Dynamic Networks Improve Remote Decision-Making
Crisis events demand decision-making networks that are dynamic and respond to frequent feedback
By Abdullah Almaatouq and Alex “Sandy” Pentland
The idea of collective intelligence is not new. Research has long shown that in a wide range of settings, groups of people working together outperform individuals toiling alone. But how do drastic shifts in circumstances, such as people working mostly at a distance during the COVID-19 pandemic, affect the quality of collective decision-making? After all, public health decisions can be a matter of life and death, and business decisions in crisis periods can have lasting effects on the economy.
During a crisis, it’s crucial to manage the flow of ideas strategically so that communication pathways and decision-making are optimized. Our recently published research shows that optimal communication networks can emerge from within an organization when decision makers interact dynamically and receive frequent performance feedback. The results have practical implications for effective decision-making in times of dramatic change.
How Feedback and Network Flexibility Affect Collective Intelligence
In two web-based experiments, each involving more than 700 people recruited online, we examined how organizational structures influence collective decision systems.
In both experiments, participants were asked to estimate, in a series of 20 rounds, the strength of statistical correlations between two variables (such as height and weight) that were graphed on a scatterplot. Without the participants’ knowledge, we introduced distracting statistical noise into the graphed data and systematically varied the degree of distraction across individuals. Then, in the middle of the 20-round series, we abruptly shuffled the noise levels (thereby inducing a drastic shift). Monetary prizes were awarded for accurate performance.
Our aims were to test whether networks of participants that were dynamically structured would make more accurate estimates than either static networks or individual participants, and whether getting frequent, high-quality feedback on performance would make participants perform better.
In the first experiment, groups of 12 participants were randomly assigned to one of three conditions: (1) solo, whereby each group member did the 20 estimation rounds in isolation; (2) static network, in which each member submitted his or her estimates after collaborating with three other preassigned participants; or (3) dynamic network, in which members chose three collaborators to interact with before submitting their individual answers. In all conditions, participants received performance feedback after each round, but those assigned to the dynamic condition were also able to choose three new collaborators after getting the feedback.
In the second experiment, the 12-member groups were randomly assigned to one of four feedback scenarios: (1) solo decision-making, with no feedback given on performance after each round; (2) network decision-making, also with no performance feedback; (3) network decision-making, in which each participant received feedback only on his or her own performance; or (4) network decision-making, in which full feedback on all participants’ performance was shown to everyone. In all but the solo condition, participants could change which three collaborators they interacted with during subsequent rounds.
Our results showed that even the best-performing individuals benefited from interacting with a network of peers — and that dynamic networks, in which peers chose their collaborators, improved individuals’ performance significantly compared with static networks.
We also found that when given full feedback, networks of participants deftly adapted to changes by shifting influence to people who had better information, thereby substantially reducing individual error and benefiting from collective wisdom.
Decision-Making Networks in a Crisis Period
Our experiments illustrate the importance of dynamically configuring network structures and enabling decision makers to obtain useful, recurring feedback. But how do you apply such findings to real-world decision-making, whether remote or face to face, when constrained by a worldwide pandemic? In such an environment, connections among individuals, teams, and networks of teams must be continually reorganized in response to shifting circumstances and challenges. No single network structure is optimal for every decision, a fact that is clear in a variety of organizational contexts.
Public sector. Consider the teams of advisers working with governments in creating guidelines to flatten the curve and help restart national economies. The teams are frequently reconfigured to leverage pertinent expertise and integrate data from many domains. They get timely feedback on how decisions affect daily realities (rates of infection, hospitalization, death) — and then adjust recommended public health protocols accordingly.
Some team members move between levels, perhaps being part of a state-level team for a while, then federal, and then back to state. This flexibility ensures that people making big-picture decisions have input from those closer to the front lines.
Witness how Germany considered putting a brake on some of its reopening measures in response to a substantial, unexpected uptick in COVID-19 infections. Such time-sensitive decisions are not made effectively without a dynamic exchange of ideas and data. Decision makers must quickly adapt to facts reported by subject-area experts and regional officials who have the relevant information and analyses at a given moment.
Higher education. At MIT, where we’re working toward reopening the campus, the central administration first consulted medical experts and their models in order to develop a plan. Administrators then realized that they also needed input from departments and facilities managers, right at the university. That’s because they had to learn what interactions really occur during normal daily operations — for example, how many people congregate in particular places on campus for specific purposes.
The administration has now involved experts from the MIT Quest for Intelligence (including both of us) to develop a way to monitor social distancing. The effort focuses on not only epidemiologic models but also campus-sourced facilities specifications and educational (classroom, lab) logistics data. The current goal: real-time organizational sensing that flags potential problems, convenes the right advisers to address those problems, and makes adjustments in response to frequent feedback before moving on to the next dilemma, involving whatever network of experts and managers is needed.
Industry. When car manufacturer Ford decided to make face shields for health workers this past April, the company didn’t just have to involve designers and engineers. To distribute the assembled products effectively, it also needed an advanced-product expert with experience in supply chain logistics, a software designer, a logistics-savvy public relations manager, a government affairs expert, and a regional communications manager with deep experience in how people collaborate remotely. In short, the network of decision makers had to change, responding frequently to new information, in order to get the job done. Millions of face shields were eventually deployed.
During a dynamically evolving pandemic, adhering to a static organizational chart to make decisions about the future, or even the present, is simply not viable. Leaders must rethink how they structure their decision-making networks and take a more flexible approach in adapting those networks to changing circumstances and to valuable feedback from a variety of domains.
Crisis is, by definition, at odds with stasis. Recognizing the new reality — and acting accordingly — will help leaders avoid dangerous pitfalls with long-term consequences and discover opportunities for re-imagining optimal performance in an altered world.
Originally published at https://sloanreview.mit.edu on June 2, 2020.
