Actuarial work revolves around probabilistic judgment, modeling uncertainty, and estimating future outcomes using data. But even the sharpest statistical minds aren’t immune to the same biases that skew decisions in everyday settings. From game nights to boardrooms, certain cognitive traps recur. These distortions in judgment don’t just affect individuals—they influence the models and assumptions actuaries build.
Cognitive Biases and Their Relevance to Actuarial Thinking
Biases are mental shortcuts, or heuristics, that help process information quickly. They aren’t inherently flawed, but they often lead to systematic errors. In actuarial science, where risk evaluation underpins real-world consequences, recognizing these tendencies matters.
Anchoring Bias
This bias occurs when people rely too heavily on an initial piece of information—the “anchor”—when making decisions.
- A common example outside actuarial work is Wordle. Players often stick too closely to their first guess, using it as a foundation despite new clues pointing in a different direction. This mirrors how actuaries might cling to early assumptions in a model instead of adjusting them as updated data emerges. Failing to shift from outdated anchors can lead to pricing errors or poor risk projections.
Overconfidence Bias
Overconfidence leads individuals to place too much trust in their own judgment. This often appears as underestimating the range of possible outcomes or assuming a forecast is more accurate than it is.
For actuaries:
- It might cause understated margins for error in reserve estimates.
- It can result in insufficient sensitivity testing, as actuaries may assume the model’s assumptions are already robust.
Mitigation: Build scenarios that explicitly stress-test confidence limits. Introduce multiple perspectives during peer reviews. Create space for dissenting model interpretations.
Confirmation Bias
People tend to seek out or interpret information in a way that confirms their preconceptions.
In actuarial practice:
- This can manifest when data is selectively validated to support a preferred model structure.
- Actuaries might dismiss contradictory evidence, thinking it’s an outlier or data quality issue.
Mitigation: Establish review protocols that challenge base assumptions. Incorporate red-teaming strategies where one group’s sole role is to question the prevailing model.
Lessons from Games with Built-In Uncertainty
Everyday games—card games, puzzles, and strategy-based challenges—present controlled environments where human decision-making under uncertainty is put to the test.
Poker and Risk Appetite
In poker, overestimating the probability of a favorable card based on a few wins can skew judgment. This is akin to assuming favorable trends in emerging risks like cyber insurance without sufficient data maturity. Actuaries must resist the urge to act on streaks and instead focus on well-calibrated risk appetites.
Chess and Information Gaps
Chess punishes premature moves based on incomplete board reading. Similar pitfalls await actuaries who rely on partial datasets. A model built with missing data or one that doesn’t account for emerging variables (like climate risks in general insurance) leads to blind spots.
Board Games and Groupthink
Collaborative games reveal how social dynamics can influence decisions. In group modeling settings, dominant voices may suppress alternative assumptions, creating echo chambers. Decision quality suffers when models aren’t subjected to diverse challenge.
Practical Strategies to Minimize Bias Impact
Actuaries can adopt structured methods to minimize the influence of cognitive biases:
1. Blind Reviews
- Have assumptions or parameters assessed independently before group discussions begin.
2. Forced Re-estimation
- Require periodic reevaluation of all core assumptions, regardless of new data availability.
3. Pre-mortem Analysis
- Encourage teams to assume the model failed and backtrack to hypothesize why. This helps surface hidden weaknesses.
4. Bayesian Updating
- Apply Bayesian methods that mathematically prioritize new information over older beliefs.
5. Cognitive Bias Training
- Include formal behavioral bias education in actuarial professional development programs.
Final Thought
The same biases that cause missteps in everyday games subtly weave into professional environments. By studying how decisions falter in low-stakes settings, actuaries can better armor their models against similar misjudgments. Bias-awareness isn’t about eliminating heuristics—it’s about structuring processes that catch their side effects before they distort actuarial judgment.
