Strategic Planning – Reducing Decision-Making Distortions

Strategic planning in business is only as good as the decisions that shape it.

Unfortunately, human judgment is often clouded by decision-making noise and cognitive biases, which can lead to inconsistent, suboptimal, or even disastrous choices. Whether in corporate boardrooms, startup strategy sessions, or executive planning meetings, these distortions affect everything from market positioning to investment decisions.

To combat these issues, leaders can apply believability-weighted decision-making, a method championed by investor Ray Dalio. His approach, combined with insights from Nobel laureate Daniel Kahneman’s work on noise and biases, provides a structured framework for improving decision quality.

This blog considers:

• The problems noise and bias introduce into business decision-making.
• How these distortions impact strategic planning.
• Practical solutions, including believability-weighted decision-making, to improve outcomes.

The Problems: Noise and Bias in Decision-Making

Noise: The Hidden Source of Inconsistency

Daniel Kahneman, Olivier Sibony and Cass Sunstein, in their (2021) book Noise: A Flaw in Human Judgment, define noise as undesirable variability in judgments. Noise occurs when different people (or even the same person at different times) make inconsistent decisions given identical circumstances. Unlike bias, which skews decisions predictably, noise creates random variations that reduce decision reliability.

Examples of Noise in Business

1. Hiring Decisions:

• Two hiring managers at the same company review the same candidate’s resume. One rates the applicant as highly qualified, while the other dismisses them as unfit. The inconsistency is noise—not bias—because it introduces random variation into the hiring process.
• Studies show that hiring managers frequently disagree on candidate rankings, even when using the same job descriptions and qualifications.

2. Loan Approvals in Banking:

• Two underwriters assessing identical loan applications may reach different conclusions based on mood, fatigue, or personal interpretation.
• One study found that decision variability in loan approvals cost banks millions in missed opportunities and unnecessary defaults.

3. Performance Reviews:

• Employee performance scores can fluctuate dramatically based on the reviewer’s personal tendencies, time of day, or how they feel about other employees reviewed earlier that day.
• A 2015 study found that over half of performance review scores reflect more about the reviewer’s personality than the employee’s actual performance—an example of noise polluting business decisions.

In strategic planning, noise can lead to misaligned decisions across teams, making it difficult to execute long-term strategies effectively.

Bias: The Systematic Distortion of Judgment

Bias differs from noise because it skews decision-making in a predictable direction. Kahneman’s research has identified numerous cognitive biases that distort business judgment.

Examples of Bias in Business

1. Confirmation Bias in Market Expansion:

• A retail company expanding internationally may rely on market research that supports its preconceived belief that a new region is a good fit.
• Ignoring contradictory data (such as cultural barriers, weak brand recognition, or local competition) can lead to costly miscalculations.

2. Overconfidence Bias in Mergers & Acquisitions:

• Many M&A deals destroy shareholder value, often because executives overestimate their ability to integrate businesses successfully.
• Microsoft’s $7.2 billion acquisition of Nokia’s mobile business in 2014 is a classic example. Microsoft overestimated its ability to compete with Apple and Android and ended up writing off the entire investment.

3. Anchoring Bias in Pricing Strategies:

• A company setting prices for a new product might rely too heavily on historical prices rather than evolving market conditions.
• Research shows that consumers’ willingness to pay can be influenced by initial price exposure, meaning companies anchored to old pricing models might miss opportunities to optimize revenue.

Strategic planning that doesn’t account for bias risks making one-sided, overly optimistic, or misaligned decisions.

One Approach to Solution: Believability-Weighted Decision Making

Ray Dalio, founder of Bridgewater Associates, developed believability-weighted decision-making to improve judgment quality. His approach ensures that the most credible and experienced voices carry more weight in decision-making, rather than relying on hierarchy, personal biases, or emotion-driven opinions.

Key Elements of Believability-Weighted Decision Making

1. Identifying Who Is “Believable”

• Dalio argues that not all opinions should carry equal weight. Instead, decision influence should be based on expertise, past success, and credibility.

Examples – believability:

• If a company is deciding whether to partner with another company, build a new company, or acquire a company, an executive who has successfully led all three of these approaches to growth should have more decision-making influence than a general strategist with less experience.
• Believability should be determined by demonstrated success—not just job titles.

2. Structured Decision-Making Process to Reduce Noise

• Scoring systems: Instead of subjective discussions, decisions should be quantified. For example, a new product launch decision might involve scoring potential products based on market fit, competitive advantage, and financial projections…weighted by the expertise of those providing input.
• Checklists and guidelines (bringing due diligence): Decision frameworks ensure that all factors are considered consistently across different teams, reducing the influence of randomness.

Example:

• An investment firm or department implementing structured pitch evaluations (for internal and/or third-party pitches] reduces noise in investment decisions, leading to better long-term portfolio performance.

3. Aggregating Multiple Perspectives to Reduce Bias

• Independent assessments: Seeking input from multiple experts, rather than a single dominant voice, prevents bias from skewing decisions.
• AI-driven analytics: AI tools can analyze historical decisions and outcomes, helping organizations make more data-driven, unbiased decisions.

Example:

• When deciding to open new store locations, a retail chain could combine machine learning models with expert insights to predict demand more accurately, avoiding biases like optimism bias or overreliance on past success.

Implementing Believability-Weighted Decision Making in Business

Step 1: Audit Past Decisions for Noise and Bias

• Review historical strategic decisions and compare predictions to actual outcomes.
• Identify patterns of inconsistency or systematic distortions.

Step 2: Define “Believability” Criteria for Decision-Makers

• Set measurable performance-based criteria for decision-making influence (e.g., track record of successful strategic initiatives).
• Ensure that those with the most relevant expertise contribute the most weight to decisions.

Step 3: Implement Decision Systems to Reduce Noise and Bias

• Use structured decision frameworks (e.g., weighted scoring models).
• Leverage data analytics and AI to reduce subjectivity.
• Establish cross-functional decision panels to prevent groupthink.

Step 4: Create a Feedback Loop for Continuous Improvement

• Regularly assess the accuracy of past decisions and refine believability weightings accordingly.
• Encourage a culture of radical transparency where bad decisions are analyzed and corrected.

Building the Future of Decision-Making in Business Strategy

By addressing decision-making noise and biases through believability weighting, businesses can significantly improve strategic decision quality. Whether in corporate strategy, financial planning, or operational execution, this approach ensures that decisions are made by those best equipped to make them and are not overly influenced by people with the highest positions of authority.

As businesses integrate AI and decision intelligence platforms, they will have even more tools to systematically improve judgment, reduce cognitive distortions, and create more predictable, effective business strategies.

Lessons Learned:

1. Noise and bias create distortions and are major barriers to good decision-making.
2. Believability-weighted decision-making ensures expertise reduces errors and brings more-successful strategies.
3. Structured processes and AI can systematically reduce judgment errors.
4. Organizations must continuously refine decision frameworks for long-term success.

By applying principles, businesses can: 

1. enhance decision accuracy,
2. improve execution, and
3. drive sustained competitive advantage.