What is a decision?

Let's start by defining what a decision is. At its core, a decision is simply the act of choosing between options in a given situation. Some decisions are made quickly and intuitively, while others require careful deliberation. The amount of effort we put into making a decision depends on factors like the potential consequences, the number of options available, and how relevant the decision is to our lives

Now, it leads us to the next question: When do we need to make decisions? Every change, no matter how big or small, prompts us to make a decision. That decision is based on certain factors, or precursors, that define the moment of decision. Let’s take an example of headache as a precursor, it now prompts us to make the decision to choose the right remedy. Take your hunger as a precursor, or a machine not working properly in an industry, or even changing time. Every change leads to a position where we need to make a decision.

Decision making process

The decision-making process typically follows these steps: First, we identify and define the problem or decision that needs to be made. Next, we gather information or context relevant to the decision. Third, we identify and evaluate the possible options or alternatives. Fourth, we choose the best option based on a predetermined set of criteria or policies. Fifth, we implement the decision and put it into action. And, lastly, we review the decision.  

In this discussion, I'd like to explore the concept of decision intelligence, which involves the integration of human and machine intelligence in the decision-making process. For a future of human-machine integration, the decision-making part is the most crucial and difficult to tackle. Let’s discuss a bit about this.  

I believe the combined decision-making framework would follow the below-given steps:  

1. (Step 1) The first step is to identify the key players in the decision-making process: humans and machines. Once we've established who's involved, we can start looking at the data and context that each entity has access to. This is crucial for understanding the problem and breaking it down into subtasks. By taking this step, we lay the groundwork for the decision-making process and ensure that everyone is working with the same information.
2. (Step 2)  The next step is to recognize what triggers the decision-making process in the first place. This trigger can be any sort of change in the environment, from something as simple as the passage of time to something more complex like a change in the operating conditions of a machine. Just like humans, machines must be able to perceive their surroundings and recognize when a change occurs that requires action.
3. (Step3) Once we recognize the need for a decision, the next step is to understand the goal or objective we're trying to achieve. Then, we need to identify the environmental variables that could affect our ability to reach that goal. Let’s break it into sub-steps:
4. This step establishes the rules of the decision-making process. It is to understand the problem we're trying to solve and develop a policy or framework to evaluate possible solutions. This policy should define the goal of the decision, the criteria we'll use to evaluate different options, and how we'll weigh the different factors involved.
5. With the policy in place, the next step is to brainstorm and research all the potential options for solving the problem. This step involves both creative thinking and information gathering. Once we've identified all the possible solutions, we can move on to the next step of evaluating them.
6. Once we've compiled a list of potential solutions, we can start evaluating each option in light of the policy we established. We'll compare the alternatives and determine which one best aligns with our goals and criteria.
7. (Step 4) The final step revolves around the implementation of the decision.

While we may not always follow a structured approach to decision-making in our daily lives, the general principles still apply. Our brains intuitively go through a similar process, even if we're not consciously aware of it.

Decision Intelligence

For humans, decision-making is often viewed as art because we rely on intuitions and pre-determined structures. But, access to data turns this art into a science. Machines have a distinct advantage over humans in steps 2 and 3 because of their ability to rapidly analyze vast amounts of data. They can detect even tiny changes in the baseline and consider a huge number of variables and options to find the best solution. This allows them to make decisions that would be impossible for humans. Similarly, the massive processing capabilities can be used to generate context and formulate policies that are more nuanced and sophisticated than what we could create on our own. And once we've made a decision, machines can help us implement them much more efficiently and effectively.

While the advantages of combining human and machine intelligence are clear, there's still a lot of work to be done in understanding how these two entities interact during the decision-making process. The work revolves around sequential interaction between humans and machines so that machines can proactively suggest users new ideas without interrupting their own thought processes. We need to explore how humans and machines can share information, access datasets, identify relevant contexts, and combinedly consider new options as they arise.

In addition to the challenges mentioned above, there are other complexities that arise when integrating human and machine intelligence for decision-making. Humans make decisions based on emotion and other factors that are difficult to quantify, while machines use a purely probabilistic approach. This creates a fundamental disconnect between the way humans and machines view decisions. For humans, a decision is either successful or unsuccessful, with little room for nuance or gray areas. Machines, on the other hand, consider every possible outcome and calculate the probability of each one. This difference in perspective can make it difficult to align human and machine decision-making.

The ultimate goal of human-machine integration is to augment human capabilities and free people to focus on living in the moment. Every aspect of the process must be designed to support that goal.

I am eagerly waiting to see the entire concept of human-machine intelligence unfold in front of us. It will not only be leading to unimaginable possibilities but also help us understand ourselves better, our decision-making in this case. In the end, what is science apart from a chance to understand ourselves a bit more than our ancestors?

I’d love to take up what shall happen if we reach a position where we understand ourselves and our environment fully, or is it even possible? What would science be at that moment? The whole collection of our knowledge?