Analysis and decision-making tools

I am passionate about analysis and decision-making tools. I am a collector.

When I estimate next year's sales or revenues (or anything uncertain), I use a triangular distribution over 3-4 scenarios.

When I choose holiday locations, or prioritize features on a roadmap, I draw a decision matrix.

Here is my collection of core tools and techniques for analysis and decision-making.

• Brainstorming
• Decomposition (e.g. X-BS), divide-et-impera
• Decision matrices
• Scenario analysis
• Triangular distributions (Worst case + 4 x Probable + Best ) ÷ 6
• Dependency modelling (DSM, DMM, MDM)
• Cost-benefit analysis
• Checklists
• SWOT
• PEST / STEEP
• Mind-maps
• Stakeholder maps
• Decision trees
• Cause-effect / Ishikawa
• Pareto (the 80-20 rule, sometimes 90-10)
• Delphi
• Focus groups
• Monte Carlo
• Causal-loop, systems thinking
• Process, workflow, component diagrams
• Toyota way
• 5 why-s
• WWWWWH (Who, What, When, Where, Why, How)

What tools do you use?

*) I have a different collection of methodologies and frameworks, e.g. here https://blog.stefanmorcov.com/2021/11/frameworks-and-methodologies-for-it.html)

**) Image generated by ChatGPT.

Online training, MIT: Fundamentals of Entrepreneurial Finance (Entrepreneurship 104).

Finished a new online training, this week, MIT: Fundamentals of Entrepreneurial Finance (Entrepreneurship 104).

The general level was a bit basic for me. But I enjoyed a lot some topics such as the VC valuation method, choosing multiples and yearly discount rates according to various factors such as industry and investment type, or convertible preferred stock.

It was video only, which is, as usual, a minus for me. Usually slow, and including boring, irrelevant parts, such as irrelevant anecdotes and life stories. So, I skipped heavily, and also went directly to transcripts.

The transcripts were not easy to read, automatically generated, verbose. The course could have benefited from actual handouts. 

I used ChatGPT heavily to complement and clarify the course materials. GPT is an amazing personal trainer and assistant. Great at maths, calculations, explaining formulas or concepts, giving practical industry examples, great at web search.

I tested Gemini in parallel with ChatGPT. Disappointing. Google AI is way behind its competition.

Didn't request the certificate. Don't need one.

Training and personal development: never stop. Finishing the year with a few hours of training feels great.

Transparency, Traceability, and Accountability in politics, public procurement, and spending

Transparency, traceability, and accountability are essential for a healthy society and organization.

Opaque government is dangerous, as it enables mistakes and accidents, some with extraordinary consequences.

These principles must first be applied to the financing of political parties and election campaigns. It is critical to know who funds a party and where the money goes.

That’s the basic step.  

Then, transparency must also apply to:

- Public spending, through sound governance principles in public procurement and spending.

- Hiring public servants and officials.

- Lobby - a functional registry of organizations and individuals that try to influence public policies, laws, taxes, subsidies, including lobbying channels and funding sources.

Democracy is not a natural phenomenon. It must be built and protected.

Engineering complexity: because it works !

Why do we build complex products, like smart phones, AI, or Hermix?

Because complexity works. It delivers benefits and value, to the economy and society. And this is particularly true of modern engineering. We build complex products, for complex organizations, even if sometimes more fragile, or expensive, or risky. Because they work. These are thoughts about product and engineering complexity, from our Hermix webinar of 27. Nov 2024, with Arian Turhani, VP Capgemini for European institutions, and Alin Izvoran, public procurement expert: AI for public procurement and sales. Complexity has 2 major aspects: structural, and dynamic. Structural complexity means composed of many varied interrelated parts. A modern car has thousands of interdependent components. A modern AI neural network has hundreds of billions, even a trillion nodes. Dynamic complexity relates to uncertainty and ambiguity. It is characterized by emergence, nonlinearity, adaptiveness, propagation, chaos. This is where we get Taleb’s black swans and Lorenz’ butterfly effects. These systems are difficult to predict and control, even if we have sufficient information about their components. It was Aristotle who first described the phenomenon of really complex environments, 2300 years ago, as he famously said that the whole is not only more, but in fact it is different than the sum of the parts. And some of the effects of complexity are positive. The whole is not only different, but also better. These complexity manifestations also occur in relation to modern engineering technology. This is also what we are also building with Hermix. We build more than a tool with significant economic and efficiency benefits. We build a complex ecosystem. We create a new paradigm, with policy implications; such as increase in transparency and competition. We help create a better market environment.

Full video on Hermix website.

AI/GPT is a genius idiot slave

 I use AI/GPT as my genius idiot slave. 

Very pedantic. Obedient. Hard working. Great memory. 

Unable to understand the context.

It works. 

You must know HOW to ask,

how to evaluate the answer, 

AND you need patience.

So, what works great for me? 

Search and summarize a very general question, a book, a movie, a problem.

Correct my spelling, find synonyms, antonyms, rephrase text.

Write standard software code.

What doesn't work: solve a niche software problem, invent a new solution to a new problem, find a niche book, especially not published in English.

 

My basic example of tonight: I had a very niche problem: 'check if the value of an Excel cell is N/A or ""'

GPT4 was not able to solve it. Excel has a weird way to evaluate expressions and errors, so the simple solution always returns N/A, instead of True or False.