Comment by gobdovan
16 hours ago
I suppose it's mostly about clear communication. If you are reviewing books for a movie, the job does not seem to be "will this become a successful adaptation?" so much as "what is the strongest movie latent in this book, and how do I communicate that to the people who can act on it?". Those people would then try to imagine how that script would be portrayed on the screen, what the acting would be like, what the scenes would look like and where the material would break under translation. Given you all have some understanding about what makes a great script and what makes a great movie, you make a pipeline that has multiple experts controlling different aspects of the transformation and generating the strongest final product based on the original book, which, from book adaptations I saw, most of the time is just a thin seed rather than a forced blueprint.
If it later turns out the material was not adaptable in the way you thought, I'd imagine that is not just a binary miss, since the reader, producer, writer and executives can discuss and try to see where their judgement failed and what went wrong. I get that the hard feedback is sparse, but it doesn't have to be researche-grade measurements as much as it has to be good judgement, constant reality checks, even if just from proxies, and good taste. I'd be curious if this sounds close to what you were doing.
PS: there's this Dalton + Michael YC advice for startups which seems relevant: when outcomes are highly uncertain, you can't judge the result-only whether you acted logically, ethically and treated people well along the way.. (https://www.youtube.com/watch?v=XgcdvIj5I-k)
> I'd be curious if this sounds to what you were doing.
Yes, there were definitely aspects of teasing out how much a poor outcome was related to the initial call and how much was in the execution. The execution breaks down into the early definition and scoping, which can be part of the initial decision, and then all the downstream decisions.
Assessing all this is inevitably clouded by confounding factors like unforeseeable external factors and even human variability. That's pretty easy if a film you green-lit hits screens the week the COVID shutdown happens but in my domain post mortems more often came down to non-binary judgements like the degree to which an unlikely outcome was unforeseeable. In my post mortems I focused a lot on how well I modeled and surfaced the more relevant risks. Unlike an IPO prospectus, just listing all the possibilities isn't enough. The value is in identifying and surfacing the factors below "obvious" but above "very unlikely", then plugging them into scenarios and playing them forward.
To my company's credit, it wasn't uncommon that during a detailed, often painful, post mortem of a horrendous failure, my work would be singled out for being outstanding. Why? Because I'd surfaced and appropriately weighed the risks, including the ones that sank the project. When the calls weren't obvious (and many aren't), my job was to ensure the stakeholders had situational awareness, including relevant risk factors. As I got better at modeling the intractably 'wicked' nature of my role, I included fewer "Go / No Go" calls and more provisional judgements like "If we choose Go, we're betting we can execute a combination of these factors better than their median probability and that the following less-likely externalities won't occur."
Conversely, I found it frustrating that in the celebratory review of one especially huge win, which I'd endorsed with as big a "Go" as I'd ever give, my esteemed stakeholders failed to notice that my "Go" was right but for the wrong reasons. My private self-assessment was quite brutal because the excellent outcome wasn't due to an unforeseeably rare "Golden Goose" (the opposite of a Black Swan). It was a less likely factor that ended up being critical for reasons I'd seen and assessed but weighed incorrectly. The silver-lining was this was one of those rare times that exactly how I'd blown the eval of that factor was discernible. It ended up being one of the single most instructive events of my career because in parsing how I'd failed, I uncovered a process error which significantly leveled up my skills. It's like finding a math mistake wasn't a calculation error but an error in the formula or, in coding, a mythical compiler bug.
It's ironic the legendary 'big win' I was probably most known for was actually one of my biggest preventable errors. Once I'd had time to really study it, I did an internal talk on the whole episode, labeling it as my best-ever 'teachable moment.' I've heard the video of that talk is still regularly shown.