Optimal Bias Forecasting

The Hidden Problem

"We think too little of the unknowns — and too much of ourselves." That's the quiet flaw buried in most infrastructure forecasts. It's not about bad intentions or lazy analysis. It's about cognitive bias — particularly the planning fallacy, a term coined by Nobel laureate Daniel Kahneman to describe our tendency to underestimate cost, duration, and risk, even when we know better.

What's Going Wrong in Projects?

From rail megaprojects to vertical infrastructure builds, decision-makers routinely fall into the "inside view" — they focus on plans, expert opinions, and best-case scenarios. It's not just human error. It's structural optimism, reinforced by Excel sheets, committee compromises, and lack of real-time feedback loops.

• Overly optimistic timelines
• Underestimated budgets
• Repeated schedule resets
• Missed early signals

Enter Optimal Bias

Daniel Kahneman's solution? Use the outside view. That means grounding forecasts in historical performance, comparable cases, and known risk patterns. But here's the challenge — most teams don't have the time or tools to do that consistently. That's where Mavryck comes in. We operationalized the concept of Optimal Bias — blending Kahneman's insight with AI-driven evidence.

How Mavryck Embeds Optimal Bias

Our comprehensive approach combines behavioral science with AI-driven pattern recognition to correct forecast bias before it compounds.

• Reference Class Forecasting (RCF): Mavryck compares your current schedule, cost, and risk posture to thousands of historic patterns — surfacing what actually happened on similar projects
• Real-Time Pattern Disruption Alerts: Using our GIGO framework and anomaly detection, we flag when your actuals are drifting from forecast long before traditional tools catch up
• Knock-On Impact Mapping: We don't just detect the problem — we show the downstream consequences. Think of it as a GPS that warns you three turns ahead
• Optimal Bias Scoring: We assign a confidence range to your current forecast and recommend adjustments using external comparables

A Quick Case: When Bias Cost 60 Days

In one recent project, Mavryck identified a silent risk: The contractor's forecast showed green, but historical data flagged similar activity clusters that slipped by 45–90 days in past builds. We triggered an Optimal Bias Alert. Days later, an RFI backlog surfaced — confirming the delay risk. The forecast was wrong, but Mavryck's bias correction caught it in time.

Why It Matters

Infrastructure doesn't fail due to lack of data — it fails due to flawed conviction in the wrong forecast. The future belongs to teams that embed probabilistic thinking and cognitive risk correction — not just better planning. With Mavryck, we're not just visualizing progress. We're rewiring it — to think better, plan smarter, and deliver on time.

Want to Know the Bias in Your Forecast?

Request a scan of your latest schedule or risk register. We'll show you where your plan diverges from reality — and how to build in Optimal Bias.