The most successful biological organisms have an organization that eschews centralized control in favor of allowing multiple agents to independently sense and quickly respond to environmental change.  Our immune system trusts millions of cells all over the body to look for, and neutralize, invading pathogens without any conference calls to our brain to plan and execute an appropriate response.  The octopus, which has a powerful central brain, nonetheless knows how to balance its advanced cognitive capabilities with the quick responsiveness afforded by having millions of color changing skin cells spread across its body.Pretend you are an octopus.  You are happily (yes, octopuses seem to have emotions) skipping over a coral reef looking for crabs to eat, when suddenly you spy a large mouth grouper swimming your way.  Your best bet at this point is to hide, but how do you do it? You have a wonderful brain, why not use it to tell your body what to do?  Okay, start shouting orders: “Arm 1, turn pink! Arm 2, turn greenish yellow! Arm 3, turn sorta red-fuschia-ish!”  You can see the problem right away.  Not only will your brain be too slow to tell a complex body how to act, but the coral reef is too complex for your central brain to even have a good sense of what it looks like in each little micro-environment at once. Fortunately the octopus has millions of skin cells that can each respond to the environment around them, changing shape and form to match the very local conditions in their immediate area.  Their collective actions give the octopus as a whole its camouflage. Research by Geerat Vermeij, who looks at broad patterns in the history of life, suggests that the most adaptable organisms use decentralized organization—where multiple semi-independent agents sense change and respond to it on behalf of a larger body, but not under the control of a central brain.  The vertebrate immune system is an excellent example, wherein many independent sensors scan the body for invading pathogens, identify and upregulate an appropriate response without deference to a central brain.In society, there are both negative and positive analogies to this type of system. For example, putting most of the U.S. security agencies into a single large centrally controlled bureaucracy (the Department of Homeland Security) after 9/11 led to ineffective responses to the next major security emergency, Hurricane Katrina. In the aftermath of Katrina, the question on everyone’s mind was, “Where’s FEMA, KE Fishing”, referring to the Federal Emergency Management Agency.  Finding an agency within a bureaucracy means finding the “org” chart, and the org chart of DHS looks like this:Can you find FEMA in that (hint: it’s the box with the dotted line around it)?  The org chart is a symbolic representation of the difficulty for ideas to find solutions or solutions to find ideas in a centralized organization.By contrast, Google Flu Trends uses the distributed sensing power of millions of Google users searching flu-related terms to accurately detect flu outbreaks.  Unlike the centralized US Center for Disease Control’s flu trends reports, which require surveys to be sent to doctors and hospitals and returned to CDC for analysis and report writing, Google Flu Trends are available (like the camouflage of an octopus) almost instantly, and up to two weeks earlier than the CDC data.But we also like to say, “Have an Open Mind, But Not So Open That Your Brain Falls Out”.  By this we mean, all that decentralization is great, but it will never work effectively without some central control.  In adaptable systems there are several key roles of a central controller, an organization, or a manager.   A central controller is useful for getting the resources that decentralized problem solvers need (all those skin cells in the octopus wouldn’t work at all without the metabolic energy provided for them by the octopus and its clever brain).  A central controller is also often useful for having a bigger vision (the octopus seeing the threat of the predatory fish) and that is essential for helping define the actual challenge.  For example, while the most effective research, mitigation, and adaptation strategies for climate change may be at the local and regional level, it is still necessary to have a body like the Intergovernmental Panel on Climate Change that can see the collective effect of our actions on the planet as a whole and across long time scales.

How adaptable systems work – Unplanned

Organisms in nature do make some predictions, but they tend to be over fairly regular events, like the turning of the tides or the shift from day to night.  Wasting energy in trying to predict high risk events that are highly uncertain would leave little resources for the more important task of solving day to day challenges. Did the animals that acted so strangely before the enormous Boxing Day tsunami in Asia predict it, or were they just really good at observing the early warning signs?  The answer lies in the rarity of a huge tsunami. Evolution has ruthlessly weeded out from organisms any desire to expend resources predicting the future state of an inherently unpredictable system, especially for such rare and unpredictable events as a tsunami.   In its place, adaptable organisms have astonishing observational skills. The signals of vibration, noise, smell and magnetism those animals experienced on the morning of the tsunami were so unlike their usual observations that they became very nervous, and domestic animals even tried to tell their human compatriots about their fears.  Too often in society, we try to substitute predictive models for intensive observation of the world.
Finally, although evolution is sometimes mistakenly referred to as, “survival of the fittest”, it is actually “survival of the just good enough.”  You don’t need to be the fittest or the biggest or the fastest or the prettiest to do well in nature.  You just have to relentlessly solve challenges in the environment long enough and well enough to get the chance to reproduce yourself.  Even the notion of identifying perfection in nature is absurd.  We are told in documentaries that the great white shark is nature’s “perfect predator,” but wouldn’t it be more perfect if it had laser beam eyes? As Andreas Wagner points out in his book, Paradoxical Life, a perfect parasite species at any given time would infect every single available host, but then there would be no more hosts for the parasite’s offspring to infect.    Unfortunately, we tend to waste untold resources in “optimization” exercises that have us endlessly chasing an elusive goal of perfection while losing sight of merely solving the problems at hand.

Research Support

We seek support for our research activities through federal grants, private foundations and individuals.  Individual and foundation support can be routed through the University of Arizona Foundation at a low overhead rate.  Our work also is supported in innumerable ways through our relationship with the University of Arizona, which provides administrative support, unparalleled library resources, and a wide range of professional colleagues and top graduate students all operating in a unique environment that places a high value on interdisciplinary and applied research.

Balancing Big Data and Deep Data

In Learning from the Octopus I mostly look at the benefits of massive, decentralized data gathering—by organisms in nature, or by networked organizations of people. To varying extents biological organisms including humans, process massive amounts of observational data and use these data to construct subconscious patterns and scenarios.  How our immediate reality coincides with or deviates widely from these patterns leads us to make changes (in how we move, where a quarterback throws a ball, how a dog determines whether to trust someone, etc.) or adapt to a novel situation.    But I also note that organisms in nature, for 3.5 billion years, haven’t been wasting their time trying to use data to predict into the far and uncertain future.


This far and uncertain future is where holistic pictures of complex situations emerge.  For example, our understanding of recent climate change and our limited predictions of what is to come, could not be developed solely on massive climatological databases.  There is, for instance, also the question of how the biosphere has been reacting to these climate changes (itself now a large and growing database of “fingerprints” of climate impact), and how human behaviors and economic decisions in the past and future will shape the climate system.  Right now, we have a lot of these data, but not nearly enough based on the data alone, to make a firm decision about what we should do.  But that doesn’t mean we should do nothing.


As I argue in the book, nature has multiple options when confronted with challenges that its own internal database can’t handle.  For example, an organism can create a symbiotic partnership with another organism.  Such symbioses will be essential in solving climate and other complex challenges, even where we lack all the data we’d want.  One small example: a project colleagues of mine at the University of Arizona have started with Navajo Indians to install solar membrane distillation desalinization pumps where once windmills pumped water so saline that not even cows would drink it, and tribe members had to drive an average of 40 miles to bring back bottled water to their homes.  The project benefits the Navajo nation whether or not particular data driven scenarios about climate change in northeastern Arizona are accurate, and it benefits the University that wants to demonstrate its prowess in renewable energy technologies R&D.  In other words, symbiosis helps partnering organisms (or organizations) solve complex problems without needing all the data.


This balance of mining data in unprecedented and amazing ways versus the need to understand a situation holistically in order to solve complex challenges is the driving the transformation of scientific inquiry, as I describe in my other recent book, Observation and Ecology: Broadening the Scope of Science to Understand a Complex World (2012, Island Press).  In that book, I show how life scientists are now combining massive, automated and technological data gathering and analysis capabilities with the old fashioned practice, long dismissed in scientific circles, of simple natural history—what paleontologist Geerat Vermeij calls “observation with the brain in gear” in his contribution to the book.  This combination of broad knowledge and deep knowledge, of technical and human capabilities, gives us our best chance at understanding an unpredictable and rapidly changing world.

Adaptable Mission Command Requires a Balance

I would further press that a mission command force must use these observational skills to better understand the true intent (as opposed to the stated intent, which may not be the same thing) of its adversaries.  A fish doesn’t try to turn a shark into a vegetarian—it accepts the risk of predation in the world—but it does use its observational experience to try to escape from the shark, trick the shark, or even form a partnership with a shark.  One of the most effective measures for reducing the IED threat in Iraq wasn’t better armor or jamming technologies, but forming symbiotic partnerships with local populations that generated greater numbers of tips about IEDs and IED makers. Understanding the intent of these populations—rather than assuming they could only be classified as an “enemy”–was essential in forming these partnerships.

Trust is fortunately deeply seeded in biology.  Essentially all organisms since the beginning of biological time needed systems for understanding what was like themselves and what was not like themselves.  All organisms have this “self/non-self” recognition system that lets them know who to trust.  For humans, this is codified in culture or “tribal identity”.  We often consider tribal identity as dangerous and conflict-generating, citing examples of militant religious identities and domestic terrorists.  We also consider tribal identity as enshrining stasis and adherence to outdated norms.  These are both biased readings of human evolutionary history, where tribal identity has overwhelmingly been a source of advancement and adaptability. The trust enshrined in tribal identity is what kept a naked and largely defenseless ape secure for most of our time on Earth and it continues to have a critical value today. The trust we place in members of our tribe give us the freedom to innovate and to take the risks to try new ways of living.


A key aspect of mission command is balance—as General Dempsey states, “understanding…must flow from both bottom-up and top-down”.  Biological organisms are neither completely decentralized nor centralized in their approach to problem solving.  Decentralized observers and responders are essential to get a localized and high tempo reading of the challenge at hand.  Centralized command, in parallel, carries key functions which include having a globalized and contextual sense of the local challenge, providing resources to support those charged with meeting the challenge, and providing a means for reproducing successful solutions.  Through natural selection, organisms like the octopus have arrived organically at this balance. Bringing this balance to present day human command structures—which are overwhelmingly centralized–will likely require leadership to relinquish some control.  General Dempsey provided some specific illustrations on how this might be accomplished in the training environment—by incorporating uncertainty, imperfect information, and the need to delegate.

These same characteristics can be generated in all operations of a future force through the process of challenge-based problem solving.  Challenges, as opposed to “orders”, put the onus of finding the best solution on decentralized agents.  A challenge that is well-tuned to the operational environment (understanding) and well-articulated (intent) will almost always yield faster, cheaper, and more effective results than a centralized mandate.  Of course, relinquishing controlled planning to the unknown and unpredictable outcomes of a challenge requires commanders to utilize the last attribute of mission command—trust.  Trust in the subordinate challenge solvers and trust in the system of challenge-based problem solving.  The former can only be generated through relationship-building. As for the latter, there are now dozens of case studies from many complex environments of successful challenge-based problem solving—from DARPA’s “Grand Challenges” to biological challenges to identify novel protein conformations (issued in the form of a multi-player online video game)—which reveal empirically the power of trusting decentralized problem solvers enough to get the job done.

As General Dempsey stated, the basic principles of mission command are not new concepts.  Indeed, they are billions of years old.  The challenge is to implement them throughout the Joint Force.  Lessons from the massive case study database of nature can inform this implementation at every level.