Preface

I write this purely because many of us make decisions in our lives that are in essence a sequence of decisions where each further decision is a sum of all prior ones. Having said that, concepts that are applicable in the context of decision making also make their way into fields like Internal Medicine, Surgery, Psychiatry, Mechanical Engineering, Psychology, Law, and the like. Abstractly speaking, these decisions are essentially tree like structures. Grammatically speaking, the word decision meaning that there is some split between at least two actions. We can find this within the etymology of the word decision.

Decision Trees

Decision trees provide humans with a way of visualizing and simplifying actions as they walk through making some decision. We can model decisions within structures, as has been done already within literature of aforementioned fields. What is interesting, is in the field of Applied Computing & Applied Mathematics, we can apply programmatic methods to solving large sets of Decision Trees, prospectively applying these over a series of subjects. For example, given some decison tree identifying the maintainability of an authentication layer of every team within a company, we could apply a Decision Tree that encompasses best practices in analyzing all internal systems comprising an authentication layer.

Alternatively, our actions would be limited to manually identifying these vectors ourselves. While the latter could provide great benefit in job security, the benefit to the former is it releases resources as a means of helping us focus on different things, thus helping us keep an open-mind.

Medicine

Overall, these are usually referred to as Clinical Decision Trees & Support Charts, along with Diagnostic Decision Trees within Clinical Decision Trees. These apply over a broad range of specialties of medicine.

Decision Trees within internal medicine exist for simplification of Diagnoses along with treatment as it relates with associated disease and disorders. For eaxmple, within: The Oxford Handbook of Clinical Medicine (M. Longmore, I. Wilkinson, A. Baldwin, & E. Wallin), and The Merck Manual of Diagnosis and Therapy (Robert S. Porter, M.D.).

Whereas for, Internal Medicine specific to Pediatrics, and Psychological aspects we could analyze decision trees from: The Red Book (Report of the Commitee on Infectious Diseases, American Academy of Pediatrics), and DSM-IV Handbook of Differential Diagnosis: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, (American Psychiatric Association).

Benefits of these are for example, within the Differential Diagnosis handbook, we can determine overlap in comorbidities, and in The Red Book, we can make sure our approach in medicine is in line with others in the field, given the frequency of The Red Book being present on at least one of one’s shelves!

Engineering

In engineering these can vary by name of: Flowchart, Decision Tree, and Flow Diagram. Experientially, these are great for when decisions must be made, and further decisions cannot be effectively made until some later period, and thus these provide a checkpoint for reference in the future.

Law

While there are some proprietary examples of decision trees in law, many of these must be manually built by who is practicing law. For example, one could work from Black’s Law Dictionary, (Bryan A. Garner), as a means of identifying appropriate definitions of terms as identified in many Titles among various Codes of law, and then building some decision tree identifying what instances a client’s alleged actions could actually be associated with some law.

Conclusion

Many fields provide an interface for simplifying handling actions within said field. Placing an importance on not knowledge-siloing could greatly benefit our ability to working with technology as a means of simplifying our actions.