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Decision Intelligence’s Introduction

Are you curious to learn what the psychology of avoiding lions on the savannah has to do with ethical AI leadership and the difficulties inherent in data warehouse design? Greetings, and welcome to decision intelligence!

Decision intelligence is a new academic discipline that examines all facets of choice selection. It unifies the greatest aspects of applied data science in malaysia, social science, and managerial science to enable individuals to use data to enhance their lives, enterprises, and the world around them. It is a critical science for the AI era, covering the competencies required to properly lead AI projects and establish objectives, metrics, and safety-nets for large-scale automation.

Let’s have a look at some of the fundamental terms and concepts. The parts have been structured to facilitate skimming (and sometimes even skip-reading, which is when you skip the uninteresting passages… and occasionally even the act of reading entirely).

What is a choice?

While data are visually appealing, it is the decisions that matter. Our decisions – our actions — have an impact on the world around us.
We use the term “decision” to refer to any entity’s choice between alternatives, so the discussion encompasses more than MBA-style challenges (like whether to open a branch of your business in London).

Our decisions – our actions — have an impact on the world around us.

In this language, adding a label such as cat versus not-cat to a user’s photo is a decision make by a computer system, however deciding whether to launch such system is a deliberate decision made by the project’s human leader (I hope!).

What exactly is a decision-maker?

In our parlance, a “decision-maker” is not the stakeholder or investor who intervenes to veto the project team’s manoeuvres, but rather the individual accountable for decision architecture and context framing. In other words, a designer of precisely articulated objectives as opposed to a demolition of them.

What exactly is decision-making?

Because the term “decision-making” is use differently in different areas, it might refer to:

taking action in the face of alternative possibilities (in this sense, one could speak of a computer or a lizard making a decision).

assuming the role of a (human) decision-maker, which includes accepting accountability for decisions. Even if a computer system is capable of making a decision, it will not be refer to as a decision-maker because it is not accountable for its outputs – that responsibility is fully on the shoulders of the people who designed it.

Calculation vs. decision-making

Not all outputs/recommendations are decision-making. In decision analysis parlance, a decision is taken only once an irreversible allocation of resources occurs. As long as you have the option of changing your mind without penalty, no decision has been taken.

Taxonomy of decision intelligence

One technique to learning about decision intelligence is to divide it into quantitative and qualitative components (which mostly overlap with applied data science course malaysia) (developed primarily by researchers in the social and managerial sciences).

On the qualitative side, there are the decision sciences.

The qualitative disciplines have long been refer to as decision sciences – a moniker I would have preferred for the entire endeavour (alas, we cannot always have what we want).

The decision sciences are concerned with issues such as:

“How should you develop your decision criteria and metrics?” (All)

“Is the metric you’ve picked incentive-compatible?” (Economics)

“At what level of quality should this decision be made, and how much should you pay for excellent information?” (Analysis of decisions)

“How do emotions, heuristics, and biases affect judgement?” (Psychology)

“How can biological elements such as cortisol levels influence judgement?” (Neuroeconomics)

“How does altering the way information is present affect choice behaviour?” (Economics of Behavior)

“How do you maximise your outcomes when making group decisions?” (Theory of Experimentation)

“How do you design a decision context that balances various restrictions and multistage objectives?” (Design)

“Who will bear the brunt of the decision’s repercussions, and how will various groups perceive that experience?” (User Experience Research)

“Is the objective’s decision ethical?” (Philosophy)

This is only a sampling… there are plenty others! This is obviously not an exhaustive list of the fields involve. Consider the decision science side to be concern with decision-making and information processing in its fuzzier storage form (the human brain), as opposed to the neatly put down in semi-permanent storage (on paper or electronically) that we refer to as data.

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