I stumbled across this fun open source simulation this afternoon. Despite its lack of nearly any documentation, Loopy 1.0 is an impressive serious game. In Loopy you can play with out of the box challenges or create your own.
The player interacts with the system model by adding “nodes” and “arrows”. Arrows can have a positive or negative effect upon the relationship between the two nodes. Consider foxes and rabbits.
In this example, the bottom arrow is positive- indicating that an increase in rabbits will likely result in an increase of foxes. The top arrow is negative indicating that an increase in foxes will likely result in a decrease in the number of rabbits. The system runs adding or subtracting from a node based upon the +/- of the arrow(s) that lead to it.
You can add more than one arrow between the same nodes to indicate a strength difference. For instance, if you double the top arrow above it would basically represent the idea that every fox would lead to a reduction of two rabbits in the system.
You can add whatever nodes you’d like to the system (hunters, a new housing developent, etc) that represent the complexity of the system.
When ready, you click “Start” and then chose one node to either add (up arrow) or reduce (down arrow) and the system starts to run. You can see where the system stresses. My one criticism of Loopy is that there is no point where the system “breaks”. The rabbits are never all killed off by the foxes and hunters. The banks never go bankrupt.
The creator of Loopy provides three challenges to play with. So I did, choosing the automation and job loss challenge.
The challenge is at this link http://bit.ly/2nCaK9p You can start the challenge and observe how it works. This one, as presented puts amazing stress on the “frustration” and “political unrest” nodes because of unemployment caused by jobs being automated.
Keying off of the big clue – “??? what goes here ???” – in the middle of the challenge system, I began with asking what, if positively affected could reduce job loss. But it didn’t seem like the answer would come from that route. Lo and behold, I came to the conclusion that what government needed to do was use some of that tax revenue that goes no where in the original system to reinvest in public education and other programs that help humans who are loosing their jobs to robots and AI to find new purpose in the new world.
My solution can be found at this link http://bit.ly/2mUIscV
Education will create a more educated workforce that will be trained in unique human competencies. I couldn’t figure out a simple way to slow the impact of automation on jobs, but realized that the key node was the frustration that led to political unrest. With a re-education program for the unemployed and new community outreach programs to empower non-workers to improve their self-image and to find worth in volunteerism. Ah, a bit utopian in concept, but when I hit the Start button and the up button on tax revenue, the system kept working and working. Sure frustration would build and there would be substantial political unrest occasionally, the systems would
Ah, a bit utopian in concept, but when I hit the Start button and the up button on tax revenue, the system kept working and working. Sure frustration would build and there would be substantial political unrest occasionally, the systems would relieve the tensions and keep on going.
I found Loopy easy to use and it did make me think about how the system would accept change and interaction between nodes. As you can see from this post, it enables the sharing of the model I create. It can the be manipulated by others. Construction of systems can be collaborative. All in all, a nice little tool.
Your turn: What do you think? Do you see a way that a tool like Loopy could fit into a learning experience? What type of learning would you try to implement it with? Please leave a comment below in the reply area.
Feature photo by NASA via unsplash.com