Cognitive bias in R&D

Introduction The mere exposure effect is a cognitive bias where individuals tend to favor things simply because they are familiar. In R&D, this bias can lead to an over-reliance on familiar ideas, technologies, or processes, even when newer solutions might offer better outcomes. In this newsletter, we’ll examine the impact of mere exposure on R&D […]

Introduction In R&D, teams often feel pressure to act quickly. But moving fast isn’t always moving smart. A McKinsey study found that 30% of R&D costs stem from premature decisions—rushed pivots, incomplete testing, and scrapped innovations. The real challenge isn’t just avoiding action bias; it’s balancing strategic patience with timely execution. Consider two biotech companies:

For years, Intel dominated the semiconductor industry, believing that CPUs would remain the foundation of computing power. Meanwhile, NVIDIA, once known mainly for gaming graphics, saw the future differently—investing heavily in GPUs for AI and high-performance computing. Intel initially dismissed GPUs as a niche technology, assuming they wouldn’t challenge CPUs for broader applications. Today, NVIDIA

Self-serving bias—the tendency to attribute successes to internal skills and failures to external factors—is a silent disruptor in R&D. While this bias may protect egos, it stifles learning, fosters repeated mistakes, and undermines innovation. For R&D leaders and business owners, fostering accountability while maintaining psychological safety is essential for driving long-term success. Let’s explore how

IntroductionIn innovation, caution is a double-edged sword. While managing risks is critical for sustainable innovation, excessive caution driven by pessimism bias can hinder teams from pursuing ambitious, high-reward projects. Pessimism bias—overestimating potential risks and underestimating possible rewards—often leads to missed opportunities and slower innovation. For R&D leaders and business owners, overcoming pessimism bias is essential

In R&D, overconfidence can lead to costly mistakes, but underestimating expertise can be equally damaging. The Dunning-Kruger effect—a bias where less-experienced individuals overestimate their abilities while highly skilled team members undervalue theirs—creates imbalances that stifle innovation and drain resources. For R&D leaders, mastering this balance isn’t just about improving team dynamics; it’s about unlocking the

In R&D, planning is a balancing act between ambition and realism. Yet, teams often fall prey to the planning fallacy, underestimating timelines and budgets by focusing on best-case scenarios while overlooking risks, complexities, and constraints. This bias can lead to missed deadlines, budget overruns, and lost opportunities, all of which are compounded by contributing factors

In cutting-edge fields like biotechnology and semiconductor development, where failure rates can exceed 90%, understanding both successes and failures is essential for effective R&D decision-making. Survivorship bias—the tendency to focus on successful outcomes while ignoring failures—can distort your understanding of what it truly takes to innovate. This bias leads to overconfidence, misallocation of resources, and

In the race to innovate, it’s easy for R&D teams to fall into the bandwagon effect—rushing to adopt trending technologies or methodologies simply because others are doing the same. But blindly following trends can lead to wasted resources, misaligned priorities, and missed opportunities for transformative innovation. For R&D leaders and industrial owners, the key lies

In R&D, how information is presented can significantly influence decision-making—a cognitive bias known as the framing effect. This bias causes teams to react differently to the same data depending on how it’s framed, potentially leading to skewed decisions. For R&D leaders and industrial owners, mitigating the framing effect is essential to ensuring that decisions are