Some problems are solvable, and some just aren't. That doesn't mean they're not worth tackling, though. Knowing the difference is half the battle.
Most tech leaders regard problem-solving as a critical component of their jobs. Whether selecting a new vendor or determining how to get thousands of employees to be productive from their homes, we’re often provided with a vague end goal, a limited amount of resources and an admonition to “get it done and call me with the result.”
For many of us, these challenging problems are what keep us engaged with technology, our teams and our organizations. As you might intuit, different types of problems require different tools, skills and information to solve. However, many of us fail to recognize the fundamentally different nature of two broad and starkly different types of problems: Kind problems and wicked ones.
Kind problems don’t always seem that way
A kind problem often is not easy or fun to solve, and there are plenty of opportunities to fail at solving the kindest of problems. Generally speaking, kind problems can ultimately be solved by applying a process or algorithm, even if it’s wildly complex.
For example, the game of chess is a kind problem. Computer scientist Jonathan Schaeffer quipped that “the possible number of chess games is so huge that no one will invest the effort to calculate the exact number,” and various estimates have been formulated to keep mathematicians busy over the years.
Computers can perform seemingly miraculous feats by besting the world’s top chess masters. Many predicted the end of human-dominated intelligence when IBM’s Deep Blue defeated world champion Garry Kasparov in a 1997 rematch.
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Despite the seemingly infinite complexity, there are a maximum of about 30 legal moves in any given turn in a chess game. There are books detailing the “standard” opening chess moves, and professional players devote much of their early careers to learning the “book moves” and appropriate countermoves.
Kind problems have known rules and outcomes. While no human or machine can calculate all possible permutations of a chess game, each move can only be countered within the confines of the rules of chess. For example, despite the desires of my 5-year old when we play chess, you can’t randomly anoint a “flying pawn” that miraculously jumps across the board to kill my king and still call the game chess.
A kind problem like chess is well-suited to an entity that can deeply master the fixed rules of the problem and quickly calculate the odds of success from applying various approaches. In technology, network design might be a kind problem, where various rules and technical limitations govern the possible moves and countermoves that can be made. There are opening moves in the form of best practices and grandmasters who have spent years studying how to solve the kind (albeit complex) problem of designing a network.
The challenge of wicked problems
On the other hand, wicked problems don’t have a well-defined set of rules and parameters. Rather than a move being subject to a set of potential and definable countermoves, a move against a wicked problem usually reveals another problem.
Wicked problems tend to have unclear and changing rules of the game that require multiple cycles to understand. A company launching a new product or service to market usually faces a wicked problem. Rather than attempting to immediately find the answer, might test a prototype with a small set of customers. That initial solution reveals more about the nature of the problem, which they then might solve through an initial product release in a single market, followed by a marketing campaign and expanded rollout, etc.
Essentially, unlike a chess game with a defined endpoint, this Wicked problem is never solved in the same way as a game of chess.
Know thy problem
Tech leaders can go awry by attempting to apply a kind solution to a wicked problem and vice versa. IBM dramatically conquered the kind problem of chess and later, the game show “Jeopardy,” only to see its Watson technology fail on the decidedly wicked problem of cancer mitigation.
At a more immediate level, an approach like Agile designed to test, refine, and solve wicked problems would likely fail when applied to a kind problem like designing an airplane wing or implementing a well-understood software package. Similarly, using narrow technical expertise to solve a kind problem is an excellent idea since these resources can help non-experts identify the moves that can be made and the potential result of each move.
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However, apply narrow technical expertise to a wicked problem, and you’ll end up focused on only one dimension of the problem rather than the more holistic understanding required to reveal the next layer of the problem. Vendors that claim to have a packaged “solution” to a wicked problem are setting you up for failure, just as bringing a diverse set of generalists to a complex technical problem (with a defined solution) will result in long timelines and failure.
When faced with a new problem, spend some time considering whether you’re facing what’s fundamentally a kind or wicked problem. Determine whether there are known rules and a defined set of moves that can be made toward a clear outcome or whether your first step is performing a set of actions that ultimately help you understand the problem more deeply.
Understanding what you’re facing will save time, resources and heartache in the long run, assuming you don’t encounter any magical flying pawns along the way.