Category: Biology

Local vs. Global Peaks: Balancing exploration and exploitation to reach our pinnacle

A local optimum is a solution that is optimal within a neighboring set of candidate solutions—a point from which no small change can generate improvement. However, this local peak may still be far from the global optimum—the optimal solution among all possible solutions, not just among nearby alternatives.

This valuable model can teach us about the inherent tradeoff between capitalizing on our current opportunities and pursuing new ones—whether in biological ecosystems, businesses, or machine learning. We can use it to better understand the complex environments we operate in, and to design more effective strategies to achieve our goals.

Getting stuck

Picture a rugged plane comprised of many peaks and valleys of various elevations, with numerous individuals or groups competing to reach the highest peaks. Nearby points tend to have similar levels of “fitness.” The landscape itself may shift dynamically, altering the peaks and valleys and transforming the available paths to reach them. This model is known as a “fitness landscape,” an extremely useful metaphor for thinking about optimization amidst local and global peaks in a variety of applications—including systems, biology, computer science, and business.1

In complex systems (such as an industry or an ecosystem), it is easy to get stuck on local peaks as the ground shifts beneath our feet (undermining our position), especially if we fail to survey new territory. We won’t know precisely how the landscape will shift, so the only way to sustain progress in the long-term is, simply, to explore.

Sometimes, we may even have to go down (temporarily worsen our situation) in order to ascend a higher peak. And this requires a lot of courage. For example, Netflix’s stock fell by almost 80% from its peak after CEO Reed Hastings announced they were getting out of the DVD business in 2011. Ten years later, Netflix had pioneered the video streaming industry, and its stock price had grown by nearly 1,300%!

Evolutionary searches can never relax. We must constantly experiment with new ideas and strategies to find better solutions and adapt as the landscape shifts.

Faster than the speed of evolution

In biological evolution, we can visualize the competition for genetic dominance as a rugged fitness landscape in which the peaks and valleys represent the highs and lows of evolutionary fitness across an ecosystem. Higher peaks represent species or organisms that are better adapted to their environment—that is, ones that are more successful than their nearby competitors at causing their own replication.

Evolution is capable of creating remarkably complex and useful features, such as the human body’s ability to repair itself or the peacock’s brilliant tail. However, because it optimizes only for the ability of genes to spread through the population, evolution will inevitably reach only local peaks of fitness within a given environment.2 It can favor genes that are useless (the human appendix), suboptimal (women’s narrow birth canals), or even destructive to the species. For instance, the peacock’s large, colorful tail that helps it find mates also makes it more vulnerable to predators.3

When the landscape shifts, even a highly adapted species will be unable to evolve toward a worse (less well-fitted) state than its current one in order to begin ascending a new, higher evolutionary peak. If the environment shifts faster than the species can adapt to it, mass extinctions can occur.4

Fortunately, we humans don’t need to be bound by evolutionary timescales. Often, we can find better hills to climb.

Let’s look to computer science and business to see why.

Getting un-stuck

Algorithms provide useful insights into optimization and into overcoming local peaks.

The simplest optimization algorithm is known as “gradient ascent,” in which the program just keeps going “up.” For instance, a video site such as YouTube might be programmed to continue recommending videos that resemble your past content consumption. But “dumb” algorithms like this one maximize only short-term advantage, leading us to local peaks but not to global ones. What if the user’s content preferences change? What if the viewer gets bored by stale recommendations? What if repetitive videos trap the user in a filter bubble?

Randomness and experimentation can help us “pogo-jump” to higher peaks that simple gradient ascent would not reach. For example, a “jitter” involves making a few random small changes (even if they seem counterproductive) when it looks like you are stuck on a local peak, then resuming hill-climbing. A “random-restart” involves completely scrambling our solution when we reach a local peak—which is particularly useful when there are lots of local peaks.5

Perhaps our video site should recommend random pieces of viral content even if the viewer hasn’t watched similar clips previously. Or show clips that contrast sharply with past viewing habits (for nuance or contrarian content). Only experimentation can reveal whether we are climbing the best hill.

The explore/exploit tradeoff

In business, it is useful to picture the strategic environment as a rugged landscape, with each “local peak” representing a coherent bundle of mutually reinforcing choices.

Every organization needs to balance experiments in exploitation of its current businesses with experiments in exploration for future innovations. In the short-term, simple “gradient ascent” strategies (keep going up) help ensure the company is exploiting its current strengths and opportunities. Over the long-term, however, companies must make occasional medium- or long-distance “pogo jumps” to prevent getting stuck on local peaks and, sometimes, to make drastic improvements. The key problem with many organizations is that when the environment seems stable, they stop experimenting because it seems costly and inefficient, and because it sometimes creates internal competition.6

This was Reed Hastings’s revelation about Netflix in 2011: its wildly successful DVD-by-mail business was merely a local peak. The landscape had shifted. The new global peak, he believed (correctly), was streaming.

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The overall lesson is that because the environment is uncertain and always changing, good strategy requires individuals and organizations to carefully cultivate and protect a portfolio of strategic experiments, creating valuable options for the future.

Even when it seems we are at a “peak,” there may be even higher peaks that we cannot yet see, and the peaks themselves are constantly shifting! In such an environment, complacency is a death sentence.

Evolution by Variation and Selection: The reigning explanation for life (plus: why humans actually are special)

Until around the 19th century, it was generally assumed that some supernatural force (such as the Greek gods) was required to explain the complexity and diversity of life. However, Charles Darwin’s theory of evolution in 1859 introduced the revolutionary idea that life’s breathtaking variety doesn’t require any supernatural intervention. The theory’s modern understanding, termed Neo-Darwinism, involves three main processes:

  1. Replication — All organic life emerges through the copying of genes from parent to offspring;
  2. Variation — However, the genetic copying process is imperfect, introducing random variation into each generation;
  3. Selection — Nature acts as a relentless genetic “filter,” favoring gene variants that confer advantages in survival and replication.

Evolution is one of the most powerful theories available to us today, with broad implications across domains and throughout our lives. Let’s dive deeper.

The copying competition

Genes are effectively molecular software programs, bits of chromosomal material encoded simply to manufacture certain chemicals. These low-level molecular programs accumulate into complex systems of control and feedback, ultimately giving rise to the marvelous life forms that we see. Most importantly, genes are “replicators,” entities capable of copying themselves. They are programmed to cause their own replication—for example, through sexual reproduction in animals.1

During the imperfect replication process, random variants emerge. These “mutations” are non-purposeful: they occur without consideration of what problem they might solve. But this random genetic “shuffling” is far from useless: it gives potentially favorable genetic variants a chance to emerge. Certain mutations, by blind luck, will be more successful replicators than others. For instance, a male peacock with bigger, more colorful tail-feathers may have more mating success than his modestly-feathered chum.

“Natural selection” occurs over generations as the gene variants that are best able to cause their own replication survive and dominate, while less effective mutations fade away.2

We observe evolution in all processes subject to some mechanisms of replication, variation, and selection—including in business, where evolution can help us understand the differential success of companies.

Companies that don’t die

In the business world, as in nature, the mechanisms of replication, variation, and selection play a crucial role.

Businesses replicate the strategies, processes, and business models of others. But like mutating genes, startups begin with some different properties from their predecessors, and incumbents adapt and reconfigure themselves over time. These variations might include new product features, pricing models, marketing strategies, or operational processes. Just as natural selection eliminates disadvantageous genes, the market filters out weaker businesses over time. When the environment changes, the organizations that survive are those best able to adapt their resources and strategies to the external selection environment.3

A great example is The New York Times, which has persisted for 170 years as America’s most Pulitzer-decorated news organization and as a thriving business. Digital upheaval from the Internet and smartphones decimated the legacy print newspaper readership and advertising revenue of most newsrooms. The Times, however, recognized the shifting landscape early on—and chose to adapt. The company (controversially) launched a paywalled digital subscription in 2011, aggressively hired and handsomely paid top journalists (even amidst mass layoffs by competitors), and invested heavily in digital and mobile experiences.4 Today, the company’s market-leading 10 million digital subscribers are over 4x its print-era peak.

Netflix offers another striking example. Starting as a DVD-by-mail disruptor in the Blockbuster-dominated DVD rental market, Netflix became a household name—but it did not stagnate. Recognizing shifts in how viewers consumed video content, the company successfully transformed into a pioneer of the digital video streaming market, then led a global revolution in the production of original streaming content.5 Netflix’s ability to foresee and adapt to consumer preferences and technological advancements secured its streaming dominance, while the market filtered out less adaptable competitors such as Blockbuster and Quibi.

Examples like these demonstrate how evolutionary dynamics can help us eliminate common misconceptions about business—such as the idea that success is linear and predictable, or that the best product or the genius CEO or the biggest company always wins. Adaptability and randomness loom large in dynamic environments.

Biological evolution, too, is frequently misunderstood. Exploring these misconceptions can be extremely enlightening.

The “appearance of design” misconception

For one, the remarkable adaptations degree that we observe in nature (such as the body’s ability to heal itself) give an “appearance of design” that leads many people to presume the existence of a supernatural designer (or designers), and to the common misconception that evolution has “goals”—that it optimizes for the species or the individual.

By definition, the designer of an adaptation must have had an intention for that design. But, again, random variation takes place without knowing what problem those mutations might solve. The variants most successful at replication will dominate. There is no goal—no design.

Evolutionary processes are capable of explaining how all life forms evolved from single-cell organisms into highly complex beings, with remarkable yet “unplanned” adaptations—such as a peacock’s extravagant tail or a giraffe’s long neck.6

If life had been “designed,” then wouldn’t all biological traits be perfectly optimized for the good of the organism? This, too, is false.

Evolution can result in useless features, such as the human appendix or the “tail bone” (evolutionary leftovers). Evolution can even favor completely disadvantageous features. For instance, the peacock’s large, colorful tail that attracts mates also makes it more vulnerable to predators.7 The dominant genes may in fact cause a species to go extinct when the environment changes, if they were only well-adapted to conditions that no longer exist.8

The reality is that natural selection is blind. It does not optimize for the “welfare” of anything other than the gene’s ability to replicate itself.

“Organisms are the slaves, or tools, that genes use to achieve their ‘purpose’ of spreading themselves through the population.”

David Deutsch, The Beginning of Infinity (2011, pg. 92)

The “fine-tuning” misconception

A related misconception is the idea that the world appears to be “fine-tuned” to promote life on Earth.

The “parable of the sentient puddle” provides a thought-provoking counterargument: one day, a puddle wakes up and finds itself in a hole that fits the puddle “staggeringly well.” The puddle therefore concludes that the world must have been created for it.

But just as the hole wasn’t created for the puddle, the universe wasn’t sculpted for life. In fact, the opposite is true: We are fine-tuned by evolution to a very limited range of environmental conditions. Mild changes to our environment (temperature, altitude, etc.) would promptly kill us, in the absence of any human knowledge or technology.9

The Earth, in fact, is often indifferent or even hostile to life. Perhaps 99% of all species that have ever existed on Earth are extinct. Much of the Earth’s life-support system was not bestowed upon humans but created by them, using their distinct ability to create knowledge.10

Why humans actually are special

This revelation leads to perhaps the most important application of the theory of evolution outside of biology: the creation of human knowledge.

According to the dominant theory of knowledge-creation, philosopher Karl Popper’s “critical rationalism,” science is a problem-solving process that resembles biological evolution.

Ideas, like genes, are replicators. New theories emerge from creative guesswork by humans, which introduces variation into the knowledge-creation process (similar to genetic mutation). Then, we subject our ideas to a selection process by which we test them logically and experimentally. When a theory fails to survive criticism, we abandon it. If a new theory displaces it, we tentatively deem our problem-solving process to have made progress.11

Unlike biological evolution, in which bad genes are eliminated through the death of their carrier organisms, we are free at any time to discard our old ideas or to invent and test new ideas. This is one reason why knowledge creation is exponentially faster and more efficient than biological evolution. We can, in Popper’s words, “let our theories die in our stead”!

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With humans, in some ways evolution produced a system of trial-and-error even more powerful than evolution itself. We evolved not only to survive but to explain and reshape nature, as evidenced by our cities, technologies, agriculture, and science. Our genes are coded only to selfishly perpetuate themselves, but we can transcend this programming and create genuinely altruistic societies. Recognizing our unique evolutionary position, we can build a future that not only ensures our survival but also amplifies our thriving!