The article “Data Science Mental Models – Optimizing your Thinking and Decision-Making” first appeared on AlgoTrading101 Blog.
Excerpt
What are Mental Models in Data Science?
Mental Models are our inner representations of external reality that we use to interact with the world around us.
In Data Science, you often need to solve problems, make decisions, and communicate… and knowing which Mental Models to use and when to use them will make you stand apart.
Why should I use Mental Models in Data Science?
- Mental Models help us make better decisions
- Mental Models allow for easier problem solving
- Make our thinking more efficient
- Help us avoid logical fallacies
- Simplify problems
- Make us better thinkers
- Improve our work quality
Why shouldn’t I use Mental Models in Data Science?
- Mental Models aren’t perfect
- Mental Models types are situational
- Some problems might not be solvable by a Mental Model (or a human at all)
- You might use the wrong ones
- You might spend too much time thinking about mental models and not the problem
- Aren’t immune to logical fallacies
Why is improving thinking and decision-making in Data Science important?
Thinking and decision-making are integral parts of Data Science and their outputs can make or break a project, potential solution, idea, and much more. These are also skills that can be made better and more efficient, and cultivating them will make you a better Data Scientist.
What decisions do Data Scientists often need to make?
Data Scientists often need to juggle the business and technical sides which often don’t get along. These decisions can span from modeling, structural, communication, goal orientation, the scope of work, responsibility, and much more.
What are the most common issues with decision-making in Data Science?
Issues with decision-making in Data Science often revolve around stakeholders asking the impossible, getting unusable data, obscure requirements, navigating a team, finding the right data, having too much or too little information, and more.
Knowing your way on how to navigate the sea of decisions and how to face its issues can be tricky. But, using Mental Models can make your life much easier.
Getting started
The inspiration for writing this article arose from me noticing that many Data Scientists, with whom I worked, tend to assimilate much of the problem-solving process to previous solutions, “jump the gun”, or subject everything to Deep Learning while the solution was a “simple” one.
I’ve often found that each new problem requires a fresh look and that the usage of Mental Models can speed up the process of problem solving and decision-making. Moreover, Mental Models can alleviate much stress and uncertainty, and are also transferable to all parts of life.
Thus, having my background in Psychology and working as a Data Scientist for more than 4 years, I think that I can offer an interesting and practical perspective on different Mental Models and their usage.
We’ll categorize Mental Models into 4 major categories inside of which I’ll be showcasing a couple of them and applying them to example scenarios. Feel free to play along with the example scenarios as there are multiple solutions to each problem.
The 4 categories which will be shown are the following:
- Thinking
- Decision-making
- Problem-solving
- Communication
Have in mind that these categories aren’t perfect and that the Mental Models can easily overlap, be borrowed, and/or combined.
Take note that the example scenarios are made up, boiled down to make the article more interesting, and might have or might have not happened to me. Any resemblance you might find with real-world examples is random and unintentional.
Episode 1: A new problem
You’re a Lead Data Scientist in a well-established company that offers various 360o Data Science solutions to banks, health-based companies, startups, hedge funds, and more. You are in charge of the decision-making process and are leading a data team.
You receive two emails on the next exciting problems that await your attention. Here are the boiled down versions of those emails:
The first one has to deal with a startup that is offering AI-based mental health treatments to users through music. They’re doing this via an app where the user comes in and select the mood that they want to be in and their algorithm impacts the user’s mood in the wished direction.
They’ve found issues with their app where users experience inflation of their current mood and/or don’t reach the required mood goal at all. The startup is confused as its models have been validated to work by scientific research.
The second email is from a bank that desires a solution that will predict the amount of money taken out from their ATMs, 30 days in advance, and daily.
Are you spotting some potential issues and/or overlooked factors with these problems?
What are some good thinking Mental Models for Data Science?
Some good thinking Mental Models for Data Science are the following:
- Concept Map
- The Iceberg Model
- Reinforcement Feedback Loop
- Balancing Feedback Loop
Now, let’s cover each of them and see how to apply them to our problem.
Concept Map
A Concept Map is a useful mental model as it allows us to visually display a system and pinpoint how the linkages between its parts. Having a concept map laid out for almost every project you’ll be dealing with will be quite beneficial.
Joseph Novak and Alberto Caňas, which are the creators of the Concept Map, say the following about it:
“Concept mapping has been shown to help learners learn, researchers create new knowledge, administrators to better structure and manage organizations, writers to write, and evaluators assess learning.”
There are mainly 3 steps that we need to go through to implement a Concept Map.
Step 1: Formulate a problem question
What are the exact questions that you need to answer to be able to visually represent the system in which the problem is situated? Start by asking “How does X work?”, “What’s the context of X in which it exists?” and “How is X linked to Y?”.
For example, we might want to ask the following:
- How does your algorithm work exactly?
- What’s the app ecosystem like in which the algorithm exists?
- How are the algorithm’s music curations linked to the user’s current mood?
Step 2: Identify key entities and sort them
Now that you have the context of the problem, try to create a list of the key entities that impact the problem and are linked to it. These entities might be people, algorithms, processes, places, protocols, and more.
Compile these entities in a list that should have about 20 entities in it. Have in mind that you might have fewer or more entities depending on the problem.
Now, that you have a list of entities try sorting it by specificity and/or importance. This will help you to uncover the hierarchy that is needed to create the Concept Map.
Step 3: Outline the map and fill it in
Use a whiteboarding tool like Miro and start adding entities according to your hierarchy and understanding of the problem. Then proceed with linking them together with various arrows that showcase the direction of impact.
Make sure to write the actual action of the said connection by adding phrases to the arrows like “adds to”, “creates”, “selects from”, “picks according to”…
When done, you should end up with something like this:
The above Concept Map is a simple one which is enough to get a sense of what the startup is doing and what questions we might have for them to see how we can help out.
Some of these boxes (e.g. algorithm) might have their specific concept maps. It all depends on the level of specificity you need/want to use.
Visit the AlgoTrading101 Blog to read the full article.
Disclosure: Interactive Brokers
Information posted on IBKR Campus that is provided by third-parties does NOT constitute a recommendation that you should contract for the services of that third party. Third-party participants who contribute to IBKR Campus are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.
This material is from AlgoTrading101 and is being posted with its permission. The views expressed in this material are solely those of the author and/or AlgoTrading101 and Interactive Brokers is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to buy or sell any security. It should not be construed as research or investment advice or a recommendation to buy, sell or hold any security or commodity. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.