What it means
Most financial models need a forecast and some idea of how likely each outcome is. In many real situations, such as a new market, a new technology or an unusual economic shock, nobody has that information.
Info-Gap Decision Theory, developed by the engineer Yakov Ben-Haim, was designed for exactly this gap between what you know and what you need to know. The method starts with a best estimate and an unpredictable error around it.
You then set a minimum acceptable result, such as a cash balance that must not fall below a stated level. The question becomes: how large can the error in my estimate be while I still meet that minimum?
The answer is a measure of resilience, sometimes called the immunity of a plan to uncertainty. A plan that still meets its target even when the estimates are badly wrong is more resilient than one that only works if the forecast is nearly perfect.
Decision makers can then compare options by their resilience rather than by their forecast return. There is a trade-off.
Aiming for a higher reward usually makes a plan more fragile, because it needs more things to go right. IGDT shows this trade-off clearly, which helps a board choose between a safer plan and a more ambitious one.
The approach has critics. It does not tell you how likely the bad outcomes are, and the result depends on how the uncertainty is modelled.
It works best as one input alongside scenario analysis, stress testing and judgement.
In practice
Real-world examples.
Example
A hospital group is deciding whether to build a new wing. Demand forecasts are unreliable because the population is changing. The finance team uses the method to see how far patient numbers can fall short before the project fails to cover its costs.
Example
A technology company is entering a market where it has no history. Rather than guess a probability for sales, the strategy team asks how much lower sales can be than the plan before cash drops under the level needed to pay staff. This gives the board a clear threshold to monitor.
Example
A water utility planning for droughts compares two investment plans. One is cheaper but fails if rainfall drops by 10%, while the other costs more but copes with a drop of 25%. The regulator can see what extra resilience the higher cost buys.
Formula
Calculation
Maximum tolerable error = the largest error margin at which the worst-case result still meets the minimum requirement
Suppose a project is forecast to bring in $1,000,000 of revenue, and the business needs at least $700,000 to cover its costs. Assume actual revenue could fall short of the forecast by some percentage, called the error margin.
Worst-case revenue = 1,000,000 x (1 - error margin). Setting this equal to the minimum: 1,000,000 x (1 - error margin) = 700,000, so 1 - error margin = 0.70 and the error margin is 30%. The plan still meets its minimum even if revenue is up to 30% below forecast.
A second plan forecasts $1,200,000 of revenue but needs at least $1,000,000. Its worst-case revenue is 1,200,000 x (1 - error margin) = 1,000,000, which gives 1 - error margin = 0.8333 and an error margin of about 16.7%. It promises more but tolerates less error, so the first plan is more resilient.Case study
Seen in the real world.
Harbourlight Energy is a fictional company deciding between two solar projects. Project A is forecast to earn $5,000,000 a year and needs at least $4,000,000 to service its debt. Project B is forecast to earn $6,000,000 and needs at least $5,400,000.
The finance team used the method to ask how far income could fall below forecast before debt payments were at risk. For Project A, 5,000,000 x (1 - error margin) = 4,000,000 gives a margin of 20%. For Project B, 6,000,000 x (1 - error margin) = 5,400,000 gives a margin of 10%.
In this illustrative case the board chose Project A. Project B had the higher forecast income, but it could tolerate only half as much forecasting error, and the company had little experience of the new technology.
Watch out
Common mistakes.
- Treating IGDT as a way to forecast the most likely outcome, when its purpose is to test how much error a plan can tolerate.
- Assuming a high resilience score means a plan is good, when the plan may still earn too little to be worthwhile.
- Ignoring how the uncertainty is modelled, since the answer changes if the shape of the possible error changes.
Questions
People also ask.
When is IGDT most useful?
It is most useful when probabilities are unknown or unreliable, such as new markets, new technologies and rare events.
How is it different from scenario analysis?
Scenario analysis tests a few chosen futures, while IGDT asks how far from the estimate the future can drift before the goal is missed.
Does it replace probability based methods?
No. It is an alternative for situations where probabilities cannot be trusted, and it is often used alongside other methods.
From the founder's library
Take it further with the book.
Build your financial confidence beyond this definition. Shihan's full-length guide, Accounting Fundamentals, takes the same plain-English approach and turns it into a complete, practical playbook for non-finance managers, business owners and students - with chapter-end quiz answers and presentation slides included.
25% off with code MMHQ25, applied at checkout. Priced in USD - checkout may show the equivalent in your local currency.
View the book and save 25%Related