Integrating Crowd Forecasting with ARC's Decomposition & Scenarios

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In complex organizational environments, decision-makers face two linked challenges: 1) understanding the underlying factors that will shape future outcomes, and 2) quantifying the uncertainty around them.

Decomposition and scenario analysis give you the structure to understand unfolding dynamics and what signals drive them. Crowdsourced forecasting gives you a way to measure and assign probabilities to those signals. Bringing these methods together turns fuzzy intuition into clear, actionable foresight.

Recap: From decomposition to scenarios

In earlier articles, we explored how decomposition breaks down a complex future-facing question into key drivers and observable indicators. Scenario analysis then takes these drivers and imagines a range of plausible futures, helping teams think expansively about uncertainty. This combination of techniques provides essential structure and context, but not probabilities. Without knowing how likely each future is, organizations risk planning for the wrong outcome. 

Forecasting: The quantitative bridge

Forecasting fills that gap. It transforms the qualitative understanding of drivers and scenarios into hard probabilities. This allows organizations to focus attention and resources on the most probable futures.

For example, a company might identify regulatory changes as a key driver, and use forecasting to estimate the likelihood that new export restrictions will actually be enacted within the next two years. This turns scenario planning from a brainstorming exercise into a decision-making tool.

Forecasting is best understood as one key step in the overall forecasting “cycle” that includes strategic goal-setting, decomposition, scenario analysis, AI monitoring, and forecasting.

• Decomposition reveals what to forecast by identifying the key drivers and signals that matter most to your organization.
• Scenario analysis frames the range of plausible outcomes, helping you understand the boundaries of uncertainty.
• Forecasting assigns likelihoods to these outcomes, quantifying risk and opportunity so teams can prioritize actions and resources.

The process is inherently iterative: as new information emerges and forecasts are updated, organizations can revisit their decomposition and scenario analysis, refining their understanding and improving their predictions over time. This cycle of continuous learning ensures that forecasts remain relevant and robust as events change and new information emerges.

Best practices: example workflow

To illustrate how forecasting integrates with decomposition and scenarios in practice, let's walk through our example of a global technology company evaluating European geopolitical risks that could disrupt its operations and competitiveness.

1. Select a forecast question	Let's suppose you've already run a Decomposition in ARC on your overall strategic question to identify key drivers and measurable indicators. You also ran Scenarios to understand the possible futures. Next, you use the Crowdsource Forecasts capability in ARC to create and prioritize forecast questions based on your decomposition analysis, such as "Will a major European country (Germany, France, or the UK) introduce new legislation restricting the export of advanced semiconductor technology to non-EU countries by 31 December 2026?"
2. Invite a diverse set of forecasters	After creating your forecast questions, you invite experts and other colleagues to submit probabilistic forecasts, ensuring you have a diverse cross-section of colleagues.  You invite colleagues from legal affairs, government relations, European operations managers, and business development to capture different perspectives. This helps to avoid groupthink, mitigate individual biases, and uncover blind spots.
3. Collect and view consensus forecasts and rationales	As forecasters respond, you're able to see all submissions in ARC’s Crowdsource Forecasts tab. You employ best practices for reviewing, updating, and incorporating forecasts into your analysis, including:  Review rationales to identify contrarian perspectives: You and your team deliberately look for viewpoints that contradict the prevailing logic or consensus. Qualitative rationales accompanying the forecasts revealed that some colleagues warned of a specific operational risk that otherwise would have been overlooked. Embrace iteration: You regularly remind peers to submit updated forecasts in ARC, you compare forecasts to actual policy developments, learn from discrepancies, and refine your overall analysis. Incorporate forecasts into regular decision cycles: Rather than treating this as a one-off exercise, the company establishes a regular cadence for reporting forecasting results to leadership and key decision-makers. The company proactively adjusts procurement strategies and supplier relationships based on evolving probability estimates, making forecasting a routine part of their planning process.
4. Update forecasts	Forecasters can use the same forecast question link to submit new forecasts as new information emerges, time passes, and their judgement changes. This ensures that the process stays current and up-to-date.

The research behind crowd forecasting 

A growing body of research shows that aggregating forecasts from diverse individuals ("crowd forecasting") can dramatically improve accuracy and reduce bias. A landmark example is the Aggregative Contingent Estimation (ACE) Program, sponsored by the Intelligence Advanced Research Projects Activity (IARPA). In ACE’s multi-year forecasting tournament, teams of researchers and thousands of participants competed to predict global events, ranging from geopolitical shifts to leadership changes. The Good Judgment Project, led by Philip Tetlock and Barbara Mellers, decisively outperformed both other research teams and the U.S. intelligence community’s own benchmarks. This demonstrated that well-structured crowd forecasting can deliver more accurate and timely intelligence than traditional expert analysis. 

Crowd forecasting works because it harnesses a wide range of perspectives, experiences, and information sources. When managed effectively, this diversity leads to more reliable predictions, as errors and biases tend to cancel each other out. 

Key findings from the literature include: 

• Aggregating independent judgments reduces bias and increases accuracy. 
• Crowd forecasting is effective across domains, from geopolitics to operations management and public health.
• Larger, more diverse groups tend to produce better forecasts, provided the process is well-managed. 

ARC incorporates these insights by making crowd forecasting a core feature, but organizations can also leverage public forecasting communities or enterprise forecasting platforms for even richer insights.

Tips for making good forecasts

When your team is providing forecasts for your analysis, and as you begin to forecast yourself, consider some tips to help reduce bias and increase accuracy:

• Start with the outside view: When making forecasts, the “outside view” refers to the perspective of the question that does not evaluate the particular circumstances of the case at hand. Instead, in the outside view we look to the “base rate” that a particular outcome occurs in similar situations. Whereas most people tend to overemphasize what they already know about the topic when creating a forecast, savvy forecasters anchor their probability to a base rate that is not specific to the question at hand. This helps to ground the forecast and prevents overconfidence.
• Adjust with the inside view: After identifying the base rate, you can now return to evaluating the “inside view,” which is what you know about this particular circumstance that makes it unique. From here, you can adjust your forecast slightly upward or downward.
• Seek out contradictory information: Make sure to play devil’s advocate with yourself by reading articles or opinions that take the opposing perspective. This helps to counter common biases like overconfidence or confirmation bias.
• Conduct a pre-mortem: Before you settle on a final forecast, you can uncover a new and helpful perspective by simply asking yourself “if my forecast turns out to be wrong, what factors might have contributed to that unexpected result?”

With these techniques, you can greatly improve your forecasting accuracy and contribute to your organization’s strategic and operational planning. Integrating forecasting with decomposition and scenario analysis will further equip your organization to move beyond intuition or outdated expert-based modes of forecasting, enabling your team to quantify uncertainty, prioritize actions, and respond dynamically to changing circumstances.

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Sources: 

Sarah Scoles, “Psychology of Intelligence Analysis” (Popular Science, 2023).

Kim Armstrong, “Crowding Out Falsehoods” (Association for Psychological Science, 2024).

Achal Bassamboo et al., “The Wisdom of Crowds in Operations: Forecasting Using Prediction Markets” (Harvard Business School, 2019).

Cultivate Labs,“Royal Dutch Shell Case Study”, accessed 2025.

“Crowd forecasting infectious disease outbreaks: how John Hopkins leveraged the wisdom of crowds” (smarter together, 2024).

Philip Tetlock and Dan Gardner, “Superforecasting: The Art and Science of Prediction” (Crown, 2016).

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