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Short answer: A 2018 computer simulation suggests that luck can strongly affect who ends up most successful, even when talent matters. It does not show that real wealth is “just chance.” The authors modeled simulated individuals, not actual wealthy people, and their result depends on the rules built into the model.
What the study actually found
In “Talent vs Luck: the role of randomness in success and failure,” Alessandro Pluchino, Alessio Emanuele Biondo and Andrea Rapisarda use an agent-based model to explore how talent and random events might combine to shape accumulated success. An agent-based model represents individuals as simulated agents with assigned characteristics; they experience events according to rules set by the researchers.
In the model, talent is not irrelevant: the authors say some degree of it is necessary for success. But talent alone does not determine who reaches the top. Randomly favorable or unfavorable events can affect an agent’s path and accumulated capital, allowing a less talented but luckier agent to overtake a more talented one. The authors’ conclusion is about outcomes generated by their simulation, not a measured pattern among real people.
Why “it’s just chance” is too strong
The provocative headline is a useful question, but its literal answer overstates the paper. The model proposes randomness as one ingredient in unequal outcomes; it does not establish that real fortunes are determined only by luck. Nor does it measure how much of any actual person’s wealth comes from talent, opportunity, inheritance, institutions, effort or chance.
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The paper treats wealth as a proxy for success and explores one possible mechanism for producing unequal accumulated outcomes. That is different from showing that this mechanism explains real-world wealth inequality. A simulation can demonstrate what follows from its assumptions; it cannot, by itself, establish that those assumptions capture the full causes of observed fortunes.
What kind of evidence is this?
The work is a research model, not a survey, field experiment or longitudinal study of real people’s wealth. The paper appeared in Advances in Complex Systems, volume 21, issues 3–4, as article 1850014; the peer-reviewed publication date is July 27, 2018. The arXiv record lists an initial submission on February 20, 2018 and a revision on July 9, 2018. Read the paper on arXiv.
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The authors’ University of Catania page provides a preprint and a link to the model’s NetLogo code, useful for examining how the simulation was set up. Access to code helps readers inspect a model; it is not independent empirical validation of its conclusions.
What the model may—and may not—mean for money
For understanding unequal outcomes
The simulation offers a way to think about how small random advantages and disadvantages can compound over time. It challenges the idea that the most successful person must also be the most talented. But it should be treated as a possible mechanism to investigate, not a complete account of how actual wealth is created or distributed.
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For funding decisions
The authors discuss implications for research funding: if chance influences success, spreading opportunities across a broader pool could be worth considering rather than concentrating resources solely on those judged most talented. That is a policy hypothesis suggested by a simulation, not proof of a universal funding rule or a tested prescription for personal investing.
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This paper cannot diagnose an individual’s finances or explain why one particular person has more or less money. Its narrower point is that talent may matter without guaranteeing the highest outcome, and that random events can shape success in the model. That is a useful caution against treating wealth as a simple scoreboard of intelligence—but not evidence that wealth is merely luck.
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MIT Technology Review’s 2018 article introduced the result with the “just chance” framing while identifying it as a computer model. Read the article.
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