Lecture VI - Computational Kindness
Programming with Python
Key Concepts
Topics from the Lecture
- Optimal Stopping: How to decide when to stop looking for better options.
- Explore/Exploit Tradeoff: Balancing between trying new things and sticking with known options.
- Caching: Storing and reusing information by sticking to a task to improve performance.
- Scheduling: Efficiently managing tasks and time.
- Randomness: Understanding and working with uncertainty.
Computational Kindness
Computational Kindness
. . .
Question: An idea what that is?
. . .
- Idea of introducing empathy in algorithms
- Consider human cognitive load and limitations
- Making choices that reduce mental burden for others
- Creating systems that are easy to understand and interact
Scheduling Meetings
- Propose specific times!
- Don’t ask “when are you free?”
- Reduces cognitive load for others
- Transforms an open-ended problem
. . .
Now a simple yes/no decision!
Communication
- Be explicit rather than implicit
- Provide clear options instead
- Avoid open-ended questions
- State your preferences
. . .
Avoid deferring → “I’m fine with whatever”!
The Cost of Flexibility
- “I’m free whenever” seems helpful
- But it is not!
. . .
- Forces others to consider all possibilities
- Increases cognitive load
- Makes decision-making more complex
Principles
- Reduce Options: Fewer choices lead to better decisions
- Be Explicit: Clear constraints help others decide
- Consider Cognitive Load: Design interactions minimizing mental effort for others
- Make Decisions: Taking responsibility can be kind
Reflection
Think about it: How could you apply computational kindness in these scenarios?
- Planning a group dinner with friends
- Asking your professor for thesis feedback
- Coordinating a team project at work
How to continue?
How to continue learning?
- We have covered a lot of topics
- But there are many more to explore!
Bayes’ Rule
- Fundamental theorem in probability theory
- Updates probability of hypothesis based on new evidence
- Used in statistics, machine learning, and decision-making
. . .
Helps make informed predictions and decisions under uncertainty in the real world!
Overfitting
- Occurs when a model learns the training data too well
- Captures noise and outliers rather than pattern
- Results in poor generalization to new data
. . .
Several ways to counter overfitting, e.g. cross-validation.
Game Theory
- Strategic interactions among rational decision-makers
- Analyzes situations with multiple agents and their strategies
. . .
Many applications in economics, politics, and biology!
Networking
- Study of information exchange over networks
- Includes understanding protocols and data transmission
- Optimizes network resources
. . .
Important in computer networks, the internet and social life!
Relaxation
- Simplifies complex problems to make them more tractable
- Involves relaxing certain constraints
- Provides insights or approximate solutions
Continue programming?
- The best way to continue learning is to keep programming in the future
- Potentially, you will continue to do so during your studies
- Coding in your Thesis is another great way to improve
- Try to find a way to apply programming in your work
- There are many interesting topics to explore!
Advent of Code
- Advent of Code is a fun way to keep programming
- Here you can solve programming puzzles during Advent
- It is completely free and ad-free and starts each year at 01.12.
That’s it for the Lecture Series!
- We now have covered the basics of Python
- I hope you enjoyed the lecture and found it helpful
- If you have questions or feedback, please let me know!
- I wish you all the best for your studies and your career!
Q&A
Literature
Interesting literature to start
- Christian, B., & Griffiths, T. (2016). Algorithms to live by: the computer science of human decisions. First international edition. New York, Henry Holt and Company.1
Books on Programming
- Downey, A. B. (2024). Think Python: How to think like a computer scientist (Third edition). O’Reilly. Here
- Elter, S. (2021). Schrödinger programmiert Python: Das etwas andere Fachbuch (1. Auflage). Rheinwerk Verlag.
. . .
Think Python is a great book to start with. It’s available online for free. Schrödinger Programmiert Python is a great alternative for German students, as it is a very playful introduction to programming with lots of examples.
More Literature
For more interesting literature, take a look at the literature list of this course.
Footnotes
The main inspiration for this lecture. We have read it and discussed it in depth, always wanting to translate it into a course.↩︎