What if much of the fragmentation discussed in our first report comes from a few hidden assumptions held by project leaders and funders in our field? Below we identify one such “risky assumption” that may impact several areas, as well as an idea for reframing.

Look for a revised report in the coming weeks. The ideas for these “assumption” posts come in large part from the feedback and ideas we have received in recent months from the community. Thank you all.

Risky Assumption #3: “The Logic Model is Obvious.” 

It is not uncommon for game projects to launch without publicly declaring how they expect impact to come about. That’s understandable — it is pretty easy to describe a vision for the outcome, but much harder to explain the causal logic that leads to success. We can describe the gap as a missing or underdeveloped logic model.

(For those new to the nonprofit sector, logic models are used by organizations to plan and account for their impact, and are often spelled out when organizations dive into strategic planning.)

Particular danger comes if design teams consider their model “obvious.” What that often means in practice is that the “logic” is only descriptive — without causal claims. For example, “the players will learn math through Dominoes” is a start, because it implies a causal factor (Dominoes). However, it does not specify how playing dominoes actually leads to math skills. To do that, you might say that “math is deeply learned through practice, and Dominoes forces players to practice basic math (especially dividing by five).” More radically, you might also say that “playing Dominoes in teams can create a ‘need to know’ that catalyzes much faster acquisition of math skills like division — including by showing players the social benefits of being skilled at dividing by five.”

What are the benefits?

• Unexpectedly, articulating your logic can be wildly generative.  Even simple models lead to new ideas — including new ideas about how to optimize design, wrap around services, and track impact.  
• For the field, there will be fewer misunderstandings between stakeholders.  That’s because all games have multiple pathways to impact; in other words, they’re complex!  (In terms of the report’s main claims, we can reduce fragmentation in claims #1 and #3 with better logic models.)
• Finally, by specifying the logic of a game, the whole field will understand the game better.  Looking across games, the logic model is what allows us to “generalize” success and try to improve a whole set of games… categorically!

Fortunately, anyone can articulate the logic model with a bit of effort. Simply state “what caused what” (or take your best guess!). Be brave. Making your logic public can feel a bit exposed and out on a limb — but it also shows a kind of deeper confidence. When the game is just being released it is tempting to keep you cards close, but there are deep benefits to the field (and the game!) of proactive transparency.

…positive reframing: Articulate HOW your impact is happening (be transparent, be brave, reveal your logic model!)

Sound useful? Let us know what you think!

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Other assumption posts include: #1 (design as separate from research), #2 (delay the ‘research design’), #4 (innovation is about game types – forthcoming), and #5 (there is one way to scale – forthcoming).

(This post was written by Benjamin Stokes, Gerad O’Shea, and Aubrey Hill.)