X-Men: God Loves, Man Kills Batman - Year One Daredevil - Born Again Superman - For All Seasons Superman - Up In The Sky Gotham Central Murder Falcon Extremity Kill or be Killed I Kill Giants Vagabond REAL Vinland Saga Planetes Blue Giant Akira # Alan Moore V for Vendetta Swamp Thing --- [[2025-07-14|25-07-14-22]] Below is a set of graphic‑novel “field texts” I would load onto my Kindle if I were the Ph.D. student behind _The Promise Vendor: Entrepreneurial Over‑promise as Rational Strategy_. I group them by the kind of research intuition they can catalyze and, for each, flag one or more “orbit hooks” you can mine: | # | Title & Creators | Why it rewards an entrepreneurial‑Bayesian read | Orbit hooks* | | ----------------------------------------------------------- | ----------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------- | | **A. Narratives of strategic over‑promise & renegotiation** | | | | | 1 | **_Transmetropolitan_** – Warren Ellis & Darick Robertson | Gonzo journalist Spider Jerusalem survives by weaponised rhetoric: he oversells, pivots, then documents the fallout. A living lab for **charismatic commitment devices** and posterior “truth revelation” under radical uncertainty. | producing ⟷ evaluating | | 2 | **_The Black Monday Murders_** – Jonathan Hickman & Tom Coker | Finance is literal blood‑magic; bargains resemble SAFEs with occult contingencies. Forces you to model information asymmetry when valuation depends on hidden state variables. | theorising | | **B. Mobility, logistics & embodied risk** | | | | | 3 | **_Motor Crush_** – Brenden Fletcher, Cameron Stewart & Babs Tarr | A startup racer hacks biotech‑fuel supply chains. Shows how **vertical integration choices** (in‑house “Crush” production vs. black‑market outsourcing) map onto delay‑vs‑cost trade‑offs—exactly your supply‑chain node problem. | producing | | 4 | ⭐️**_Planetes_** – Makoto Yukimura | Blue‑collar orbital‑debris salvagers turn low‑status work into a stepping‑stone venture. Great case for **option value of mundane markets** and incremental learning under long timelines. | evaluating | | **C. Bayesian time‑loops & adaptive belief revision** | | | | | 5 | **_Paper Girls_** – Brian K. Vaughan & Cliff Chiang | Teenage delivery riders are thrown into a multiverse war; every encounter is a noisy signal that forces them to update priors on who to trust. Superb allegory for **non‑stationary priors and exploration–exploitation tension**. | theorising ⟷ evaluating | | 6 | **_The Incal_** – Alejandro Jodorowsky & Moebius | A reluctant courier inherits a McGuffin that different factions value inconsistently; promises are renegotiated at galactic scale. Use it to think through **multi‑stakeholder Bayesian games**. | theorising | | **D. Resource‑rational scrappiness** | | | | | 7 | **_The Unbeatable Squirrel Girl_** – Ryan North & Erica Henderson | Doreen beats cosmic‑level threats with gumption, memes, and cheap heuristics—textbook **resource‑rationality**. Episodes often end with a reality‑check Bayesian update: the easiest path was to talk, not fight. | producing | | 8 | **_Hawkeye: My Life as a Weapon_** – Matt Fraction & David Aja | Clint Barton juggles landlord duties, mob threats, and Avenger gigs on a shoestring. Illuminates **bounded‑budget prioritisation** and “fail cheaply” experimentation. | producing | | **E. Moral hazard & the cost of partial truths** | | | | | 9 | **_Kill or be Killed_** – Ed Brubaker & Sean Phillips | A grad‑school protagonist is coerced into vigilantism under uncertain rules—morality and utility collide. Useful for modelling **commitment under imperfect information** and the psychological cost of strategic misrepresentation. | evaluating | | 10 | **_Vinland Saga_** – Makoto Yukimura | Vikings chase glory they oversell to followers; voyages illustrate **path‑dependence** and sunk‑cost fallacy. A historical sandbox for posterior regret analysis. | theorising ⟷ producing | * **Orbit hooks:** _Theorising = outer‑orbit abstraction, Producing = middle‑orbit implementation, Evaluating = inner‑orbit lived feedback._ --- ### How to harvest insight systematically 1. **Map characters to decision variables.** Treat each promise or pivot as a node in a dynamic Bayesian network; trace how new evidence shifts their policy. 2. **Annotate over‑promises.** Mark speeches or deals where a protagonist commits beyond available resources. Ask: _what posterior probability would justify that leap?_ 3. **Run light‑weight coding exercises.** After each volume, sketch a Stan or NumPyro toy model that captures one strategic dilemma you observed (e.g., salvage‑orbital debris vs. build‑new‑ship in _Planetes_). Parameterise priors with plausible real‑world data. 4. **Three‑orbit reflection.** Close every reading session by jotting: - _Outer_: What normative theory is implied? - _Middle_: What design/operational move mirrors it in your Gen‑based spreadsheet model? - _Inner_: How would a founder feel after acting on that move? --- ### A note on the classics list you found Your shortlist already includes gems like **_Planetes_**, **_Vinland Saga_**, **_Kill or be Killed_**, and **_Akira_**—all highly compatible with the research lenses above. --- **Next step** Pick one title from each bloc, interleave them with your modelling sprints, and treat the exercise as qualitative “scenario priors” generation. Over a semester you’ll accrue a miniature ethnography of strategic over‑promise in fictional worlds, ready to be formalised in your dissertation’s Bayesian entrepreneurship chapter.