Schrage builds a strong case that 'serious play', or extensive use of modelling, creates behavioural changes and generates a form of interaction that greatly enhances efficiency in getting innovations to market. "How organizations play with their models determines how successfully they manage themselves and their markets."
• Changing the economics of play transforms the economics of innovation ... [and] individual and institutional behaviors.
• Behavior change matters more than technological change. It is cultural variables that define how the 'technological toys' succeed or fail to effect profitable innovation.
• In general, the economics of modelling and prototyping has been transformed from managing scarcity to managing potential excess (the author has a chapter on the value of spreadsheets and the perils of 'spreadsheet wars').
• Models can be 'engines of surprise', by generating 'results utterly at odds with the assumptions embedded in them'. 'The challenge is recognizing and exploiting that unanticipated value.'
Here are the book's organizational structure and key ideas:
Part I: Getting Real
1. The New Economics of Innovation (it's usually cheaper to spend time and money on simulations than to make mistakes in the marketplace)
2. A Spreadsheet Way of Knowledge (spreadsheets allow companies to look at more alternatives and explain them better, but there are dangers in relying on faulty ones)
Part II: Model Behavior
3. Our Models, Ourselves (models reflect how we think about innovation and our assumptions more than the real world)
4. Productive Waste (the more we waste in thinking through alternatives, the better the final result and eventual economic returns are as long as we are focused on speed to implementation)
5. Preparing for Surprise (the most valuable benefits come from surprises we don't expect -- be sure to keep your eyes open and follow up)
6. Perils of Pathological Prototyping (ways to make simulations worthless or harmful -- lessons of what to avoid doing)
Part III: S(t)imulating Innovation
7. S(t)imulating Interventions (creating shared space and information flows allows more types of stakeholders to participate including suppliers, other internal functions, and customers)
8. Measuring Prototyping Paybacks (understanding how you generate the most value from your project can improve your process)
9. Going Meta: Evolution as a Business Practice (future steps for simulation improvements)
User's Guide (10 key lessons):
(1) Ask who benefits? This may create bias. Eliminate or reduce the bias.
(2) Decide what the main paybacks should be and measure them. Rigorously. Without this focus, your process can miss the most important elements of the activity for you.
(3) Fail early and often. Iterations are more important than making the most progress with each prototype.
(4) Manage a diversified prototype portfolio. Each way you prototype will have biases and errors in it. By duplication of prototypes in different forms, you can avoid those mistakes.
(5) Commit to a migration path. Honor that commitment. This means that you integrate simulations into a business process.
(6) Prototypes should encourage play. Otherwise, finished models simply cast ideas in concrete (clay models of cars often had this effect)
(7) Create markets around the prototype. This means getting customers involved through methods such as beta testing.
(8) Encourage role playing. This is an effective way to create empathy and a shared view of the problem.
(9) Determining the points of diminishing return. This means to spend your time and efforts in those areas that are most productive, and to manage your total time to implementation against the cost of errors you can eliminate.
(10) Record and review relentlessly and rigorously. This is the idea of how to improve your overall simulation process.
This is an important book. Playing around with a prototype changes the dynamic between proposer and proposee, turning them both into collaborators who focus on the model rather than judging each others ideas.
Up to a point, the more prototypes, the more innovation. Now that computers have made prototypes dirt cheap, it's vital for innovators to assess how much is enough.
I borrowed this book from Mechanics Library. If I'd bought it, every other page would be marked up with yellow highlighter. Since that's not the case, I ripped off a couple of reviews from Amazon (left).
Schrage's last chapter (before his user's guide) is "Going Meta.: Evolution as a Business Practice." Today, CAD/CAE stands for Computer-Aided Design and Computer-Aided Engineering. Tomorrow, the same acronyms may stand for Computer-Aided Darwinism and Computer-Aided Evolution... State-of-the-art software breakthroughs won't merely be written, they will be grown. "I think of it as being like a software brewmaster," says Danny Hillis, an evolutionary-programming pioneer.
In the future, innovators may want to prototype how they prototype...
