Jul 17 2020 The Basics of Decision Intelligence

·      What Is Decision Intelligence?

Decision intelligence is an academic discipline that uses social science, decision theory, and management science to enhance data science. By turning information into better action, decision-making intelligence embodies the power to improve lives, businesses and the surrounding environment.

This method of supporting business leaders to make complex decisions is beyond the quantitative scientific scope of mathematical calculations and machine learning algorithms. It enhances these operations through human behaviors and decision-making tendencies, thus integrating quantitative science and qualitative science.

Google decision intelligence scientist and decision intelligence director Cassie Kozyrkov believes that this is a crucial science in the AI era, it covers the skills required to lead AI projects responsibly, and design goals, indicators and security for large-scale automation network. She compared the decision-making intelligence with the kitchen analogy. She went on to say: “If research-based AI is making microwave ovens and applied AI is using microwaves, then decision-making intelligence will safely use microwaves to meet your goals without requiring them. Use other microwave ovens. The goal is always the starting point for decision-making intelligence.”

In short, decision intelligence is ahead of artificial intelligence and data analysis. Combining the power of the two and the research of social science and management science can help business leaders make decisions faster and better. The main advantage of decision intelligence is that it can adapt to the advantages of intuition and other human judgments, and can eliminate errors such as deviations.

·      Decision intelligence taxonomy

One way to learn decision intelligence is to decompose it into quantitative aspects (which generally overlap with applied data science) and qualitative aspects (mainly developed by researchers in the social sciences and management sciences) along the traditional lines of thought.

·      DI is much more than technology

However, DI is not only limited to this definition, but it is much broader than this. Lorien says that if your job involves understanding or helping how actions lead to results, and/or the thought process that you go through before taking actions to help you achieve the desired result, then you are a direct Investment practitioner. Therefore, this means that the DI umbrella includes economists, social scientists, neuropsychologists, teachers, leaders, etc.  DI is about:

  1. The integration of these previously separate disciplines.
  2. These disciplines focus on how to support decision-making. Many people have realized that this is the right focus for collaboration between humans and scientific fields, and of course, the technology to solve major problems.

Lorien and other pioneers of DI, including Element Data’s Chuck Davis, Google’s Cassie Kozyrkov and’s Vishal Chatrath, are all trying to solve this problem. Their driving force is to realize that if we focus on understanding and improving decision-making, then we can do better.

·      DI from an AI perspective

From the perspective of AI experts, DI can be seen as a method of combining multiple AI systems and analyzing the causal structure between tangible and intangible multiple factors in order to determine the best measures to produce a specific result.

Lorien explained that from this perspective, DI binds multiple AI systems together to generate a more comprehensive decision-making method. Traditional AI is largely designed for direct single link systems. In the field of science, the norm is to publish papers or gain new insights to accumulate knowledge. Historically, the focus of science has always been to discover new things about the world, which is fundamentally different from the analysis of the causal structure of the world: the chain of events can be combined to enhance our understanding of the results of possible actions.

The reason why DI is different from traditional AI is that DI’s methods and potential goals stem from different longings for understanding the long-term impact of decision-making and having more value in human reasoning. DI seeks social science because it attempts to better understand the relationships in an increasingly global society. The focus has shifted to the use of visual maps, discussion through decision-making, and brainstorming of the results and effects of events.

Therefore, DI is worthy of a new field, because it covers not only the technical field, but also academia and other disciplines, and bridges the gap between technology and the natural way people think and make decisions.



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