Summary: A collaborative mathematical study reconciled conflicting pieces of cultural advice by mapping the exact parameters of human ambition. The research utilizes sequential search modeling to prove that optimal success occurs when individuals set a satisfaction threshold that is strictly above average but strictly finite.
The model unmasks a severe structural asymmetry in decision-making: setting an ambition threshold too high is far costlier to personal or professional performance than setting it too low by an identical margin, establishing that chronic over-satiation is mathematically more damaging than being easily satisfied.
Key Facts
- The Geometry of Strategic Search: Researchers investigated a model focused on finding the best available strategy, a framework designed to generalize across real-world dynamics including career choice, entrepreneurship, romantic partnerships, and political campaigns. At each step of the chronological search timeline, the actor must decide whether to settle for their current asset or expend resources to continue searching.
- The Mathematical Law of Moderation: Led by Dr. Kath Landgren of Stanford’s Doerr School of Sustainability, the team mathematically validated the intuition behind classical folk wisdom. The proofs demonstrate that individuals maximize their average outcomes not by “shooting for the moon,” but by targeting a precise middle ground: aiming higher than the baseline average while strictly avoiding the pursuit of perfection.
- The Left-Skewed Ambition Surge: The study demonstrated that the statistical distribution of environmental outcomes should radically shift an individual’s target baseline. When real-world scenarios are left-skewed, meaning catastrophic, way-below-average failures are statistically more common than historic windfalls (such as in national macroeconomic policymaking, where recessions outsize booms)—individuals must minimize risk while simultaneously increasing their ambition relative to the average.
- The Right-Skewed Entrepreneurial Paradox: Conversely, when environments are right-skewed—meaning a tiny handful of elite “unicorns” artificially inflate the mathematical average (such as in venture capitalism and billionaire wealth creation), individuals should actually lower their ambition relative to that distorted average. This exposes a vital biological divergence between healthy risk-taking and uncalibrated, discouraging expectations.
- The Systemic Cost of Upward Social Comparison: The mathematical model exposed that evaluating success exclusively against top-performing peers causes human performance to drop substantially. Co-author Ryan Langendorf notes that focusing solely on the curated highlight reels of elite peers, a phenomenon heavily amplified by modern social media algorithmic design, creates chronic dissatisfaction and forces actors to consistently pass up highly optimized, achievable rewards.
- Empirical Validation Across Human Datasets: To test the real-world predictability of their model, titled “Optimal ambition in business, politics, and life,” the authors cross-referenced it with actual human behavioral data spanning online dating metrics, university applications, swing-state polling, and wealth distribution. In several domains, human behavior mirrored the math; online daters, for instance, naturally concentrate their messaging resources on partners who register as only slightly more desirable than themselves.
Source: University of Wyoming
How ambitious should you be? Folk wisdom offers conflicting advice: “Shoot for the moon,” but also, “Don’t let the perfect be the enemy of the good.”
A new study from researchers at the University of Wyoming, Stanford University and the University of Colorado-Boulder used a mathematical model to show that ambition lies in the middle — above average but finite.
“Conventional wisdom tells people not to settle, but also not to let the perfect be the enemy of the good,” says lead author Kath Landgren, a postdoctoral scholar at Stanford’s Doerr School of Sustainability. “We wanted to see whether the math actually supports that intuition. It does, with some interesting twists.”
The researchers studied a model of searching for the best available strategy — where a strategy could represent anything from a job to a business idea, to a romantic partner, to a public policy or political campaign. At each time step, the searcher either settles for what they’ve already found or they keep searching.
The researchers proved that people achieve the best results, on average, when they use a satisfaction threshold that is strictly above average, but also strictly finite. In other words: Aim higher than average, but don’t shoot for the moon. The researchers also found that setting the threshold too high is costlier than setting it too low by the same amount. In other words, being too hard to satisfy is worse than being too easy to satisfy.
The study, “Optimal ambition in business, politics, and life,” appears today in the journal Physical Review E.
The study looked at how the statistical distribution of possible outcomes should change one’s ambition. When outcomes are rugged (less correlated from one attempt to the next) or left-skewed (way-below-average outcomes are more common than way-above-average ones), people should be more ambitious compared to the average. When outcomes are right-skewed (way-above-average outcomes are more common) — as in entrepreneurship, where a few “unicorns” pull the average up — people should actually be less ambitious relative to that inflated average.
“This shows a counterintuitive but important difference between ambition and risk-taking,” says co-author Matt Burgess, an assistant professor of economics at UW. “When outcomes are left-skewed, like in economic policymaking, where recessions are larger than booms, you should avoid risks, but you should be more ambitious compared to average. You shouldn’t let the large recessions drag down your growth target for a typical year. It’s the opposite in entrepreneurship: You want to take risks but also not be discouraged if you don’t become the next billionaire.”
The study also found that upward social comparison is costly. When people judged their success only in comparison to peers who were doing better than they were, their performance dropped substantially in the model. They were chronically dissatisfied and missed achievable rewards.
“Upward social comparison sets us up for disappointment,” says co-author Ryan Langendorf, a lecturer at the University of Colorado Boulder. “It’s great to be inspired by others’ accomplishments, but focusing only on our most successful peers distorts our view of what’s achievable. This is especially true with social media, where we mostly see each others’ curated highlight reels.”
The researchers illustrated their results using real-world data from online dating, college applications, U.S. economic growth, billionaire wealth and 2020 swing-state polling. In several cases, people’s actual behavior closely tracks the model’s predictions. For example, online daters concentrate their messaging on partners just slightly more desirable than themselves.
The researchers emphasized that their model is simpler than real-world decision-making, but they argue that its core insights broadly generalize.
“We lack complete information in most everyday decisions,” Landgren says. “Our work offers a precise but accessible way to think about how ambitious you want to be in different contexts.”
Key Questions Answered:
A: The math proves that being a perfectionist is far more damaging to your success. The model revealed a severe asymmetry: setting your expectations too high is significantly costlier than setting them too low by the exact same amount. Being too hard to satisfy causes you to experience chronic disappointment and routinely throw away excellent, highly optimized opportunities in pursuit of a statistical mirage.
A: Because market averages in entrepreneurship are completely distorted by right-skewed data. A tiny handful of “unicorn” companies and hyper-visible billionaires pull the mathematical average sky-high, far past what is typical or realistic. If you calibrate your personal ambition directly to that inflated average, you set yourself up for immediate discouragement, whereas the correct mathematical path is to embrace the risk of the venture but target a grounded, finite reward threshold.
A: It drastically degrades your performance by warping your view of what is achievable. When you engage in upward social comparison, measuring your value solely against peers who are performing better than you, your ability to make smart decisions drops sharply in the model. You become chronically dissatisfied, lose your grip on reality, and end up missing out on real, achievable rewards right in front of you.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this math modeling and ambition research news
Author: Chad Baldwin
Source: University of Wyoming
Contact: Chad Baldwin – University of Wyoming
Image: The image is credited to Neuroscience News
Original Research: Closed access.
“Optimal ambition in business, politics, and life” by Ekaterina Landgren, Ryan E. Langendorf, and Matthew G. Burgess. Physical Review E
DOI:10.1103/dfw8-vhjk
Abstract
Optimal ambition in business, politics, and life
In business, politics, and life, folk wisdom encourages people to aim for above-average results, but not to let the perfect be the enemy of the good.
Here, we mathematically formalize and extend this folk wisdom. We model a time-limited search for strategies having uncertain rewards. At each time step, the searcher is either satisfied with their current reward or continues searching.
We prove that the optimal satisfaction threshold is both finite and strictly larger than the mean of available rewards—matching folk wisdom. This result is robust to search costs, unless they are high enough to prohibit all search.
We show that being too ambitious has a higher expected cost than being too cautious. We show that the optimal satisfaction threshold increases if the search time is longer, or if the reward distribution is rugged (i.e., has low autocorrelation) or left-skewed. The skewness result reveals counterintuitive contrasts between optimal ambition and optimal risk-taking.
We show that using upward social comparison to assess the reward landscape substantially harms expected performance. We show how these insights can be applied qualitatively to real-world settings, using examples from entrepreneurship, economic policy, political campaigns, online dating, and college admissions.
We discuss implications of several possible extensions of our model, including intelligent search, reward landscape uncertainty, and risk aversion.
