Volume VI: Nested Causal Models, Maps, and Interventions
Provides causal graphs, claim ledgers, and explicit frontier questions.
Media / Conflict systems
The Chain of Decisions
A war becomes legible when events are connected to the decisions that made them possible.
This program moves beyond a chronology of strikes and casualties toward branch points, authority, constraints, alternatives, and downstream consequences. The chain is offered as an accountable causal map, with unknown links left open rather than invented.
Terms in this film
Explore the same term in other films through the transcript glossary.
Continue into the research
Related work
War Maps Project
The operational map that exposes events and unresolved causal branches.
Read the video transcript
Published captions, with their original wording.
Welcome to this explainer. Today we're diving into some pretty incredible concepts and tools
designed by Sidjay Hubbard to help us really understand global conflict. We're going to zoom
way way out. We're talking about moving from our every day honestly chaotic news consumption
all the way down to the foundational structural data that explains how the world actually works.
If you've ever felt totally exhausted by the daily news cycle or if you've ever suspected you're
only getting a heavily filtered slice of reality, well you are definitely in the right place. We're
going to explore some powerful alternatives today. Putting a special spotlight on an international
news tool called Newsboob along with an open source atlas known as the War Maps Project.
Okay, let's jump right into this with a quote from Hubbard that I think sets the stage perfectly.
War is not a picture. It is a chain of decisions passing through people, governments, institutions,
weapons, roads, markets, alliances and information systems before arriving in the lives of strangers.
Think about that for a second. The highly visual, deeply emotional images we see on our screens,
a burning building, maybe a moving convoy. That is just where the chain of decisions finally landed.
It is absolutely not where it began. But unfortunately, that final, tragic picture is often the very
only part of the chain we're ever shown. And that brings us to section one, The Picture of War,
a narrow channel. So to really grasp how narrow our typical media channel is, we have to look at
the problem of how war is generally consumed. We are so often fed through one highly emotional
channel, right? Images are selected from maximum visual impact. Language is optimized for pure
speed. We get the heat of a distant event, but we almost never get the actual instruments we need
to understand the root causes. To put this into perspective, contrast the standard ad-driven
American news diet with an alternative tool Hubbard created. It has, admittedly, a bit of a quirky
name, Newsboob. But its purpose is incredibly serious. While your standard news might just repeat the
exact same government assumptions across a handful of networks, Newsboob intentionally opens up a broad,
open-source, multicultural field. The goal here isn't to say that one specific foreign channel
is the absolute unquestionable truth, not at all. It's about exposing us to the omissions
and the varying emphases of totally different cultures. It's about simply hearing how people
in other nations are describing what is happening right in their own backyards. Which brings us to
Section 2, the Newsboob antidote, a free-speaking world. Now what's really fascinating here is
that Newsboob acts as a direct antidote to the very real danger of mistaking one single news source
for objective reality. And it pulls this off by intentionally stripping things away. We're talking
no tracking, no internet ads, no annoying skip button video ads, and absolutely no censorship.
It is built from the ground up as a plain, transparent inspection tool for international news.
It's not some corporate brand trying to monetize your attention. Hubbard actually refers to it as
a machine for being nourished by the free-speaking world completely stripped of all that commercial
noise that normally dictates how our news is packaged and forced fed to us. Hubbard sums up the
whole philosophy behind Newsboob with this fantastic point. A broader field does not remove judgment.
It gives judgment something to work on. Because it's not about finding one pure foreign channel,
or assuming a broadcaster is somehow unbiased just because they happen to criticize Washington.
It's about volume. It's about diversity. When you use Newsboob to tap into a wide array of
global sources, you start to notice what each specific newsroom considers normal. You see their
unique distinct assumptions. And that, finally, gives your own critical judgment the context it
actually needs to function. All right, moving on to section three, the scaffolding of reality
behind the report. So let's say you've widened your news diet using Newsboob. That is a massive
step, but we still hit a deeper problem with reporting itself. The polished headline almost
always hides the scaffolding. Even the most diverse news coverage often lacks the strict
rigor you'd find in, say, peer-reviewed science. Just to consider this question, was a casualty
figure counted by a hospital, a ministry, a local journalist, a combatant, or maybe just a person
repeating a post online? When we read a clean, perfectly grammatically correct sentence,
it entirely hides the messy, complicated reality of how that data was actually collected.
What time period is the word "today" even cover? Was a location independently confirmed?
A dramatic high-resolution image can actually be way less honest than a complicated data table,
simply because it completely hides the uncertainty. So the crucial point is, how do we fix that?
Well, that brings us to nested causal modeling. This is the foundational philosophy behind
Hubbard's data work. Instead of taking a bunch of messy data and just blending it into one
authoritative-looking score that completely hides where the information came from,
nested causal modeling preserves the enclosures. Think of it kind of like a super-detailed food
label that doesn't just say cake but tells you exactly which farm the eggs came from.
It keeps every single observation firmly attached to its specific source, the actor,
the exact time, and the coding method. It makes the conclusion totally transparent
and, importantly, available for correction. This rigorous methodology is the beating heart of
Section 4, the War Maps Project, and Open Atlas. Now, if newsboob exists to widen your daily news
perspective, the War Maps Project is there to widen your historical evidence. The project
specifically uses the year 1946 as its starting point for observations on organized conflict,
and why is that? Because the project relies strictly on loaded, pure-reviewed evidence.
It completely refuses to backfill unsupported centuries. The Atlas doesn't appoint itself
as the ultimate unquestionable authority of truth. It simply makes the data sources,
the coverage, and the methods completely explicit. That way, you can see exactly what the map supports,
and just as importantly, what it doesn't. Let's actually walk through the strict architecture
of how this project processes data, because it's pretty brilliant. Step 1. Preserve the source
row and data set boundary. Step 2. Normalize names only to join them to reference geometry,
keeping that original source identity perfectly intact. Step 3. Render measurements in their
native units. Visuals are treated as comparison aids, never as replacement values. And Step 4,
which, honestly, might be the most impressive part, keep missing values missing. The map refuses
to guess. It never synthesizes some premature conclusion. It never invents history, and if
the data simply isn't there, it remains totally blank. And this brilliantly illustrates Hubbard's
absolute dedication to explicit boundaries. The War Maps project defines exactly where its data
begins, where it ends, and where it remains completely silent. Whether we're talking about
organized conflict, governance data, or population health metrics like mortality and fertility,
the Atlas never extrapolates beyond the source boundary. It tells you exactly what is observed,
and explicitly states that outside that specific range, the data is just unavailable.
No guesswork? To build all this, the Atlas relies on some incredibly rigorous upstream data sets,
like the Uppsala Conflict Data Program and VDM. But it treats them with absolute discipline.
It uses them to distinguish conflict types, to record exact locations, and to separate
institutional conditions from economic baselines. But most importantly, it prevents collapsing
unlike things into a single, dramatic, misleading count. It treats these databases not as magic
windows that show pure unfiltered truth, but as distinct human-coded sources with distinct
boundaries that we can inspect, and if needed, even disagree with. Section 5, the Human Denominator,
the Cost of Conflict. A map of conflict is, frankly, incomplete without looking at the human cost.
You really have to view two incredibly different types of data side-by-side to understand reality
here. On one hand, you have conflict data, the immediate battle reports, the locations of violence,
the military episodes. But on the other hand, you have population health data. This reveals the
much longer timescale of household survival. We're talking untreated disease, displacement,
lost medical capacity, drastic changes in fertility rates. They have totally different
denominators and totally different timescales. But viewed side-by-side, they show the true,
heavy weight of a conflict. Adding this health data does something genuinely profound, captured
perfectly in this idea. Population health gives meaning to conflict without pretending that
a rate can explain a decision. By placing mortality and fertility data right next to conflict data,
the real-world consequences of war become impossible to just abstract away.
It prevents us from looking at a map and somehow forgetting that real human lives are the denominator
here. Which brings us to our final part, Section 6, Modeling Reality, Changing Outcomes.
So let's see how this all builds together. How do tools like a broader news diet with
Newsboob, combined with the structural evidence from war maps, actually change real-world outcomes?
Because this isn't just about sitting back and admiring a beautiful data dashboard while
the world burns. The practical hopes of reality modeling are to make causal paths legible early
on. When uncertainty is clearly marked, rumors lose their power to become pretext for violence.
When civilian costs are directly, mathematically connected to institutional decisions,
leaders can't use distance as an excuse anymore. Ultimately, tools like Newsboob and
war maps build civic capacity. They give us enough shared evidence to actually notice danger
long before it becomes destiny. So I want to leave you with this really provocative thought
to ponder today. Can a map prevent a war? If we expose the chain of decisions, if we widen our
daily news diet with tools like Newsboob, refuse those simple, convenient stories, and trace the
true scaffolding of reality before violence becomes inevitable, can we actually alter the future?
It's a powerful question, and it's exactly why understanding and utilizing these tools matters
so much. Thanks so much for joining this explainer. Keep asking the hard questions,
and keep looking at the data.