Redesigning the Economics of Conflict: How a Data Analyst Reworked the US-Iran War Oil Price Models

Main Facts: Visualizing Energy Markets Under Duress

In the high-stakes arena of global energy markets, clear visualization can mean the difference between sound policy analysis and guesswork. Since early 2026, economists and energy analysts tracking the geopolitical fallout of the US-Iran conflict have frequently cited a specific theoretical model detailing the impact of crude oil supply shocks. Prominently featured by Robin J. Brooks of the Brookings Institution and originally attributed to financial data provider Haver Analytics, the model attempts to map how drops in Gulf oil exports drive up global crude prices.

However, the original graphic—a three-dimensional perspective plot—has long frustrated data professionals. While theoretically sound, relying on standard economic principles of price elasticity, the chart’s format makes intuitive reading difficult.

Addressing this analytical roadblock, data scientist and blogger freerangestats.info has undertaken a complete visual overhaul of the model. By translating the underlying dataset into a 2D line plot utilizing R programming language, the redesign prioritizes human readability over complex multidimensional geometry. The revised graphic not only clarifies the direct relationship between export volumes and price surges but also integrates dual-axis dollar valuations per barrel, offering an accessible tool for evaluating market stress amid ongoing geopolitical volatility.

Chronology: The Evolution of a Conflict-Driven Economic Model

• March 2026: As geopolitical tensions surrounding the US-Iran conflict escalate, economic commentators begin grappling with potential worst-case scenarios for energy supply chains. Robin J. Brooks adopts a Haver Analytics diagram to illustrate the theoretical impacts on crude oil.
• Mid-2026: Analysts note that while up to 20% of the world’s oil supply is exposed to regional risk, active disruptions peak at lower levels—around 10% of normal global supply. Despite fluctuating export rates, Brooks maintains a bearish outlook on crude prices, relying heavily on the Haver Analytics framework.
• Late 2026: Observers increasingly criticize the cognitive load required to interpret the 3D perspective plot. The necessity for a more intuitive, user-friendly visualization becomes apparent to researchers trying to communicate market vulnerabilities to broader audiences.
• Present Day: Independent data analysis leads to the creation of alternative visualizations—first via heatmaps and ultimately culminating in a streamlined, multi-line 2D graph equipped with secondary axes for real-world dollar conversions.

Supporting Data: Elasticity, Exports, and the Mechanics of Shortages

At the heart of both the original Haver Analytics diagram and the new visualization lies a fundamental economic principle: the demand curve for crude oil is not vertical. Although introductory economics frequently treats the price elasticity of demand as inelastic—often cited with a standard value of -0.1—the real-world implications during a supply shock are dramatic.

An elasticity of -0.1 dictates that a 1% price increase leads to a 0.1% decrease in quantity demanded. Conversely, in the context of a supply disruption, if market equilibrium is forced downward by 0.1% due to a shifting supply curve, the resulting price jumps upward by roughly 1%.

When applied to the scale of the US-Iran conflict:

• Supply Disruptions: At the peak of recent disruptions, approximately 10% of normal global oil supply was removed from the market.
• Price Projections: Assuming a short-term price elasticity of demand between -0.1 and -0.2, a 10% cut in supply implies a staggering 100% price increase (a doubling of prices) if elasticity sits at -0.1, or a 50% increase if it sits at -0.2.
• Baseline Parameters: The simulation models a global consumption baseline of 100 million barrels per day, pre-war Gulf exports of 20 million barrels per day, and a pre-war baseline oil price of US$72.50 per barrel.

While these calculations operate on a linear elasticity approximation—and purposefully set aside factors such as demand curve shifts, green energy transitions, and emergency inventory drawdowns—they provide a crucial bounding box for realistic market expectations.

Official Responses and Analytical Perspectives: From 3D Confusion to 2D Clarity

The core critique leveled against the original Haver Analytics diagram is not one of economic theory, but of graphic design and cognitive ergonomics.

The original perspective plot forces the viewer to interpret three variables simultaneously:

1. The volume of oil successfully exported from the Gulf (x-axis).
2. The resulting percentage impact on global prices (z-axis).
3. An uncertain short-term price elasticity parameter ranging from -0.1 to -0.2 (y-axis).

In practice, reading values off a flexing 3D surface is notoriously difficult. As the independent analyst noted, while color shading (such as a gradient from misty rose to dark red) helps slightly, general trends remain obscured.

The Heatmap Experiment

The first iteration of the redesign converted the 3D surface into a 2D heatmap, mapping the price increase variable to color fill rather than vertical height. While this made individual cell values vastly easier to read, it functionally reduced the graphic to a data table, failing to leverage the analytical power of dynamic visual trends.

The Definitive Line Plot Solution

The ultimate breakthrough came with the adoption of a multi-line 2D plot. By placing the two most critical variables—Gulf export volume and percentage price rise—on the standard x and y axes, the chart immediately aligns with human visual processing habits.

To account for the unknown price elasticity of demand, the designer plotted a "fan" of multiple lines representing different plausible elasticity values (from 0.1 to 0.2). Crucially, these lines are color-coded based on their distance from the midpoint (0.15) rather than a simple low-to-high spectrum, visually emphasizing the zone of uncertainty.

Furthermore, the revised plot introduces a secondary y-axis converting percentage increases directly into implied US dollar values per barrel (anchored to the $72.50 pre-war baseline). This transforms a theoretical abstraction into concrete, actionable financial intelligence.

Implications: What the New Chart Reveals About Current Markets

Beyond solving an aesthetic and functional design problem, the newly refactored line plot offers sharp insights into current market psychology regarding the US-Iran conflict.

When analysts cross-reference today’s estimated Gulf export volumes (fluctuating roughly between 7 and 15 million barrels per day) with the chart’s fan of plausible price lines, a distinct pattern emerges: actual market prices appear to be pricing in a premium beyond what baseline models predict.

This divergence suggests that contemporary crude prices factor in severe risk hedging. Market participants are not merely reacting to current physical flows, which have largely stabilized compared to the conflict’s initial shockwaves; rather, they are pricing in the persistent, existential fear that regional instability could cause exports to plummet rapidly once again.

Ultimately, this analytical exercise demonstrates that data visualization is never truly neutral. By stripping away the confusing geometry of traditional 3D charts and placing core economic variables front and center, analysts are better equipped to separate rational market mathematics from fear-driven speculation.