In advance of reviewing Karl Kapp's Gadgets, Games and Gizmos for Learning tomorrow, today I'm revealing some of my own experiences as a computer gamer of sorts. My first "game" that I played repeatedly was Microsoft Flight Simulator on my Compaq "luggable" circa 1983. I still recall the spellbinding, immersive experience of landing a small Cessna plane at O'Hare Airport. The screen was green and black, but that did not detract from how captivating it seemed to be in complete control of the plane at a location I had flown into countless times on management consulting gigs.
Next I was introduced to SimCity 1.0 after dinner at friends I was visiting in 1989. The first time I looked up after being immersed for awhile, it was 12:45am! Beyond the spellbinding quality of the gameplay, SimCity offered emergent, nonlinear phenomena. I could build roads, real estate and city services. I could change the tax rate and add mass transit. That much conformed to straightforward, make-it-happen, cause-effect dynamics. However, I could not directly control whether a neighborhood turned into a slum, what happened to the crime rate, how snarled traffic congestion became or the ways voter approval of me fluctuated. I was fascinated by the recursive complexity of what was occurring and the surprising feedback I was getting. I was compelled to play that game repeatedly to try different strategies and see what evolved from the indeterminate model in the game engine.
Next I was spellbound by Tetris and a LucasArts derivative called Pipe Dream. Both of these games were gaming me. Both had AI (artificial intelligence) to recognize patterns in my maneuvers and deprive me of future opportunities to score in the ways I had become accustomed to. I was being taught to "up my game" by having my successful routines eliminated. I found myself getting smarter by getting out-smarted. Both games had that addictive reward of breaking into a higher level with different music and graphics. Both increased the pace of action coming at me as I became more proficient. Both redefined "learning from experience" as a very different endeavor from learning to ride a bike or to use a new tool. Changing challenges attuned to my understanding assessed by the thing being learned -- had entered into the mix.
Finally I've been enraptured by Railroad Tycoon II a decade ago and the 3D version more recently. The last time I played it was two nights ago. Like most RTS games (real time strategy) there are long lists of different scenarios with specific objectives to accomplish. Any scenario can be played repeatedly to fine tune the strategy deployed. The process of building networks of rail lines between cities has eventual effects on rail traffic, revenue, profitably, and stock valuation. The game includes rival railroads building networks and unpredictable variations between different cities' economies and growth. As I've reflected on the kinds of learning occurring as I've played this game, I came up with the following list:
- A small change in strategy can yield an enormous impact on outcome measures - e.g. starting with several disconnected routes instead of one continuous rail line, building large stations initially instead of later.
- The AI rivals have limited objectives that can be baited or neutralized once they no longer appear intimidating, overwhelming or unlimited, as-if fear is our worst enemy.
- There are better ways to spend the money, use the available time frame and generate additional revenue-- that can only be discovered by running the same scenario several times to play out the consequences of different strategy mixes.
- Success and failure are cyclical and self-reinforcing: success breeds success and patterns of failure are very costly to turnaround and often unproductive
- There's an unstated need to focus one's endeavors with no clue how to avoid: getting spread too thin, distracted by easy wins, fooled by apparent shortcuts or set-up to pursue dead ends.
- Rewards are more likely to be achieved by slow build, indirect and long term strategies, than by get-rich-quick, straightforward and short-sighted maneuvers.
- The best alternatives are not obvious and require the mind of a detective to uncover them, recognize what's missing, challenge the obvious evidence and suspect oneself as overlooking subtle clues.
The value of this breed of learning to the successful operation of business operations is potentially huge.