Animals Follow a Hidden Algorithm, Whether They’re Meerkats or Hyenas todayheadline

From the scorpion-hunting meerkats of the Kalahari to Kenya’s powerful hyenas, mammals with vastly different lifestyles appear to follow remarkably similar behavioral patterns, according to research published this week in the Proceedings of the National Academy of Sciences.

The surprising findings suggest an underlying architecture that may organize how animals sequence their daily activities, regardless of species, environment, or individual differences.

Scientists from the Max Planck Institute of Animal Behavior and collaborating institutions tracked three distinct mammal species in their natural habitats using accelerometers—the same motion-sensing technology found in smartphones—to record their movements with unprecedented detail over extended periods.

Common Patterns Emerge Where Scientists Expected Differences

“We assumed there would be differences,” said Pranav Minasandra, a postdoctoral researcher at MPI-AB and lead author of the study. After all, differences are apparent when comparing meerkats, coatis, and hyenas, which occupy dissimilar environments and ecological roles. “But we found common patterns in how animals switch between behaviors, regardless of what species and which individual. It’s as if their behavior was built on the same hidden algorithm.”

What makes this discovery particularly intriguing is how it contradicts conventional wisdom about animal behavior. Most researchers—and indeed most of us—would expect animals to grow increasingly likely to switch activities the longer they’ve been doing one thing. Instead, the opposite appears true.

The Surprising “Lock-In” Effect

The most striking pattern identified was what researchers call a “decreasing hazard function”—essentially meaning that the longer an animal maintains a particular behavior, the less likely it is to change in the next moment. This self-reinforcing behavioral momentum was consistent across all animals studied.

“This was unexpected,” adds Minasandra.

Imagine a hyena walking continuously for 10 minutes. Most people would probably guess that the hyena would be more likely to stop over time, and the authors did too. “We originally thought the probability of switching behaviors would increase over time, as we assumed it would not be optimal to lock-in to any behavior.”

How Researchers Captured The Hidden Patterns

The research team equipped wild animals with accelerometers to track their movements with exceptional precision. The study included:

• Meerkats: Small, burrowing social mammals in the Kalahari Desert
• Coatis: Raccoon-sized tree-dwellers in Panama’s rainforests
• Spotted hyenas: Large carnivores roaming Kenya’s savanna

Using machine learning algorithms, researchers translated raw movement data into behavioral states such as lying down, foraging, or walking. This approach allowed them to construct detailed behavioral sequences spanning days or weeks.

“This approach allowed us to capture detailed behavioral sequences over days and even weeks from multiple individuals across three distinct species,” says Ariana Strandburg-Peshkin, group leader at MPI-AB and senior author on the study.

Predictable Unpredictability

The researchers also examined how well current behavior predicts future actions—what they term “predictivity decay.” As expected, prediction accuracy decreases the further into the future one tries to forecast. But how does this predictivity fade? The pattern of decay followed a remarkably consistent mathematical form across all animals studied.

The authors further examined how current behavior predicts future actions—a concept they call “predictivity decay.” Predictivity decay reflects the increasing difficulty in predicting behavior the further we look into the future, primarily due to random, unpredictable variations. The shape of the decay graph conveys how decision-making systems across different timescales interact to generate animals’ behavioral sequences. “We found that the pattern of predictivity decay was remarkably consistent across all animals studied, implying a shared architecture beneath the surface.”

Why Do Such Similar Patterns Exist?

What could explain these shared behavioral structures across such diverse species? The researchers propose two main possibilities:

First is positive feedback: the longer an animal remains in a state—say, lying down—the more likely that staying put is rewarded, whether because it’s warm, safe, or socially reinforced. Behavior becomes self-reinforcing.

The second possibility is multi-timescale decision-making. Instead of a single internal clock governing when to switch behaviors, animals may integrate cues from many processes—internal hunger, external threats, social context—each with its own tempo. The interplay of these overlapping signals could generate the observed patterns.

Could these shared behavioral structures explain broader patterns we see in nature, such as the famously heavy-tailed distributions of animal movement known as Lévy flights? The authors suggest this is possible, with potential implications for how we understand fundamental aspects of animal behavior.

More Questions Than Answers

This research raises as many questions as it answers. Do non-social animals show the same patterns? What about different developmental stages or ecological pressures? Do these behavioral structures actually confer advantages in survival or reproduction?

Whatever the answers, the study hints at something profound about the organization of behavior across the animal kingdom.

Says co-author Meg Crofoot, Director of the Department for the Ecology of Animal Societies: “What this study suggests is that real animals, be they hunting, hiding, or resting, are guided by hidden structures that seem to echo across life’s branches.”

The complete study is available in the Proceedings of the National Academy of Sciences, published May 15, 2025 (DOI: 10.1073/pnas.2503962122).

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