摘要: |
Decisions are guided by processes and influenced by organizational culture, biases, reason, emotion, experience and memories, benefits and costs, and context. Decisions are also affected the type of
decision (emergent activity or established practice), degree of collaboration required, role clarity, the urgency, completeness and quality of data, and availability of resources.
State DOTs make many decisions in many different contexts. As our work becomes more multimodal and multidisciplinary, the complexity of decision making is increasing. As we seek to provide equity,
we need to review our decision-making practices for systemic bias. New practices are often evaluated using decision models designed for operational activities. We are also at the precipice of using more
automation, machine learning, and artificial intelligence and need to understand the types of decisions that are best suited for these practices.
The goal of this project is to identify factors and frameworks that support different types of decisions, common pitfalls in decision-making, and strategies for successful and sustainable decisions. It is anticipated that this may include systems thinking, common cognitive biases, decision roles, and practices such as recognition-primed decision making, the Cynefin model, and more.
The objective of this project is to identify and review decision-making models for their potential to improve the fit with the type of decision being made and the desired outcomes. This includes
supporting decision quality, reducing rework, supporting transparency and equity, promoting sustainable outcomes, and providing timely support for changes in business strategies. |