Abraham Herzog-Arbeitman, SB’19, SM’19, makes a $26,000 suggestion: Let’s find out.
In February the National Science Foundation announced the winners of the first-ever NSF 2026 Idea Machine, a competition to help set the US agenda for fundamental research in science; engineering; and STEM education (science, technology, engineering, and mathematics). Anyone aged 14 or older—from the scientific community, industry, nonprofits, the public—could submit an idea.
Of the 800 entries, the NSF chose four grand prize winners, awarding $26,000 to each. (Of course “the real prize,” the press release stated, was “the opportunity to promote the progress of science and engineering by helping NSF identify possible new areas of research.”) Among the grand prize winners was the submission “Emergence: Complexity from the Bottom Up,” by then-undergrad Abraham Herzog-Arbeitman, SB’19, SM’19, now a graduate student in organic chemistry at MIT.
“I should note that emergence is not my idea, nor is it a new idea,” Herzog-Arbeitman explained by email. “My contribution was to expand interest in this approach to understanding complex systems at this unique point in time,” when increased computing power has made sophisticated simulations possible.
A month after the Idea Machine winners were announced, the University made its own announcement: Spring Quarter would be remote. “Yes, emergence is extremely relevant in Coronatime,” Herzog-Arbeitman wrote. “Pandemics are emergent phenomena, as are herd immunity and viral interactions with the human body.”
A better understanding of emergence, he continued, could have helped policy makers give clearer advice to the public, distribute food and personal protective equipment more efficiently, and manage societal effects like unemployment. “Of course, these systems are a long way from being understood. But we can move beyond simple model systems now, so … progress.” Here is the full text of Herzog-Arbeitman’s entry.—Carrie Golus, AB’91, AM’93
Emergence: Complexity from the Bottom Up
The intricate design of a snowflake, a school of fish swimming in unison, the thought patterns of a human mind. Each of these are complex systems that began with something simple—a water molecule, a minnow, a single neuron. As groups of these elements interact, a complex structure unfolds with new characteristics. Out of chaos, a sophisticated order seems suddenly to emerge without effort or guidance. But this is far from chance. It is an efficient and versatile process prevalent throughout nature known as emergence—a phenomenon that describes how simple components interact to form elaborate things.
Emergence is found wherever complexity is. It is a process relevant to all the sciences and the humanities—as prominent in computing or cryptography as it is in predicting traffic patterns or viral videos. Virtually every intricacy in our world depends upon emergence, and evidence suggests that these complex systems rely on it in a similar way. That means that the more we understand how emergence works, the more we can understand and influence all kinds of elaborate systems. Ultimately, harnessing emergent design could help us create our own complex systems or behaviors with the same efficiency as nature.
This deeper understanding of emergence promises far-reaching impacts for science and society. It could help us to influence economic trends or to untangle the cellular interactions that lead to cancer. The efficiency of emergent design is especially useful when creating ordered systems affected by limited resources. For example, energy grids, postal services, factories, and waste-management plants could all be designed to produce more and waste less. Entire cities could be planned with greater efficiency, inviting a new era of urban design inspired, ironically, by nature.
To predict the complex behavior of emergent systems, we must understand how their components communicate and interact with each other over time. This may require analyzing massive amounts of data. Like many modern research ideas, advancements in machine learning and supercomputing stand to greatly enhance our study of emergence, making now an optimal time to explore this idea and to build on existing efforts.
Perhaps more than any other research question, the study of emergence has interdisciplinarity at its core. Researchers must work across scientific and social disciplines to gather and compare examples of emergent systems, helping them to understand their common elements and to develop a unified vocabulary for describing them. Through this careful but universal lens, we can begin to extract general design principles that could be applied to various fields and societal priorities.
As our challenges grow more and more complex, so too must our solutions and our understanding of complexity itself. Fortunately, in nature we have a blueprint for complexity that is both efficient and effective, promising new emergent solutions for challenges of every kind.