# Why Overconfidence Survives: The Evolutionary Case for Entrepreneurs

Cite as: Gorak, R. (2026). Why Overconfidence Survives: The Evolutionary Case for Entrepreneurs. Tradicted. https://www.tradicted.com/research/bernardo-overconfidence-2001/
Paper: Antonio E. Bernardo and Ivo Welch — *On the Evolution of Overconfidence and Entrepreneurs*
Published in: Journal of Economics and Management Strategy (2001)
Original: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=275516

Key finding: In a group of 500 individuals with signal precision p = 0.51, the expected group benefit of the first overconfident entrepreneur is approximately 114 times larger than the expected cost to that individual.

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Overconfidence is the tendency of individuals to overweight their own private information relative to public information. In a sample of 2,994 entrepreneurs, 81% believed their chances of success were at least 70%. In reality, about 75% of new businesses fail within five years. In On the Evolution of Overconfidence and Entrepreneurs, Bernardo and Welch (2001) model informational cascades where overconfident entrepreneurs broadcast their private signals. In a group of 500 with signal precision p = 0.51, one entrepreneur's group benefit is roughly 114 times the individual cost.

## What the Study Found

In a no-entrepreneur group with signal precision p = 0.51, the probability of an incorrect cascade approaches 48% even in large groups. The limiting probability of an incorrect cascade equals q²/(p²+q²) when the entrepreneur proportion is zero, and falls to qᵏ/(pᵏ+qᵏ) for any positive proportion. In a 500-person group with p = 0.51, the twentieth entrepreneur's marginal cost is 0.0035 against a 0.11 benefit to the other 499 members. In a contest with N = 100, p = 0.6, and k = 4, entrepreneurs rise from 7.5% to about 8.2% next generation. At p = 0.51, k = 12, and N = 100, entrepreneurs survive at only 1–2%, far below the group optimum of 0.425.

## Methodology

The paper develops a theoretical model rather than an empirical dataset, extending the informational-cascade framework with overconfident agents. It compares normal, fully rational individuals against entrepreneurs who underweight public information, and compares groups containing entrepreneurs against groups without them. Because no closed-form solution exists for the cascade probabilities, the authors compute comparative statics numerically using a state-time recursion. A group-selection displacement model then traces how the proportion of entrepreneurs evolves across generations.

## Key Statistics

| Metric | Finding | Context |
|---|---|---|
| Bad-cascade probability without entrepreneurs | Approaches 48% | Signal precision p = 0.51, large groups |
| Group benefit vs. individual cost | ~114× larger | N = 500, p = 0.51, first entrepreneur with k = 4 |
| Marginal cost vs. benefit of 20th entrepreneur | 0.0035 cost vs. 0.11 benefit | N = 500, p = 0.51, k = 4 |
| Surviving entrepreneur frequency | 1–2% vs. group optimum 0.425 | N = 100, p = 0.51, k = 12 |
| Social welfare function | E[V(λ)] = λ·E[V_OC(λ)] + (1−λ)·E[V_R(λ)] | λ = entrepreneur proportion; maximized to find λ* |
| Limiting incorrect-cascade probability | qᵏ/(pᵏ+qᵏ) for λ > 0 | q = 1−p, k = entrepreneur critical state |

## Why This Matters

The model offers a discipline for when behavioral assumptions like overconfidence belong in economic theory. It frames the bias as a positive externality. Individuals who follow their own signals pay a private cost but reveal information that rescues their group from herd-driven errors. Behavioral finance models that treat overconfidence as a primitive gain a survival mechanism that explains why the trait persists. The framework predicts overconfidence should be most useful in large groups, with low-precision information, and at moderate rather than extreme levels. Calibration requires comparing what you expected against what happened, across enough trades to matter. A [trading journal](/trading-journal/) keeps both.

## FAQ

### What is overconfidence in behavioral finance?

81% of 2,994 surveyed entrepreneurs believed their success odds were at least 70%, illustrating overconfidence — the tendency to overweight one's own private information. Bernardo and Welch (2001) contrast this with reality, where roughly 75% of new businesses fail within five years of founding.

### How large is the group benefit of overconfident entrepreneurs?

Approximately 114 times the individual cost, in the authors' benchmark calibration. With signal precision p = 0.51 and a group of 500, the first entrepreneur's group benefit far exceeds their expected loss. Their dissent breaks the poor information aggregation in the herd.

### When does overconfidence help a group the most?

0.425 is the group-optimal entrepreneur proportion in one extreme-overconfidence case, but the optimum is highest with large groups, low-precision information, and moderate overconfidence. The optimal proportion falls as overconfidence becomes extreme, because the extra information arrives only after the public state is already informative.

### How does overconfidence survive evolutionary selection?

7.5% to 8.2% was the single-generation rise in entrepreneur share in one contest with N = 100, p = 0.6, and k = 4. Group selection counterbalances individual selection because the fitter group displaces its rival. Group benefits can exceed individual costs by two orders of magnitude.

## Source

Bernardo, A. E., & Welch, I. (2001). On the Evolution of Overconfidence and Entrepreneurs. Journal of Economics and Management Strategy.

[Read the full paper →](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=275516)