Dr. Marcial Losada is the founder and executive director of Meta Learning, a consulting organization that specializes in developing high performance teams. He currently consults with executives and their teams at several corporations in the U.S. and around the world. More information here. His articles are here.
Here’s a link to Part II of this series: Work Teams and the Losada Line: New Results.
Some things in this world are linear, like the accelerator in a car: the more I press it, the faster the car goes. It is a good thing that the accelerator is linear; it would be disastrous if it were nonlinear. Like the accelerator, there are many other devices that are useful because they are linear.
Marriages are Non-Linear!
Linear means what I put in is proportional to what I get out. Non-linear means the input is not proportional to its output; i.e., with little, but clever, effort I can get disproportionably more out of a complex system, and with a lot of dumb effort I can get disproportionably less. In marriages that work, it could be that what matters most is not how much they do for each other, but rather when and how they do it. In other words, nonlinear means that I have to understand the context in order to find the lever that provides the clue to smart actions.
When it comes to complex systems, where components interact strongly, linearity is practically useless. Marriages and teams are such complex interacting systems: there are no simple linear recipes to be successful, which makes it both challenging and fascinating. The best linear models can explain about 30% of the variance in output (team performance). About 70% of the variance remains unexplained.
What Is the “Meta Learning Model”?
On the other hand, a nonlinear model like the meta learning model accounts for 92% of the output variance; only 8% remains unexplained. Most linear models require many variables and parameters to explain a small amount of variance. In science, we like parsimony: explaining the most with the least.
The meta learning model shown in Figure 1 consists of three bipolar variables:
- inquiry-advocacy (how much people ask vs. talk),
- positivity-negativity (how much people are positive vs. negative),
- other-self (how much people are focused on others vs. on themselves),
one control parameter, connectivity, and two other parameters (a and b in Fig. 1):
- viscosity (how the environment resists change), and
- negativity bias (our speed of response to negative events to avoid harm).
Fig.1 The Meta Learning Model
PPND readers may be familiar with the positivity-negativity ratio (P/N) from previous articles (including “Flourishing with the Positive” by Doug Turner and The “Right Fit at Work” by Jen Hausmann). As a reminder, the P/N ratio falls within the Losada zone, when it is greater than or equal to 2.9013, but lower than 11.6346. In this zone, nonlinearity rules.Above the Losada zone, the system goes to limit cycles; and below it goes to fixed-point attractors. A fixed point is like being confined to a solitary cell with almost no space to move. In a limit cycle, while you are still confined, you have more room, but can only walk around and around in a tedious, repetitive, way.
Furthermore, the Losada zone reminds us that the key does not lie in runaway positivity. If the key were linear, then we could say that the more positivity the better, as is sometimes wrongly assumed. But the key is nonlinear: The ratio of positivity to negativity has lower and upper bounds for it to function optimally. We have not observed teams that go above the Losada zone, not even after intense training in meta learning (around a year). The vast majority of the teams we have worked with in a variety of countries and cultures start below the Losada zone and all of them, without exception, end up above the Losada line (which is the lower bound of the Losada zone), but well below the upper bound––typically, teams end up not higher than a P/N ratio of 6.
Why Is Meta Learning Good for Teams and Marriage?The ancient Chinese sage Lao Tsu wrote, “nonlinearity begets completeness; misjudgment creates linearity.” Let’s focus on the key words that Lao Tsu uses: completeness and misjudgment. Misjudge is to estimate wrongly. Trying to see complex phenomena as linear is tempting because the human mind looks for simplifications in order to apprehend what otherwise might not be within the grasp of our understanding. Because of our success in using linearity to control many devices, mostly mechanical, we tend to look for similar answers to try to understand and control complex situations for which mechanical analogies are insufficient. The danger lies in estimating these complex situations wrongly and believing our estimation is sufficiently accurate.
Let’s now focus on completeness, the second term Lao Tsu uses to refer to the power of nonlinearity. In statistical theory, completeness is a property of a statistic that allows us to obtain optimal information about the unknown parameters characterizing the distribution of the underlying data. This definition is directly relevant to the way the meta learning model was developed.
First, hundreds of thousands of data points were gathered at two labs in Ann Arbor and Cambridge that represented the interaction patterns of business teams. Then in 1999, I developed a model that captured the essential features of these data. The time series generated by the meta learning model were cross-correlated with the empirical time series gathered at the labs. It was determined that the model approximated the actual data with a very low probability of error (p < .01). Hence, we were able to comply with Lao Tsu’s completeness requirement.
The power of the meta learning model comes from its nonlinear structure (see the nonlinear differential equations that define the model in Fig. 1). When its control parameter, connectivity, is below a certain threshold, we have one kind of dynamic regime, a linear regime that is represented by fixed points. But if it is above that threshold, we have a nonlinear dynamics regime, represented by what I call a complexor – union of two words: complex order.
We can see in Fig. 1 that complexor dynamics lead to flourishing, while fixed points and limit cycles lead to languishing. Meta learning is a nonlinear methodology that allows a system (person, relationships, teams or organizations) to move from languishing to flourishing in a sustainable way. I explain this in detail in my forthcoming book, Meta Learning: The Nonlinear Path from Languishing to Flourishing in Relationships and Teams (more info will be available on my site).
Tomorrow’s article is about a new result in work teams and the Losada line – a threshold that overcomes the barrier to flourishing by using the power of nonlinearity.
Light chaos (nonlinear optics) courtesy of KevinDooley
black-and-white from Pyramid Encoder with Nonlinear Prediction by Panu Chaichanavong at Stanford University,
Lorenz attractor courtesy of ganjalf007
Fredrickson, B. L. & Losada, M. (2005). Positive affect and the complex dynamics of human flourishing. American Psychologist, 60(7), 678-686.
Losada, M. (1999). The complex dynamics of high performance teams. Mathematical and Computer Modelling, 30(9-10), 179-192. Abstract and order information here.
Losada, M. & Heaphy, E. (2004). The role of positivity and connectivity in the performance of business teams: A nonlinear dynamics model. American Behavioral Scientist, 47(6), 740-765. Abstract and order information here.
Tong, H. (1990). Non-linear time series: A dynamical system approach. Oxford, UK: Clarendon Press.
Tucker, W. (2002). A Rigorous ODE Solver and Smale’s 14th Problem. Foundations of Computational Mathematics, 2, 53-117.