Based on Smaldino & McElreath (2016), “The Natural Selection of Bad Science”
Introduction: Fraud Doesn’t Begin With Villains—It Begins With Incentives
When people imagine scientific fraud, they picture caricatures:
a rogue scientist faking data to become famous; a malicious figure cooking numbers for personal gain.
Reality is much more subtle—and much more disturbing.
Fraud evolves.
Just like biological traits arise in response to environmental pressures, fraudulent behaviors arise within scientific ecosystems because those behaviors confer competitive advantage under certain institutional conditions. Smaldino & McElreath’s paper argues that the same incentives that select for poor methodological rigor also select for increasingly bold forms of cheating.
This evolutionary view challenges the idea that individual misconduct is the root of the crisis. Instead:
Fraud is an adaptive response to misaligned incentives, not a personal flaw in an otherwise healthy system.
In this post, we explore:
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How small methodological shortcuts evolve into systemic fraud
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Why fraud emerges even among people with good intentions
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Historical and modern examples of fraud evolution
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How fraudulent strategies spread within academic lineages
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Why policing fraud is so difficult in an ecosystem selecting for it
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And critically: how we can redesign incentives to prevent fraud’s natural selection
1. The Continuum of Cheating: From Innocent Flexibility to Full Fabrication
Fraud rarely begins with outright forgery. It evolves gradually through a dangerous continuum:
Stage 1: Innocent flexibility
Researchers try multiple statistical models because “they want to understand the data better.”
Stage 2: Selective reporting
Negative results are dropped “because the journal won’t accept them.”
Stage 3: HARKing (Hypothesizing After Results are Known)
Researchers rewrite hypotheses post-hoc to match significant results.
Stage 4: Data massaging
Removal of outliers that “don’t make sense,”
or reclassifying categories to achieve significance.
Stage 5: Fabrication-lite
Inventing a few missing values, adjusting means slightly, or copying data points to “fix noise.”
Stage 6: Full data fabrication
Creating entire datasets from imagination.
The key insight is this:
At each step, competitive advantage increases while detection risk remains low.
Evolution always favors strategies with the highest payoff relative to cost.
Misaligned incentives—publish or perish, novelty over accuracy, prestige over honesty—act as selective pressures moving individuals along this continuum.
2. Why Good People Drift Toward Bad Science
People do not enter science as cheaters. They enter as idealists.
But as evolutionary biologists know, behavior adapts to the environment.
Three forces push researchers toward unethical behavior:
2.1 Selection for productivity over accuracy
A researcher who produces 12 papers a year—thanks to flexible methods or data manipulation—is more likely to get:
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job offers
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grants
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tenure
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speaking invitations
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media attention
Meanwhile, the careful, slower researcher is deemed “less productive.”
This is pure Darwinian selection, not moral selection.
2.2 Lack of punishment mechanisms
In nature, cheating thrives when policing is absent.
In academia:
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Fraud detection rates are extremely low
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Replication rarely occurs
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Retractions are rare and slow
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Institutions protect successful researchers
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Journals avoid scandals to protect their reputation
Low policing + high reward = the perfect conditions for cheating to thrive.
2.3 Cognitive dissonance and rationalization
Once minor cheating yields rewards, researchers begin to rationalize:
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“Everyone does it.”
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“The result is basically true.”
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“This helps me survive until tenure.”
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“The reviewers won’t understand anyway.”
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“I know the effect is real—I just need cleaner numbers.”
This psychological lubricant allows unethical behavior to seem justified.
3. Fraud Evolves Because It Works
Smaldino & McElreath’s core argument is simple and devastating:
The system selects for those who succeed—not those who are honest.
Every generation of researchers learns from the successful.
And who is successful?
The ones who:
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publish frequently
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produce flashy claims
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get into prestigious journals
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secure big grants
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attract media coverage
If these successes are achieved through questionable practices, then those practices become heritable—not genetically, but culturally, through lab training and mentoring.
4. Fraud Spreads Through Academic Lineages
Just as biological traits spread through reproduction, research practices spread through academic genealogy.
4.1 “Descendants” adopt their mentors’ strategies
If a PI produces statistically improbable results regularly, their trainees absorb:
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their data methods
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their publication strategies
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their analysis shortcuts
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their attitude toward p-values and significance
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their tolerance for exaggeration
This creates academic “lineages” with distinct methodological cultures.
Evidence: David L. Stern & the “lineage effect”
Stern’s work showed that even fruit flies inherit certain behavior patterns culturally across generations.
Labs do too.
Good practices and bad practices propagate through lineages.
4.2 Fraud clusters geographically and institutionally
Just like infections spreading in populations, fraud patterns cluster:
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similar manipulation techniques
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same statistical artifacts
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same impossible distributions
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same writing styles
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same figure duplications
These clusters reveal that fraud is not random—it is learned.
5. Historical Examples: Fraud Evolution in Action
Fraud is not new, but it is increasingly detected in clusters, consistent with evolutionary models.
5.1 The Cyril Burt IQ scandal
Cyril Burt published “twin studies” claiming extremely high heritability of intelligence.
Later, investigators found:
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nonexistent coauthors
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fabricated correlations
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copied data patterns
For decades, he thrived.
His fraudulent work shaped education policies.
And his students inherited his methods.
This is classic evolutionary propagation: successful phenotype → expanded lineage.
5.2 Anil Potti (Duke University cancer genomics)
Potti published numerous high-impact cancer biomarker papers.
Later:
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analyses showed fabricated patient data
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bioinformatic methods were manipulated
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clinical trials were influenced
His lab’s success created a generation of scientists trained on toxic practices.
5.3 Diederik Stapel (Social psychology)
Stapel produced extremely clean datasets that were “too perfect.”
His fraud persisted because:
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he trained students with similar data expectations
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his results matched reviewers’ theoretical biases
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replication was rare
The ecosystem protected him.
5.4 Yoshinori Watanabe (Cell biology)
Watanabe’s lab was caught manipulating blots and fluorescence images.
Investigations revealed:
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systemic training in visual data manipulation
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multiple students involved
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institutional reluctance to punish
Fraud had become a lab culture, not individual misconduct.
6. Why Fraud Thrives in the Current Scientific Ecosystem
Fraud spreads because the ecosystem selects for it.
Smaldino & McElreath highlight several systemic pressures:
6.1 Lack of replication removes constraints
Replication is the natural predator of fraud.
But when replication is rare, fraud proliferates.
6.2 High competition intensifies selective pressure
When survival depends on out-producing rivals:
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statistical flexibility becomes adaptive
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selective reporting becomes strategic
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fabrication becomes tempting
This is akin to bacteria evolving antibiotic resistance under selective pressure.
6.3 Journals reward “too good to be true” results
Fraudsters know what reviewers want:
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large effect sizes
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perfect curves
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clean p-values
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dramatic conclusions
This mirrors sexual selection in nature: whatever trait is preferred, individuals evolve to exaggerate it.
6.4 Institutions protect high-performers
Universities benefit from:
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prestige
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funding
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high-impact publications
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media attention
They often resist investigating misconduct because the fraudster benefits the institution.
This is group-level incentive misalignment.
7. The Fragility of Policing Mechanisms
Unlike biological evolution, which often has built-in constraints, scientific culture has weak policing:
7.1 Journal peer review rarely checks raw data
Reviewers lack time, expertise, or access.
7.2 Retractions take years
Retraction Watch tracks retractions that took decades.
7.3 Whistleblowers face retaliation
Whistleblowing can destroy careers.
7.4 Detection methods lag behind fabrication techniques
For instance:
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easy digital manipulation of images
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generative AI for synthetic data
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deep statistical obfuscation
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complex bioinformatic pipelines
The result:
Fraud evolves faster than policing.
8. Can Fraud Ever Be Eliminated? Evolutionary Theory Says No—But It Can Be Minimized
In natural ecosystems, cheating strategies never disappear entirely.
But they can be controlled by making cheating:
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less rewarding
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more risky
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more detectable
The same must be done in science.
8.1 Increase the cost of cheating
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mandatory raw data and code availability
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unblinded access to analysis pipelines
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random replication audits
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statistical anomaly detectors
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funding agency spot checks
8.2 Reduce the rewards
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prioritize quality over quantity
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reward transparency
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value incremental progress
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shift journal prestige toward replicable work
8.3 Enhance policing
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fast-track retractions
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strong whistleblower protections
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specialized forensic-statistics units
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replication consortia to investigate suspicious papers
8.4 Change cultural expectations
The real transformation begins when lab culture shifts from “show me significance” to “show me validity.”
Conclusion: Fraud Is Not a Disease of Individuals—It Is an Evolutionary Outcome of the System
This is the most sobering conclusion of Smaldino & McElreath’s work:
Fraud is inevitable in a system that rewards fraudulent strategies.
Unless incentives change, the evolution of scientific misconduct will continue—and accelerate.
Fraud is not merely a failure of morality.
It is a failure of ecology.
A failure of institutional design.
A failure of evolutionary pressures.
We can restore integrity only by reshaping the selective landscape so that:
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honesty becomes adaptive
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replication becomes central
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transparency becomes mandatory
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quality becomes rewarded
Only then will the evolution of fraud slow—and perhaps stabilize at manageable levels.
