Aim Above Average, Not for the Moon: New Model Shows Moderate Ambition Wins
Researchers from the University of Wyoming, Stanford and the University of Colorado Boulder built a simple mathematical model that shows people achieve the best outcomes when they set goals slightly above the average, but avoid extreme over‑ambition.
Why Researchers Replaced “Shoot for the Moon” with “Above‑Average” Targets
The model treats individuals as agents with a reward threshold. Agents reject offers below their threshold and accept those that meet or exceed it. By varying thresholds and reward distributions, the team discovered that agents who set modestly higher‑than‑average targets consistently outperformed both under‑ambitious and overly ambitious peers.
Quantitative Findings: How Modest Over‑Ambition Beats Extremes
- Optimal satisfaction occurs when the threshold is above the mean reward but not far beyond it.
- Agents with thresholds far above the mean performed worse than those whose thresholds were equally far below the mean.
- In a related marathon study cited by the authors, merely asking runners to set a goal boosted performance by 13.5%, equivalent to a nine‑year age advantage for a 42‑year‑old.
Implications for Careers, Housing, and Policy Decisions
The insights apply to real‑world choices such as salary negotiations, apartment hunting, and economic policy. Over‑ambitious expectations can lead to chronic dissatisfaction, especially when individuals compare themselves only to the highlight reels posted on social media. By recognizing the full range of possible outcomes, decision‑makers can set realistic, slightly above‑average goals that maximize satisfaction.
What the Model Predicts for Future Goal‑Setting Strategies
Future research will likely explore how dynamic thresholds adapt to changing environments and how social information influences perceived reward distributions. For now, the authors advise a pragmatic tweak to classic motivational slogans: aim a little lower than the moon, shoot for the stars you can actually see.