Wednesday, February 4, 2026

Post 6 -- The Ecology of Modern Science: Competition, Cooperation, and Collapse

Based on Smaldino & McElreath (2016), “The Natural Selection of Bad Science”

Introduction: Science as an Ecosystem—But a Degraded One

If you walk into a rainforest, you witness dynamic interactions: predator and prey, mutualism, competition, niche partitioning, evolutionary trade-offs. Ecology teaches us that systems adapt—but not always toward greater “goodness”. Sometimes they adapt toward survival shortcuts, parasitism, invasive dominance, or collapse.

Modern science behaves very much like such an ecosystem. This is the argument that sits at the heart of Smaldino & McElreath’s 2016 paper: research institutions do not select for truth-finding efficiency; they select for strategies that maximize professional survival, often at the cost of scientific integrity.

In this post, we step away from equations and instead interpret the paper through a broader ecological lens. We ask:

What “species” exist in the academic ecosystem?
What competition pressures distort adaptation?
Why does “cheating” (or corner-cutting) evolve so naturally?
How do these pressures produce runaway selection for low-quality research?

Let’s explore.

1. The Scientific Ecosystem: Who Lives Here?

Ecologists categorize organisms by roles—producers, consumers, decomposers. Science has its own functional guilds:

1.1 Explorers (slow, careful, high-quality)

These align most closely with the ideal of science:

thoughtful hypothesis construction
rigorous statistical reasoning
careful replication
incremental but robust discoveries

In the analogy, they are slow-growing trees—deep roots, solid wood, long lifespan.

1.2 Exploiters (fast, flashy, low-quality)

These labs or researchers produce:

many papers per year
flashy statistical significance
weakly designed experiments
exaggerated statements
irreproducible claims

Ecologically, they resemble invasive species—quick growth, low resource investment, rapid colonization.

1.3 Predators (journals, rankings, funders)

Predators shape prey behavior. Journals and funding agencies exert:

aggressive selection for novelty
“predatory” attention toward surprising results
pressure to publish frequently
biases toward positive results

They don’t “eat” scientists literally; they consume scientists’ time, energy, and incentives.

1.4 Scavengers (meta-analysts, critics, reformers)

They pick up the remains:

replication failures
systematic reviews of conflicted data
post-mortems of entire research fields

They recycle waste—an essential role, but one overwhelmed by the scale of what must be cleaned.

You can begin to see already why problems emerge: fast-growing invasive species outcompete slow-growing trees when the environment rewards speed over durability.

2. Environmental Pressures: The Selective Forces Distorting Science

In ecology, environmental pressures shape evolutionary direction. In academia, the environmental pressures include:

2.1 Publish-or-perish metrics

This is the strongest selection force.

Tenure depends on publication count.
Grants depend on publication count.
Promotions depend on publication count.

Slow, careful, thoughtful (but fewer) papers lose to fast, frequent, flashy output.

2.2 Journal prestige as habitat quality

Top journals function like patches of high-quality habitat with limited space. The individuals that reach them are often those who:

exaggerate novelty
optimize statistically for significance
oversell or overspeculate

Slow, cautious, nuanced research often cannot thrive in these patches.

2.3 Grant funding as a limiting resource

Like food scarcity in an ecosystem, scarce funding leads to:

fierce competition
favoritism for risky, sexy, newsworthy ideas
penalties for “boring” but necessary replication

2.4 Career bottlenecks: Postdoc → Faculty transition

This bottleneck creates evolutionary sweeps:

only the most prolific survive
survival probabilities depend on output speed
quality becomes less relevant
risk-taking (in the statistical sense) is rewarded

Together, these pressures create a landscape where invasive strategies thrive.

3. Evolutionarily Stable Strategies: Why Bad Practices Survive

In ecology, an evolutionarily stable strategy (ESS) arises when a strategy, once common, cannot be outcompeted by alternatives.

In modern academia, the ESS is distressingly simple:

Produce as many statistically significant, novel results as possible using minimal time per project.

This ESS is not in line with truth discovery. But once adopted widely, it is difficult to reverse because:

3.1 Slow science loses competitions

Careful labs never reach the publication numbers of fast labs. So they fail in grant competitions and hiring rounds.

3.2 Reputation becomes decoupled from truth

A lab that publishes 15 papers a year appears more “successful” than one that produces two carefully validated papers.

3.3 The ecosystem becomes “locked in”

When every institution measures success using the same metrics, every participant must adapt or perish. Even well-meaning, careful scientists are forced to play the game or risk extinction.

4. Ecological Collapse: What Happens When Bad Science Takes Over?

When an ecosystem is dominated by opportunistic invaders, you get collapse:

soil nutrient loss
biodiversity crashes
long-term resilience disappears

In science, the analogs are:

4.1 Replicability crisis

Field after field demonstrates:

low reproducibility
inflated effect sizes
contradictory results
entire literatures built on fragile foundations

4.2 Epistemic pollution

Low-quality publications accumulate like pollution:

meta-analyses become impossible
true effects are masked
pseudoscience gains legitimacy
real progress becomes slower

4.3 Career disillusionment and attrition

Talented scientists burn out when forced to compete on quantity rather than quality.

4.4 Loss of public trust

When the public sees contradictory findings, fraud scandals, and frequent retractions, trust erodes.

This is the scientific equivalent of ecological desertification—once the soil is lost, recovery is extremely hard.

5. Ecological Anecdotes That Mirror Academic Dysfunction

Anecdote 1: The cane toad (Australia)

Introduced to control beetles, the cane toad multiplied explosively and destabilized ecosystems. It adapted well to the incentives but generated harmful outcomes.

Academic parallel:
Inventing “impact factor” was like introducing cane toads. It solved one problem but introduced many more.

Anecdote 2: The chestnut blight fungus (North America)

A fast-growing pathogen wiped out slow-growing, foundational species.

Academic parallel:
Fast-publication labs crowd out foundational, rigorous labs.

Anecdote 3: The tragedy of the commons

Each individual herder benefits from adding more cattle, but collectively they destroy the pasture.

Academic parallel:
Each scientist benefits individually from publishing more—even low-quality papers.
Collectively, academia becomes a wasteland of irreproducible findings.

6. The Paper’s Core Claim in Ecological Terms

To recast Smaldino & McElreath in ecological language:

The incentives of modern academia create a habitat where invasive, fast-replicating research strategies thrive, driving out slow, careful, high-quality science through natural selection.

This is not moral failure, individual laziness, or corruption.
It is ecological inevitability under the current environment.

7. Toward a Restoration Ecology of Science

If we think like restoration ecologists, what interventions help restore ecosystems?

7.1 Change the selective environment

reward replication
reward transparency
reward null results
reduce dependence on publication count

7.2 Diversify habitats

establish journals that value careful, long-term research
create grant categories for incremental or confirmatory work

7.3 Reintroduce apex predators

Predators regulate ecosystems. In science, the predators could be:

replicability audits
statistical screening tools
meta-analytic policing
data availability requirements

These would eat away at low-quality work.

7.4 Create refugia for slow science

Institutions like the IAS (Princeton) or EMBL partially serve this role by giving scientists time without pressure to produce.

7.5 Facilitate succession

Allow the ecosystem to shift toward more stable, long-lived scientific strategies.

Conclusion: Science Needs Ecological Thinking

The ecosystem analogy is powerful because it reframes the conversation away from blaming individuals and toward understanding systemic evolution.

In ecology, systems adapt to whatever pressures exist. If the pressures reward destructive behaviors, destructive organisms thrive.
The same is true in academia.

Smaldino & McElreath’s insight is that bad science is not an accident—it is the product of natural selection in a distorted environment.

To fix science, we must change the environment.

Tuesday, February 3, 2026

Post 5 — How Scientific Fields Collapse: Lessons from Psychology, Genomics, Economics, and Cancer Research

One of the most uncomfortable insights in Smaldino & McElreath’s The Natural Selection of Bad Science is that scientific collapse is not an anomaly. It is a recurring, predictable, evolutionary outcome — a form of cultural extinction event triggered by misaligned incentives. Fields don’t collapse because bad people ruin them. Fields collapse because adaptation to the wrong incentives gradually hollows them out from the inside.

This post examines why entire scientific disciplines sometimes enter periods of crisis, retrenchment, or mass retraction — and why these collapses follow predictable patterns. We will walk through four influential examples:

Social Psychology and the Priming Crisis
Early Genomics and the Biomarker Bubble
Macroeconomics and the Austerity Shock
Cancer Biomarkers and the Reproducibility Meltdown

These case studies reveal the same evolutionary dynamics in action:

Incentives reward discoverability, not verifiability.
Labs evolve toward high-output, low-effort strategies.
Hype cycles amplify low-quality discoveries.
Replication is too slow and too weak.
The field enters an ecological collapse where noise drowns signal.

Understanding these collapses isn’t just historical curiosity — it’s a blueprint for diagnosing the health of scientific ecosystems today.

1. What Does It Mean for a Scientific Field to “Collapse”?

A scientific collapse is not a sudden event. It is a long, slow attrition of reliability.

You know a field is collapsing when:

Replication rates fall below noise levels.
Key findings become unstable or contradictory.
Statistical tools are misused and normalized.
Methodological shortcuts become standard.
Top journals reward surprising results over robustness.
Industry partners lose trust in the field’s output.
Foundational theories must be rewritten or abandoned.

Collapses often culminate in:

mass retractions
major methodological reforms
devastating replication studies
an exodus of credibility
a shift in intellectual prestige to competing disciplines

The model by Smaldino & McElreath predicts exactly this process:
low-effort strategies multiply faster than corrective mechanisms can contain them.

Eventually, reliability becomes unsalvageable and the field must rebuild from scratch.

2. Case Study 1: Social Psychology and the Priming Collapse

Few scientific collapses are as famous — or as thoroughly documented — as the downfall of social priming research in the 2000s.

The Incentives

Publish cute, surprising results.
Use small samples (cheap, fast).
Don’t preregister — flexibility helps results appear significant.
Maximize media attention.

This created the ideal evolutionary environment for:

low-effort experiments
flexible analysis (“the garden of forking paths”)
inflated false positives
publication bias

Psychologists weren’t malicious — they were adapting to their environment.

The Peak of the Bubble

Between 1995 and 2010, dozens of sensational papers emerged:

Priming people with the elderly stereotype made them walk slower.
Thinking about money made one less social.
Subtle cues could alter voting patterns.

Journals loved it. TED Talks loved it.
It was a golden age — for a while.

The Collapse

In 2011–2016:

Large-scale replication attempts failed spectacularly.
Many priming effects could not be reproduced even with much larger samples.
The field entered a crisis of credibility.

Daniel Kahneman, Nobel laureate, described the situation as:

“A train wreck.”

Kahneman advised priming researchers to “clean up their act,” but — as the model predicts — replication was too slow and too weak. The incentives remained unchanged for decades, allowing weak methods to evolve unchecked.

3. Case Study 2: Early Human Genomics — The Biomarker Bubble

Before GWAS became rigorous, the early 2000s saw a massive surge of “candidate gene” studies.

The Incentives

Link any gene to any disease with small samples.
Publish novel associations quickly.
Use lenient statistical thresholds.
Avoid replication because it’s expensive.

Small labs could produce dozens of “gene X predicts trait Y” papers each year.

The Outcome

A 2009 meta-analysis concluded:

“Over 90% of candidate gene associations were false.”

Why?

Because the field rewarded:

speed over sample size
novelty over rigor
positive results over null results
exploratory p-hacking over confirmatory research

The evolutionary analogy is clear:

Labs that produced splashy claims reproduced academically.
Labs that insisted on high effort died out.

The Collapse

By the mid-2010s, the field was forced to abandon most of its foundational claims.

GWAS later showed that:

Most common complex traits involve hundreds of genes.
Individual candidate genes rarely explain meaningful variance.
Many early associations were artifacts of population structure.

This collapse led to massive reallocations of funding and prestige.

But the damage was done: a decade of biomedical research had been built on unreliable foundations.

4. Case Study 3: Economics and the Reinhart & Rogoff Shock

Macroeconomics rarely faces replication pressure — yet it experienced one of the most famous data-driven collapses of the 21st century.

The Claim

A massively influential paper by Reinhart & Rogoff (2010) claimed:

Countries with >90% debt-to-GDP ratio experience sharply reduced growth.

This result justified widespread global austerity policies.

The Incentives

Prestigious economists influence policy.
Top journals favor macro-wide conclusions.
Replication datasets are often restricted.

This created an ecosystem where high-impact results multiplied without robust verification.

The Collapse

In 2013, a group of graduate students replicated the analysis and found:

Coding errors
Excluded countries
Incorrect weighting procedures
Statistical miscalculations

When the errors were fixed, the 90% threshold disappeared.

The Fallout

Despite the correction:

Policy consequences had already played out.
Countries had adopted austerity measures.
Billions in economic decisions were made based on flawed evidence.

According to Smaldino & McElreath’s logic:

Replication came after the selective advantage (policy impact) was realized.
Thus, replication had no evolutionary power.

R&R’s original paper remained highly cited.

The field quickly moved on without structural reform.

5. Case Study 4: Cancer Biomarker Research — A Structural Meltdown

The cancer literature provides one of the clearest real-world confirmations of the model.

A 2005 paper in Nature analyzed cancer biomarker studies and concluded:

88% of them could not be reproduced.

Eighty-eight percent.

This wasn’t a series of bad apples — it was systemic.

The Incentives

Pharma companies reward promising preliminary data.
Journals love breakthroughs.
Novel biomarkers attract enormous grants.
Clinical translation is slow, so failed replication is invisible.

Labs evolved toward maximum publication output:

flexible analyses
small sample sizes
no preregistration
selective reporting
low methodological effort

The Collapse

When industry groups attempted replication:

almost none of the biomarkers validated
many were statistical mirages
entire avenues of clinical trial design were affected

And yet, even after the collapse:

many labs continued to publish low-quality biomarker studies
replication remained rare
journals continued favoring novelty

Again, replication arrived too late and with too little force.

6. Why Collapses Follow Predictable Patterns

Across these fields, the same evolutionary mechanisms emerge:

(1) Incentives reward rapid, positive, surprising results.

This lowers the “effort threshold” for survival.

(2) Labs evolve low-effort, high-output strategies.

These labs have higher reproductive fitness.

(3) Noise accumulates faster than replication can eliminate it.

False positives proliferate exponentially.

(4) Replication attempts appear late, often after a field matures.

The ecosystem is already saturated with unreliable findings.

(5) Replication has weak punitive power.

Failed replications do not harm lab survival.

(6) The field hits a tipping point where signal-to-noise ratio collapses.

Once noise dominates, theory collapses.

(7) A painful reform period begins.

Reforms often include:

preregistration
large-scale consortia
stricter statistical norms
data sharing
high-powered studies
adversarial collaborations

This is the “ecological reset” phase — analogous to a burned forest slowly regrowing.

7. Why Some Fields Avoid Collapse

Not all fields collapse. Some stay robust.

Fields that avoid collapse tend to have:

1. Strong replication culture

(e.g., physics, some areas of chemistry)

2. Large, expensive experiments where p-hacking is impossible

(e.g., particle physics, astrophysics)

3. Community-wide data sharing

(genetics after 2010)

4. Strict statistical conventions

(e.g., neuroimaging after the “dead salmon” paper)

5. No reward for novelty without rigor

(e.g., clinical trial pipelines)

Fields with these traits experience slow, stable cumulative progress.

They are evolutionarily stable strategies under the model.

8. Warning Signs: Is a Field Approaching Collapse?

Based on the model and history, signs of impending collapse include:

rapid proliferation of positive results
high publication volume with small sample sizes
widespread p-values just below 0.05
low rates of data sharing
lack of preregistration
theoretical fragmentation (each lab has its own model)
replication studies consistently failing
large gaps between media claims and real effects
heavy reliance on “hidden moderators” to explain failures

If multiple signs appear simultaneously, the field may be entering a pre-collapse trajectory.

9. Lessons for Scientists Today

The collapses above are not moral failures.
They are adaptive responses to maladaptive incentives.

The key lesson:

If a field rewards volume over rigor, it will evolve toward low effort and eventually collapse.

Smaldino & McElreath capture this evolutionary truth with mathematical precision:
selection pressures shape scientific methods as surely as natural selection shapes beak sizes.

To preserve scientific integrity, we must shift fitness away from speed and novelty, and toward accuracy, transparency, and theoretical stability.

10. Coming Up Next

Post 6 — The Role of Hype Cycles: How Media, Journals, and Funding Agencies Accelerate the Spread of Bad Science

This post will explore:

Why hype amplifies low-quality discoveries
The social psychology of “breakthrough culture”
How journalists, TED Talks, and grant committees shape the evolution of scientific methods
Historical examples of hype-driven bubbles
How hype interacts with the evolutionary model in the paper

Monday, February 2, 2026

Post 4 — Why Replication Isn’t Enough: The Evolutionary Trap of Scientific Incentives

Among all the lessons in The Natural Selection of Bad Science, one strikes particularly hard: even strong replication efforts cannot, by themselves, reverse the evolutionary decline in scientific quality.

This is deeply unintuitive. Most scientists agree that the “replication movement” — from the Open Science Framework to large-scale reproducibility projects — is one of the most important reforms of the modern academic era. And yet, according to the model by Smaldino & McElreath, replication, even when well-funded and rigorous, cannot fix the fundamental evolutionary pressures that select for low-effort research.

This article explains why replication fails as a corrective mechanism, what the math shows, and how real-world scientific history aligns perfectly with the model’s predictions. We will also examine several case studies — from priming research to fMRI social neuroscience to cancer biomarker studies — that illustrate the “replication trap” in action.

1. Why People Think Replication Should Work

Replication seems like the perfect immune system for science.
If a result is false, just replicate it.
If it doesn’t repeat, discard it.

Simple.

But this reasoning assumes:

Replications are common.
Failed replications lead to consequences.
Low-quality labs suffer reputational damage.
The system rewards trustworthy results.

Unfortunately, none of these assumptions are true.

Replication is not the default. Science has no built-in self-correcting machinery.
It has the potential to self-correct, but only under the right pressures — and those pressures are currently too weak.

Smaldino & McElreath quantify this problem and show that:

Replication has only a tiny effect on the evolutionary trajectory of scientific methods unless it is extremely punitive to low-effort labs.

Which it rarely is.

2. The Model’s Logic: Replicators Cannot Compete with Producers

In the model:

Some labs specialize in production: quick studies, low effort, high false-positive rate.
Other labs specialize in replication: they repeat studies to verify their truthfulness.

What happens when we simulate a population containing both?

Result 1: Low-effort producers produce more papers and outcompete replicators.

Replicators:

publish less frequently
spend more time on confirmations
cannot generate flashy findings
rarely receive top-tier grants
don’t produce sensational media-worthy results

Meanwhile, low-effort producers:

publish frequently and visibly
get grants
train more students
create more academic successors
dominate institutional resources

If fitness = publication output, then:

Producers reproduce faster than replicators, causing replicators to be outcompeted.

This is identical to how parasitic strategies in nature can overwhelm cooperative ones.

3. Replication Has Almost No Punitive Power in the Real World

The model assumes that failed replications might harm a lab.

But in practice:

Replication failures are rarely published in high-impact journals.
Original authors face little consequence.
Failed replication papers get fewer citations.
Journals prefer novel claims over verification.
Null results are undervalued.
Universities don’t reward replication studies at promotion time.

Even when replication failures happen, authors often:

invoke “hidden moderators”
claim the field has moved on
suggest conceptual misinterpretation
publicly dispute the findings

Replication often becomes a public debate, not a correction.

The producer has already extracted career value from their original flashy result.
A failed replication five years later affects nothing.

Thus:

Replication does not reduce the reproductive fitness of low-effort labs.
So low-effort labs continue to grow.

4. The Mathematical Trap: Replication Pressure Is Too Slow

Another key point in the paper is about the time lag:

Low-effort labs can outrun replication

Because:

Replications take more time than flashy original studies.
Producers generate multiple new papers in the time it takes for one failed replication to emerge.
Low-quality labs can pivot quickly to new topics.
Replicators remain tied to verifying old problems.

This resembles Red Queen dynamics:

The replicators must run as fast as they can just to stay in place,
while low-effort labs sprint ahead unhindered.

5. Real-World Case Study #1: Social Priming

Few fields provide a better illustration of this dynamic.

Early 2000s psychology was full of:

very small sample sizes
flexible analysis pipelines
researcher degrees of freedom
surprising “cute” findings

Classic examples:

priming people with words related to old age makes them walk slower
holding a warm cup makes you judge people as kinder
thinking about money makes you less social

These studies were published because:

they were novel
statistically significant (p < 0.05)
quick to run
highly publishable in top journals

Replication attempts began years later.

By then:

Many of the original authors had built entire careers
The most famous results appeared in textbooks
High-impact journals resisted null replications
Tenure committees didn’t care about replication failures

Even after the field-wide replication crisis, many original researchers insisted the failures were due to:

cultural differences
subtle context shifts
experimenter effects
conceptual misunderstanding

This is exactly what Smaldino & McElreath’s model predicts:
the producers had already won the evolutionary race.

The immune system activated too late.

6. Case Study #2: fMRI Social Neuroscience and “Dead Salmon” Problems

In 2009, Bennett et al. famously showed that an fMRI analysis pipeline detected “brain activity” in a dead salmon.
The result: without rigorous correction, false positives ran rampant.

Did this humiliation lead to the downfall of low-effort fMRI studies?
Not really.

Labs kept publishing underpowered fMRI studies.
Multiverse analysis showed high false discovery rates.
The average fMRI sample size remained too small for years.
Replication attempts were rare and underfunded.

Why?
Because flashy fMRI studies:

made headlines
generated TED Talks
attracted major grants
produced visually compelling brain images

Replicators — who were slower and less flashy — were selected against.

7. Case Study #3: Cancer Biomarker Research

A 2005 paper in Nature found that 88% of cancer biomarker literature was irreproducible.

And yet:

The field did not collapse.
Labs continued producing low-quality biomarker studies.
Replication studies were not rewarded.
Novel positive results dominated publication incentives.

Companies and journals prefer exciting claims:

“New blood biomarker predicts cancer risk!”

—even if statistically flawed.

This creates the exact ecological environment where low-effort labs thrive.

8. Replication is Not Evolutionary Pressure — It is Ecological Feedback

A key conceptual error many scientists make is assuming replication will automatically shape behavior.

But in evolutionary terms:

Replication is post-hoc ecological feedback.
Evolutionary selection is determined by reproductive success.

If failed replication does NOT affect a lab’s reproduction (its ability to secure students, grants, jobs, tenure), then:

Replication has no power as a selective force.

For replication to matter evolutionarily, two things must happen:

(1) Failed replication must be strongly punished

– loss of grants
– loss of prestige
– loss of student recruitment
– slowing of lab growth

(2) Successful replication must be rewarded

– career advancement
– grant funding
– hiring and promotion credit
– institutional prestige

But the current system does none of this.

Thus, as the paper says:

“Replication alone will have little effect unless it affects the differential reproduction of labs.”

In plainer terms:

Scientists must lose by producing bad science, not merely be embarrassed by it.

9. Why Journals Defeat Replication

Even if replicators do their job perfectly, journals undermine their effect.

Replications are not glamorous

Science incentives promote “impact,” not verification.

Replication studies:

have lower citation potential
rarely produce new mechanisms or theories
do not attract media coverage
are harder to publish in top journals

Editors prefer:

breakthroughs
paradigm shifts
counterintuitive findings
novel experimental paradigms

This creates an asymmetry:

False positives have many outlets.
False negatives have few.

And asymmetry drives evolution.

10. Replication in Other Fields: A Historical View

The replication trap is not new.
It’s just more visible now.

A few examples:

Classical Anthropology

Margaret Mead’s controversial findings on Samoan adolescent sexuality were criticized by later ethnographers — but the replication attempts did not erase Mead’s influence.

Economics

Reinhart & Rogoff’s paper on national debt thresholds was debunked by replication.
Yet the original paper shaped global austerity policy for years.

Replication came too late.

Nutritional Epidemiology

Contradictory diet studies appear weekly.

Nobody replicates them because:

replication is expensive
null findings are unpublishable
dietary questionnaires are unreliable
flashy claims drive media coverage

The field evolves based on visibility, not reliability.

11. The Deeper Evolutionary Lesson

Replication is vital for truth — but weak for evolution.

Evolution does not reward truth-seeking.
It rewards success.

If the system rewards:

speed
quantity
novelty
media visibility

…then evolution will select for labs that maximize those traits.

Replication cannot stop this any more than the occasional predator stops a rapidly multiplying prey species — unless predation is intense and targeted.

This is the “evolutionary trap” of scientific incentives.

12. Can Replication Ever Work as a Corrective Force?

Yes — but only under certain extreme conditions:

1. Replications must be common.

(e.g., 10–20% of published studies should be replications)

2. Failed replications must have major career consequences.

(denial of grants, loss of institutional credibility)

3. Replicators must receive strong institutional and financial rewards.

4. Journals must give equal prestige to replications and novel findings.

5. Funding agencies must incentivize adversarial replication.

6. Pre-registration and transparency must be standard.

These policies would change the evolutionary calculus.

Labs that produce unreliable work would:

lose funding
lose recruits
decline in prestige
shrink
eventually disappear

Labs that produce reliable work would:

survive
reproduce
shape the next generation

Only then does replication become an evolutionary force.

13. Conclusion: Replication is Necessary — but Not Sufficient

Replication is essential for a healthy scientific ecosystem.

But it is not enough.

The model shows — and history affirms — that:

Replicators cannot win an evolutionary race against low-effort producers.
Replication pressure is slow, weak, and rarely punitive.
The incentive structure protects flashy producers.
Failed replications seldom harm careers.
Replications themselves are under-incentivized.

The core insight:

Replication cleans up messes, but does not prevent them.
Only incentive reform can prevent their creation.

In the next post, we will explore how scientific fields have historically collapsed under their own incentive structures — and what they teach us about the future.

Sunday, February 1, 2026

Post 3 — The Maths of Misaligned Incentives: Modeling the Evolution of Bad Science

In the first two posts, we discussed a striking proposition: modern scientific practices evolve under natural selection. Labs that publish more quickly have higher “fitness,” and traits that boost publication rates — even if they weaken the reliability of findings — spread across generations of scientists.

But how do we formalize such a claim?

Smaldino & McElreath didn’t simply rely on anecdotes or intuition. They constructed mathematical and computational models to test how scientific cultures evolve under different incentives. The results were stark:

When productivity (number of publications) is rewarded over accuracy, scientific rigor inevitably declines.

This decline isn’t slow. It’s rapid, predictable, and difficult to avoid without deliberate counter-selection.

In this post, we explore:

the core structure of their model,
how “lab traits” are encoded mathematically,
how selection pressure is simulated,
why low methodological rigor outcompetes high rigor,
and how these results mirror real-world science.

We’ll also use examples from population genetics, epidemiology, and cultural evolution to illustrate why the model behaves the way it does.

1. Why Model Science at All?

Science is complicated. Labs vary enormously:

Some are huge industrial-scale operations.
Others are tiny one-person groups.
Some fields can run 200 experiments per year.
Others take 2 years for a single dataset.

So why did the authors model scientific evolution using simplified assumptions?

Because models clarify causality.

Real scientific cultures are messy — but the underlying logic of incentives is simple. By stripping away the noise, the model reveals a core truth:

Even with well-intentioned scientists, a system that rewards quantity will select for low-quality methods.

This is the same reason evolutionary biologists model complex ecosystems using simple genetic or ecological equations: clarity emerges from abstraction.

2. The Core Entities of the Model: Labs, Traits, and Fitness

The model treats labs as the evolving unit — not individual scientists.

Each lab is defined by three key traits:

Effort (E)
How much methodological rigor the lab applies (large samples, careful controls, slow pace).
Higher effort → lower false positives but fewer studies per year.
Power (W)
The statistical power of research produced by the lab.
Higher power → more true discoveries, but more expensive studies.
Replication Rate (R)
How often the lab attempts to replicate existing findings.

Each trait comes with a cost:

Higher power → fewer total studies
Higher effort → even fewer studies
Higher replication → fewer original publications

Meanwhile, the scientific environment rewards total publication count, not accuracy.

That reward system is encoded into a fitness function.

3. Fitness = Publication Count × Reward Structure

A lab’s fitness in the model is determined by:

the number of studies it runs,
the probability each study yields a publishable result,
the reward associated with each result
(original positive > replication > null results).

Crucial point:

Positive results almost always produce higher rewards than nulls, regardless of truth.

Thus, labs face pressure to:

increase throughput,
maximize positive outcomes,
minimize time spent on replications,
avoid costly, high-effort research.

The fitness function creates a landscape where low effort and low power produce evolutionary advantages.

4. How Do Labs “Reproduce”?

Just as organisms with more offspring spread their genes, labs with high fitness:

train more students,
place more postdocs in new positions,
expand into new subfields,
receive more grant money,
split or spawn new labs.

In the simulation, “offspring” labs inherit their parent’s traits with slight mutations. This mirrors real-world mentorship:

A student leaves the lab carrying its habits — sample size norms, statistical techniques, even its attitudes toward novelty vs. rigor.

Over generations, labs that publish more (even unreliably) produce more academic descendants.
High-rigor labs produce fewer.

Thus, traits that maximize publication count spread.

5. The Model’s Heart: The Trade-Off Between Power and Productivity

Scientific studies have costs.

A high-power study (e.g., N = 200) provides robust statistical inference.
But it is expensive.

A low-power study (e.g., N = 10) is cheap and fast.

In the model:

Low power = many studies per year.
Many studies = more chances at positive results.
Positive results = more publications.
More publications = higher evolutionary fitness.

The logic is evolutionary dynamite.

Mathematically, the expected number of publishable results for a lab is:

Expected Positives = Number of Studies × P(Significant Result)

But P(Significant Result) includes:

true positives (depends on power & reality)
false positives (depends on effort & statistical standards)

This means:

Low power increases false positives → increasing publication output → increasing fitness.

This is the central paradox of modern science:

Low-power labs produce less reliable results,
but
Low-power labs produce more publishable results.

Thus low power is selected for.

6. Replication in the Model: A Weak Immune System

The model incorporates replication attempts. But there are incentives against replication:

Replications earn less prestige.
Replications of null results rarely get published.
Labs conducting replications lose precious time.

Mathematically, replications have:

lower reward,
lower impact,
higher cost.

Thus in the simulation:

Replication rates evolve downward.
Fields drift toward poor self-correction.

Even when replication is present, it cannot overcome the selection pressure toward low power unless the rewards for replication are radically increased.

This reflects real-world patterns:

Replications are rare in psychology, biology, and ecology.
Journals routinely reject replications.
Funding agencies rarely support them.

Replication becomes evolutionarily disadvantageous.

7. The Model’s Results: Decline Is Inevitable Under Current Incentives

After many generations in the simulation, labs evolve toward:

Low effort

(minimal methodological rigor)

Low power

(maximal throughput)

Low replication

(minimal time spent correcting errors)

This is not just one possible outcome. It is the stable outcome of the system.

The authors show that:

Even if all labs begin as high-quality, high-effort, high-power groups…
Evolutionary pressure rapidly degrades methods.
The average false-positive rate skyrockets.
Replication does not save the system.
High-quality labs go extinct (outcompeted by low-quality ones).

This reflects reality across many fields where:

underpowered studies dominate,
novelty outruns replication,
flashy claims outnumber reliable findings.

The model formalizes what many scientists intuitively observe.

8. Real-World Parallels: Why the Model Matches Reality

(a) Population Genetics Parallel

Low-power labs resemble advantageous alleles:

They reproduce more,
Their “offspring” spread,
They take over the population.

High-power labs resemble disadvantageous alleles:

They reproduce less,
They gradually disappear.

(b) Epidemiology Parallel

False positives behave like infectious agents:

They spread rapidly,
Transmission is easy,
Labs are susceptible hosts.

Replication is like an immune response:

Slow,
Underfunded,
Often neutralized by social pressures.

(c) Cultural Evolution Parallel

Just as religious rituals or political ideologies spread when they confer social benefits, bad scientific methods spread when they confer career benefits.

The parallels are mathematically and conceptually tight.

9. Why Good Intentions Do Not Change the Outcome

A key contribution of the paper is dispelling a myth:

Bad science is not the result of bad intentions.

Even if every scientist:

wants to uncover truth,
values rigor,
disapproves of p-hacking,

…the system pushes them toward:

cutting corners,
reducing sample sizes,
publishing prematurely,
avoiding replications.

This is why the authors emphasize:

“Good science will not survive unless good scientists are rewarded for doing good work.”

The mechanism is evolutionary, not ethical.

10. When Does Good Science Win? Rare but Possible

The authors simulated strong reforms:

high rewards for replication,
penalties for false positives,
mandatory methodological standards.

Under these artificial conditions, high-effort, high-power labs flourish.

This reflects real-world domains like:

particle physics (high standardization)
genomics (large collaborative consortia)
mathematics (proof-based verification)

These fields have strict norms that counterbalance productivity incentives.

The lesson is optimistic:

If we change the incentive landscape, evolution will favor good science.

But until then, decline is inevitable.

11. Conclusion: The Mathematics Make It Clear — Incentives Shape Evolution

Smaldino & McElreath’s model shows that:

Publication incentives are misaligned with truth-seeking.
Natural selection acts on labs based on those incentives.
Low rigor and low power spread through academic “lineages.”
Replication is too weak to stop this trend.
Only structural reforms can reverse the decline.

This is evolutionary theory applied not to organisms, but to epistemic culture.

The model is not meant to be a perfect mirror of science, but a lens to reveal its hidden structure. And it reveals something stark: we have built an evolutionary environment that selects against good science.

In the next post, we examine a critical question:

Why do bad methods spread faster than good ones — even when scientists know they’re bad?

This will take us deeper into incentives, lab sociology, and the dynamics of statistical shortcuts.