Decision Intelligence Navigator: More about the Silicon, Carbon and Oxygen Dimensions

I briefly introduced the Decision Intelligence Navigator (Version 4.0) in my last Decision Model Innovation newsletter issue. In a very short time, I got lots of feedback about how important it is that we, as "Decision Intelligence" enthusiasts, shouldn't forget how many practical concepts already exist to help decision-makers in their work. I totally agree, and that's precisely why the Decision Intelligence NAVIGATOR (V4.0) is not another framework to support decision-makers in a very specific decision-making situation but serves as a GUIDE (or a tool) to 'structure' all sorts of valuable frameworks, concepts or models when facing (almost) any decision challenge.

Exhibit 1: Decision Model Navigator (Version 4.0)

In the following, I want to specify in more detail the three core dimensions of the Decision Intelligence Navigator. These three INTERDEPENDENT dimensions (as of now) represent the three aspects that any decision-maker needs to consider throughout an entire decision process.

I have labelled these three dimensions as silicon, carbon and oxygen because I want to keep these three dimensions as wide open as possible. It's basically an hommage to the interdisciplinary nature of good decision-making. The silicon dimension stands for all that technologies and (digital) data science have to offer. The carbon dimension represents all human/cognitive aspects - represented by the human brain. Finally, the oxygen dimension symbolises the interpersonal aspects/interaction challenges between people involved in a decision (for more details and explanations of these three labels, please check the following article). Now, let me briefly explain each of these three core dimensions.

BTW: The idea for these three labels comes from an interdisciplinary workshop that my colleague Sarah Bankins and I organized at 澳大利亚麦考瑞大学 as part of the Macquarie University Minds & Intelligences Initiative (MMII) under the leadership of Andrew Barron . When I tried to create a visualization based on the DI Cube and wanted each faculty to find themselves somehow represented...that's when I started to use the periodic elements representing different schools of thought/faculties....social sciences (oxygen), natural sciences (carbon) and data sciences (silicon). I know it's not perfect, and I am happy to receive other ideas or extensions if you don't feel represented in any of the three elements.

The SILICON Dimension

The silicon dimension currently incorporates two major aspects (but I am happy to include more if you see other extensions that can add to the overall structuring of decision-making challenges):

The silicon dimension generally deals with data, information, knowledge or insights and the filters to move from one level to another (e.g., through structure, context or wisdom). The primary objective of the silicon dimension is to reduce uncertainty or ambiguity & equivocality.

• Models vs. Frameworks: In many cases, decision-makers define the decision context and end up with a list of questions they need to get answered before making a decision. Models or frameworks are actually representing the 'structure' of the answers that decision-makers are looking for. We use the term frameworks when the input required (i.e. the parameters) can't be put into 'numbers' while we prefer the term model when we deal with answers that are based on probabilities (i.e. risks that can be 'calculated').
• Data-to-Insights: Depending on whether the answers required are rather structured through frameworks (dealing with different forms of uncertainty or ambiguity/equivocality) or models, decision-makers need to organize their data-to-insights activities accordingly. Obviously, the recent rise of #AI/ML has empowered decision-makers and their organizations to completely re-arrange their intelligence gathering and processing activities.

The CARBON Dimension

The carbon dimension currently incorporates two major aspects (but I am happy to include more if you see other extensions that can add to the overall structuring of decision-making challenges):

The carbon dimension generally deals with the avoidance (and sometimes the leverage) of cognitive biases and the leverage of cognitive diversity. The primary objective of the carbon dimension is to make informed opinions, apply rational assessments where possible and allow for ideology where necessary.

• Cognitive Biases: Throughout a decision process, decision-makers are prone to cognitive biases of all sorts (check here for a comprehensive list) - it starts with the definition of the decision context, goes on with the selection of suitable frameworks, the collection of intelligence, its interpretation and how we are influenced by other stakeholders' perspectives etc. Depending on a decision-maker's objectives and the overall setting, more or less rationality vs. ideology are required or allowed.
• Cognitive Diversity: The natural countermeasure to fight cognitive biases might be labelled as cognitive diversity. There are all sorts and definitions of what cognitive diversity includes. My favourite is the concept of mental models, which help executives understand their situation (i.e., which questions are crucial to answer) or develop and assess potential answers.

The OXYGEN Dimension

The oxygen dimension currently incorporates two major aspects (but I am happy to include more if you see other extensions that can add to the overall structuring of decision-making challenges):

The oxygen dimension generally deals with identifying dissent and fostering consensus (where appropriate) among the decision stakeholders. The primary objective of the oxygen dimension is to integrate the different perspectives of people involved throughout the decision process and ensure that their input is either improving the decision preparation and/or the implementation of decisions made..

• Identification of Dissent: Throughout a decision process, decision-makers face the challenge of ensuring that they understand where stakeholders (e.g. data experts, analysts, outsiders affected by decisions etc.) agree or disagree (and why?) with their definition of the decision context (e.g. which questions are crucial to get answered), the selection of frameworks or models, their data-to-insights journey and/or how to draw conclusions from the intelligence gathered. In my personal opinion, this aspect is the most undervalued aspect of what Niels Van Hove would call #DecisionQuality.
• Fostering Consensus: The mirror dimension of identifying dissent is fostering consensus wherever possible and appropriate. Again, decision-makers must have better tools and concepts to do this throughout the decision process. Let me give you an example: In the past, I worked for different MNCs helping them to develop scenarios of their future business contexts in emerging markets. While this kind of consulting work was more part of the silicon dimension, the follow-up workshops at each MNC for executives to develop implications from the scenarios for each department's resource allocations revealed lots of hidden dissents but also finally fostered consensus among the involved decision-makers about what kind of resource reallocations in one department would result in severe consequences for others. I firmly believe that this part of the entire scenario development & analysis consulting was much more valuable for the decision quality in a company than the focus on the most accurate description of multiple scenarios.

Exhibit 2: The Three Core Dimensions of the Decision Intelligence Navigator

I hope this brief overview of the three core dimensions of the Decision Intelligence Navigator (V 4.0) helps you better understand the interdependencies of good decision-making. Whether it's the selection of the proper framework or model to structure the answers to crucial questions, the avoidance of cognitive biases or the management of dissent or consensus, decision-makers are challenged to draw from a rich pool of existing concepts, tools and solutions to overcome any decision challenge they face.

The Decision Intelligence Navigator's purpose isn't to provide you with the best tool to do this but to serve as a GUIDE to understand which concept, tool or solution might serve you best throughout a decision process.

If you have ideas for additional elements etc., please do not hesitate to contact me directly.

I want to thank SatSure for its continued support of this newsletter. SatSure is a great example of how Decision Model Innovation can lead to competitive solutions!