Thinking Algorithmically during the “New Normal”
Given the urgency with which the COVID-19 outbreak has mandated remote collaboration, it has become incumbent on work teams to quickly adapt to a changing execution and experience. The implementation of a universal work from home policy is a profound change for many companies, and forging a “new normal” falls largely on the shoulders of those who lead.
Coming from a mathematical background and having worked on application development projects at Kenway, I have become interested in how we can apply the principles of computer science to everyday life. A computational thinking approach depends on an algorithm design or sequential, finite steps to dictate how to perform a task. Although human behavior is far more idiosyncratic than a computer, we can employ the same structured, algorithmic approach to solving real world problems.
“Some of the biggest challenges faced by computers and human minds alike: how to manage finite space, finite time, limited attention, unknown unknowns, incomplete information, and an unforeseeable future; how to do so with grace and confidence; and how to do so in a community with others who are all simultaneously trying to do the same.” (Algorithms to Live By: The Computer Science of Human Decisions)
New organizational paradigms now demand more dynamic and adaptive management, requiring leaders to engage teams without the physical cues and rhythms of an office. According to a Harvard Business Review study on managing remote workers, employees working from home may experience a decline in job performance and engagement due to distractions and the inability to disassociate work from personal time. Leveraging algorithmic thinking can assist leaders in guiding teams through some of these key challenges.
So, Where Was I?
Children yelling, dogs barking, roommates talking … Anyone who has worked from home has likely encountered similar distractions, all atypical of an office setting. Moreover, mandated stay-at-home orders are increasing people’s time spent at home, and thus intensifying these distractions. After an interruption, have you ever found yourself spending more time asking, “So, where was I?” than executing the task at hand?
Frequent interruptions pose a large productivity risk and take valuable time from which to recover. The concept of thrashing, or becoming overloaded with switching between too many tasks, is a common reason our computers freeze. When a computer processor is preoccupied with metawork, it spends more time swapping information in and out of memory, than doing actual work.
Like computers, humans also need to solve for unplanned interruptions and sporadic context switches to avoid a collapse in performance. By approaching this problem algorithmically, we can start by evaluating the variables we can control (the frequency of context switches at work) and the variables we cannot control (the frequency in which a baby cries or a dog needs a walk).
In computer science, thrashing can be combated by interrupt coalescing, or allowing interruptions to accumulate. Ultimately, there is a trade-off between responsiveness to the interruption and the throughput of work. As a leader, interruptive coalescing can be applied with regularly scheduled one-on-ones or meetings with team members. By setting expectations of when non-urgent inquiries can be addressed and how employees should aggregate questions, leaders can decrease the cost of context switching to both themselves and their employees.
Time to Pack Up and Go Home
In a typical office setting, we can physically see what time coworkers are getting to work and heading home. If most of the office is going home at 5 p.m., we can draw upon others’ behavior and conclude that leaving at 11 p.m. may not be appropriate. When working remotely, however, the ability to gauge our peers’ working hours becomes challenging and employees are left to define the limits themselves. Therefore, the delineation between work and personal life decreases and virtual hours may become abnormally high, leading to burnout and hindered work performance.
Understanding the Nash equilibrium of Game Theory can help leaders design a system for employees to act in beneficial ways. In Game Theory, the concept of Nash equilibrium is that the optimal outcome of a game is one where no player has an incentive to deviate from his or her chosen strategy after considering an opponent’s choice. Inherently, employees want to leave at a reasonable hour and the Nash equilibrium converges at a certain number. The optimal outcome for both the employer and employee is a workday that allows employees time to complete all necessary tasks, while also maximizing personal time.
When a “player” does not have the luxury of considering the “opponent’s” choice, the player will act independently to maximize the outcome for themselves. Consequently, employees acting in isolation may perceive that more working hours results in competitive advantage and thus increasing the Nash equilibrium.
As a leader, altering the game “pay-offs” can shift the equilibrium by disincentivizing behaviors that produce a negative outcome to the group. Clearly communicating the importance of personal time can change the equilibrium of the game by altering employees’ perception of the definition of a competitive advantage.
By leveraging Nash equilibrium, we can shift team members’ strategies with an incentive to balance work and personal life. For example, a team lead could ask team members to share an anecdote or photo of a recreational activity outside of work with the larger group. By employing positive administration that may not have existed previously, a leader can enable people to act in ways mutually beneficial to both themselves and the larger group.
Leading through Uncertainty
By examining human rationality and intuition with an algorithmic mindset, we can better solve for innately human problems with extreme unknowns and rapid changes. The current health crisis is an inflection point, after which our personal and professional lives will be changed in ways we cannot yet fully comprehend. We have been forced to live in a constant state of uncertainty, with a scarcity of similar events to provide useful empirical guidance.
As our new reality evolves, we can be certain that we will not be returning to the pre-pandemic world that we inhabited a few months ago. During a time of extreme uncertainty, when even the arcane machinations of computer science cannot solve for the efficient algorithms, we can draw on parallels predicated by computer science to elucidate vexing questions.