance those relating to multiple options, constraints and objectives; human-based knowledge and experience; the environment for which we are designing and the people affected by our designs / decisions. Inherent uncertainty and poor problem definition initially exacerbate the situation. Preliminary computer-based problem representations may comprise fuzzy concepts and sparse information. Further, such representations change as information and knowledge accumulates from initial search and exploration and associated human assimilation. Human experience plays a major role. People-centred systems are therefore required that meld such knowledge with computer-based simulation, search and exploration, data processing and data visualization. The decision-maker should become immersed in the system involved in an iterative process of data generation; evaluation and analysis; subsequent modification of the computer-based representation and further exploration of the redefined space. The utility of relatively established computational intelligence technologies will be reassessed in the light of their current usage. Network investigations will extend into current associated areas of research and the potential of new, emerging technologies. These will not be considered stand-alone but will be investigated alongside a range of well-established enabling computational technologies to ensure successful integration. The complexity of the domain is such that much further collaborative working is required to identify such primary research and development areas that will result in generic, people-centred computational systems for early design and decision-making processes. Very significant cross-disciplinary interest is evident. The potential of people-centred computation across varied decision-making processes is now being identified. Business/organizational structure, financial systems and resource allocation/scheduling are just a small example of areas that can benefit from the envisaged systems. It is our intention to develop the Network membership to ensure that such areas are well-covered and are in a position to benefit from collective reasoning, collaborative research, workshops and training events. It is also essential that members representing the large body of research relating to statistical, intelligent systems and more deterministic approaches to dealing with uncertainty and risk in decision processes are included. Much mutual benefit could arise from a melding of established techniques and strategies and emerging people-centred systems. The strength of the Network will lie in the collaboration of seemingly disparate cognitive disciplines that require a common core expertise to support decision-making processes. The result will be the identification of mutually symbiotic design and decision-making environments which create new potential interfaces for capturing and enabling discovery and innovation.