Learning Game Trees and Forgetting Wrong Paths

This is the second of two blog posts delineating the pedagogical approach of Herb Simon, the man credited with inventing the field of artificial intelligence, for which he won a Turing award in 1975. (Read the first post here.) Simon was a polyglot social scientist, computer scientist and economics professor at Carnegie Mellon University, He later won the Nobel Prize in 1978 in economics for his work in organizational decision-making.

Game Tree
Tic Tac Toe Game Tree, Gdr from Wikimedia Commons

Dr Simon would often tell his students that he liked to think about human learning as a game tree: when you start out learning about a new topic, you begin at the root of the tree with what you already know, and follow connections to related topics, discovering new “nodes” in the tree. You employ a variety of search strategies to follow connections both broadly and deeply through related topics, loading as much of the explorable tree into memory as possible. As you discover and master each “node” on the tree, you learn which branches of the tree are fruitful and which are fruitless.

During and after exploration though, the entire game tree remains in your working memory, slowing you down. When you take breaks, not only are you relaxing, but you are also forgetting wrong paths – pruning those fruitless branches from your working memory. When you next return to the task at hand, you resume exploring connections and mastering concepts not at the very top of the tree, but in the most fruitful subtrees where you left off, making better use of your working memory.

At Pedago, we believe in learning by doing, and we want to break complex topics and concepts down into what Seymour Papert in the book Mindstorms calls “mind-sized bites.” One of the benefits of breaking complicated topics into “bites” is that it is easier to build learning content that learners can work through when they only have a few minutes free, on whatever device they have on hand.

As we build our database of short concepts and lessons, we find ourselves also building a rich tree structure of topic relation metadata that in structure is not unlike Simon’s game tree of learning. A nice side-effect of a learning solution with rich, encapsulated, short lessons is that you don’t have to commit to a thirty minute video – you can learn in bits and pieces throughout your day. And by doing this, you are unintentionally building and then pruning your learning game tree in an efficient way, forgetting wrong paths and making the best use of your working memory each time you return to your lessons.

 

Herb Simon on Learning and Satisficing

This is the first of two posts delineating the pedagogical approach of Herb Simon, credited with inventing the field of AI, for which he won a Turing award in 1975.

This is the first of two blog posts delineating the pedagogical approach of Herb Simon, the man credited with inventing the field of artificial intelligence, for which he won a Turing award in 1975. Simon was a polyglot social scientist, computer scientist and economics professor at Carnegie Mellon University. He later won the Nobel Prize in 1978 in economics for his work in organizational decision-making.

Herbert Simon in front of blackboard
Herbert Simon, Pittsburg Post Gazette Archives

“Learning results from what the student does and thinks and only from what the student does and thinks. The teacher can advance learning only by influencing what the student does to learn.” –Herb Simon

Among his many accomplishments, Herb Simon was a pioneer in the field of adaptive production systems. He also identified the decision-making strategy “satisficing,” which describes the goal of finding a solution that is “good enough” and which meets an acceptability threshold, as opposed to “optimizing,” which aims to find an ideal solution.

Simon believed that human beings lack the cognitive resources to optimize, and are usually operating under imperfect information or inaccurate probabilities of outcomes. In both computer algorithm optimization and human decision-making, satisficing can save significant resources, as the cost of collecting the additional information needed to make the optimal decision can often exceed the total benefit of the current decision.

We live in a world where overwhelming amounts of information are at our very fingertips. Every month new educational software offerings are on the market. You can find tutorials to fix anything in your house, learn a new language for free, find lessons that teach you to dance, and watch video lectures from top universities in the topics of your choice.

I like to think of myself as a polyglot learner: I would love nothing better than to just take a year, or two, or ten, and learn as much as I can about everything. But unfortunately, I have limited time. How do I know which tutorials, lessons, and classes are worth the commitment of my time? How can I find a satisficing solution to the problem of becoming a more well-rounded learner and human being?

In Simon’s words, “information is not the scarce resource; what is scarce is the time for us humans to attend to it.” At Pedago we’ve been inspired by thinkers such as Simon to build a learning solution that makes the most of the scarce resource of your time, by employing curated streams of bite-sized lessons; rich, explorable connections between topics; interactive learn-by-doing experiences; and just the right amount of gamification. We want to enable you to craft your own learning experience, so that you can, as Simon would say, positively influence what you do and what you think.

Stay tuned for the second post in this series as we examine Simon’s modeling of human learning.