When a prediction is wrong, the productive response is to diagnose why — in the Iran case Bishop was too focused on Israeli military capabilities and not enough on Trump's political appetite to intervene — yielding a lesson that there is no single 'one rule to rule them all' and analysts must constantly reassess.

The 'logic of political survival' framework explains leader behavior — leaders place their own power and advantage ahead of everything, which is why Netanyahu would never agree to a hostage deal that risks his coalition just to satisfy Trump — but the framework is not foolproof, as shown by Hamas surrendering hostages twice despite the leverage logic predicting otherwise.

Clients value the analysis primarily not for the outcome prediction itself (for which they could use prediction markets) but for the rehearsal of all possible scenarios in advance — like an F1 driver memorizing every turn of the track before a race so they are pre-prepared to react when events unfold.

A Chinese move against Taiwan within five years is expected, but it is more likely to be a blockade than an amphibious invasion; the underappreciated risks are that a blockade could be rolled out literally overnight with no buildup, and that a sustained bloodless 'anaconda squeeze' blockade of 3-6 months could be far worse for markets than a one-month war regardless of outcome, because of the prolonged disruption to business operations.

A base case scenario can be market-constructive while the aggregate of all non-base-case scenarios produces a more likely market-negative outcome — clustering non-base scenarios revealed this in a Uruguay election analysis.

Prediction markets are currently of limited use to clients mainly because of low liquidity, and they are more useful to the analyst than to the end user because their rigidity — fixing predictions to specific falsifiable dates — narrows their value when the truly relevant cutoff (e.g., a Trump visit) differs from the market's stated date.

A useful lesson early in an analyst's career is to not get overly excited by every new headline, citing the perennially overhyped 'death of the dollar' stories (first gas contract settled in euros) and the BRICS narrative, which has promised a 'glorious future' for nearly 20 years without materializing.

Predicting geopolitical outcomes is actually harder than flipping a coin or throwing darts, because most situations have far more than two possible scenarios, making accurate probability assignment extremely difficult.

It is better to have rigorous analysis with a wrong call than a correct call with bad analysis, because good process is repeatable while a lucky correct call without process is just throwing darts.

Studies showing geopolitical risk doesn't move markets are short-sighted because they typically measure the S&P over the long term, whereas the firm's clients trade short-term and in niche assets (travel/tourism, oil and gas, Israeli shekel) where geopolitical events produce major sharp volatility.

Assigning probabilities to sub-events before publishing serves as an internal logic check: if the cumulative math doesn't add up to 100, it signals you are overstating or understating some aspect of the dynamic, even though the value is logical rather than mathematical.

Because there are many independent ways to reach a given outcome, compounding the probabilities of independent variables can tip the cumulative odds past 50% even when no single scenario is your modal (most likely) scenario — for example, US intervention in Iran exceeded 50% overall even with a low probability assigned to Trump wanting to intervene.

Intel-based geopolitical analysis (insider information from Congress, golf with officials, etc.) does not make for good predictions because even insiders like the Treasury Secretary did not know whether Trump would back down on tariffs, and Assad did not know if he would be toppled — proximity to power does not confer foresight.

Trump's pattern of backing down (the 'taco' phenomenon) — backing down in roughly 21 of 23 documented threats — was a valid pattern, but it was already fading by the time it became a popular label, illustrating an 'Economist cover' effect where the pattern becomes less true once widely recognized, requiring constant reassessment.

Identifying Trump's motivation in each specific case is key to prediction: if he is after tariff revenue he cannot back down (you can't collect revenue if you retreat), whereas transactional motivations like fentanyl or immigration concerns with Mexico made him likely to back down once he could claim he had acted — and the same country can be targeted by completely different motivations at different times.

An overarching grand theory like 'Trump's trade policy is all about China' can be accurate over four years yet useless in the short term, because for the first three months the administration was obsessed with Mexico and Canada — so analysts should avoid grand theories and assess each prediction individually (fox vs hedgehog).

Rapidly changing geopolitics makes analysis harder because it shrinks the already-tiny historical data set — e.g., the long-established 'axis of convenience' read on the Russia-China relationship became genuinely unclear after the 2022 Ukraine invasion — and the temptation to 'make it up as you go' is where analysis gets dangerous.

Trump allows his advisers to say anything, including the craziest statements, as long as they do not reduce his optionality — he benefits from them going to extremes provided he doesn't get cornered by their comments, which is why parsing his spokespeople literally (e.g., 'sign the executive order' vs 'implement the tariffs') can be predictively useful.

Geopolitical regime changes (e.g., shift to a multipolar world, US abandoning the hegemon role) do happen but unfold much more slowly than financial-market regime changes, giving more time to price them in; even the isolationist, pacifist-leaning Trump intervening militarily in Iran demonstrates how slow that process is.

Technical knowledge (e.g., missile ranges, weapons capabilities) is necessary for geopolitical forecasting but need not be held in-house: the firm breaks a big issue into sub-questions and sources answers from open source or external experts while never outsourcing the actual analysis or predictions.